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events_workshops Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/D7V2ImRWpG00ZwtqcglAiaZQbZisCcJYW0i47NNLl2KPtrNgWne1pMRlqdchhpySycR5GkO8UmHpykqzjoV2Ia8=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/D7V2ImRWpG00ZwtqcglAiaZQbZisCcJYW0i47NNLl2KPtrNgWne1pMRlqdchhpySycR5GkO8UmHpykqzjoV2Ia8=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # FQA Scalable Verification for Safety-Critical Deep Networks Driver Assistance Systems and Vision-Based System Validates Driver Monitoring The present invention relates to a system for providing advertisement contents based on facial analysis. The system consists of an image acquisition device, a face detection module, an analysis module, a classification module, a database, a computation module, a matching module and a display device. The image acquisition device acquires an image of a user, the face detection module detects the face of the user in the image, the analysis module analyses the facial features statistically using classification models, the database stores matching rules, weighted advertisements and a plurality of advertisement contents and the display device displays the advertisement contents. The computation module computes the weighted image of the user and the matching module matches the weighted image of the user with the weighted advertisement to select an advertisement content based on facial analysis of the user. The system aims to provide advertisement contents via a digital standee by extracting salient demographic from a user to indirectly obtain user information and behavioral preference. The present invention relates to a system and method for providing advertisement contents based on facial analysis using a digital standee. The system (100) is embedded in the digital standee and comprises an image acquisition device, a face detection module, a classification module, a data analysis module, a computation module, a database and a matching module. The image acquisition device is configured to acquire an image of a user, and the face detection module uses deep learning technology to detect the user's face in the image. The classification module classifies the user's facial features into a plurality of classification models, such as gender, age range, emotion, style and attention. The data analysis module obtains behavioral preference and information of the user by analyzing the classified facial features. The matching module matches the information with types of businesses to provide suitable advertisement contents to the user based on rules set by the advertisement provider. The advertisement contents are displayed on a display device in the system. This patent describes a system for providing advertisements based on facial analysis. The system consists of an image acquisition device, a face detection module, an analysis module, a computation module, a matching module, a database, and a display device. The image acquisition device captures an image of the user, the face detection module identifies the user's face and facial features, the analysis module analyses the facial features using statistical parameters and classification models, the computation module computes the weighted image of the user, the matching module matches the weighted image of the user with weighted advertisements, and the display device displays the advertisement contents. The system operates in real-time and updates the classification models continuously. The advertisement contents are based on the user's age, gender, emotion, style, and attention and are provided by the advertisement providers with matching rules. The process described in this patent involves matching a user's weighted image with a weighted advertisement based on matching rules established by the advertisement providers. The matching rules may include order of features, most similar features, important features, and nearest similar features. The matching is done from left to right of the binary sequence. The selected advertisement content is then displayed by the display device. The terms used in the patent are defined as specified. The invention is open to changes in form and details. The system (100) is a device for providing advertisement contents based on facial analysis. It consists of: an image acquisition device (10) to acquire an image of a user, a face detection module (20) to detect the face and obtain facial features, an analysis module (40) to analyze the facial features statistically using classification models, a database (60) to store matching rules and advertisements, and a display device (80) to display the selected advertisement content. The system also has a computation module (50) to compute a weighted image of the user based on the analyzed facial features, and a matching module (70) to match the weighted image of the user with the weighted advertisement to select the advertisement content. The system can work for a single user or a group of users. The method (200) of providing advertisement content follows similar steps as the system (100). The steps include acquiring an image of the user, detecting the face, analyzing the facial features, computing a weighted image of the user, obtaining matching rules, and matching the weighted image with the weighted advertisement. The method also includes steps of training the classification models and providing display of the selected advertisement content. This patent describes a system for providing advertisements based on facial analysis using a digital standee. The system consists of an image acquisition device, a face detection module, an analysis module, a computation module, a matching module, a database, and a display device. The image acquisition device captures an image of the user, the face detection module identifies the user's face and facial features, the analysis module analyzes the facial features using statistical parameters and classification models, the computation module computes the weighted image of the user, the matching module matches the weighted image of the user with weighted advertisements based on matching rules set by the advertisement providers, and the display device displays the advertisement contents. The system operates in real-time and updates the classification models continuously. The advertisement contents are based on the user's age, gender, emotion, style, and attention and are provided by the advertisement providers with matching rules. # [Scalable Verification for Safety-Critical Deep Networks](https://arxiv.org/pdf/1801.05950.pdf) [https://arxiv.org/pdf/1801.05950.pdf](https://arxiv.org/pdf/1801.05950.pdf) "Verifying that neural networks behave as intended may soon become a limiting factor in their applicability to real-world, safetycritical systems such as those used to control autonomous vehicles safety and reliability on DNNs. verify properties of DNNs. A major challenge of verifying properties of DNNs with satisfiability modulo theories (SMT) solvers is in handling the networks’ activation functions such as, Reluplex (domain-specific theory solvers; through a lazy approach). 1)devising scalable verification techniques. 2)identifying design choices -> amenable to verification. " Each neuron of a neural network computes a weighted sum of its inputs according to learned weights. It then passes that sum through an activation function to produce the neuron’s final output. Typically, the activation functions introduce nonlinearity to the network, making DNNs capable of learning arbitrarily complex functions, but also making the job of automated verification tools much harder. # Driver Assistance Systems and Vision-Based System Validates Driver Monitoring [Vision-based convolutional neural network system detects phone usage, eating, and drinking.](https://www.techbriefs.com/component/content/article/tb/supplements/pit/features/technology- leaders/36144) cameras with active infrared lighting; 30 Hz and delivered 8-bit grayscale images at 1280 × 1024-pixel resolution; ResNeXt-34; [video-based driver assistance systems, such as automated driving; ](https://www.bosch-mobility-solutions.com/en/products-and-services/passenger- cars-and-light-commercial-vehicles/driver-assistance-systems/lane-departure- warning/multi-purpose-camera/)resilient object detection and tracking; camera: ± 50°field of view (horizontal); +27°/ -21°field of view (vertical); > 150 m detection range; 2.6 MP resolution. multi path approach: 1. classifier: for pattern recognition; resilient object detection 2. dense optical flow and structure from motion; to detect static objects; 3D structure 3. deep learning: classify objects, road, edge road, orientation; " Operation principle of the multi purpose camera: During assisted and automated driving, the vehicle must know what is happening in its surroundings at all times. It must reliably detect objects and people, and be able to react to these appropriately. Here, the latest generation of the front video camera from Bosch plays a crucial part: The multi purpose camera for assisted and partially automated driving utilizes an innovative, high-performance system- on-chip (SoC) with a Bosch microprocessor for image-processing algorithms. Its unique multipath approach combines classic image-processing algorithms with artificial-intelligence methods for comprehensive scene interpretation and reliable object detection. With its algorithmic multipath approach and the innovative system-on-chip, this camera generation has been specially developed for high-performance driver assistance systems. In line with this approach, the multi purpose camera uses for example the following technical paths at once for image processing: The first of these is the conventional approach already in use today. Via preprogrammed algorithms, the cameras recognize the typical appearance of object categories such as vehicles, cyclists, or road markings. The second and third paths are new, however. For the second path, the camera uses the optical flow and the structure from motion (SfM) to recognize raised objects along the roadside, such as curbs, central reserves, or safety barriers. The motion of associated pixels is tracked. A three- dimensional structure is then approximated based on the two-dimensional camera image. The third path relies on artificial intelligence. Thanks to machine- learning processes, the camera has learned to classify objects such as cars parked by the side of the road. The latest generation can differentiate between surfaces on the road and those alongside the road via neuronal networks and semantic segmentation. Additional paths are used as required: These include classic line scanning, light detection, and stereo disparity. " [Link](https://www.bosch-mobility-solutions.com/en/products-and- services/passenger-cars-and-light-commercial-vehicles/driver-assistance- systems/lane-departure-warning/multi-purpose-camera/) Face recognition/attributes ![](https://lh4.googleusercontent.com/TgbkO5gpReB1fENVpm7Sn00xnwzAjbeqdP5_TQDuHnsxXxCQX1wQI93AxE5mlxTErsklFmmYImbGCWG8W1Y7Bdle8yiXhKWxXXeDuJdeeCHAqlijWN8vLjiWy0lEGxwWzg=w1280) I use citation plugin 1. add path to the JabRef database "reading notes/dh.bib" 2. create folder "Reading notes" 3. use Ctrl+Shift+O to select reference 4. automatically create file based on that reference 5. Ctrl+Shift+E to insert link to citation page 6. Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/D7V2ImRWpG00ZwtqcglAiaZQbZisCcJYW0i47NNLl2KPtrNgWne1pMRlqdchhpySycR5GkO8UmHpykqzjoV2Ia8=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/D7V2ImRWpG00ZwtqcglAiaZQbZisCcJYW0i47NNLl2KPtrNgWne1pMRlqdchhpySycR5GkO8UmHpykqzjoV2Ia8=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # FQA Scalable Verification for Safety-Critical Deep Networks Driver Assistance Systems and Vision-Based System Validates Driver Monitoring The present invention relates to a system for providing advertisement contents based on facial analysis. The system consists of an image acquisition device, a face detection module, an analysis module, a classification module, a database, a computation module, a matching module and a display device. The image acquisition device acquires an image of a user, the face detection module detects the face of the user in the image, the analysis module analyses the facial features statistically using classification models, the database stores matching rules, weighted advertisements and a plurality of advertisement contents and the display device displays the advertisement contents. The computation module computes the weighted image of the user and the matching module matches the weighted image of the user with the weighted advertisement to select an advertisement content based on facial analysis of the user. The system aims to provide advertisement contents via a digital standee by extracting salient demographic from a user to indirectly obtain user information and behavioral preference. The present invention relates to a system and method for providing advertisement contents based on facial analysis using a digital standee. The system (100) is embedded in the digital standee and comprises an image acquisition device, a face detection module, a classification module, a data analysis module, a computation module, a database and a matching module. The image acquisition device is configured to acquire an image of a user, and the face detection module uses deep learning technology to detect the user's face in the image. The classification module classifies the user's facial features into a plurality of classification models, such as gender, age range, emotion, style and attention. The data analysis module obtains behavioral preference and information of the user by analyzing the classified facial features. The matching module matches the information with types of businesses to provide suitable advertisement contents to the user based on rules set by the advertisement provider. The advertisement contents are displayed on a display device in the system. This patent describes a system for providing advertisements based on facial analysis. The system consists of an image acquisition device, a face detection module, an analysis module, a computation module, a matching module, a database, and a display device. The image acquisition device captures an image of the user, the face detection module identifies the user's face and facial features, the analysis module analyses the facial features using statistical parameters and classification models, the computation module computes the weighted image of the user, the matching module matches the weighted image of the user with weighted advertisements, and the display device displays the advertisement contents. The system operates in real-time and updates the classification models continuously. The advertisement contents are based on the user's age, gender, emotion, style, and attention and are provided by the advertisement providers with matching rules. The process described in this patent involves matching a user's weighted image with a weighted advertisement based on matching rules established by the advertisement providers. The matching rules may include order of features, most similar features, important features, and nearest similar features. The matching is done from left to right of the binary sequence. The selected advertisement content is then displayed by the display device. The terms used in the patent are defined as specified. The invention is open to changes in form and details. The system (100) is a device for providing advertisement contents based on facial analysis. It consists of: an image acquisition device (10) to acquire an image of a user, a face detection module (20) to detect the face and obtain facial features, an analysis module (40) to analyze the facial features statistically using classification models, a database (60) to store matching rules and advertisements, and a display device (80) to display the selected advertisement content. The system also has a computation module (50) to compute a weighted image of the user based on the analyzed facial features, and a matching module (70) to match the weighted image of the user with the weighted advertisement to select the advertisement content. The system can work for a single user or a group of users. The method (200) of providing advertisement content follows similar steps as the system (100). The steps include acquiring an image of the user, detecting the face, analyzing the facial features, computing a weighted image of the user, obtaining matching rules, and matching the weighted image with the weighted advertisement. The method also includes steps of training the classification models and providing display of the selected advertisement content. This patent describes a system for providing advertisements based on facial analysis using a digital standee. The system consists of an image acquisition device, a face detection module, an analysis module, a computation module, a matching module, a database, and a display device. The image acquisition device captures an image of the user, the face detection module identifies the user's face and facial features, the analysis module analyzes the facial features using statistical parameters and classification models, the computation module computes the weighted image of the user, the matching module matches the weighted image of the user with weighted advertisements based on matching rules set by the advertisement providers, and the display device displays the advertisement contents. The system operates in real-time and updates the classification models continuously. The advertisement contents are based on the user's age, gender, emotion, style, and attention and are provided by the advertisement providers with matching rules. # [Scalable Verification for Safety-Critical Deep Networks](https://arxiv.org/pdf/1801.05950.pdf) [https://arxiv.org/pdf/1801.05950.pdf](https://arxiv.org/pdf/1801.05950.pdf) "Verifying that neural networks behave as intended may soon become a limiting factor in their applicability to real-world, safetycritical systems such as those used to control autonomous vehicles safety and reliability on DNNs. verify properties of DNNs. A major challenge of verifying properties of DNNs with satisfiability modulo theories (SMT) solvers is in handling the networks’ activation functions such as, Reluplex (domain-specific theory solvers; through a lazy approach). 1)devising scalable verification techniques. 2)identifying design choices -> amenable to verification. " Each neuron of a neural network computes a weighted sum of its inputs according to learned weights. It then passes that sum through an activation function to produce the neuron’s final output. Typically, the activation functions introduce nonlinearity to the network, making DNNs capable of learning arbitrarily complex functions, but also making the job of automated verification tools much harder. # Driver Assistance Systems and Vision-Based System Validates Driver Monitoring [Vision-based convolutional neural network system detects phone usage, eating, and drinking.](https://www.techbriefs.com/component/content/article/tb/supplements/pit/features/technology- leaders/36144) cameras with active infrared lighting; 30 Hz and delivered 8-bit grayscale images at 1280 × 1024-pixel resolution; ResNeXt-34; [video-based driver assistance systems, such as automated driving; ](https://www.bosch-mobility-solutions.com/en/products-and-services/passenger- cars-and-light-commercial-vehicles/driver-assistance-systems/lane-departure- warning/multi-purpose-camera/)resilient object detection and tracking; camera: ± 50°field of view (horizontal); +27°/ -21°field of view (vertical); > 150 m detection range; 2.6 MP resolution. multi path approach: 1. classifier: for pattern recognition; resilient object detection 2. dense optical flow and structure from motion; to detect static objects; 3D structure 3. deep learning: classify objects, road, edge road, orientation; " Operation principle of the multi purpose camera: During assisted and automated driving, the vehicle must know what is happening in its surroundings at all times. It must reliably detect objects and people, and be able to react to these appropriately. Here, the latest generation of the front video camera from Bosch plays a crucial part: The multi purpose camera for assisted and partially automated driving utilizes an innovative, high-performance system- on-chip (SoC) with a Bosch microprocessor for image-processing algorithms. Its unique multipath approach combines classic image-processing algorithms with artificial-intelligence methods for comprehensive scene interpretation and reliable object detection. With its algorithmic multipath approach and the innovative system-on-chip, this camera generation has been specially developed for high-performance driver assistance systems. In line with this approach, the multi purpose camera uses for example the following technical paths at once for image processing: The first of these is the conventional approach already in use today. Via preprogrammed algorithms, the cameras recognize the typical appearance of object categories such as vehicles, cyclists, or road markings. The second and third paths are new, however. For the second path, the camera uses the optical flow and the structure from motion (SfM) to recognize raised objects along the roadside, such as curbs, central reserves, or safety barriers. The motion of associated pixels is tracked. A three- dimensional structure is then approximated based on the two-dimensional camera image. The third path relies on artificial intelligence. Thanks to machine- learning processes, the camera has learned to classify objects such as cars parked by the side of the road. The latest generation can differentiate between surfaces on the road and those alongside the road via neuronal networks and semantic segmentation. Additional paths are used as required: These include classic line scanning, light detection, and stereo disparity. " [Link](https://www.bosch-mobility-solutions.com/en/products-and- services/passenger-cars-and-light-commercial-vehicles/driver-assistance- systems/lane-departure-warning/multi-purpose-camera/) Face recognition/attributes ![](https://lh4.googleusercontent.com/TgbkO5gpReB1fENVpm7Sn00xnwzAjbeqdP5_TQDuHnsxXxCQX1wQI93AxE5mlxTErsklFmmYImbGCWG8W1Y7Bdle8yiXhKWxXXeDuJdeeCHAqlijWN8vLjiWy0lEGxwWzg=w1280) I use citation plugin 1. add path to the JabRef database "reading notes/dh.bib" 2. create folder "Reading notes" 3. use Ctrl+Shift+O to select reference 4. automatically create file based on that reference 5. Ctrl+Shift+E to insert link to citation page 6. Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/VnELnzCZElXe9gLxGYU00_xF7qju2MljSVlgUMwWsc50I88T6vB5ahQjH2kGA --o3hIeJYu2N--BO_uidCis2Ow=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/VnELnzCZElXe9gLxGYU00_xF7qju2MljSVlgUMwWsc50I88T6vB5ahQjH2kGA --o3hIeJYu2N--BO_uidCis2Ow=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # FQA Scalable Verification for Safety-Critical Deep Networks Driver Assistance Systems and Vision-Based System Validates Driver Monitoring The present invention relates to a system for providing advertisement contents based on facial analysis. The system consists of an image acquisition device, a face detection module, an analysis module, a classification module, a database, a computation module, a matching module and a display device. The image acquisition device acquires an image of a user, the face detection module detects the face of the user in the image, the analysis module analyses the facial features statistically using classification models, the database stores matching rules, weighted advertisements and a plurality of advertisement contents and the display device displays the advertisement contents. The computation module computes the weighted image of the user and the matching module matches the weighted image of the user with the weighted advertisement to select an advertisement content based on facial analysis of the user. The system aims to provide advertisement contents via a digital standee by extracting salient demographic from a user to indirectly obtain user information and behavioral preference. The present invention relates to a system and method for providing advertisement contents based on facial analysis using a digital standee. The system (100) is embedded in the digital standee and comprises an image acquisition device, a face detection module, a classification module, a data analysis module, a computation module, a database and a matching module. The image acquisition device is configured to acquire an image of a user, and the face detection module uses deep learning technology to detect the user's face in the image. The classification module classifies the user's facial features into a plurality of classification models, such as gender, age range, emotion, style and attention. The data analysis module obtains behavioral preference and information of the user by analyzing the classified facial features. The matching module matches the information with types of businesses to provide suitable advertisement contents to the user based on rules set by the advertisement provider. The advertisement contents are displayed on a display device in the system. This patent describes a system for providing advertisements based on facial analysis. The system consists of an image acquisition device, a face detection module, an analysis module, a computation module, a matching module, a database, and a display device. The image acquisition device captures an image of the user, the face detection module identifies the user's face and facial features, the analysis module analyses the facial features using statistical parameters and classification models, the computation module computes the weighted image of the user, the matching module matches the weighted image of the user with weighted advertisements, and the display device displays the advertisement contents. The system operates in real-time and updates the classification models continuously. The advertisement contents are based on the user's age, gender, emotion, style, and attention and are provided by the advertisement providers with matching rules. The process described in this patent involves matching a user's weighted image with a weighted advertisement based on matching rules established by the advertisement providers. The matching rules may include order of features, most similar features, important features, and nearest similar features. The matching is done from left to right of the binary sequence. The selected advertisement content is then displayed by the display device. The terms used in the patent are defined as specified. The invention is open to changes in form and details. The system (100) is a device for providing advertisement contents based on facial analysis. It consists of: an image acquisition device (10) to acquire an image of a user, a face detection module (20) to detect the face and obtain facial features, an analysis module (40) to analyze the facial features statistically using classification models, a database (60) to store matching rules and advertisements, and a display device (80) to display the selected advertisement content. The system also has a computation module (50) to compute a weighted image of the user based on the analyzed facial features, and a matching module (70) to match the weighted image of the user with the weighted advertisement to select the advertisement content. The system can work for a single user or a group of users. The method (200) of providing advertisement content follows similar steps as the system (100). The steps include acquiring an image of the user, detecting the face, analyzing the facial features, computing a weighted image of the user, obtaining matching rules, and matching the weighted image with the weighted advertisement. The method also includes steps of training the classification models and providing display of the selected advertisement content. This patent describes a system for providing advertisements based on facial analysis using a digital standee. The system consists of an image acquisition device, a face detection module, an analysis module, a computation module, a matching module, a database, and a display device. The image acquisition device captures an image of the user, the face detection module identifies the user's face and facial features, the analysis module analyzes the facial features using statistical parameters and classification models, the computation module computes the weighted image of the user, the matching module matches the weighted image of the user with weighted advertisements based on matching rules set by the advertisement providers, and the display device displays the advertisement contents. The system operates in real-time and updates the classification models continuously. The advertisement contents are based on the user's age, gender, emotion, style, and attention and are provided by the advertisement providers with matching rules. # [Scalable Verification for Safety-Critical Deep Networks](https://arxiv.org/pdf/1801.05950.pdf) [https://arxiv.org/pdf/1801.05950.pdf](https://arxiv.org/pdf/1801.05950.pdf) "Verifying that neural networks behave as intended may soon become a limiting factor in their applicability to real-world, safetycritical systems such as those used to control autonomous vehicles safety and reliability on DNNs. verify properties of DNNs. A major challenge of verifying properties of DNNs with satisfiability modulo theories (SMT) solvers is in handling the networks’ activation functions such as, Reluplex (domain-specific theory solvers; through a lazy approach). 1)devising scalable verification techniques. 2)identifying design choices -> amenable to verification. " Each neuron of a neural network computes a weighted sum of its inputs according to learned weights. It then passes that sum through an activation function to produce the neuron’s final output. Typically, the activation functions introduce nonlinearity to the network, making DNNs capable of learning arbitrarily complex functions, but also making the job of automated verification tools much harder. # Driver Assistance Systems and Vision-Based System Validates Driver Monitoring [Vision-based convolutional neural network system detects phone usage, eating, and drinking.](https://www.techbriefs.com/component/content/article/tb/supplements/pit/features/technology- leaders/36144) cameras with active infrared lighting; 30 Hz and delivered 8-bit grayscale images at 1280 × 1024-pixel resolution; ResNeXt-34; [video-based driver assistance systems, such as automated driving; ](https://www.bosch-mobility-solutions.com/en/products-and-services/passenger- cars-and-light-commercial-vehicles/driver-assistance-systems/lane-departure- warning/multi-purpose-camera/)resilient object detection and tracking; camera: ± 50°field of view (horizontal); +27°/ -21°field of view (vertical); > 150 m detection range; 2.6 MP resolution. multi path approach: 1. classifier: for pattern recognition; resilient object detection 2. dense optical flow and structure from motion; to detect static objects; 3D structure 3. deep learning: classify objects, road, edge road, orientation; " Operation principle of the multi purpose camera: During assisted and automated driving, the vehicle must know what is happening in its surroundings at all times. It must reliably detect objects and people, and be able to react to these appropriately. Here, the latest generation of the front video camera from Bosch plays a crucial part: The multi purpose camera for assisted and partially automated driving utilizes an innovative, high-performance system- on-chip (SoC) with a Bosch microprocessor for image-processing algorithms. Its unique multipath approach combines classic image-processing algorithms with artificial-intelligence methods for comprehensive scene interpretation and reliable object detection. With its algorithmic multipath approach and the innovative system-on-chip, this camera generation has been specially developed for high-performance driver assistance systems. In line with this approach, the multi purpose camera uses for example the following technical paths at once for image processing: The first of these is the conventional approach already in use today. Via preprogrammed algorithms, the cameras recognize the typical appearance of object categories such as vehicles, cyclists, or road markings. The second and third paths are new, however. For the second path, the camera uses the optical flow and the structure from motion (SfM) to recognize raised objects along the roadside, such as curbs, central reserves, or safety barriers. The motion of associated pixels is tracked. A three- dimensional structure is then approximated based on the two-dimensional camera image. The third path relies on artificial intelligence. Thanks to machine- learning processes, the camera has learned to classify objects such as cars parked by the side of the road. The latest generation can differentiate between surfaces on the road and those alongside the road via neuronal networks and semantic segmentation. Additional paths are used as required: These include classic line scanning, light detection, and stereo disparity. " [Link](https://www.bosch-mobility-solutions.com/en/products-and- services/passenger-cars-and-light-commercial-vehicles/driver-assistance- systems/lane-departure-warning/multi-purpose-camera/) Face recognition/attributes ![](https://lh4.googleusercontent.com/jaITkeRo5He8RQ7Ex5lQenMTINRaJi3DWiz5p2YMQnTFQfAlXBCuKraOqz0_DfaGFr0Wj2zR54Bx0x7zhgwmfi47PpGQFXs2TbclXfQFXjuOGfIaFPNWqtUbAlWvYXoDgg=w1280) I use citation plugin 1. add path to the JabRef database "reading notes/dh.bib" 2. create folder "Reading notes" 3. use Ctrl+Shift+O to select reference 4. automatically create file based on that reference 5. Ctrl+Shift+E to insert link to citation page 6. Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. 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[#aiforbusiness](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Daiforbusiness%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw16X1M4iwrzdjYdU_ulFK3T) [#science](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dscience%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw1vmLH__ugmE11ZB7WjmLmx) [#researcher](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dresearcher%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3pIEq9N1emXExu0-191RiG) [#phd](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dphd%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2OcifGSB6XNuqH2gKzK6YY) [#cameracalibration](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dcameracalibration%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2RlWcAsgtAUNVg6OBGbkKd) [#opticalflow](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dopticalflow%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw18QjZi7x9SLhVukcmZAVgD) [#videostablization](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dvideostablization%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3WSAJkfYr1vIjbcnfpodFl) [#humanoidrobot](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dhumanoidrobot%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2xFO1kRd2oXLQJq10M-_XJ) [#localization](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dlocalization%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0MIyR__Y8eRDB3dZTecf2Z) [#3dSLAM](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3D3dslam%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2UY6pRlrLvoOiDP8URxRz5) [#reconstruction](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dreconstruction%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0N_h3Cd2AY7DVCXLQUSZ_r) [#pointcloud](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dpointcloud%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0S3yinkSJeDwnOoX27HKOr) [#mixedreality](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dmixedreality%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3BC3QpLp-w3P2WCrusaB02) [#edgecomputing](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dedgecomputing%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2do7CB7fc5a8IDPUINj916) [#raspberrypi](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Draspberrypi%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw05lTlcUMZAvgLJvCrdYOT0) [#intelstick](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dintelstick%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3nSDy4bef8ItMQDBY1AHc2) [#googlecoral](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dgooglecoral%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2flvDoGrRtzJ6AVKvsqwsF) [#jetsonnano](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Djetsonnano%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3FBZ8F6yWzRF85Qc1isy2O) [#nvidiavgpu](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dnvidiavgpu%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw1X0Zxn25NXWOT0YlLcUlPP) [#tensorflowjs](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dtensorflowjs%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2K-Wq_3mt-X1pq4mJWurnU) [#pytorch](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dpytorch%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0MKjo_xUTSXMHwpeG5pU9I) [#opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dopencv%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2WzPIfJb8SPE6hE0LcvqKp) [#aikit](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Daikit%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3rsc3lNPLGvTgNgYKccJQu) [#caffee](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dcaffee%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0VhKNPDM34JSJZe4QtqcXc) [#DIGITS](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Ddigits%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0UivwkkpqApDtx2okylxxR) [#c](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dc%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0jrMZfs5pBJdwrTFWjNWJ8)++ [#python](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dpython%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0-vYAEwDIVuEH- Fq-OrgIb) [#ubuntu](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dubuntu%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw1R_p- FRFOxdmZ9CYFTmYzL) [#farshidpirahansiah](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dfarshidpirahansiah%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw1qvC98TN9G7Di_rOxuWWk7) [#pirahansiah](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dpirahansiah%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0R-BbyLh11PJ2jVzgdk7zn).com [#farshid](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dfarshid%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2hrd3c0cgU-0ePNJwPxscm) [#pirahansiah](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dpirahansiah%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0AbYCv97yWC3MZG0QBI3L-) [#robotics](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Drobotics%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0c-v7d0S979BakkNasVEmb) #pirahansiah.com #farshid #pirahansiah [#MultiCameraMultiClassMultiObjectTracking](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dmulticameramulticlassmultiobjecttracking%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0xHni8xf5NFjd9IvccxvdN) [#deeplearning](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Ddeeplearning%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0_4BL9ENEZAPN9I_ZyTHfw) [#machinelearning](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dmachinelearning%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0GJQE0pYJLn1KpAlBULTve) [#artificialintelligence](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dartificialintelligence%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0kghSLOS6QnRK1JyVmLYgX) [#tensorflow](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dtensorflow%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw1esVtdflQf4ZZx0624h1N9) [#robotics](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Drobotics%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0c-v7d0S979BakkNasVEmb) [#3dvision](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3D3dvision%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2ZJO6ohCZ1NgSqX0wvFNlX) [#sterovision](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dsterovision%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0AmcHHfNGY_zeMJbmBcbur) [#depthmap](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Ddepthmap%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3wM --ayMcpa0-XdKt_RPO8) [#RCNN](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Drcnn%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2m0g4VZrmYsjPcW7wB_ZTR) [#machinevision](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dmachinevision%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2liIXvJ6DKQRJXtztRKEG3) [#imageprocessing](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dimageprocessing%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw1L71Lp1-CV50XPHcixp9hE) [#patternrecognition](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dpatternrecognition%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw1WoEPUJMhUpSCRh9Ta4Mta) [#compiler](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dcompiler%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw12szcaIf6dpkJAjPYM7V-e) [#RISC](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Drisc%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3YM_foJf7Hf3jZFwteLIfj)-V [#RNN](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Drnn%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3bCj47R1qTzd4MNk-1exEF) [#fullStackDeepLearning](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dfullstackdeeplearning%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw23eDRV9AhY4o007CEKu2X9) [#productinnovation](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dproductinnovation%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2AsIcbuD5dqUJA28qtDgSe) [#patents](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dpatents%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3kv-k5ws7NEzqF9ML5lbHd) [#TensorRT](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dtensorrt%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw34rToqV4FEI6a8qUEvgY_i) [#ApacheTVM](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dapachetvm%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw231JTCeb5OyZgt6ck89BnA) [#TFLite](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dtflite%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2iX_x5Fj5RXQNur3YCdCmx) [#PyTorchmobile](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dpytorchmobile%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw37QEC00u5OJDhhHOXqXpfa) [#dockers](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Ddockers%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2f-TuKUh24azBqsvKlVBmI) [#gRPC](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dgrpc%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw11hYAjKgAPw1MTu7ihz9De) [#RESTAPIs](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Drestapis%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3DKEVm0FUJzdhKMLl6QoTd) [#GRPC](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dgrpc%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw11hYAjKgAPw1MTu7ihz9De) [#GraphQL](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dgraphql%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw30 --htOGW4sG7y2BjM2He5) #imageprocessing #patternrecognition **#** **Enabling** **E** **fficient #high-performance** **#** **Accelerators** **#** **Optimization** ****[#computervision](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dcomputervision%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0cCdJ3Vmk5N1l3cp6O_su8) [#AI](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dai%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2dPEMgAJEghAGxPRWF43xJ) [#objectdetection](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dobjectdetection%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3iPjh0s9Xx0kiUns1ngiIB) [#objecttracking](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dobjecttracking%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0c5NH2HyRE4LM81xtfyMGC) [#ml](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dml%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw1_WgU3yRidQpKEvm5A-z8l) [#research](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dresearch%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw1vKlf5iOpwLhK-0F4foQ3e) [#CNN](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dcnn%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw19QWtSDWm3hhBhz3-AChAa) [#gans](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dgans%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw1lk1ECXOJN- mXDWUOJNSyq) [#convolutionalneuralnetworks](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dconvolutionalneuralnetworks%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0c8noF_OiCn- zFjustxEUJ) [#ai](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dai%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2dPEMgAJEghAGxPRWF43xJ) [#vr](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dvr%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2emO4DfNb- fRcZrRa5KMFg) [#reinforcementlearning](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dreinforcementlearning%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0P0YF-D2TdSHbkavX52nx2) [#mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dmlops%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3dvE9cPCHjH0CkdyuCTIA8) [#aiforbusiness](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Daiforbusiness%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw16X1M4iwrzdjYdU_ulFK3T) [#science](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dscience%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw1vmLH__ugmE11ZB7WjmLmx) [#researcher](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dresearcher%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3pIEq9N1emXExu0-191RiG) [#phd](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dphd%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2OcifGSB6XNuqH2gKzK6YY) [#cameracalibration](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dcameracalibration%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2RlWcAsgtAUNVg6OBGbkKd) [#opticalflow](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dopticalflow%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw18QjZi7x9SLhVukcmZAVgD) [#videostablization](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dvideostablization%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3WSAJkfYr1vIjbcnfpodFl) [#humanoidrobot](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dhumanoidrobot%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2xFO1kRd2oXLQJq10M-_XJ) [#localization](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dlocalization%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0MIyR__Y8eRDB3dZTecf2Z) [#3dSLAM](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3D3dslam%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2UY6pRlrLvoOiDP8URxRz5) [#reconstruction](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dreconstruction%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0N_h3Cd2AY7DVCXLQUSZ_r) [#pointcloud](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dpointcloud%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0S3yinkSJeDwnOoX27HKOr) [#AR](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dar%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0gk- Nuu4ZaAxv_LKdSgLEA)/VR [#mixedreality](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dmixedreality%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3BC3QpLp-w3P2WCrusaB02) [#edgecomputing](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dedgecomputing%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2do7CB7fc5a8IDPUINj916) [#raspberrypi](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Draspberrypi%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw05lTlcUMZAvgLJvCrdYOT0) [#intelstick](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dintelstick%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3nSDy4bef8ItMQDBY1AHc2) [#googlecoral](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dgooglecoral%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2flvDoGrRtzJ6AVKvsqwsF) [#jetsonnano](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Djetsonnano%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3FBZ8F6yWzRF85Qc1isy2O) [#nvidiavgpu](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dnvidiavgpu%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw1X0Zxn25NXWOT0YlLcUlPP) [#tensorflowjs](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dtensorflowjs%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2K-Wq_3mt-X1pq4mJWurnU) [#pytorch](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dpytorch%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0MKjo_xUTSXMHwpeG5pU9I) [#opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dopencv%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2WzPIfJb8SPE6hE0LcvqKp) [#aikit](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Daikit%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3rsc3lNPLGvTgNgYKccJQu) [#caffee](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dcaffee%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0VhKNPDM34JSJZe4QtqcXc) [#DIGITS](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Ddigits%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0UivwkkpqApDtx2okylxxR) [#c](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dc%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0jrMZfs5pBJdwrTFWjNWJ8)++ [#python](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dpython%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0-vYAEwDIVuEH- Fq-OrgIb) [#ubuntu](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dubuntu%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw1R_p- FRFOxdmZ9CYFTmYzL) [#farshidpirahansiah](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dfarshidpirahansiah%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw1qvC98TN9G7Di_rOxuWWk7) [#pirahansiah](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dpirahansiah%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0R-BbyLh11PJ2jVzgdk7zn).com [#farshid](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dfarshid%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2hrd3c0cgU-0ePNJwPxscm) [#pirahansiah](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dpirahansiah%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0AbYCv97yWC3MZG0QBI3L-) [#robotics](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Drobotics%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0c-v7d0S979BakkNasVEmb) [#SingleObjecttracking](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dsingleobjecttracking%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2knhqyR7nh7fpmxnxl1Jn5) [#SOT](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dsot%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw062tuLWdObgGpo5g34V8Fu) [#MultiObjecttracking](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dmultiobjecttracking%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw1rSOO9ndnf3jWPm8DD__Ka) [#MOT](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dmot%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw18W7sxGpQ22ZsiFiqhT6TX) [#MultiTargetTracking](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dmultitargettracking%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw1s-kLp6KVMz85P_VRU1Yhd) [#MTT](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dmtt%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3Q4HD1CShuvIZAZD- myjaA) [#MultiClassMultiObjecttracking](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dmulticlassmultiobjecttracking%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2RY9vHf7Vmdj3N0-NVi2LR) [#MCMOT](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dmcmot%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2ZpgAV4w3SDkDd1VciqFE3) [#MultiCameraMultiClassMultiObjectTracking](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dmulticameramulticlassmultiobjecttracking%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0xHni8xf5NFjd9IvccxvdN) [#MCMCMOT](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dmcmcmot%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw38UBYhA- jT868guoRcn8_Z) [#deeplearning](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Ddeeplearning%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0_4BL9ENEZAPN9I_ZyTHfw) [#machinelearning](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dmachinelearning%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0GJQE0pYJLn1KpAlBULTve) [#artificialintelligence](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dartificialintelligence%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0kghSLOS6QnRK1JyVmLYgX) [#computervision](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dcomputervision%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0cCdJ3Vmk5N1l3cp6O_su8) [#video](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dvideo%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3Y3ZMpartNxjyWNhDcBB4Z) [#objectdetection](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dobjectdetection%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3iPjh0s9Xx0kiUns1ngiIB) [#objecttracking](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dobjecttracking%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0c5NH2HyRE4LM81xtfyMGC) [#tensorflow](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dtensorflow%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw1esVtdflQf4ZZx0624h1N9) [#innovation](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dinnovation%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw29IajN9Pzy7GGtovK_Kquk) [#learning](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dlearning%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw1ZASSoW21pBmLxbEz02Yxl) [#datascience](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Ddatascience%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0gQ790KskULL4oxc30aNTx) [#robotics](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Drobotics%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0c-v7d0S979BakkNasVEmb) [#3dvision](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3D3dvision%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2ZJO6ohCZ1NgSqX0wvFNlX) [#sterovision](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dsterovision%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0AmcHHfNGY_zeMJbmBcbur) [#depthmap](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Ddepthmap%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3wM --ayMcpa0-XdKt_RPO8) [#RCNN](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Drcnn%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2m0g4VZrmYsjPcW7wB_ZTR) [#machinevision](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dmachinevision%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2liIXvJ6DKQRJXtztRKEG3) [#imageprocessing](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dimageprocessing%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw1L71Lp1-CV50XPHcixp9hE) [#patternrecognition](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dpatternrecognition%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw1WoEPUJMhUpSCRh9Ta4Mta) [#compiler](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dcompiler%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw12szcaIf6dpkJAjPYM7V-e) [#RISC](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Drisc%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3YM_foJf7Hf3jZFwteLIfj)-V [#RNN](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Drnn%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3bCj47R1qTzd4MNk-1exEF) [#fullStackDeepLearning](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dfullstackdeeplearning%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw23eDRV9AhY4o007CEKu2X9) [#productinnovation](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dproductinnovation%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2AsIcbuD5dqUJA28qtDgSe) [#patents](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dpatents%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3kv-k5ws7NEzqF9ML5lbHd) [#TensorRT](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dtensorrt%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw34rToqV4FEI6a8qUEvgY_i) [#ApacheTVM](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dapachetvm%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw231JTCeb5OyZgt6ck89BnA) [#TFLite](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dtflite%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2iX_x5Fj5RXQNur3YCdCmx) [#PyTorchmobile](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dpytorchmobile%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw37QEC00u5OJDhhHOXqXpfa) [#TensorFlow](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dtensorflow%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw1esVtdflQf4ZZx0624h1N9).js [#CoreML](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dcoreml%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2ZXGZtxrsEyl4W-lnZ_mpM) [#MLkit](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dmlkit%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0vadDBE6AbUX1O_6JjGuok) [#DataDog](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Ddatadog%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3Sh0T2qWLoIEpoGdsovN3e) [#NewRelic](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dnewrelic%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0Xty08MHg7izhbmUw99I0d) [#AmazonCloudWatch](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Damazoncloudwatch%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0brP3L3UwD6s9wrP5rVJ8z) [#dockers](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Ddockers%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2f-TuKUh24azBqsvKlVBmI) [#gRPC](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dgrpc%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw11hYAjKgAPw1MTu7ihz9De) [#RESTAPIs](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Drestapis%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3DKEVm0FUJzdhKMLl6QoTd) [#GRPC](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dgrpc%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw11hYAjKgAPw1MTu7ihz9De) [#GraphQL](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dgraphql%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw30 --htOGW4sG7y2BjM2He5) #farshidpirahansiah #pirahansiah.com #farshid #pirahansiah #robotics #SingleObjecttracking #SOT #MultiObjecttracking #MOT #MultiTargetTracking #MTT #MultiClassMultiObjecttracking #MCMOT #MultiCameraMultiClassMultiObjectTracking #MCMCMOT #deeplearning #machinelearning #artificialintelligence #computervision #video #objectdetection #objecttracking #tensorflow #innovation #learning #datascience #robotics #3dvision #sterovision #depthmap #RCNN #objectdetection #objecttracking #ml #research #CNN #gans #convolutionalneuralnetworks #ai #vr #reinforcementlearning #mlops #aiforbusiness #science #researcher #phd #cameracalibration #opticalflow #videostablization #humanoidrobot #localization #3dSLAM #reconstruction #pointcloud #AR/VR #mixedreality #edgecomputing #raspberrypi #intelstick #googlecoral #jetsonnano #nvidiavgpu #tensorflowjs #pytorch #opencv #aikit #caffee #DIGITS #c++ #python #ubuntu #machinevision #imageprocessing #patternrecognition [#SingleObjecttracking](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dsingleobjecttracking%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6780044658807472128&sa=D&sntz=1&usg=AOvVaw3YwvbWxTQDiRbi3Tm2uqpf)[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dsot%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6780044658807472128&sa=D&sntz=1&usg=AOvVaw2qer2nQ_iaOJydmjpZJ68t)[#SOT](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dsot%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6780044658807472128&sa=D&sntz=1&usg=AOvVaw2qer2nQ_iaOJydmjpZJ68t) [#MultiObjecttracking](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dmultiobjecttracking%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6780044658807472128&sa=D&sntz=1&usg=AOvVaw1wX74Ebhp- ECPoFeCkCMct)[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dmot%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6780044658807472128&sa=D&sntz=1&usg=AOvVaw2zMMwiCHRv7zm81eU3p-pO)[#MOT](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dmot%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6780044658807472128&sa=D&sntz=1&usg=AOvVaw2zMMwiCHRv7zm81eU3p-pO)[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dmultitargettracking%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6780044658807472128&sa=D&sntz=1&usg=AOvVaw3SQmnZgPkH0pJBjsztRG5p)[#MultiTargetTracking](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dmultitargettracking%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6780044658807472128&sa=D&sntz=1&usg=AOvVaw3SQmnZgPkH0pJBjsztRG5p)[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dmtt%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6780044658807472128&sa=D&sntz=1&usg=AOvVaw0mZir0I2WXG91-SIZPtdRO)[#MTT](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dmtt%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6780044658807472128&sa=D&sntz=1&usg=AOvVaw0mZir0I2WXG91-SIZPtdRO) [#MultiClassMultiObjecttracking](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dmulticlassmultiobjecttracking%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6780044658807472128&sa=D&sntz=1&usg=AOvVaw20iwKRZXaVS7DgBOvK6562)[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dmcmot%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6780044658807472128&sa=D&sntz=1&usg=AOvVaw2VhrixTfDkYRmyE7lij1SK)[#MCMOT](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dmcmot%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6780044658807472128&sa=D&sntz=1&usg=AOvVaw2VhrixTfDkYRmyE7lij1SK) [#MultiCameraMultiClassMultiObjectTracking](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dmulticameramulticlassmultiobjecttracking%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6780044658807472128&sa=D&sntz=1&usg=AOvVaw2HH- uWNghBY5FEXistcDB3)[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dmcmcmot%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6780044658807472128&sa=D&sntz=1&usg=AOvVaw2PqdIYfNl8Bb- gg93oJ8xs)[#MCMCMOT](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dmcmcmot%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6780044658807472128&sa=D&sntz=1&usg=AOvVaw2PqdIYfNl8Bb- gg93oJ8xs) [#deeplearning](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Ddeeplearning%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6780044658807472128&sa=D&sntz=1&usg=AOvVaw0xFx9t-yYIzgzagdpzUIsO)[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dmachinelearning%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6780044658807472128&sa=D&sntz=1&usg=AOvVaw0cYttnUUrc6am2CgXQsOOZ)[#machinelearning](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dmachinelearning%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6780044658807472128&sa=D&sntz=1&usg=AOvVaw0cYttnUUrc6am2CgXQsOOZ)[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dartificialintelligence%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6780044658807472128&sa=D&sntz=1&usg=AOvVaw03xR4UQw7q4HR2mSrrl5tG)[#artificialintelligence](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dartificialintelligence%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6780044658807472128&sa=D&sntz=1&usg=AOvVaw03xR4UQw7q4HR2mSrrl5tG) [#computervision](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dcomputervision%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6779686219396739072&sa=D&sntz=1&usg=AOvVaw0Za2W-E3D2yYrkT9qqdT-W)[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dvideo%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6779686219396739072&sa=D&sntz=1&usg=AOvVaw0m0XV9aWTxOfZYRkKIAPPV)[#video](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dvideo%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6779686219396739072&sa=D&sntz=1&usg=AOvVaw0m0XV9aWTxOfZYRkKIAPPV)[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dobjectdetection%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6779686219396739072&sa=D&sntz=1&usg=AOvVaw2LjzNOzcVA6USEGDh4I6jI)[#objectdetection](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dobjectdetection%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6779686219396739072&sa=D&sntz=1&usg=AOvVaw2LjzNOzcVA6USEGDh4I6jI)[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dobjecttracking%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6779686219396739072&sa=D&sntz=1&usg=AOvVaw1Oh0dtOTWUJI2Iim6S0AN7)[#objecttracking](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dobjecttracking%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6779686219396739072&sa=D&sntz=1&usg=AOvVaw1Oh0dtOTWUJI2Iim6S0AN7) [#tensorflow](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dtensorflow%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6778369331723034624&sa=D&sntz=1&usg=AOvVaw1wCTy9_6UXNF0PB0910n9T)[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dinnovation%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6778369331723034624&sa=D&sntz=1&usg=AOvVaw1-46_OT1MzHuYxGVr638wN)[#innovation](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dinnovation%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6778369331723034624&sa=D&sntz=1&usg=AOvVaw1-46_OT1MzHuYxGVr638wN)[ 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/EvERVn15AzJZ7aNloDEKv1byWxFboB4Sbe_aPtX2ng9-SLNTjTIPJnwKRUBCWPFdXAOrqOFYlZeEgYJkKq7lPbM=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/EvERVn15AzJZ7aNloDEKv1byWxFboB4Sbe_aPtX2ng9-SLNTjTIPJnwKRUBCWPFdXAOrqOFYlZeEgYJkKq7lPbM=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Links pirahansiah, Personal, Roadmap, Books, Online Courses [](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze- oXQexwY "Open Spreadsheet, Links in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/sheets_32dp.png)Links Links from my research and interest AI in RISC-V Mixed reality Old Links Reference Link Groups Top website Python Computer Vision Documents Business Tools Patents Books Journal Papers Conference Papers Articles on LinkedIn Slides slideshare Online Courses Learning path Completed Reference Link of best Groups Books Videos Papers Site Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning training loops. GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp) [https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to- install-remmina/) [https://readme.so/](https://readme.so/) # Links from my research and interest [Build Better Generative Adversarial Networks (GANs) - Andrew Ng - 2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_- VnF) [https://www.oreilly.com/library/view/flow- architectures/9781492075882/](https://www.oreilly.com/library/view/flow- architectures/9781492075882/) ## AI in RISC-V * [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020) ## Mixed reality * [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera) * Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU. # Old Links ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Top website * [https://paperswithcode.com/](https://paperswithcode.com/) * [https://www.learnthepart.com/](https://www.learnthepart.com/) * [https://hackaday.com/](https://hackaday.com/) * [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762) * hbrew install youtube-dl * [https://www.masterclass.com/](https://www.masterclass.com/) * [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp) * [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona) * [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/) * [https://explainshell.com/](https://explainshell.com/) * [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20) * [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4) * [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4) ### Python * [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/) * [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/) * [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594) ### Computer Vision * [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/) * [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md) * [https://github.com/google/mediapipe](https://github.com/google/mediapipe) ### Documents * [https://book.keybase.io/docs](https://book.keybase.io/docs) * [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind) * [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/) * [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/) * [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/) * [https://kepler.gl/demo](https://kepler.gl/demo) * [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/) * [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE) ### Business * [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006) * [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5) ### Tools * Free convert video for mac opensource * [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake) * [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg) * [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c) * [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises * [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap * [Create and share beautiful images of your source code.](https://carbon.now.sh/) * [Web IDE](https://replit.com/templates) * [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2) ### Patents [https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) 2. A method for augmenting a plurality of face images (WO2021060971A1) 3. A method for detecting a moving vehicle (WO2021107761A1) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) The present invention relates to a system (100) for providing advertisement contents based on facial analysis comprising an image acquisition device (10) to acquire an image of a user, a face detection module (20) to detect face of the user in the image, an analysis module (40) to analyse the facial features statistically using classification models retrieved from a classification module (30), a database (60) to store matching rules, weighted advertisements and a plurality of advertisement contents; and a display device (80) to display the advertisement contents. The system (100) further comprises a computation module (50) to compute weighted image of the user and a matching module (70) to match the weighted image of the user with the weighted advertisement to select an advertisement content based on facial analysis of the user. A method of providing the advertisement contents based on facial analysis is also provided thereof. [https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf) 2. A method for augmenting a plurality of face images (WO2021060971A1) The present invention relates to a method for increasing data for face analysis in video surveillance. The method comprises the steps of acquiring at least one face image from an image acquisition module (102), acquiring a plurality of face images available on the internet using a data input module (104), increasing face images by at least one data augmentation module (106 and 107), generating a plurality of face images based on a trained Generative Adversarial Network, GAN technique by using a GAN module, selecting proper images based on quality of the face images using a fuzzy logic module (111), saving the selected images into a fifth database, and training the deep learning module (113). [https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf) 3. A method for detecting a moving vehicle (WO2021107761A1) The present invention relates to a method for detecting a moving vehicle. The method comprises the steps of grabbing an initial image from a video stream by a vehicle detection module (1100), wherein the vehicle detection module (1100) is a part of a system (1000) to identify moving vehicle, enhancing the illumination of the initial image by the vehicle detection module (1100), enhancing the edges within the initial image by the vehicle detection module (1100), and finding vehicle based on homogenous properties of the body of the vehicle by the vehicle detection module (1100). The step of finding vehicle based on homogenous property of the body of the vehicle by the vehicle detection module (1100) further comprising the sub-steps of closing open edges, inverting the binary image, segmenting an inverted binary image, filtering the noise based on geometric feature, and filtering the noise based on relation. [https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf) ### Books 1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394) 2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/) ### Journal Papers * Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21) * * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf) * GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6) * * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf) * Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9). * * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM) * * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf) * Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2) * * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf) * Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52 * * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf) ### Conference Papers * Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017) * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342) * Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015) * [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf) * Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015) * [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336) * 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012) * [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf) * Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics * [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918) * License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011) * [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627) * Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011) * [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf) * [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649) * Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85. * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532) * Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010) * [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125) * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050 * [http://waset.org/publications/3636](http://waset.org/publications/3636) * An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010) * [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en) ### Articles on LinkedIn * [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/)) * [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) ) * [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/)) * [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/) * [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/) ### Slides slideshare * Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation) * Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) ) * tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) ) * Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) ) * Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) ) * Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) ) * Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) ) * How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) ) * Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) ) * Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) ) ### Online Courses * [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact) * [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life) * [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2) * [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends) * [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business) * [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity) * [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2) * [Understanding Business ](https://www.linkedin.com/learning/understanding-business) * [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea) * [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship) * [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations) * [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations) * [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations) * [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips) * [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation) * [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments) * [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow) * [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families) * [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances) * [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2) * [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2) * [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv) * [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers) * [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea) * [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital) * [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving) * [Professional Networking ](https://www.linkedin.com/learning/professional-networking) * [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home) * [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital) * [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling) * [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence) * [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers) ### Learning path * [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance) * [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner) # Completed ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link of best Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Books * [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y) * [Mathematics for Machine Learning](https://mml-book.github.io/) * Principles of Economics (6th edition) * The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It * Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) * Reinventing Your Life: The Breakthough Program to End Negative Behavior * Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence * [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf) * * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/) * [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/) * [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/) * Multiple view geometry in computer vision * Learning OpenCV Book by Adrian Kaehler and Gary Bradski * Digital Image Processing, Global Edition by Rafael C. Gonzalez * Introduction to Algorithms, 3rd Edition (The MIT Press) * The Mythical Man-Month: Essays on Software Engineering * [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition) ### Videos * [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning) * [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos) * [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/) * [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY) ### Papers * CALTag: High Precision Fiducial Markers for Camera * Diatom Autofocusing in Brightfield Microscopy: a Comparative Study * Analysis of focus measure operators in shape-from-focus * Optical flow modeling and computation: A survey * Toward general type 2 fuzzy logic systems based on zSlices ### Site * [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#)) ![](https://www.google.com/images/icons/product/drive-32.png)Reference- ComputerVisionDeepLearning.pdf * [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w) * [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679) * [https://www.freertos.org/index.html](https://www.freertos.org/index.html) * [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation) * [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/) * [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413) * [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917) * [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf) * [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/) * [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833) * [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238) * [https://koinly.io/](https://koinly.io/) site map: octopus.do [https://www.startupgermany.nrw/startup- contest/](https://www.startupgermany.nrw/startup-contest/) [https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth- camera-4k-cv-edge-object- detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai- kit-oak-depth-camera-4k-cv-edge-object-detection/description) [HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube in 2021! - YouTube ](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg) [محمد رضا شجریان، آلبوم کامل در خیال - YouTube](https://www.youtube.com/watch?v=cFdApmqlllg) [دوره جامع گوگل آنالیتیکس(UA) | آنالیتیپس](https://analytips.io/product/google-analyticsua/) [رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا - YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc) [Social Selling Index | Sales Navigator](https://www.linkedin.com/sales/ssi?src=or- search&veh=www.google.com) [https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/EvERVn15AzJZ7aNloDEKv1byWxFboB4Sbe_aPtX2ng9-SLNTjTIPJnwKRUBCWPFdXAOrqOFYlZeEgYJkKq7lPbM=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/EvERVn15AzJZ7aNloDEKv1byWxFboB4Sbe_aPtX2ng9-SLNTjTIPJnwKRUBCWPFdXAOrqOFYlZeEgYJkKq7lPbM=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Links pirahansiah, Personal, Roadmap, Books, Online Courses [](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze- oXQexwY "Open Spreadsheet, Links in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/sheets_32dp.png)Links Links from my research and interest AI in RISC-V Mixed reality Old Links Reference Link Groups Top website Python Computer Vision Documents Business Tools Patents Books Journal Papers Conference Papers Articles on LinkedIn Slides slideshare Online Courses Learning path Completed Reference Link of best Groups Books Videos Papers Site Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning training loops. GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp) [https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to- install-remmina/) [https://readme.so/](https://readme.so/) # Links from my research and interest [Build Better Generative Adversarial Networks (GANs) - Andrew Ng - 2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_- VnF) [https://www.oreilly.com/library/view/flow- architectures/9781492075882/](https://www.oreilly.com/library/view/flow- architectures/9781492075882/) ## AI in RISC-V * [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020) ## Mixed reality * [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera) * Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU. # Old Links ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Top website * [https://paperswithcode.com/](https://paperswithcode.com/) * [https://www.learnthepart.com/](https://www.learnthepart.com/) * [https://hackaday.com/](https://hackaday.com/) * [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762) * hbrew install youtube-dl * [https://www.masterclass.com/](https://www.masterclass.com/) * [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp) * [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona) * [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/) * [https://explainshell.com/](https://explainshell.com/) * [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20) * [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4) * [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4) ### Python * [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/) * [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/) * [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594) ### Computer Vision * [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/) * [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md) * [https://github.com/google/mediapipe](https://github.com/google/mediapipe) ### Documents * [https://book.keybase.io/docs](https://book.keybase.io/docs) * [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind) * [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/) * [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/) * [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/) * [https://kepler.gl/demo](https://kepler.gl/demo) * [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/) * [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE) ### Business * [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006) * [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5) ### Tools * Free convert video for mac opensource * [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake) * [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg) * [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c) * [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises * [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap * [Create and share beautiful images of your source code.](https://carbon.now.sh/) * [Web IDE](https://replit.com/templates) * [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2) ### Patents [https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) 2. A method for augmenting a plurality of face images (WO2021060971A1) 3. A method for detecting a moving vehicle (WO2021107761A1) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) The present invention relates to a system (100) for providing advertisement contents based on facial analysis comprising an image acquisition device (10) to acquire an image of a user, a face detection module (20) to detect face of the user in the image, an analysis module (40) to analyse the facial features statistically using classification models retrieved from a classification module (30), a database (60) to store matching rules, weighted advertisements and a plurality of advertisement contents; and a display device (80) to display the advertisement contents. The system (100) further comprises a computation module (50) to compute weighted image of the user and a matching module (70) to match the weighted image of the user with the weighted advertisement to select an advertisement content based on facial analysis of the user. A method of providing the advertisement contents based on facial analysis is also provided thereof. [https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf) 2. A method for augmenting a plurality of face images (WO2021060971A1) The present invention relates to a method for increasing data for face analysis in video surveillance. The method comprises the steps of acquiring at least one face image from an image acquisition module (102), acquiring a plurality of face images available on the internet using a data input module (104), increasing face images by at least one data augmentation module (106 and 107), generating a plurality of face images based on a trained Generative Adversarial Network, GAN technique by using a GAN module, selecting proper images based on quality of the face images using a fuzzy logic module (111), saving the selected images into a fifth database, and training the deep learning module (113). [https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf) 3. A method for detecting a moving vehicle (WO2021107761A1) The present invention relates to a method for detecting a moving vehicle. The method comprises the steps of grabbing an initial image from a video stream by a vehicle detection module (1100), wherein the vehicle detection module (1100) is a part of a system (1000) to identify moving vehicle, enhancing the illumination of the initial image by the vehicle detection module (1100), enhancing the edges within the initial image by the vehicle detection module (1100), and finding vehicle based on homogenous properties of the body of the vehicle by the vehicle detection module (1100). The step of finding vehicle based on homogenous property of the body of the vehicle by the vehicle detection module (1100) further comprising the sub-steps of closing open edges, inverting the binary image, segmenting an inverted binary image, filtering the noise based on geometric feature, and filtering the noise based on relation. [https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf) ### Books 1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394) 2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/) ### Journal Papers * Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21) * * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf) * GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6) * * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf) * Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9). * * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM) * * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf) * Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2) * * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf) * Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52 * * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf) ### Conference Papers * Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017) * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342) * Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015) * [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf) * Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015) * [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336) * 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012) * [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf) * Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics * [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918) * License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011) * [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627) * Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011) * [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf) * [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649) * Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85. * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532) * Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010) * [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125) * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050 * [http://waset.org/publications/3636](http://waset.org/publications/3636) * An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010) * [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en) ### Articles on LinkedIn * [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/)) * [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) ) * [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/)) * [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/) * [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/) ### Slides slideshare * Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation) * Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) ) * tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) ) * Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) ) * Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) ) * Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) ) * Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) ) * How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) ) * Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) ) * Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) ) ### Online Courses * [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact) * [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life) * [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2) * [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends) * [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business) * [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity) * [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2) * [Understanding Business ](https://www.linkedin.com/learning/understanding-business) * [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea) * [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship) * [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations) * [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations) * [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations) * [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips) * [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation) * [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments) * [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow) * [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families) * [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances) * [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2) * [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2) * [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv) * [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers) * [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea) * [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital) * [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving) * [Professional Networking ](https://www.linkedin.com/learning/professional-networking) * [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home) * [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital) * [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling) * [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence) * [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers) ### Learning path * [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance) * [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner) # Completed ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link of best Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Books * [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y) * [Mathematics for Machine Learning](https://mml-book.github.io/) * Principles of Economics (6th edition) * The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It * Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) * Reinventing Your Life: The Breakthough Program to End Negative Behavior * Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence * [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf) * * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/) * [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/) * [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/) * Multiple view geometry in computer vision * Learning OpenCV Book by Adrian Kaehler and Gary Bradski * Digital Image Processing, Global Edition by Rafael C. Gonzalez * Introduction to Algorithms, 3rd Edition (The MIT Press) * The Mythical Man-Month: Essays on Software Engineering * [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition) ### Videos * [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning) * [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos) * [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/) * [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY) ### Papers * CALTag: High Precision Fiducial Markers for Camera * Diatom Autofocusing in Brightfield Microscopy: a Comparative Study * Analysis of focus measure operators in shape-from-focus * Optical flow modeling and computation: A survey * Toward general type 2 fuzzy logic systems based on zSlices ### Site * [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#)) ![](https://www.google.com/images/icons/product/drive-32.png)Reference- ComputerVisionDeepLearning.pdf * [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w) * [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679) * [https://www.freertos.org/index.html](https://www.freertos.org/index.html) * [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation) * [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/) * [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413) * [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917) * [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf) * [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/) * [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833) * [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238) * [https://koinly.io/](https://koinly.io/) site map: octopus.do [https://www.startupgermany.nrw/startup- contest/](https://www.startupgermany.nrw/startup-contest/) [https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth- camera-4k-cv-edge-object- detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai- kit-oak-depth-camera-4k-cv-edge-object-detection/description) [HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube in 2021! - YouTube ](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg) [محمد رضا شجریان، آلبوم کامل در خیال - YouTube](https://www.youtube.com/watch?v=cFdApmqlllg) [دوره جامع گوگل آنالیتیکس(UA) | آنالیتیپس](https://analytips.io/product/google-analyticsua/) [رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا - YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc) [Social Selling Index | Sales Navigator](https://www.linkedin.com/sales/ssi?src=or- search&veh=www.google.com) [https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/EvERVn15AzJZ7aNloDEKv1byWxFboB4Sbe_aPtX2ng9-SLNTjTIPJnwKRUBCWPFdXAOrqOFYlZeEgYJkKq7lPbM=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/EvERVn15AzJZ7aNloDEKv1byWxFboB4Sbe_aPtX2ng9-SLNTjTIPJnwKRUBCWPFdXAOrqOFYlZeEgYJkKq7lPbM=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Links pirahansiah, Personal, Roadmap, Books, Online Courses [](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze- oXQexwY "Open Spreadsheet, Links in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/sheets_32dp.png)Links Links from my research and interest AI in RISC-V Mixed reality Old Links Reference Link Groups Top website Python Computer Vision Documents Business Tools Patents Books Journal Papers Conference Papers Articles on LinkedIn Slides slideshare Online Courses Learning path Completed Reference Link of best Groups Books Videos Papers Site Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning training loops. GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp) [https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to- install-remmina/) [https://readme.so/](https://readme.so/) # Links from my research and interest [Build Better Generative Adversarial Networks (GANs) - Andrew Ng - 2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_- VnF) [https://www.oreilly.com/library/view/flow- architectures/9781492075882/](https://www.oreilly.com/library/view/flow- architectures/9781492075882/) ## AI in RISC-V * [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020) ## Mixed reality * [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera) * Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU. # Old Links ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Top website * [https://paperswithcode.com/](https://paperswithcode.com/) * [https://www.learnthepart.com/](https://www.learnthepart.com/) * [https://hackaday.com/](https://hackaday.com/) * [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762) * hbrew install youtube-dl * [https://www.masterclass.com/](https://www.masterclass.com/) * [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp) * [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona) * [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/) * [https://explainshell.com/](https://explainshell.com/) * [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20) * [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4) * [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4) ### Python * [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/) * [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/) * [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594) ### Computer Vision * [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/) * [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md) * [https://github.com/google/mediapipe](https://github.com/google/mediapipe) ### Documents * [https://book.keybase.io/docs](https://book.keybase.io/docs) * [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind) * [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/) * [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/) * [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/) * [https://kepler.gl/demo](https://kepler.gl/demo) * [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/) * [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE) ### Business * [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006) * [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5) ### Tools * Free convert video for mac opensource * [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake) * [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg) * [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c) * [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises * [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap * [Create and share beautiful images of your source code.](https://carbon.now.sh/) * [Web IDE](https://replit.com/templates) * [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2) ### Patents [https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) 2. A method for augmenting a plurality of face images (WO2021060971A1) 3. A method for detecting a moving vehicle (WO2021107761A1) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) The present invention relates to a system (100) for providing advertisement contents based on facial analysis comprising an image acquisition device (10) to acquire an image of a user, a face detection module (20) to detect face of the user in the image, an analysis module (40) to analyse the facial features statistically using classification models retrieved from a classification module (30), a database (60) to store matching rules, weighted advertisements and a plurality of advertisement contents; and a display device (80) to display the advertisement contents. The system (100) further comprises a computation module (50) to compute weighted image of the user and a matching module (70) to match the weighted image of the user with the weighted advertisement to select an advertisement content based on facial analysis of the user. A method of providing the advertisement contents based on facial analysis is also provided thereof. [https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf) 2. A method for augmenting a plurality of face images (WO2021060971A1) The present invention relates to a method for increasing data for face analysis in video surveillance. The method comprises the steps of acquiring at least one face image from an image acquisition module (102), acquiring a plurality of face images available on the internet using a data input module (104), increasing face images by at least one data augmentation module (106 and 107), generating a plurality of face images based on a trained Generative Adversarial Network, GAN technique by using a GAN module, selecting proper images based on quality of the face images using a fuzzy logic module (111), saving the selected images into a fifth database, and training the deep learning module (113). [https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf) 3. A method for detecting a moving vehicle (WO2021107761A1) The present invention relates to a method for detecting a moving vehicle. The method comprises the steps of grabbing an initial image from a video stream by a vehicle detection module (1100), wherein the vehicle detection module (1100) is a part of a system (1000) to identify moving vehicle, enhancing the illumination of the initial image by the vehicle detection module (1100), enhancing the edges within the initial image by the vehicle detection module (1100), and finding vehicle based on homogenous properties of the body of the vehicle by the vehicle detection module (1100). The step of finding vehicle based on homogenous property of the body of the vehicle by the vehicle detection module (1100) further comprising the sub-steps of closing open edges, inverting the binary image, segmenting an inverted binary image, filtering the noise based on geometric feature, and filtering the noise based on relation. [https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf) ### Books 1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394) 2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/) ### Journal Papers * Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21) * * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf) * GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6) * * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf) * Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9). * * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM) * * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf) * Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2) * * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf) * Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52 * * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf) ### Conference Papers * Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017) * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342) * Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015) * [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf) * Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015) * [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336) * 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012) * [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf) * Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics * [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918) * License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011) * [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627) * Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011) * [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf) * [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649) * Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85. * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532) * Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010) * [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125) * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050 * [http://waset.org/publications/3636](http://waset.org/publications/3636) * An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010) * [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en) ### Articles on LinkedIn * [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/)) * [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) ) * [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/)) * [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/) * [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/) ### Slides slideshare * Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation) * Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) ) * tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) ) * Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) ) * Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) ) * Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) ) * Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) ) * How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) ) * Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) ) * Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) ) ### Online Courses * [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact) * [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life) * [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2) * [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends) * [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business) * [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity) * [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2) * [Understanding Business ](https://www.linkedin.com/learning/understanding-business) * [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea) * [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship) * [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations) * [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations) * [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations) * [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips) * [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation) * [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments) * [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow) * [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families) * [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances) * [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2) * [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2) * [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv) * [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers) * [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea) * [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital) * [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving) * [Professional Networking ](https://www.linkedin.com/learning/professional-networking) * [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home) * [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital) * [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling) * [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence) * [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers) ### Learning path * [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance) * [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner) # Completed ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link of best Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Books * [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y) * [Mathematics for Machine Learning](https://mml-book.github.io/) * Principles of Economics (6th edition) * The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It * Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) * Reinventing Your Life: The Breakthough Program to End Negative Behavior * Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence * [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf) * * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/) * [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/) * [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/) * Multiple view geometry in computer vision * Learning OpenCV Book by Adrian Kaehler and Gary Bradski * Digital Image Processing, Global Edition by Rafael C. Gonzalez * Introduction to Algorithms, 3rd Edition (The MIT Press) * The Mythical Man-Month: Essays on Software Engineering * [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition) ### Videos * [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning) * [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos) * [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/) * [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY) ### Papers * CALTag: High Precision Fiducial Markers for Camera * Diatom Autofocusing in Brightfield Microscopy: a Comparative Study * Analysis of focus measure operators in shape-from-focus * Optical flow modeling and computation: A survey * Toward general type 2 fuzzy logic systems based on zSlices ### Site * [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#)) ![](https://www.google.com/images/icons/product/drive-32.png)Reference- ComputerVisionDeepLearning.pdf * [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w) * [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679) * [https://www.freertos.org/index.html](https://www.freertos.org/index.html) * [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation) * [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/) * [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413) * [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917) * [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf) * [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/) * [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833) * [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238) * [https://koinly.io/](https://koinly.io/) site map: octopus.do [https://www.startupgermany.nrw/startup- contest/](https://www.startupgermany.nrw/startup-contest/) [https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth- camera-4k-cv-edge-object- detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai- kit-oak-depth-camera-4k-cv-edge-object-detection/description) [HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube in 2021! - YouTube ](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg) [محمد رضا شجریان، آلبوم کامل در خیال - YouTube](https://www.youtube.com/watch?v=cFdApmqlllg) [دوره جامع گوگل آنالیتیکس(UA) | آنالیتیپس](https://analytips.io/product/google-analyticsua/) [رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا - YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc) [Social Selling Index | Sales Navigator](https://www.linkedin.com/sales/ssi?src=or- search&veh=www.google.com) [https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/EvERVn15AzJZ7aNloDEKv1byWxFboB4Sbe_aPtX2ng9-SLNTjTIPJnwKRUBCWPFdXAOrqOFYlZeEgYJkKq7lPbM=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/EvERVn15AzJZ7aNloDEKv1byWxFboB4Sbe_aPtX2ng9-SLNTjTIPJnwKRUBCWPFdXAOrqOFYlZeEgYJkKq7lPbM=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Links pirahansiah, Personal, Roadmap, Books, Online Courses [](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze- oXQexwY "Open Spreadsheet, Links in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/sheets_32dp.png)Links Links from my research and interest AI in RISC-V Mixed reality Old Links Reference Link Groups Top website Python Computer Vision Documents Business Tools Patents Books Journal Papers Conference Papers Articles on LinkedIn Slides slideshare Online Courses Learning path Completed Reference Link of best Groups Books Videos Papers Site Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning training loops. GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp) [https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to- install-remmina/) [https://readme.so/](https://readme.so/) # Links from my research and interest [Build Better Generative Adversarial Networks (GANs) - Andrew Ng - 2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_- VnF) [https://www.oreilly.com/library/view/flow- architectures/9781492075882/](https://www.oreilly.com/library/view/flow- architectures/9781492075882/) ## AI in RISC-V * [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020) ## Mixed reality * [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera) * Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU. # Old Links ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Top website * [https://paperswithcode.com/](https://paperswithcode.com/) * [https://www.learnthepart.com/](https://www.learnthepart.com/) * [https://hackaday.com/](https://hackaday.com/) * [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762) * hbrew install youtube-dl * [https://www.masterclass.com/](https://www.masterclass.com/) * [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp) * [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona) * [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/) * [https://explainshell.com/](https://explainshell.com/) * [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20) * [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4) * [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4) ### Python * [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/) * [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/) * [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594) ### Computer Vision * [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/) * [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md) * [https://github.com/google/mediapipe](https://github.com/google/mediapipe) ### Documents * [https://book.keybase.io/docs](https://book.keybase.io/docs) * [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind) * [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/) * [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/) * [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/) * [https://kepler.gl/demo](https://kepler.gl/demo) * [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/) * [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE) ### Business * [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006) * [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5) ### Tools * Free convert video for mac opensource * [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake) * [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg) * [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c) * [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises * [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap * [Create and share beautiful images of your source code.](https://carbon.now.sh/) * [Web IDE](https://replit.com/templates) * [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2) ### Patents [https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) 2. A method for augmenting a plurality of face images (WO2021060971A1) 3. A method for detecting a moving vehicle (WO2021107761A1) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) The present invention relates to a system (100) for providing advertisement contents based on facial analysis comprising an image acquisition device (10) to acquire an image of a user, a face detection module (20) to detect face of the user in the image, an analysis module (40) to analyse the facial features statistically using classification models retrieved from a classification module (30), a database (60) to store matching rules, weighted advertisements and a plurality of advertisement contents; and a display device (80) to display the advertisement contents. The system (100) further comprises a computation module (50) to compute weighted image of the user and a matching module (70) to match the weighted image of the user with the weighted advertisement to select an advertisement content based on facial analysis of the user. A method of providing the advertisement contents based on facial analysis is also provided thereof. [https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf) 2. A method for augmenting a plurality of face images (WO2021060971A1) The present invention relates to a method for increasing data for face analysis in video surveillance. The method comprises the steps of acquiring at least one face image from an image acquisition module (102), acquiring a plurality of face images available on the internet using a data input module (104), increasing face images by at least one data augmentation module (106 and 107), generating a plurality of face images based on a trained Generative Adversarial Network, GAN technique by using a GAN module, selecting proper images based on quality of the face images using a fuzzy logic module (111), saving the selected images into a fifth database, and training the deep learning module (113). [https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf) 3. A method for detecting a moving vehicle (WO2021107761A1) The present invention relates to a method for detecting a moving vehicle. The method comprises the steps of grabbing an initial image from a video stream by a vehicle detection module (1100), wherein the vehicle detection module (1100) is a part of a system (1000) to identify moving vehicle, enhancing the illumination of the initial image by the vehicle detection module (1100), enhancing the edges within the initial image by the vehicle detection module (1100), and finding vehicle based on homogenous properties of the body of the vehicle by the vehicle detection module (1100). The step of finding vehicle based on homogenous property of the body of the vehicle by the vehicle detection module (1100) further comprising the sub-steps of closing open edges, inverting the binary image, segmenting an inverted binary image, filtering the noise based on geometric feature, and filtering the noise based on relation. [https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf) ### Books 1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394) 2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/) ### Journal Papers * Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21) * * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf) * GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6) * * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf) * Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9). * * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM) * * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf) * Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2) * * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf) * Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52 * * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf) ### Conference Papers * Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017) * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342) * Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015) * [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf) * Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015) * [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336) * 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012) * [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf) * Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics * [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918) * License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011) * [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627) * Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011) * [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf) * [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649) * Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85. * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532) * Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010) * [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125) * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050 * [http://waset.org/publications/3636](http://waset.org/publications/3636) * An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010) * [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en) ### Articles on LinkedIn * [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/)) * [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) ) * [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/)) * [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/) * [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/) ### Slides slideshare * Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation) * Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) ) * tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) ) * Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) ) * Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) ) * Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) ) * Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) ) * How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) ) * Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) ) * Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) ) ### Online Courses * [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact) * [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life) * [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2) * [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends) * [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business) * [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity) * [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2) * [Understanding Business ](https://www.linkedin.com/learning/understanding-business) * [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea) * [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship) * [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations) * [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations) * [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations) * [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips) * [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation) * [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments) * [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow) * [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families) * [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances) * [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2) * [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2) * [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv) * [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers) * [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea) * [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital) * [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving) * [Professional Networking ](https://www.linkedin.com/learning/professional-networking) * [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home) * [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital) * [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling) * [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence) * [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers) ### Learning path * [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance) * [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner) # Completed ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link of best Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Books * [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y) * [Mathematics for Machine Learning](https://mml-book.github.io/) * Principles of Economics (6th edition) * The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It * Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) * Reinventing Your Life: The Breakthough Program to End Negative Behavior * Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence * [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf) * * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/) * [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/) * [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/) * Multiple view geometry in computer vision * Learning OpenCV Book by Adrian Kaehler and Gary Bradski * Digital Image Processing, Global Edition by Rafael C. Gonzalez * Introduction to Algorithms, 3rd Edition (The MIT Press) * The Mythical Man-Month: Essays on Software Engineering * [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition) ### Videos * [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning) * [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos) * [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/) * [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY) ### Papers * CALTag: High Precision Fiducial Markers for Camera * Diatom Autofocusing in Brightfield Microscopy: a Comparative Study * Analysis of focus measure operators in shape-from-focus * Optical flow modeling and computation: A survey * Toward general type 2 fuzzy logic systems based on zSlices ### Site * [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#)) ![](https://www.google.com/images/icons/product/drive-32.png)Reference- ComputerVisionDeepLearning.pdf * [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w) * [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679) * [https://www.freertos.org/index.html](https://www.freertos.org/index.html) * [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation) * [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/) * [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413) * [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917) * [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf) * [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/) * [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833) * [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238) * [https://koinly.io/](https://koinly.io/) site map: octopus.do [https://www.startupgermany.nrw/startup- contest/](https://www.startupgermany.nrw/startup-contest/) [https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth- camera-4k-cv-edge-object- detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai- kit-oak-depth-camera-4k-cv-edge-object-detection/description) [HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube in 2021! - YouTube ](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg) [محمد رضا شجریان، آلبوم کامل در خیال - YouTube](https://www.youtube.com/watch?v=cFdApmqlllg) [دوره جامع گوگل آنالیتیکس(UA) | آنالیتیپس](https://analytips.io/product/google-analyticsua/) [رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا - YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc) [Social Selling Index | Sales Navigator](https://www.linkedin.com/sales/ssi?src=or- search&veh=www.google.com) [https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/EvERVn15AzJZ7aNloDEKv1byWxFboB4Sbe_aPtX2ng9-SLNTjTIPJnwKRUBCWPFdXAOrqOFYlZeEgYJkKq7lPbM=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/EvERVn15AzJZ7aNloDEKv1byWxFboB4Sbe_aPtX2ng9-SLNTjTIPJnwKRUBCWPFdXAOrqOFYlZeEgYJkKq7lPbM=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Links pirahansiah, Personal, Roadmap, Books, Online Courses [](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze- oXQexwY "Open Spreadsheet, Links in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/sheets_32dp.png)Links Links from my research and interest AI in RISC-V Mixed reality Old Links Reference Link Groups Top website Python Computer Vision Documents Business Tools Patents Books Journal Papers Conference Papers Articles on LinkedIn Slides slideshare Online Courses Learning path Completed Reference Link of best Groups Books Videos Papers Site Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning training loops. GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp) [https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to- install-remmina/) [https://readme.so/](https://readme.so/) # Links from my research and interest [Build Better Generative Adversarial Networks (GANs) - Andrew Ng - 2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_- VnF) [https://www.oreilly.com/library/view/flow- architectures/9781492075882/](https://www.oreilly.com/library/view/flow- architectures/9781492075882/) ## AI in RISC-V * [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020) ## Mixed reality * [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera) * Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU. # Old Links ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Top website * [https://paperswithcode.com/](https://paperswithcode.com/) * [https://www.learnthepart.com/](https://www.learnthepart.com/) * [https://hackaday.com/](https://hackaday.com/) * [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762) * hbrew install youtube-dl * [https://www.masterclass.com/](https://www.masterclass.com/) * [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp) * [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona) * [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/) * [https://explainshell.com/](https://explainshell.com/) * [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20) * [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4) * [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4) ### Python * [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/) * [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/) * [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594) ### Computer Vision * [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/) * [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md) * [https://github.com/google/mediapipe](https://github.com/google/mediapipe) ### Documents * [https://book.keybase.io/docs](https://book.keybase.io/docs) * [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind) * [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/) * [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/) * [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/) * [https://kepler.gl/demo](https://kepler.gl/demo) * [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/) * [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE) ### Business * [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006) * [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5) ### Tools * Free convert video for mac opensource * [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake) * [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg) * [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c) * [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises * [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap * [Create and share beautiful images of your source code.](https://carbon.now.sh/) * [Web IDE](https://replit.com/templates) * [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2) ### Patents [https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) 2. A method for augmenting a plurality of face images (WO2021060971A1) 3. A method for detecting a moving vehicle (WO2021107761A1) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) The present invention relates to a system (100) for providing advertisement contents based on facial analysis comprising an image acquisition device (10) to acquire an image of a user, a face detection module (20) to detect face of the user in the image, an analysis module (40) to analyse the facial features statistically using classification models retrieved from a classification module (30), a database (60) to store matching rules, weighted advertisements and a plurality of advertisement contents; and a display device (80) to display the advertisement contents. The system (100) further comprises a computation module (50) to compute weighted image of the user and a matching module (70) to match the weighted image of the user with the weighted advertisement to select an advertisement content based on facial analysis of the user. A method of providing the advertisement contents based on facial analysis is also provided thereof. [https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf) 2. A method for augmenting a plurality of face images (WO2021060971A1) The present invention relates to a method for increasing data for face analysis in video surveillance. The method comprises the steps of acquiring at least one face image from an image acquisition module (102), acquiring a plurality of face images available on the internet using a data input module (104), increasing face images by at least one data augmentation module (106 and 107), generating a plurality of face images based on a trained Generative Adversarial Network, GAN technique by using a GAN module, selecting proper images based on quality of the face images using a fuzzy logic module (111), saving the selected images into a fifth database, and training the deep learning module (113). [https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf) 3. A method for detecting a moving vehicle (WO2021107761A1) The present invention relates to a method for detecting a moving vehicle. The method comprises the steps of grabbing an initial image from a video stream by a vehicle detection module (1100), wherein the vehicle detection module (1100) is a part of a system (1000) to identify moving vehicle, enhancing the illumination of the initial image by the vehicle detection module (1100), enhancing the edges within the initial image by the vehicle detection module (1100), and finding vehicle based on homogenous properties of the body of the vehicle by the vehicle detection module (1100). The step of finding vehicle based on homogenous property of the body of the vehicle by the vehicle detection module (1100) further comprising the sub-steps of closing open edges, inverting the binary image, segmenting an inverted binary image, filtering the noise based on geometric feature, and filtering the noise based on relation. [https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf) ### Books 1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394) 2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/) ### Journal Papers * Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21) * * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf) * GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6) * * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf) * Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9). * * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM) * * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf) * Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2) * * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf) * Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52 * * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf) ### Conference Papers * Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017) * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342) * Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015) * [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf) * Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015) * [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336) * 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012) * [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf) * Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics * [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918) * License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011) * [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627) * Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011) * [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf) * [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649) * Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85. * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532) * Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010) * [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125) * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050 * [http://waset.org/publications/3636](http://waset.org/publications/3636) * An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010) * [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en) ### Articles on LinkedIn * [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/)) * [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) ) * [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/)) * [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/) * [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/) ### Slides slideshare * Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation) * Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) ) * tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) ) * Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) ) * Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) ) * Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) ) * Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) ) * How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) ) * Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) ) * Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) ) ### Online Courses * [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact) * [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life) * [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2) * [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends) * [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business) * [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity) * [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2) * [Understanding Business ](https://www.linkedin.com/learning/understanding-business) * [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea) * [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship) * [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations) * [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations) * [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations) * [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips) * [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation) * [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments) * [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow) * [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families) * [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances) * [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2) * [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2) * [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv) * [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers) * [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea) * [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital) * [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving) * [Professional Networking ](https://www.linkedin.com/learning/professional-networking) * [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home) * [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital) * [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling) * [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence) * [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers) ### Learning path * [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance) * [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner) # Completed ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link of best Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Books * [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y) * [Mathematics for Machine Learning](https://mml-book.github.io/) * Principles of Economics (6th edition) * The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It * Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) * Reinventing Your Life: The Breakthough Program to End Negative Behavior * Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence * [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf) * * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/) * [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/) * [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/) * Multiple view geometry in computer vision * Learning OpenCV Book by Adrian Kaehler and Gary Bradski * Digital Image Processing, Global Edition by Rafael C. Gonzalez * Introduction to Algorithms, 3rd Edition (The MIT Press) * The Mythical Man-Month: Essays on Software Engineering * [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition) ### Videos * [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning) * [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos) * [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/) * [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY) ### Papers * CALTag: High Precision Fiducial Markers for Camera * Diatom Autofocusing in Brightfield Microscopy: a Comparative Study * Analysis of focus measure operators in shape-from-focus * Optical flow modeling and computation: A survey * Toward general type 2 fuzzy logic systems based on zSlices ### Site * [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#)) ![](https://www.google.com/images/icons/product/drive-32.png)Reference- ComputerVisionDeepLearning.pdf * [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w) * [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679) * [https://www.freertos.org/index.html](https://www.freertos.org/index.html) * [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation) * [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/) * [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413) * [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917) * [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf) * [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/) * [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833) * [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238) * [https://koinly.io/](https://koinly.io/) site map: octopus.do [https://www.startupgermany.nrw/startup- contest/](https://www.startupgermany.nrw/startup-contest/) [https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth- camera-4k-cv-edge-object- detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai- kit-oak-depth-camera-4k-cv-edge-object-detection/description) [HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube in 2021! - YouTube ](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg) [محمد رضا شجریان، آلبوم کامل در خیال - YouTube](https://www.youtube.com/watch?v=cFdApmqlllg) [دوره جامع گوگل آنالیتیکس(UA) | آنالیتیپس](https://analytips.io/product/google-analyticsua/) [رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا - YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc) [Social Selling Index | Sales Navigator](https://www.linkedin.com/sales/ssi?src=or- search&veh=www.google.com) [https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/EvERVn15AzJZ7aNloDEKv1byWxFboB4Sbe_aPtX2ng9-SLNTjTIPJnwKRUBCWPFdXAOrqOFYlZeEgYJkKq7lPbM=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/EvERVn15AzJZ7aNloDEKv1byWxFboB4Sbe_aPtX2ng9-SLNTjTIPJnwKRUBCWPFdXAOrqOFYlZeEgYJkKq7lPbM=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Links pirahansiah, Personal, Roadmap, Books, Online Courses [](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze- oXQexwY "Open Spreadsheet, Links in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/sheets_32dp.png)Links Links from my research and interest AI in RISC-V Mixed reality Old Links Reference Link Groups Top website Python Computer Vision Documents Business Tools Patents Books Journal Papers Conference Papers Articles on LinkedIn Slides slideshare Online Courses Learning path Completed Reference Link of best Groups Books Videos Papers Site Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning training loops. GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp) [https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to- install-remmina/) [https://readme.so/](https://readme.so/) # Links from my research and interest [Build Better Generative Adversarial Networks (GANs) - Andrew Ng - 2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_- VnF) [https://www.oreilly.com/library/view/flow- architectures/9781492075882/](https://www.oreilly.com/library/view/flow- architectures/9781492075882/) ## AI in RISC-V * [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020) ## Mixed reality * [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera) * Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU. # Old Links ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Top website * [https://paperswithcode.com/](https://paperswithcode.com/) * [https://www.learnthepart.com/](https://www.learnthepart.com/) * [https://hackaday.com/](https://hackaday.com/) * [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762) * hbrew install youtube-dl * [https://www.masterclass.com/](https://www.masterclass.com/) * [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp) * [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona) * [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/) * [https://explainshell.com/](https://explainshell.com/) * [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20) * [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4) * [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4) ### Python * [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/) * [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/) * [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594) ### Computer Vision * [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/) * [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md) * [https://github.com/google/mediapipe](https://github.com/google/mediapipe) ### Documents * [https://book.keybase.io/docs](https://book.keybase.io/docs) * [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind) * [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/) * [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/) * [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/) * [https://kepler.gl/demo](https://kepler.gl/demo) * [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/) * [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE) ### Business * [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006) * [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5) ### Tools * Free convert video for mac opensource * [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake) * [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg) * [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c) * [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises * [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap * [Create and share beautiful images of your source code.](https://carbon.now.sh/) * [Web IDE](https://replit.com/templates) * [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2) ### Patents [https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) 2. A method for augmenting a plurality of face images (WO2021060971A1) 3. A method for detecting a moving vehicle (WO2021107761A1) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) The present invention relates to a system (100) for providing advertisement contents based on facial analysis comprising an image acquisition device (10) to acquire an image of a user, a face detection module (20) to detect face of the user in the image, an analysis module (40) to analyse the facial features statistically using classification models retrieved from a classification module (30), a database (60) to store matching rules, weighted advertisements and a plurality of advertisement contents; and a display device (80) to display the advertisement contents. The system (100) further comprises a computation module (50) to compute weighted image of the user and a matching module (70) to match the weighted image of the user with the weighted advertisement to select an advertisement content based on facial analysis of the user. A method of providing the advertisement contents based on facial analysis is also provided thereof. [https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf) 2. A method for augmenting a plurality of face images (WO2021060971A1) The present invention relates to a method for increasing data for face analysis in video surveillance. The method comprises the steps of acquiring at least one face image from an image acquisition module (102), acquiring a plurality of face images available on the internet using a data input module (104), increasing face images by at least one data augmentation module (106 and 107), generating a plurality of face images based on a trained Generative Adversarial Network, GAN technique by using a GAN module, selecting proper images based on quality of the face images using a fuzzy logic module (111), saving the selected images into a fifth database, and training the deep learning module (113). [https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf) 3. A method for detecting a moving vehicle (WO2021107761A1) The present invention relates to a method for detecting a moving vehicle. The method comprises the steps of grabbing an initial image from a video stream by a vehicle detection module (1100), wherein the vehicle detection module (1100) is a part of a system (1000) to identify moving vehicle, enhancing the illumination of the initial image by the vehicle detection module (1100), enhancing the edges within the initial image by the vehicle detection module (1100), and finding vehicle based on homogenous properties of the body of the vehicle by the vehicle detection module (1100). The step of finding vehicle based on homogenous property of the body of the vehicle by the vehicle detection module (1100) further comprising the sub-steps of closing open edges, inverting the binary image, segmenting an inverted binary image, filtering the noise based on geometric feature, and filtering the noise based on relation. [https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf) ### Books 1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394) 2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/) ### Journal Papers * Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21) * * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf) * GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6) * * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf) * Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9). * * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM) * * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf) * Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2) * * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf) * Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52 * * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf) ### Conference Papers * Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017) * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342) * Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015) * [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf) * Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015) * [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336) * 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012) * [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf) * Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics * [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918) * License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011) * [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627) * Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011) * [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf) * [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649) * Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85. * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532) * Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010) * [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125) * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050 * [http://waset.org/publications/3636](http://waset.org/publications/3636) * An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010) * [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en) ### Articles on LinkedIn * [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/)) * [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) ) * [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/)) * [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/) * [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/) ### Slides slideshare * Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation) * Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) ) * tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) ) * Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) ) * Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) ) * Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) ) * Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) ) * How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) ) * Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) ) * Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) ) ### Online Courses * [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact) * [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life) * [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2) * [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends) * [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business) * [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity) * [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2) * [Understanding Business ](https://www.linkedin.com/learning/understanding-business) * [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea) * [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship) * [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations) * [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations) * [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations) * [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips) * [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation) * [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments) * [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow) * [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families) * [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances) * [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2) * [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2) * [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv) * [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers) * [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea) * [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital) * [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving) * [Professional Networking ](https://www.linkedin.com/learning/professional-networking) * [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home) * [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital) * [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling) * [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence) * [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers) ### Learning path * [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance) * [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner) # Completed ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link of best Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Books * [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y) * [Mathematics for Machine Learning](https://mml-book.github.io/) * Principles of Economics (6th edition) * The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It * Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) * Reinventing Your Life: The Breakthough Program to End Negative Behavior * Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence * [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf) * * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/) * [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/) * [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/) * Multiple view geometry in computer vision * Learning OpenCV Book by Adrian Kaehler and Gary Bradski * Digital Image Processing, Global Edition by Rafael C. Gonzalez * Introduction to Algorithms, 3rd Edition (The MIT Press) * The Mythical Man-Month: Essays on Software Engineering * [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition) ### Videos * [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning) * [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos) * [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/) * [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY) ### Papers * CALTag: High Precision Fiducial Markers for Camera * Diatom Autofocusing in Brightfield Microscopy: a Comparative Study * Analysis of focus measure operators in shape-from-focus * Optical flow modeling and computation: A survey * Toward general type 2 fuzzy logic systems based on zSlices ### Site * [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#)) ![](https://www.google.com/images/icons/product/drive-32.png)Reference- ComputerVisionDeepLearning.pdf * [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w) * [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679) * [https://www.freertos.org/index.html](https://www.freertos.org/index.html) * [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation) * [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/) * [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413) * [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917) * [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf) * [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/) * [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833) * [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238) * [https://koinly.io/](https://koinly.io/) site map: octopus.do [https://www.startupgermany.nrw/startup- contest/](https://www.startupgermany.nrw/startup-contest/) [https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth- camera-4k-cv-edge-object- detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai- kit-oak-depth-camera-4k-cv-edge-object-detection/description) [HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube in 2021! - YouTube ](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg) [محمد رضا شجریان، آلبوم کامل در خیال - YouTube](https://www.youtube.com/watch?v=cFdApmqlllg) [دوره جامع گوگل آنالیتیکس(UA) | آنالیتیپس](https://analytips.io/product/google-analyticsua/) [رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا - YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc) [Social Selling Index | Sales Navigator](https://www.linkedin.com/sales/ssi?src=or- search&veh=www.google.com) [https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/EvERVn15AzJZ7aNloDEKv1byWxFboB4Sbe_aPtX2ng9-SLNTjTIPJnwKRUBCWPFdXAOrqOFYlZeEgYJkKq7lPbM=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/EvERVn15AzJZ7aNloDEKv1byWxFboB4Sbe_aPtX2ng9-SLNTjTIPJnwKRUBCWPFdXAOrqOFYlZeEgYJkKq7lPbM=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Links pirahansiah, Personal, Roadmap, Books, Online Courses [](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze- oXQexwY "Open Spreadsheet, Links in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/sheets_32dp.png)Links Links from my research and interest AI in RISC-V Mixed reality Old Links Reference Link Groups Top website Python Computer Vision Documents Business Tools Patents Books Journal Papers Conference Papers Articles on LinkedIn Slides slideshare Online Courses Learning path Completed Reference Link of best Groups Books Videos Papers Site Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning training loops. GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp) [https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to- install-remmina/) [https://readme.so/](https://readme.so/) # Links from my research and interest [Build Better Generative Adversarial Networks (GANs) - Andrew Ng - 2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_- VnF) [https://www.oreilly.com/library/view/flow- architectures/9781492075882/](https://www.oreilly.com/library/view/flow- architectures/9781492075882/) ## AI in RISC-V * [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020) ## Mixed reality * [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera) * Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU. # Old Links ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Top website * [https://paperswithcode.com/](https://paperswithcode.com/) * [https://www.learnthepart.com/](https://www.learnthepart.com/) * [https://hackaday.com/](https://hackaday.com/) * [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762) * hbrew install youtube-dl * [https://www.masterclass.com/](https://www.masterclass.com/) * [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp) * [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona) * [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/) * [https://explainshell.com/](https://explainshell.com/) * [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20) * [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4) * [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4) ### Python * [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/) * [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/) * [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594) ### Computer Vision * [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/) * [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md) * [https://github.com/google/mediapipe](https://github.com/google/mediapipe) ### Documents * [https://book.keybase.io/docs](https://book.keybase.io/docs) * [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind) * [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/) * [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/) * [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/) * [https://kepler.gl/demo](https://kepler.gl/demo) * [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/) * [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE) ### Business * [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006) * [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5) ### Tools * Free convert video for mac opensource * [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake) * [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg) * [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c) * [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises * [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap * [Create and share beautiful images of your source code.](https://carbon.now.sh/) * [Web IDE](https://replit.com/templates) * [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2) ### Patents [https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) 2. A method for augmenting a plurality of face images (WO2021060971A1) 3. A method for detecting a moving vehicle (WO2021107761A1) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) The present invention relates to a system (100) for providing advertisement contents based on facial analysis comprising an image acquisition device (10) to acquire an image of a user, a face detection module (20) to detect face of the user in the image, an analysis module (40) to analyse the facial features statistically using classification models retrieved from a classification module (30), a database (60) to store matching rules, weighted advertisements and a plurality of advertisement contents; and a display device (80) to display the advertisement contents. The system (100) further comprises a computation module (50) to compute weighted image of the user and a matching module (70) to match the weighted image of the user with the weighted advertisement to select an advertisement content based on facial analysis of the user. A method of providing the advertisement contents based on facial analysis is also provided thereof. [https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf) 2. A method for augmenting a plurality of face images (WO2021060971A1) The present invention relates to a method for increasing data for face analysis in video surveillance. The method comprises the steps of acquiring at least one face image from an image acquisition module (102), acquiring a plurality of face images available on the internet using a data input module (104), increasing face images by at least one data augmentation module (106 and 107), generating a plurality of face images based on a trained Generative Adversarial Network, GAN technique by using a GAN module, selecting proper images based on quality of the face images using a fuzzy logic module (111), saving the selected images into a fifth database, and training the deep learning module (113). [https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf) 3. A method for detecting a moving vehicle (WO2021107761A1) The present invention relates to a method for detecting a moving vehicle. The method comprises the steps of grabbing an initial image from a video stream by a vehicle detection module (1100), wherein the vehicle detection module (1100) is a part of a system (1000) to identify moving vehicle, enhancing the illumination of the initial image by the vehicle detection module (1100), enhancing the edges within the initial image by the vehicle detection module (1100), and finding vehicle based on homogenous properties of the body of the vehicle by the vehicle detection module (1100). The step of finding vehicle based on homogenous property of the body of the vehicle by the vehicle detection module (1100) further comprising the sub-steps of closing open edges, inverting the binary image, segmenting an inverted binary image, filtering the noise based on geometric feature, and filtering the noise based on relation. [https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf) ### Books 1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394) 2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/) ### Journal Papers * Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21) * * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf) * GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6) * * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf) * Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9). * * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM) * * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf) * Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2) * * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf) * Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52 * * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf) ### Conference Papers * Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017) * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342) * Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015) * [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf) * Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015) * [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336) * 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012) * [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf) * Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics * [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918) * License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011) * [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627) * Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011) * [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf) * [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649) * Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85. * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532) * Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010) * [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125) * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050 * [http://waset.org/publications/3636](http://waset.org/publications/3636) * An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010) * [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en) ### Articles on LinkedIn * [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/)) * [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) ) * [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/)) * [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/) * [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/) ### Slides slideshare * Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation) * Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) ) * tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) ) * Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) ) * Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) ) * Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) ) * Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) ) * How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) ) * Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) ) * Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) ) ### Online Courses * [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact) * [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life) * [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2) * [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends) * [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business) * [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity) * [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2) * [Understanding Business ](https://www.linkedin.com/learning/understanding-business) * [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea) * [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship) * [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations) * [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations) * [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations) * [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips) * [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation) * [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments) * [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow) * [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families) * [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances) * [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2) * [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2) * [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv) * [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers) * [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea) * [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital) * [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving) * [Professional Networking ](https://www.linkedin.com/learning/professional-networking) * [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home) * [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital) * [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling) * [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence) * [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers) ### Learning path * [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance) * [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner) # Completed ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link of best Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Books * [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y) * [Mathematics for Machine Learning](https://mml-book.github.io/) * Principles of Economics (6th edition) * The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It * Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) * Reinventing Your Life: The Breakthough Program to End Negative Behavior * Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence * [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf) * * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/) * [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/) * [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/) * Multiple view geometry in computer vision * Learning OpenCV Book by Adrian Kaehler and Gary Bradski * Digital Image Processing, Global Edition by Rafael C. Gonzalez * Introduction to Algorithms, 3rd Edition (The MIT Press) * The Mythical Man-Month: Essays on Software Engineering * [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition) ### Videos * [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning) * [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos) * [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/) * [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY) ### Papers * CALTag: High Precision Fiducial Markers for Camera * Diatom Autofocusing in Brightfield Microscopy: a Comparative Study * Analysis of focus measure operators in shape-from-focus * Optical flow modeling and computation: A survey * Toward general type 2 fuzzy logic systems based on zSlices ### Site * [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#)) ![](https://www.google.com/images/icons/product/drive-32.png)Reference- ComputerVisionDeepLearning.pdf * [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w) * [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679) * [https://www.freertos.org/index.html](https://www.freertos.org/index.html) * [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation) * [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/) * [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413) * [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917) * [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf) * [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/) * [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833) * [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238) * [https://koinly.io/](https://koinly.io/) site map: octopus.do [https://www.startupgermany.nrw/startup- contest/](https://www.startupgermany.nrw/startup-contest/) [https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth- camera-4k-cv-edge-object- detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai- kit-oak-depth-camera-4k-cv-edge-object-detection/description) [HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube in 2021! - YouTube ](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg) [محمد رضا شجریان، آلبوم کامل در خیال - YouTube](https://www.youtube.com/watch?v=cFdApmqlllg) [دوره جامع گوگل آنالیتیکس(UA) | آنالیتیپس](https://analytips.io/product/google-analyticsua/) [رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا - YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc) [Social Selling Index | Sales Navigator](https://www.linkedin.com/sales/ssi?src=or- search&veh=www.google.com) [https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/EvERVn15AzJZ7aNloDEKv1byWxFboB4Sbe_aPtX2ng9-SLNTjTIPJnwKRUBCWPFdXAOrqOFYlZeEgYJkKq7lPbM=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/EvERVn15AzJZ7aNloDEKv1byWxFboB4Sbe_aPtX2ng9-SLNTjTIPJnwKRUBCWPFdXAOrqOFYlZeEgYJkKq7lPbM=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Links pirahansiah, Personal, Roadmap, Books, Online Courses [](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze- oXQexwY "Open Spreadsheet, Links in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/sheets_32dp.png)Links Links from my research and interest AI in RISC-V Mixed reality Old Links Reference Link Groups Top website Python Computer Vision Documents Business Tools Patents Books Journal Papers Conference Papers Articles on LinkedIn Slides slideshare Online Courses Learning path Completed Reference Link of best Groups Books Videos Papers Site Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning training loops. GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp) [https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to- install-remmina/) [https://readme.so/](https://readme.so/) # Links from my research and interest [Build Better Generative Adversarial Networks (GANs) - Andrew Ng - 2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_- VnF) [https://www.oreilly.com/library/view/flow- architectures/9781492075882/](https://www.oreilly.com/library/view/flow- architectures/9781492075882/) ## AI in RISC-V * [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020) ## Mixed reality * [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera) * Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU. # Old Links ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Top website * [https://paperswithcode.com/](https://paperswithcode.com/) * [https://www.learnthepart.com/](https://www.learnthepart.com/) * [https://hackaday.com/](https://hackaday.com/) * [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762) * hbrew install youtube-dl * [https://www.masterclass.com/](https://www.masterclass.com/) * [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp) * [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona) * [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/) * [https://explainshell.com/](https://explainshell.com/) * [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20) * [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4) * [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4) ### Python * [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/) * [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/) * [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594) ### Computer Vision * [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/) * [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md) * [https://github.com/google/mediapipe](https://github.com/google/mediapipe) ### Documents * [https://book.keybase.io/docs](https://book.keybase.io/docs) * [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind) * [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/) * [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/) * [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/) * [https://kepler.gl/demo](https://kepler.gl/demo) * [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/) * [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE) ### Business * [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006) * [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5) ### Tools * Free convert video for mac opensource * [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake) * [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg) * [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c) * [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises * [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap * [Create and share beautiful images of your source code.](https://carbon.now.sh/) * [Web IDE](https://replit.com/templates) * [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2) ### Patents [https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) 2. A method for augmenting a plurality of face images (WO2021060971A1) 3. A method for detecting a moving vehicle (WO2021107761A1) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) The present invention relates to a system (100) for providing advertisement contents based on facial analysis comprising an image acquisition device (10) to acquire an image of a user, a face detection module (20) to detect face of the user in the image, an analysis module (40) to analyse the facial features statistically using classification models retrieved from a classification module (30), a database (60) to store matching rules, weighted advertisements and a plurality of advertisement contents; and a display device (80) to display the advertisement contents. The system (100) further comprises a computation module (50) to compute weighted image of the user and a matching module (70) to match the weighted image of the user with the weighted advertisement to select an advertisement content based on facial analysis of the user. A method of providing the advertisement contents based on facial analysis is also provided thereof. [https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf) 2. A method for augmenting a plurality of face images (WO2021060971A1) The present invention relates to a method for increasing data for face analysis in video surveillance. The method comprises the steps of acquiring at least one face image from an image acquisition module (102), acquiring a plurality of face images available on the internet using a data input module (104), increasing face images by at least one data augmentation module (106 and 107), generating a plurality of face images based on a trained Generative Adversarial Network, GAN technique by using a GAN module, selecting proper images based on quality of the face images using a fuzzy logic module (111), saving the selected images into a fifth database, and training the deep learning module (113). [https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf) 3. A method for detecting a moving vehicle (WO2021107761A1) The present invention relates to a method for detecting a moving vehicle. The method comprises the steps of grabbing an initial image from a video stream by a vehicle detection module (1100), wherein the vehicle detection module (1100) is a part of a system (1000) to identify moving vehicle, enhancing the illumination of the initial image by the vehicle detection module (1100), enhancing the edges within the initial image by the vehicle detection module (1100), and finding vehicle based on homogenous properties of the body of the vehicle by the vehicle detection module (1100). The step of finding vehicle based on homogenous property of the body of the vehicle by the vehicle detection module (1100) further comprising the sub-steps of closing open edges, inverting the binary image, segmenting an inverted binary image, filtering the noise based on geometric feature, and filtering the noise based on relation. [https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf) ### Books 1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394) 2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/) ### Journal Papers * Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21) * * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf) * GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6) * * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf) * Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9). * * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM) * * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf) * Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2) * * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf) * Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52 * * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf) ### Conference Papers * Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017) * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342) * Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015) * [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf) * Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015) * [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336) * 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012) * [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf) * Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics * [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918) * License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011) * [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627) * Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011) * [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf) * [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649) * Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85. * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532) * Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010) * [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125) * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050 * [http://waset.org/publications/3636](http://waset.org/publications/3636) * An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010) * [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en) ### Articles on LinkedIn * [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/)) * [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) ) * [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/)) * [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/) * [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/) ### Slides slideshare * Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation) * Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) ) * tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) ) * Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) ) * Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) ) * Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) ) * Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) ) * How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) ) * Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) ) * Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) ) ### Online Courses * [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact) * [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life) * [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2) * [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends) * [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business) * [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity) * [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2) * [Understanding Business ](https://www.linkedin.com/learning/understanding-business) * [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea) * [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship) * [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations) * [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations) * [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations) * [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips) * [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation) * [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments) * [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow) * [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families) * [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances) * [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2) * [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2) * [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv) * [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers) * [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea) * [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital) * [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving) * [Professional Networking ](https://www.linkedin.com/learning/professional-networking) * [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home) * [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital) * [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling) * [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence) * [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers) ### Learning path * [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance) * [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner) # Completed ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link of best Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Books * [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y) * [Mathematics for Machine Learning](https://mml-book.github.io/) * Principles of Economics (6th edition) * The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It * Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) * Reinventing Your Life: The Breakthough Program to End Negative Behavior * Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence * [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf) * * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/) * [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/) * [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/) * Multiple view geometry in computer vision * Learning OpenCV Book by Adrian Kaehler and Gary Bradski * Digital Image Processing, Global Edition by Rafael C. Gonzalez * Introduction to Algorithms, 3rd Edition (The MIT Press) * The Mythical Man-Month: Essays on Software Engineering * [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition) ### Videos * [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning) * [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos) * [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/) * [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY) ### Papers * CALTag: High Precision Fiducial Markers for Camera * Diatom Autofocusing in Brightfield Microscopy: a Comparative Study * Analysis of focus measure operators in shape-from-focus * Optical flow modeling and computation: A survey * Toward general type 2 fuzzy logic systems based on zSlices ### Site * [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#)) ![](https://www.google.com/images/icons/product/drive-32.png)Reference- ComputerVisionDeepLearning.pdf * [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w) * [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679) * [https://www.freertos.org/index.html](https://www.freertos.org/index.html) * [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation) * [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/) * [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413) * [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917) * [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf) * [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/) * [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833) * [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238) * [https://koinly.io/](https://koinly.io/) site map: octopus.do [https://www.startupgermany.nrw/startup- contest/](https://www.startupgermany.nrw/startup-contest/) [https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth- camera-4k-cv-edge-object- detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai- kit-oak-depth-camera-4k-cv-edge-object-detection/description) [HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube in 2021! - YouTube ](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg) [محمد رضا شجریان، آلبوم کامل در خیال - YouTube](https://www.youtube.com/watch?v=cFdApmqlllg) [دوره جامع گوگل آنالیتیکس(UA) | آنالیتیپس](https://analytips.io/product/google-analyticsua/) [رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا - YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc) [Social Selling Index | Sales Navigator](https://www.linkedin.com/sales/ssi?src=or- search&veh=www.google.com) [https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/EvERVn15AzJZ7aNloDEKv1byWxFboB4Sbe_aPtX2ng9-SLNTjTIPJnwKRUBCWPFdXAOrqOFYlZeEgYJkKq7lPbM=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/EvERVn15AzJZ7aNloDEKv1byWxFboB4Sbe_aPtX2ng9-SLNTjTIPJnwKRUBCWPFdXAOrqOFYlZeEgYJkKq7lPbM=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Links pirahansiah, Personal, Roadmap, Books, Online Courses [](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze- oXQexwY "Open Spreadsheet, Links in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/sheets_32dp.png)Links Links from my research and interest AI in RISC-V Mixed reality Old Links Reference Link Groups Top website Python Computer Vision Documents Business Tools Patents Books Journal Papers Conference Papers Articles on LinkedIn Slides slideshare Online Courses Learning path Completed Reference Link of best Groups Books Videos Papers Site Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning training loops. GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp) [https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to- install-remmina/) [https://readme.so/](https://readme.so/) # Links from my research and interest [Build Better Generative Adversarial Networks (GANs) - Andrew Ng - 2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_- VnF) [https://www.oreilly.com/library/view/flow- architectures/9781492075882/](https://www.oreilly.com/library/view/flow- architectures/9781492075882/) ## AI in RISC-V * [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020) ## Mixed reality * [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera) * Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU. # Old Links ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Top website * [https://paperswithcode.com/](https://paperswithcode.com/) * [https://www.learnthepart.com/](https://www.learnthepart.com/) * [https://hackaday.com/](https://hackaday.com/) * [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762) * hbrew install youtube-dl * [https://www.masterclass.com/](https://www.masterclass.com/) * [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp) * [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona) * [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/) * [https://explainshell.com/](https://explainshell.com/) * [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20) * [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4) * [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4) ### Python * [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/) * [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/) * [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594) ### Computer Vision * [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/) * [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md) * [https://github.com/google/mediapipe](https://github.com/google/mediapipe) ### Documents * [https://book.keybase.io/docs](https://book.keybase.io/docs) * [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind) * [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/) * [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/) * [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/) * [https://kepler.gl/demo](https://kepler.gl/demo) * [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/) * [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE) ### Business * [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006) * [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5) ### Tools * Free convert video for mac opensource * [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake) * [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg) * [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c) * [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises * [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap * [Create and share beautiful images of your source code.](https://carbon.now.sh/) * [Web IDE](https://replit.com/templates) * [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2) ### Patents [https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) 2. A method for augmenting a plurality of face images (WO2021060971A1) 3. A method for detecting a moving vehicle (WO2021107761A1) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) The present invention relates to a system (100) for providing advertisement contents based on facial analysis comprising an image acquisition device (10) to acquire an image of a user, a face detection module (20) to detect face of the user in the image, an analysis module (40) to analyse the facial features statistically using classification models retrieved from a classification module (30), a database (60) to store matching rules, weighted advertisements and a plurality of advertisement contents; and a display device (80) to display the advertisement contents. The system (100) further comprises a computation module (50) to compute weighted image of the user and a matching module (70) to match the weighted image of the user with the weighted advertisement to select an advertisement content based on facial analysis of the user. A method of providing the advertisement contents based on facial analysis is also provided thereof. [https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf) 2. A method for augmenting a plurality of face images (WO2021060971A1) The present invention relates to a method for increasing data for face analysis in video surveillance. The method comprises the steps of acquiring at least one face image from an image acquisition module (102), acquiring a plurality of face images available on the internet using a data input module (104), increasing face images by at least one data augmentation module (106 and 107), generating a plurality of face images based on a trained Generative Adversarial Network, GAN technique by using a GAN module, selecting proper images based on quality of the face images using a fuzzy logic module (111), saving the selected images into a fifth database, and training the deep learning module (113). [https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf) 3. A method for detecting a moving vehicle (WO2021107761A1) The present invention relates to a method for detecting a moving vehicle. The method comprises the steps of grabbing an initial image from a video stream by a vehicle detection module (1100), wherein the vehicle detection module (1100) is a part of a system (1000) to identify moving vehicle, enhancing the illumination of the initial image by the vehicle detection module (1100), enhancing the edges within the initial image by the vehicle detection module (1100), and finding vehicle based on homogenous properties of the body of the vehicle by the vehicle detection module (1100). The step of finding vehicle based on homogenous property of the body of the vehicle by the vehicle detection module (1100) further comprising the sub-steps of closing open edges, inverting the binary image, segmenting an inverted binary image, filtering the noise based on geometric feature, and filtering the noise based on relation. [https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf) ### Books 1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394) 2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/) ### Journal Papers * Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21) * * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf) * GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6) * * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf) * Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9). * * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM) * * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf) * Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2) * * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf) * Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52 * * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf) ### Conference Papers * Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017) * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342) * Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015) * [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf) * Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015) * [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336) * 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012) * [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf) * Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics * [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918) * License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011) * [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627) * Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011) * [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf) * [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649) * Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85. * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532) * Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010) * [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125) * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050 * [http://waset.org/publications/3636](http://waset.org/publications/3636) * An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010) * [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en) ### Articles on LinkedIn * [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/)) * [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) ) * [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/)) * [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/) * [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/) ### Slides slideshare * Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation) * Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) ) * tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) ) * Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) ) * Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) ) * Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) ) * Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) ) * How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) ) * Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) ) * Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) ) ### Online Courses * [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact) * [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life) * [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2) * [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends) * [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business) * [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity) * [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2) * [Understanding Business ](https://www.linkedin.com/learning/understanding-business) * [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea) * [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship) * [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations) * [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations) * [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations) * [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips) * [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation) * [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments) * [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow) * [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families) * [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances) * [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2) * [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2) * [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv) * [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers) * [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea) * [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital) * [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving) * [Professional Networking ](https://www.linkedin.com/learning/professional-networking) * [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home) * [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital) * [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling) * [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence) * [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers) ### Learning path * [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance) * [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner) # Completed ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link of best Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Books * [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y) * [Mathematics for Machine Learning](https://mml-book.github.io/) * Principles of Economics (6th edition) * The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It * Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) * Reinventing Your Life: The Breakthough Program to End Negative Behavior * Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence * [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf) * * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/) * [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/) * [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/) * Multiple view geometry in computer vision * Learning OpenCV Book by Adrian Kaehler and Gary Bradski * Digital Image Processing, Global Edition by Rafael C. Gonzalez * Introduction to Algorithms, 3rd Edition (The MIT Press) * The Mythical Man-Month: Essays on Software Engineering * [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition) ### Videos * [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning) * [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos) * [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/) * [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY) ### Papers * CALTag: High Precision Fiducial Markers for Camera * Diatom Autofocusing in Brightfield Microscopy: a Comparative Study * Analysis of focus measure operators in shape-from-focus * Optical flow modeling and computation: A survey * Toward general type 2 fuzzy logic systems based on zSlices ### Site * [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#)) ![](https://www.google.com/images/icons/product/drive-32.png)Reference- ComputerVisionDeepLearning.pdf * [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w) * [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679) * [https://www.freertos.org/index.html](https://www.freertos.org/index.html) * [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation) * [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/) * [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413) * [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917) * [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf) * [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/) * [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833) * [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238) * [https://koinly.io/](https://koinly.io/) site map: octopus.do [https://www.startupgermany.nrw/startup- contest/](https://www.startupgermany.nrw/startup-contest/) [https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth- camera-4k-cv-edge-object- detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai- kit-oak-depth-camera-4k-cv-edge-object-detection/description) [HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube in 2021! - YouTube ](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg) [محمد رضا شجریان، آلبوم کامل در خیال - YouTube](https://www.youtube.com/watch?v=cFdApmqlllg) [دوره جامع گوگل آنالیتیکس(UA) | آنالیتیپس](https://analytips.io/product/google-analyticsua/) [رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا - YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc) [Social Selling Index | Sales Navigator](https://www.linkedin.com/sales/ssi?src=or- search&veh=www.google.com) [https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/EvERVn15AzJZ7aNloDEKv1byWxFboB4Sbe_aPtX2ng9-SLNTjTIPJnwKRUBCWPFdXAOrqOFYlZeEgYJkKq7lPbM=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/EvERVn15AzJZ7aNloDEKv1byWxFboB4Sbe_aPtX2ng9-SLNTjTIPJnwKRUBCWPFdXAOrqOFYlZeEgYJkKq7lPbM=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Links pirahansiah, Personal, Roadmap, Books, Online Courses [](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze- oXQexwY "Open Spreadsheet, Links in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/sheets_32dp.png)Links Links from my research and interest AI in RISC-V Mixed reality Old Links Reference Link Groups Top website Python Computer Vision Documents Business Tools Patents Books Journal Papers Conference Papers Articles on LinkedIn Slides slideshare Online Courses Learning path Completed Reference Link of best Groups Books Videos Papers Site Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning training loops. GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp) [https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to- install-remmina/) [https://readme.so/](https://readme.so/) # Links from my research and interest [Build Better Generative Adversarial Networks (GANs) - Andrew Ng - 2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_- VnF) [https://www.oreilly.com/library/view/flow- architectures/9781492075882/](https://www.oreilly.com/library/view/flow- architectures/9781492075882/) ## AI in RISC-V * [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020) ## Mixed reality * [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera) * Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU. # Old Links ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Top website * [https://paperswithcode.com/](https://paperswithcode.com/) * [https://www.learnthepart.com/](https://www.learnthepart.com/) * [https://hackaday.com/](https://hackaday.com/) * [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762) * hbrew install youtube-dl * [https://www.masterclass.com/](https://www.masterclass.com/) * [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp) * [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona) * [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/) * [https://explainshell.com/](https://explainshell.com/) * [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20) * [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4) * [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4) ### Python * [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/) * [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/) * [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594) ### Computer Vision * [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/) * [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md) * [https://github.com/google/mediapipe](https://github.com/google/mediapipe) ### Documents * [https://book.keybase.io/docs](https://book.keybase.io/docs) * [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind) * [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/) * [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/) * [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/) * [https://kepler.gl/demo](https://kepler.gl/demo) * [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/) * [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE) ### Business * [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006) * [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5) ### Tools * Free convert video for mac opensource * [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake) * [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg) * [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c) * [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises * [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap * [Create and share beautiful images of your source code.](https://carbon.now.sh/) * [Web IDE](https://replit.com/templates) * [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2) ### Patents [https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) 2. A method for augmenting a plurality of face images (WO2021060971A1) 3. A method for detecting a moving vehicle (WO2021107761A1) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) The present invention relates to a system (100) for providing advertisement contents based on facial analysis comprising an image acquisition device (10) to acquire an image of a user, a face detection module (20) to detect face of the user in the image, an analysis module (40) to analyse the facial features statistically using classification models retrieved from a classification module (30), a database (60) to store matching rules, weighted advertisements and a plurality of advertisement contents; and a display device (80) to display the advertisement contents. The system (100) further comprises a computation module (50) to compute weighted image of the user and a matching module (70) to match the weighted image of the user with the weighted advertisement to select an advertisement content based on facial analysis of the user. A method of providing the advertisement contents based on facial analysis is also provided thereof. [https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf) 2. A method for augmenting a plurality of face images (WO2021060971A1) The present invention relates to a method for increasing data for face analysis in video surveillance. The method comprises the steps of acquiring at least one face image from an image acquisition module (102), acquiring a plurality of face images available on the internet using a data input module (104), increasing face images by at least one data augmentation module (106 and 107), generating a plurality of face images based on a trained Generative Adversarial Network, GAN technique by using a GAN module, selecting proper images based on quality of the face images using a fuzzy logic module (111), saving the selected images into a fifth database, and training the deep learning module (113). [https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf) 3. A method for detecting a moving vehicle (WO2021107761A1) The present invention relates to a method for detecting a moving vehicle. The method comprises the steps of grabbing an initial image from a video stream by a vehicle detection module (1100), wherein the vehicle detection module (1100) is a part of a system (1000) to identify moving vehicle, enhancing the illumination of the initial image by the vehicle detection module (1100), enhancing the edges within the initial image by the vehicle detection module (1100), and finding vehicle based on homogenous properties of the body of the vehicle by the vehicle detection module (1100). The step of finding vehicle based on homogenous property of the body of the vehicle by the vehicle detection module (1100) further comprising the sub-steps of closing open edges, inverting the binary image, segmenting an inverted binary image, filtering the noise based on geometric feature, and filtering the noise based on relation. [https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf) ### Books 1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394) 2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/) ### Journal Papers * Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21) * * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf) * GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6) * * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf) * Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9). * * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM) * * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf) * Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2) * * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf) * Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52 * * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf) ### Conference Papers * Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017) * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342) * Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015) * [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf) * Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015) * [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336) * 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012) * [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf) * Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics * [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918) * License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011) * [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627) * Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011) * [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf) * [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649) * Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85. * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532) * Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010) * [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125) * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050 * [http://waset.org/publications/3636](http://waset.org/publications/3636) * An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010) * [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en) ### Articles on LinkedIn * [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/)) * [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) ) * [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/)) * [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/) * [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/) ### Slides slideshare * Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation) * Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) ) * tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) ) * Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) ) * Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) ) * Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) ) * Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) ) * How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) ) * Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) ) * Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) ) ### Online Courses * [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact) * [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life) * [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2) * [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends) * [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business) * [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity) * [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2) * [Understanding Business ](https://www.linkedin.com/learning/understanding-business) * [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea) * [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship) * [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations) * [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations) * [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations) * [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips) * [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation) * [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments) * [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow) * [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families) * [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances) * [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2) * [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2) * [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv) * [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers) * [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea) * [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital) * [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving) * [Professional Networking ](https://www.linkedin.com/learning/professional-networking) * [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home) * [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital) * [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling) * [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence) * [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers) ### Learning path * [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance) * [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner) # Completed ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link of best Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Books * [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y) * [Mathematics for Machine Learning](https://mml-book.github.io/) * Principles of Economics (6th edition) * The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It * Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) * Reinventing Your Life: The Breakthough Program to End Negative Behavior * Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence * [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf) * * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/) * [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/) * [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/) * Multiple view geometry in computer vision * Learning OpenCV Book by Adrian Kaehler and Gary Bradski * Digital Image Processing, Global Edition by Rafael C. Gonzalez * Introduction to Algorithms, 3rd Edition (The MIT Press) * The Mythical Man-Month: Essays on Software Engineering * [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition) ### Videos * [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning) * [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos) * [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/) * [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY) ### Papers * CALTag: High Precision Fiducial Markers for Camera * Diatom Autofocusing in Brightfield Microscopy: a Comparative Study * Analysis of focus measure operators in shape-from-focus * Optical flow modeling and computation: A survey * Toward general type 2 fuzzy logic systems based on zSlices ### Site * [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#)) ![](https://www.google.com/images/icons/product/drive-32.png)Reference- ComputerVisionDeepLearning.pdf * [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w) * [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679) * [https://www.freertos.org/index.html](https://www.freertos.org/index.html) * [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation) * [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/) * [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413) * [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917) * [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf) * [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/) * [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833) * [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238) * [https://koinly.io/](https://koinly.io/) site map: octopus.do [https://www.startupgermany.nrw/startup- contest/](https://www.startupgermany.nrw/startup-contest/) [https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth- camera-4k-cv-edge-object- detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai- kit-oak-depth-camera-4k-cv-edge-object-detection/description) [HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube in 2021! - YouTube ](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg) [محمد رضا شجریان، آلبوم کامل در خیال - YouTube](https://www.youtube.com/watch?v=cFdApmqlllg) [دوره جامع گوگل آنالیتیکس(UA) | آنالیتیپس](https://analytips.io/product/google-analyticsua/) [رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا - YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc) [Social Selling Index | Sales Navigator](https://www.linkedin.com/sales/ssi?src=or- search&veh=www.google.com) [https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/EvERVn15AzJZ7aNloDEKv1byWxFboB4Sbe_aPtX2ng9-SLNTjTIPJnwKRUBCWPFdXAOrqOFYlZeEgYJkKq7lPbM=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/EvERVn15AzJZ7aNloDEKv1byWxFboB4Sbe_aPtX2ng9-SLNTjTIPJnwKRUBCWPFdXAOrqOFYlZeEgYJkKq7lPbM=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Links pirahansiah, Personal, Roadmap, Books, Online Courses [](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze- oXQexwY "Open Spreadsheet, Links in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/sheets_32dp.png)Links Links from my research and interest AI in RISC-V Mixed reality Old Links Reference Link Groups Top website Python Computer Vision Documents Business Tools Patents Books Journal Papers Conference Papers Articles on LinkedIn Slides slideshare Online Courses Learning path Completed Reference Link of best Groups Books Videos Papers Site Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning training loops. GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp) [https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to- install-remmina/) [https://readme.so/](https://readme.so/) # Links from my research and interest [Build Better Generative Adversarial Networks (GANs) - Andrew Ng - 2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_- VnF) [https://www.oreilly.com/library/view/flow- architectures/9781492075882/](https://www.oreilly.com/library/view/flow- architectures/9781492075882/) ## AI in RISC-V * [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020) ## Mixed reality * [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera) * Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU. # Old Links ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Top website * [https://paperswithcode.com/](https://paperswithcode.com/) * [https://www.learnthepart.com/](https://www.learnthepart.com/) * [https://hackaday.com/](https://hackaday.com/) * [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762) * hbrew install youtube-dl * [https://www.masterclass.com/](https://www.masterclass.com/) * [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp) * [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona) * [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/) * [https://explainshell.com/](https://explainshell.com/) * [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20) * [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4) * [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4) ### Python * [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/) * [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/) * [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594) ### Computer Vision * [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/) * [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md) * [https://github.com/google/mediapipe](https://github.com/google/mediapipe) ### Documents * [https://book.keybase.io/docs](https://book.keybase.io/docs) * [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind) * [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/) * [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/) * [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/) * [https://kepler.gl/demo](https://kepler.gl/demo) * [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/) * [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE) ### Business * [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006) * [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5) ### Tools * Free convert video for mac opensource * [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake) * [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg) * [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c) * [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises * [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap * [Create and share beautiful images of your source code.](https://carbon.now.sh/) * [Web IDE](https://replit.com/templates) * [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2) ### Patents [https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) 2. A method for augmenting a plurality of face images (WO2021060971A1) 3. A method for detecting a moving vehicle (WO2021107761A1) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) The present invention relates to a system (100) for providing advertisement contents based on facial analysis comprising an image acquisition device (10) to acquire an image of a user, a face detection module (20) to detect face of the user in the image, an analysis module (40) to analyse the facial features statistically using classification models retrieved from a classification module (30), a database (60) to store matching rules, weighted advertisements and a plurality of advertisement contents; and a display device (80) to display the advertisement contents. The system (100) further comprises a computation module (50) to compute weighted image of the user and a matching module (70) to match the weighted image of the user with the weighted advertisement to select an advertisement content based on facial analysis of the user. A method of providing the advertisement contents based on facial analysis is also provided thereof. [https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf) 2. A method for augmenting a plurality of face images (WO2021060971A1) The present invention relates to a method for increasing data for face analysis in video surveillance. The method comprises the steps of acquiring at least one face image from an image acquisition module (102), acquiring a plurality of face images available on the internet using a data input module (104), increasing face images by at least one data augmentation module (106 and 107), generating a plurality of face images based on a trained Generative Adversarial Network, GAN technique by using a GAN module, selecting proper images based on quality of the face images using a fuzzy logic module (111), saving the selected images into a fifth database, and training the deep learning module (113). [https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf) 3. A method for detecting a moving vehicle (WO2021107761A1) The present invention relates to a method for detecting a moving vehicle. The method comprises the steps of grabbing an initial image from a video stream by a vehicle detection module (1100), wherein the vehicle detection module (1100) is a part of a system (1000) to identify moving vehicle, enhancing the illumination of the initial image by the vehicle detection module (1100), enhancing the edges within the initial image by the vehicle detection module (1100), and finding vehicle based on homogenous properties of the body of the vehicle by the vehicle detection module (1100). The step of finding vehicle based on homogenous property of the body of the vehicle by the vehicle detection module (1100) further comprising the sub-steps of closing open edges, inverting the binary image, segmenting an inverted binary image, filtering the noise based on geometric feature, and filtering the noise based on relation. [https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf) ### Books 1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394) 2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/) ### Journal Papers * Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21) * * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf) * GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6) * * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf) * Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9). * * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM) * * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf) * Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2) * * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf) * Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52 * * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf) ### Conference Papers * Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017) * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342) * Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015) * [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf) * Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015) * [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336) * 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012) * [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf) * Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics * [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918) * License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011) * [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627) * Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011) * [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf) * [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649) * Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85. * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532) * Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010) * [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125) * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050 * [http://waset.org/publications/3636](http://waset.org/publications/3636) * An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010) * [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en) ### Articles on LinkedIn * [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/)) * [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) ) * [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/)) * [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/) * [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/) ### Slides slideshare * Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation) * Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) ) * tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) ) * Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) ) * Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) ) * Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) ) * Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) ) * How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) ) * Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) ) * Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) ) ### Online Courses * [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact) * [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life) * [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2) * [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends) * [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business) * [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity) * [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2) * [Understanding Business ](https://www.linkedin.com/learning/understanding-business) * [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea) * [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship) * [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations) * [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations) * [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations) * [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips) * [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation) * [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments) * [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow) * [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families) * [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances) * [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2) * [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2) * [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv) * [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers) * [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea) * [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital) * [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving) * [Professional Networking ](https://www.linkedin.com/learning/professional-networking) * [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home) * [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital) * [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling) * [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence) * [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers) ### Learning path * [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance) * [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner) # Completed ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link of best Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Books * [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y) * [Mathematics for Machine Learning](https://mml-book.github.io/) * Principles of Economics (6th edition) * The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It * Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) * Reinventing Your Life: The Breakthough Program to End Negative Behavior * Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence * [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf) * * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/) * [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/) * [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/) * Multiple view geometry in computer vision * Learning OpenCV Book by Adrian Kaehler and Gary Bradski * Digital Image Processing, Global Edition by Rafael C. Gonzalez * Introduction to Algorithms, 3rd Edition (The MIT Press) * The Mythical Man-Month: Essays on Software Engineering * [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition) ### Videos * [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning) * [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos) * [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/) * [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY) ### Papers * CALTag: High Precision Fiducial Markers for Camera * Diatom Autofocusing in Brightfield Microscopy: a Comparative Study * Analysis of focus measure operators in shape-from-focus * Optical flow modeling and computation: A survey * Toward general type 2 fuzzy logic systems based on zSlices ### Site * [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#)) ![](https://www.google.com/images/icons/product/drive-32.png)Reference- ComputerVisionDeepLearning.pdf * [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w) * [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679) * [https://www.freertos.org/index.html](https://www.freertos.org/index.html) * [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation) * [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/) * [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413) * [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917) * [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf) * [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/) * [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833) * [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238) * [https://koinly.io/](https://koinly.io/) site map: octopus.do [https://www.startupgermany.nrw/startup- contest/](https://www.startupgermany.nrw/startup-contest/) [https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth- camera-4k-cv-edge-object- detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai- kit-oak-depth-camera-4k-cv-edge-object-detection/description) [HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube in 2021! - YouTube ](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg) [محمد رضا شجریان، آلبوم کامل در خیال - YouTube](https://www.youtube.com/watch?v=cFdApmqlllg) [دوره جامع گوگل آنالیتیکس(UA) | آنالیتیپس](https://analytips.io/product/google-analyticsua/) [رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا - YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc) [Social Selling Index | Sales Navigator](https://www.linkedin.com/sales/ssi?src=or- search&veh=www.google.com) [https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/EvERVn15AzJZ7aNloDEKv1byWxFboB4Sbe_aPtX2ng9-SLNTjTIPJnwKRUBCWPFdXAOrqOFYlZeEgYJkKq7lPbM=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/EvERVn15AzJZ7aNloDEKv1byWxFboB4Sbe_aPtX2ng9-SLNTjTIPJnwKRUBCWPFdXAOrqOFYlZeEgYJkKq7lPbM=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Links pirahansiah, Personal, Roadmap, Books, Online Courses [](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze- oXQexwY "Open Spreadsheet, Links in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/sheets_32dp.png)Links Links from my research and interest AI in RISC-V Mixed reality Old Links Reference Link Groups Top website Python Computer Vision Documents Business Tools Patents Books Journal Papers Conference Papers Articles on LinkedIn Slides slideshare Online Courses Learning path Completed Reference Link of best Groups Books Videos Papers Site Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning training loops. GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp) [https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to- install-remmina/) [https://readme.so/](https://readme.so/) # Links from my research and interest [Build Better Generative Adversarial Networks (GANs) - Andrew Ng - 2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_- VnF) [https://www.oreilly.com/library/view/flow- architectures/9781492075882/](https://www.oreilly.com/library/view/flow- architectures/9781492075882/) ## AI in RISC-V * [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020) ## Mixed reality * [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera) * Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU. # Old Links ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Top website * [https://paperswithcode.com/](https://paperswithcode.com/) * [https://www.learnthepart.com/](https://www.learnthepart.com/) * [https://hackaday.com/](https://hackaday.com/) * [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762) * hbrew install youtube-dl * [https://www.masterclass.com/](https://www.masterclass.com/) * [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp) * [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona) * [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/) * [https://explainshell.com/](https://explainshell.com/) * [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20) * [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4) * [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4) ### Python * [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/) * [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/) * [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594) ### Computer Vision * [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/) * [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md) * [https://github.com/google/mediapipe](https://github.com/google/mediapipe) ### Documents * [https://book.keybase.io/docs](https://book.keybase.io/docs) * [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind) * [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/) * [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/) * [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/) * [https://kepler.gl/demo](https://kepler.gl/demo) * [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/) * [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE) ### Business * [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006) * [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5) ### Tools * Free convert video for mac opensource * [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake) * [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg) * [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c) * [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises * [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap * [Create and share beautiful images of your source code.](https://carbon.now.sh/) * [Web IDE](https://replit.com/templates) * [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2) ### Patents [https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) 2. A method for augmenting a plurality of face images (WO2021060971A1) 3. A method for detecting a moving vehicle (WO2021107761A1) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) The present invention relates to a system (100) for providing advertisement contents based on facial analysis comprising an image acquisition device (10) to acquire an image of a user, a face detection module (20) to detect face of the user in the image, an analysis module (40) to analyse the facial features statistically using classification models retrieved from a classification module (30), a database (60) to store matching rules, weighted advertisements and a plurality of advertisement contents; and a display device (80) to display the advertisement contents. The system (100) further comprises a computation module (50) to compute weighted image of the user and a matching module (70) to match the weighted image of the user with the weighted advertisement to select an advertisement content based on facial analysis of the user. A method of providing the advertisement contents based on facial analysis is also provided thereof. [https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf) 2. A method for augmenting a plurality of face images (WO2021060971A1) The present invention relates to a method for increasing data for face analysis in video surveillance. The method comprises the steps of acquiring at least one face image from an image acquisition module (102), acquiring a plurality of face images available on the internet using a data input module (104), increasing face images by at least one data augmentation module (106 and 107), generating a plurality of face images based on a trained Generative Adversarial Network, GAN technique by using a GAN module, selecting proper images based on quality of the face images using a fuzzy logic module (111), saving the selected images into a fifth database, and training the deep learning module (113). [https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf) 3. A method for detecting a moving vehicle (WO2021107761A1) The present invention relates to a method for detecting a moving vehicle. The method comprises the steps of grabbing an initial image from a video stream by a vehicle detection module (1100), wherein the vehicle detection module (1100) is a part of a system (1000) to identify moving vehicle, enhancing the illumination of the initial image by the vehicle detection module (1100), enhancing the edges within the initial image by the vehicle detection module (1100), and finding vehicle based on homogenous properties of the body of the vehicle by the vehicle detection module (1100). The step of finding vehicle based on homogenous property of the body of the vehicle by the vehicle detection module (1100) further comprising the sub-steps of closing open edges, inverting the binary image, segmenting an inverted binary image, filtering the noise based on geometric feature, and filtering the noise based on relation. [https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf) ### Books 1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394) 2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/) ### Journal Papers * Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21) * * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf) * GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6) * * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf) * Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9). * * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM) * * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf) * Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2) * * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf) * Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52 * * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf) ### Conference Papers * Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017) * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342) * Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015) * [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf) * Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015) * [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336) * 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012) * [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf) * Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics * [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918) * License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011) * [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627) * Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011) * [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf) * [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649) * Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85. * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532) * Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010) * [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125) * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050 * [http://waset.org/publications/3636](http://waset.org/publications/3636) * An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010) * [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en) ### Articles on LinkedIn * [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/)) * [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) ) * [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/)) * [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/) * [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/) ### Slides slideshare * Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation) * Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) ) * tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) ) * Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) ) * Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) ) * Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) ) * Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) ) * How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) ) * Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) ) * Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) ) ### Online Courses * [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact) * [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life) * [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2) * [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends) * [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business) * [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity) * [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2) * [Understanding Business ](https://www.linkedin.com/learning/understanding-business) * [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea) * [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship) * [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations) * [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations) * [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations) * [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips) * [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation) * [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments) * [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow) * [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families) * [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances) * [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2) * [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2) * [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv) * [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers) * [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea) * [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital) * [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving) * [Professional Networking ](https://www.linkedin.com/learning/professional-networking) * [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home) * [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital) * [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling) * [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence) * [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers) ### Learning path * [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance) * [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner) # Completed ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link of best Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Books * [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y) * [Mathematics for Machine Learning](https://mml-book.github.io/) * Principles of Economics (6th edition) * The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It * Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) * Reinventing Your Life: The Breakthough Program to End Negative Behavior * Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence * [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf) * * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/) * [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/) * [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/) * Multiple view geometry in computer vision * Learning OpenCV Book by Adrian Kaehler and Gary Bradski * Digital Image Processing, Global Edition by Rafael C. Gonzalez * Introduction to Algorithms, 3rd Edition (The MIT Press) * The Mythical Man-Month: Essays on Software Engineering * [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition) ### Videos * [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning) * [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos) * [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/) * [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY) ### Papers * CALTag: High Precision Fiducial Markers for Camera * Diatom Autofocusing in Brightfield Microscopy: a Comparative Study * Analysis of focus measure operators in shape-from-focus * Optical flow modeling and computation: A survey * Toward general type 2 fuzzy logic systems based on zSlices ### Site * [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#)) ![](https://www.google.com/images/icons/product/drive-32.png)Reference- ComputerVisionDeepLearning.pdf * [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w) * [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679) * [https://www.freertos.org/index.html](https://www.freertos.org/index.html) * [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation) * [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/) * [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413) * [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917) * [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf) * [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/) * [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833) * [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238) * [https://koinly.io/](https://koinly.io/) site map: octopus.do [https://www.startupgermany.nrw/startup- contest/](https://www.startupgermany.nrw/startup-contest/) [https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth- camera-4k-cv-edge-object- detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai- kit-oak-depth-camera-4k-cv-edge-object-detection/description) [HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube in 2021! - YouTube ](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg) [محمد رضا شجریان، آلبوم کامل در خیال - YouTube](https://www.youtube.com/watch?v=cFdApmqlllg) [دوره جامع گوگل آنالیتیکس(UA) | آنالیتیپس](https://analytips.io/product/google-analyticsua/) [رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا - YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc) [Social Selling Index | Sales Navigator](https://www.linkedin.com/sales/ssi?src=or- search&veh=www.google.com) [https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/EvERVn15AzJZ7aNloDEKv1byWxFboB4Sbe_aPtX2ng9-SLNTjTIPJnwKRUBCWPFdXAOrqOFYlZeEgYJkKq7lPbM=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/EvERVn15AzJZ7aNloDEKv1byWxFboB4Sbe_aPtX2ng9-SLNTjTIPJnwKRUBCWPFdXAOrqOFYlZeEgYJkKq7lPbM=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Links pirahansiah, Personal, Roadmap, Books, Online Courses [](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze- oXQexwY "Open Spreadsheet, Links in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/sheets_32dp.png)Links Links from my research and interest AI in RISC-V Mixed reality Old Links Reference Link Groups Top website Python Computer Vision Documents Business Tools Patents Books Journal Papers Conference Papers Articles on LinkedIn Slides slideshare Online Courses Learning path Completed Reference Link of best Groups Books Videos Papers Site Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning training loops. GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp) [https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to- install-remmina/) [https://readme.so/](https://readme.so/) # Links from my research and interest [Build Better Generative Adversarial Networks (GANs) - Andrew Ng - 2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_- VnF) [https://www.oreilly.com/library/view/flow- architectures/9781492075882/](https://www.oreilly.com/library/view/flow- architectures/9781492075882/) ## AI in RISC-V * [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020) ## Mixed reality * [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera) * Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU. # Old Links ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Top website * [https://paperswithcode.com/](https://paperswithcode.com/) * [https://www.learnthepart.com/](https://www.learnthepart.com/) * [https://hackaday.com/](https://hackaday.com/) * [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762) * hbrew install youtube-dl * [https://www.masterclass.com/](https://www.masterclass.com/) * [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp) * [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona) * [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/) * [https://explainshell.com/](https://explainshell.com/) * [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20) * [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4) * [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4) ### Python * [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/) * [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/) * [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594) ### Computer Vision * [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/) * [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md) * [https://github.com/google/mediapipe](https://github.com/google/mediapipe) ### Documents * [https://book.keybase.io/docs](https://book.keybase.io/docs) * [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind) * [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/) * [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/) * [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/) * [https://kepler.gl/demo](https://kepler.gl/demo) * [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/) * [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE) ### Business * [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006) * [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5) ### Tools * Free convert video for mac opensource * [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake) * [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg) * [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c) * [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises * [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap * [Create and share beautiful images of your source code.](https://carbon.now.sh/) * [Web IDE](https://replit.com/templates) * [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2) ### Patents [https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) 2. A method for augmenting a plurality of face images (WO2021060971A1) 3. A method for detecting a moving vehicle (WO2021107761A1) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) The present invention relates to a system (100) for providing advertisement contents based on facial analysis comprising an image acquisition device (10) to acquire an image of a user, a face detection module (20) to detect face of the user in the image, an analysis module (40) to analyse the facial features statistically using classification models retrieved from a classification module (30), a database (60) to store matching rules, weighted advertisements and a plurality of advertisement contents; and a display device (80) to display the advertisement contents. The system (100) further comprises a computation module (50) to compute weighted image of the user and a matching module (70) to match the weighted image of the user with the weighted advertisement to select an advertisement content based on facial analysis of the user. A method of providing the advertisement contents based on facial analysis is also provided thereof. [https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf) 2. A method for augmenting a plurality of face images (WO2021060971A1) The present invention relates to a method for increasing data for face analysis in video surveillance. The method comprises the steps of acquiring at least one face image from an image acquisition module (102), acquiring a plurality of face images available on the internet using a data input module (104), increasing face images by at least one data augmentation module (106 and 107), generating a plurality of face images based on a trained Generative Adversarial Network, GAN technique by using a GAN module, selecting proper images based on quality of the face images using a fuzzy logic module (111), saving the selected images into a fifth database, and training the deep learning module (113). [https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf) 3. A method for detecting a moving vehicle (WO2021107761A1) The present invention relates to a method for detecting a moving vehicle. The method comprises the steps of grabbing an initial image from a video stream by a vehicle detection module (1100), wherein the vehicle detection module (1100) is a part of a system (1000) to identify moving vehicle, enhancing the illumination of the initial image by the vehicle detection module (1100), enhancing the edges within the initial image by the vehicle detection module (1100), and finding vehicle based on homogenous properties of the body of the vehicle by the vehicle detection module (1100). The step of finding vehicle based on homogenous property of the body of the vehicle by the vehicle detection module (1100) further comprising the sub-steps of closing open edges, inverting the binary image, segmenting an inverted binary image, filtering the noise based on geometric feature, and filtering the noise based on relation. [https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf) ### Books 1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394) 2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/) ### Journal Papers * Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21) * * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf) * GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6) * * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf) * Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9). * * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM) * * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf) * Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2) * * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf) * Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52 * * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf) ### Conference Papers * Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017) * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342) * Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015) * [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf) * Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015) * [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336) * 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012) * [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf) * Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics * [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918) * License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011) * [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627) * Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011) * [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf) * [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649) * Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85. * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532) * Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010) * [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125) * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050 * [http://waset.org/publications/3636](http://waset.org/publications/3636) * An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010) * [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en) ### Articles on LinkedIn * [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/)) * [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) ) * [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/)) * [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/) * [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/) ### Slides slideshare * Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation) * Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) ) * tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) ) * Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) ) * Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) ) * Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) ) * Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) ) * How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) ) * Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) ) * Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) ) ### Online Courses * [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact) * [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life) * [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2) * [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends) * [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business) * [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity) * [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2) * [Understanding Business ](https://www.linkedin.com/learning/understanding-business) * [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea) * [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship) * [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations) * [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations) * [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations) * [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips) * [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation) * [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments) * [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow) * [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families) * [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances) * [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2) * [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2) * [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv) * [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers) * [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea) * [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital) * [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving) * [Professional Networking ](https://www.linkedin.com/learning/professional-networking) * [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home) * [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital) * [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling) * [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence) * [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers) ### Learning path * [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance) * [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner) # Completed ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link of best Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Books * [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y) * [Mathematics for Machine Learning](https://mml-book.github.io/) * Principles of Economics (6th edition) * The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It * Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) * Reinventing Your Life: The Breakthough Program to End Negative Behavior * Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence * [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf) * * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/) * [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/) * [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/) * Multiple view geometry in computer vision * Learning OpenCV Book by Adrian Kaehler and Gary Bradski * Digital Image Processing, Global Edition by Rafael C. Gonzalez * Introduction to Algorithms, 3rd Edition (The MIT Press) * The Mythical Man-Month: Essays on Software Engineering * [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition) ### Videos * [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning) * [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos) * [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/) * [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY) ### Papers * CALTag: High Precision Fiducial Markers for Camera * Diatom Autofocusing in Brightfield Microscopy: a Comparative Study * Analysis of focus measure operators in shape-from-focus * Optical flow modeling and computation: A survey * Toward general type 2 fuzzy logic systems based on zSlices ### Site * [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#)) ![](https://www.google.com/images/icons/product/drive-32.png)Reference- ComputerVisionDeepLearning.pdf * [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w) * [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679) * [https://www.freertos.org/index.html](https://www.freertos.org/index.html) * [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation) * [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/) * [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413) * [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917) * [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf) * [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/) * [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833) * [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238) * [https://koinly.io/](https://koinly.io/) site map: octopus.do [https://www.startupgermany.nrw/startup- contest/](https://www.startupgermany.nrw/startup-contest/) [https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth- camera-4k-cv-edge-object- detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai- kit-oak-depth-camera-4k-cv-edge-object-detection/description) [HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube in 2021! - YouTube ](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg) [محمد رضا شجریان، آلبوم کامل در خیال - YouTube](https://www.youtube.com/watch?v=cFdApmqlllg) [دوره جامع گوگل آنالیتیکس(UA) | آنالیتیپس](https://analytips.io/product/google-analyticsua/) [رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا - YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc) [Social Selling Index | Sales Navigator](https://www.linkedin.com/sales/ssi?src=or- search&veh=www.google.com) [https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/EvERVn15AzJZ7aNloDEKv1byWxFboB4Sbe_aPtX2ng9-SLNTjTIPJnwKRUBCWPFdXAOrqOFYlZeEgYJkKq7lPbM=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/EvERVn15AzJZ7aNloDEKv1byWxFboB4Sbe_aPtX2ng9-SLNTjTIPJnwKRUBCWPFdXAOrqOFYlZeEgYJkKq7lPbM=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Links pirahansiah, Personal, Roadmap, Books, Online Courses [](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze- oXQexwY "Open Spreadsheet, Links in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/sheets_32dp.png)Links Links from my research and interest AI in RISC-V Mixed reality Old Links Reference Link Groups Top website Python Computer Vision Documents Business Tools Patents Books Journal Papers Conference Papers Articles on LinkedIn Slides slideshare Online Courses Learning path Completed Reference Link of best Groups Books Videos Papers Site Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning training loops. GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp) [https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to- install-remmina/) [https://readme.so/](https://readme.so/) # Links from my research and interest [Build Better Generative Adversarial Networks (GANs) - Andrew Ng - 2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_- VnF) [https://www.oreilly.com/library/view/flow- architectures/9781492075882/](https://www.oreilly.com/library/view/flow- architectures/9781492075882/) ## AI in RISC-V * [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020) ## Mixed reality * [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera) * Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU. # Old Links ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Top website * [https://paperswithcode.com/](https://paperswithcode.com/) * [https://www.learnthepart.com/](https://www.learnthepart.com/) * [https://hackaday.com/](https://hackaday.com/) * [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762) * hbrew install youtube-dl * [https://www.masterclass.com/](https://www.masterclass.com/) * [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp) * [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona) * [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/) * [https://explainshell.com/](https://explainshell.com/) * [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20) * [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4) * [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4) ### Python * [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/) * [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/) * [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594) ### Computer Vision * [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/) * [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md) * [https://github.com/google/mediapipe](https://github.com/google/mediapipe) ### Documents * [https://book.keybase.io/docs](https://book.keybase.io/docs) * [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind) * [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/) * [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/) * [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/) * [https://kepler.gl/demo](https://kepler.gl/demo) * [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/) * [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE) ### Business * [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006) * [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5) ### Tools * Free convert video for mac opensource * [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake) * [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg) * [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c) * [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises * [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap * [Create and share beautiful images of your source code.](https://carbon.now.sh/) * [Web IDE](https://replit.com/templates) * [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2) ### Patents [https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) 2. A method for augmenting a plurality of face images (WO2021060971A1) 3. A method for detecting a moving vehicle (WO2021107761A1) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) The present invention relates to a system (100) for providing advertisement contents based on facial analysis comprising an image acquisition device (10) to acquire an image of a user, a face detection module (20) to detect face of the user in the image, an analysis module (40) to analyse the facial features statistically using classification models retrieved from a classification module (30), a database (60) to store matching rules, weighted advertisements and a plurality of advertisement contents; and a display device (80) to display the advertisement contents. The system (100) further comprises a computation module (50) to compute weighted image of the user and a matching module (70) to match the weighted image of the user with the weighted advertisement to select an advertisement content based on facial analysis of the user. A method of providing the advertisement contents based on facial analysis is also provided thereof. [https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf) 2. A method for augmenting a plurality of face images (WO2021060971A1) The present invention relates to a method for increasing data for face analysis in video surveillance. The method comprises the steps of acquiring at least one face image from an image acquisition module (102), acquiring a plurality of face images available on the internet using a data input module (104), increasing face images by at least one data augmentation module (106 and 107), generating a plurality of face images based on a trained Generative Adversarial Network, GAN technique by using a GAN module, selecting proper images based on quality of the face images using a fuzzy logic module (111), saving the selected images into a fifth database, and training the deep learning module (113). [https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf) 3. A method for detecting a moving vehicle (WO2021107761A1) The present invention relates to a method for detecting a moving vehicle. The method comprises the steps of grabbing an initial image from a video stream by a vehicle detection module (1100), wherein the vehicle detection module (1100) is a part of a system (1000) to identify moving vehicle, enhancing the illumination of the initial image by the vehicle detection module (1100), enhancing the edges within the initial image by the vehicle detection module (1100), and finding vehicle based on homogenous properties of the body of the vehicle by the vehicle detection module (1100). The step of finding vehicle based on homogenous property of the body of the vehicle by the vehicle detection module (1100) further comprising the sub-steps of closing open edges, inverting the binary image, segmenting an inverted binary image, filtering the noise based on geometric feature, and filtering the noise based on relation. [https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf) ### Books 1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394) 2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/) ### Journal Papers * Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21) * * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf) * GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6) * * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf) * Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9). * * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM) * * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf) * Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2) * * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf) * Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52 * * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf) ### Conference Papers * Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017) * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342) * Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015) * [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf) * Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015) * [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336) * 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012) * [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf) * Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics * [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918) * License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011) * [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627) * Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011) * [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf) * [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649) * Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85. * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532) * Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010) * [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125) * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050 * [http://waset.org/publications/3636](http://waset.org/publications/3636) * An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010) * [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en) ### Articles on LinkedIn * [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/)) * [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) ) * [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/)) * [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/) * [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/) ### Slides slideshare * Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation) * Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) ) * tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) ) * Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) ) * Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) ) * Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) ) * Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) ) * How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) ) * Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) ) * Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) ) ### Online Courses * [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact) * [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life) * [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2) * [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends) * [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business) * [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity) * [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2) * [Understanding Business ](https://www.linkedin.com/learning/understanding-business) * [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea) * [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship) * [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations) * [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations) * [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations) * [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips) * [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation) * [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments) * [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow) * [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families) * [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances) * [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2) * [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2) * [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv) * [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers) * [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea) * [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital) * [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving) * [Professional Networking ](https://www.linkedin.com/learning/professional-networking) * [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home) * [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital) * [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling) * [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence) * [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers) ### Learning path * [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance) * [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner) # Completed ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link of best Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Books * [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y) * [Mathematics for Machine Learning](https://mml-book.github.io/) * Principles of Economics (6th edition) * The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It * Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) * Reinventing Your Life: The Breakthough Program to End Negative Behavior * Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence * [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf) * * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/) * [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/) * [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/) * Multiple view geometry in computer vision * Learning OpenCV Book by Adrian Kaehler and Gary Bradski * Digital Image Processing, Global Edition by Rafael C. Gonzalez * Introduction to Algorithms, 3rd Edition (The MIT Press) * The Mythical Man-Month: Essays on Software Engineering * [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition) ### Videos * [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning) * [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos) * [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/) * [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY) ### Papers * CALTag: High Precision Fiducial Markers for Camera * Diatom Autofocusing in Brightfield Microscopy: a Comparative Study * Analysis of focus measure operators in shape-from-focus * Optical flow modeling and computation: A survey * Toward general type 2 fuzzy logic systems based on zSlices ### Site * [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#)) ![](https://www.google.com/images/icons/product/drive-32.png)Reference- ComputerVisionDeepLearning.pdf * [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w) * [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679) * [https://www.freertos.org/index.html](https://www.freertos.org/index.html) * [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation) * [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/) * [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413) * [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917) * [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf) * [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/) * [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833) * [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238) * [https://koinly.io/](https://koinly.io/) site map: octopus.do [https://www.startupgermany.nrw/startup- contest/](https://www.startupgermany.nrw/startup-contest/) [https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth- camera-4k-cv-edge-object- detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai- kit-oak-depth-camera-4k-cv-edge-object-detection/description) [HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube in 2021! - YouTube ](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg) [محمد رضا شجریان، آلبوم کامل در خیال - YouTube](https://www.youtube.com/watch?v=cFdApmqlllg) [دوره جامع گوگل آنالیتیکس(UA) | آنالیتیپس](https://analytips.io/product/google-analyticsua/) [رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا - YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc) [Social Selling Index | Sales Navigator](https://www.linkedin.com/sales/ssi?src=or- search&veh=www.google.com) [https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/EvERVn15AzJZ7aNloDEKv1byWxFboB4Sbe_aPtX2ng9-SLNTjTIPJnwKRUBCWPFdXAOrqOFYlZeEgYJkKq7lPbM=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/EvERVn15AzJZ7aNloDEKv1byWxFboB4Sbe_aPtX2ng9-SLNTjTIPJnwKRUBCWPFdXAOrqOFYlZeEgYJkKq7lPbM=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Links pirahansiah, Personal, Roadmap, Books, Online Courses [](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze- oXQexwY "Open Spreadsheet, Links in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/sheets_32dp.png)Links Links from my research and interest AI in RISC-V Mixed reality Old Links Reference Link Groups Top website Python Computer Vision Documents Business Tools Patents Books Journal Papers Conference Papers Articles on LinkedIn Slides slideshare Online Courses Learning path Completed Reference Link of best Groups Books Videos Papers Site Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning training loops. GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp) [https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to- install-remmina/) [https://readme.so/](https://readme.so/) # Links from my research and interest [Build Better Generative Adversarial Networks (GANs) - Andrew Ng - 2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_- VnF) [https://www.oreilly.com/library/view/flow- architectures/9781492075882/](https://www.oreilly.com/library/view/flow- architectures/9781492075882/) ## AI in RISC-V * [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020) ## Mixed reality * [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera) * Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU. # Old Links ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Top website * [https://paperswithcode.com/](https://paperswithcode.com/) * [https://www.learnthepart.com/](https://www.learnthepart.com/) * [https://hackaday.com/](https://hackaday.com/) * [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762) * hbrew install youtube-dl * [https://www.masterclass.com/](https://www.masterclass.com/) * [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp) * [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona) * [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/) * [https://explainshell.com/](https://explainshell.com/) * [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20) * [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4) * [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4) ### Python * [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/) * [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/) * [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594) ### Computer Vision * [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/) * [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md) * [https://github.com/google/mediapipe](https://github.com/google/mediapipe) ### Documents * [https://book.keybase.io/docs](https://book.keybase.io/docs) * [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind) * [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/) * [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/) * [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/) * [https://kepler.gl/demo](https://kepler.gl/demo) * [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/) * [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE) ### Business * [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006) * [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5) ### Tools * Free convert video for mac opensource * [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake) * [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg) * [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c) * [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises * [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap * [Create and share beautiful images of your source code.](https://carbon.now.sh/) * [Web IDE](https://replit.com/templates) * [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2) ### Patents [https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) 2. A method for augmenting a plurality of face images (WO2021060971A1) 3. A method for detecting a moving vehicle (WO2021107761A1) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) The present invention relates to a system (100) for providing advertisement contents based on facial analysis comprising an image acquisition device (10) to acquire an image of a user, a face detection module (20) to detect face of the user in the image, an analysis module (40) to analyse the facial features statistically using classification models retrieved from a classification module (30), a database (60) to store matching rules, weighted advertisements and a plurality of advertisement contents; and a display device (80) to display the advertisement contents. The system (100) further comprises a computation module (50) to compute weighted image of the user and a matching module (70) to match the weighted image of the user with the weighted advertisement to select an advertisement content based on facial analysis of the user. A method of providing the advertisement contents based on facial analysis is also provided thereof. [https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf) 2. A method for augmenting a plurality of face images (WO2021060971A1) The present invention relates to a method for increasing data for face analysis in video surveillance. The method comprises the steps of acquiring at least one face image from an image acquisition module (102), acquiring a plurality of face images available on the internet using a data input module (104), increasing face images by at least one data augmentation module (106 and 107), generating a plurality of face images based on a trained Generative Adversarial Network, GAN technique by using a GAN module, selecting proper images based on quality of the face images using a fuzzy logic module (111), saving the selected images into a fifth database, and training the deep learning module (113). [https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf) 3. A method for detecting a moving vehicle (WO2021107761A1) The present invention relates to a method for detecting a moving vehicle. The method comprises the steps of grabbing an initial image from a video stream by a vehicle detection module (1100), wherein the vehicle detection module (1100) is a part of a system (1000) to identify moving vehicle, enhancing the illumination of the initial image by the vehicle detection module (1100), enhancing the edges within the initial image by the vehicle detection module (1100), and finding vehicle based on homogenous properties of the body of the vehicle by the vehicle detection module (1100). The step of finding vehicle based on homogenous property of the body of the vehicle by the vehicle detection module (1100) further comprising the sub-steps of closing open edges, inverting the binary image, segmenting an inverted binary image, filtering the noise based on geometric feature, and filtering the noise based on relation. [https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf) ### Books 1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394) 2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/) ### Journal Papers * Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21) * * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf) * GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6) * * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf) * Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9). * * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM) * * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf) * Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2) * * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf) * Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52 * * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf) ### Conference Papers * Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017) * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342) * Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015) * [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf) * Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015) * [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336) * 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012) * [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf) * Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics * [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918) * License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011) * [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627) * Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011) * [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf) * [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649) * Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85. * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532) * Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010) * [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125) * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050 * [http://waset.org/publications/3636](http://waset.org/publications/3636) * An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010) * [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en) ### Articles on LinkedIn * [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/)) * [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) ) * [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/)) * [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/) * [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/) ### Slides slideshare * Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation) * Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) ) * tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) ) * Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) ) * Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) ) * Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) ) * Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) ) * How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) ) * Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) ) * Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) ) ### Online Courses * [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact) * [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life) * [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2) * [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends) * [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business) * [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity) * [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2) * [Understanding Business ](https://www.linkedin.com/learning/understanding-business) * [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea) * [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship) * [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations) * [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations) * [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations) * [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips) * [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation) * [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments) * [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow) * [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families) * [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances) * [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2) * [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2) * [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv) * [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers) * [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea) * [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital) * [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving) * [Professional Networking ](https://www.linkedin.com/learning/professional-networking) * [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home) * [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital) * [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling) * [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence) * [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers) ### Learning path * [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance) * [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner) # Completed ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link of best Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Books * [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y) * [Mathematics for Machine Learning](https://mml-book.github.io/) * Principles of Economics (6th edition) * The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It * Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) * Reinventing Your Life: The Breakthough Program to End Negative Behavior * Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence * [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf) * * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/) * [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/) * [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/) * Multiple view geometry in computer vision * Learning OpenCV Book by Adrian Kaehler and Gary Bradski * Digital Image Processing, Global Edition by Rafael C. Gonzalez * Introduction to Algorithms, 3rd Edition (The MIT Press) * The Mythical Man-Month: Essays on Software Engineering * [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition) ### Videos * [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning) * [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos) * [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/) * [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY) ### Papers * CALTag: High Precision Fiducial Markers for Camera * Diatom Autofocusing in Brightfield Microscopy: a Comparative Study * Analysis of focus measure operators in shape-from-focus * Optical flow modeling and computation: A survey * Toward general type 2 fuzzy logic systems based on zSlices ### Site * [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#)) ![](https://www.google.com/images/icons/product/drive-32.png)Reference- ComputerVisionDeepLearning.pdf * [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w) * [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679) * [https://www.freertos.org/index.html](https://www.freertos.org/index.html) * [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation) * [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/) * [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413) * [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917) * [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf) * [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/) * [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833) * [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238) * [https://koinly.io/](https://koinly.io/) site map: octopus.do [https://www.startupgermany.nrw/startup- contest/](https://www.startupgermany.nrw/startup-contest/) [https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth- camera-4k-cv-edge-object- detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai- kit-oak-depth-camera-4k-cv-edge-object-detection/description) [HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube in 2021! - YouTube ](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg) [محمد رضا شجریان، آلبوم کامل در خیال - YouTube](https://www.youtube.com/watch?v=cFdApmqlllg) [دوره جامع گوگل آنالیتیکس(UA) | آنالیتیپس](https://analytips.io/product/google-analyticsua/) [رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا - YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc) [Social Selling Index | Sales Navigator](https://www.linkedin.com/sales/ssi?src=or- search&veh=www.google.com) [https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/EvERVn15AzJZ7aNloDEKv1byWxFboB4Sbe_aPtX2ng9-SLNTjTIPJnwKRUBCWPFdXAOrqOFYlZeEgYJkKq7lPbM=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/EvERVn15AzJZ7aNloDEKv1byWxFboB4Sbe_aPtX2ng9-SLNTjTIPJnwKRUBCWPFdXAOrqOFYlZeEgYJkKq7lPbM=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Links pirahansiah, Personal, Roadmap, Books, Online Courses [](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze- oXQexwY "Open Spreadsheet, Links in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/sheets_32dp.png)Links Links from my research and interest AI in RISC-V Mixed reality Old Links Reference Link Groups Top website Python Computer Vision Documents Business Tools Patents Books Journal Papers Conference Papers Articles on LinkedIn Slides slideshare Online Courses Learning path Completed Reference Link of best Groups Books Videos Papers Site Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning training loops. GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp) [https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to- install-remmina/) [https://readme.so/](https://readme.so/) # Links from my research and interest [Build Better Generative Adversarial Networks (GANs) - Andrew Ng - 2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_- VnF) [https://www.oreilly.com/library/view/flow- architectures/9781492075882/](https://www.oreilly.com/library/view/flow- architectures/9781492075882/) ## AI in RISC-V * [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020) ## Mixed reality * [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera) * Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU. # Old Links ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Top website * [https://paperswithcode.com/](https://paperswithcode.com/) * [https://www.learnthepart.com/](https://www.learnthepart.com/) * [https://hackaday.com/](https://hackaday.com/) * [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762) * hbrew install youtube-dl * [https://www.masterclass.com/](https://www.masterclass.com/) * [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp) * [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona) * [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/) * [https://explainshell.com/](https://explainshell.com/) * [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20) * [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4) * [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4) ### Python * [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/) * [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/) * [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594) ### Computer Vision * [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/) * [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md) * [https://github.com/google/mediapipe](https://github.com/google/mediapipe) ### Documents * [https://book.keybase.io/docs](https://book.keybase.io/docs) * [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind) * [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/) * [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/) * [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/) * [https://kepler.gl/demo](https://kepler.gl/demo) * [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/) * [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE) ### Business * [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006) * [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5) ### Tools * Free convert video for mac opensource * [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake) * [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg) * [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c) * [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises * [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap * [Create and share beautiful images of your source code.](https://carbon.now.sh/) * [Web IDE](https://replit.com/templates) * [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2) ### Patents [https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) 2. A method for augmenting a plurality of face images (WO2021060971A1) 3. A method for detecting a moving vehicle (WO2021107761A1) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) The present invention relates to a system (100) for providing advertisement contents based on facial analysis comprising an image acquisition device (10) to acquire an image of a user, a face detection module (20) to detect face of the user in the image, an analysis module (40) to analyse the facial features statistically using classification models retrieved from a classification module (30), a database (60) to store matching rules, weighted advertisements and a plurality of advertisement contents; and a display device (80) to display the advertisement contents. The system (100) further comprises a computation module (50) to compute weighted image of the user and a matching module (70) to match the weighted image of the user with the weighted advertisement to select an advertisement content based on facial analysis of the user. A method of providing the advertisement contents based on facial analysis is also provided thereof. [https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf) 2. A method for augmenting a plurality of face images (WO2021060971A1) The present invention relates to a method for increasing data for face analysis in video surveillance. The method comprises the steps of acquiring at least one face image from an image acquisition module (102), acquiring a plurality of face images available on the internet using a data input module (104), increasing face images by at least one data augmentation module (106 and 107), generating a plurality of face images based on a trained Generative Adversarial Network, GAN technique by using a GAN module, selecting proper images based on quality of the face images using a fuzzy logic module (111), saving the selected images into a fifth database, and training the deep learning module (113). [https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf) 3. A method for detecting a moving vehicle (WO2021107761A1) The present invention relates to a method for detecting a moving vehicle. The method comprises the steps of grabbing an initial image from a video stream by a vehicle detection module (1100), wherein the vehicle detection module (1100) is a part of a system (1000) to identify moving vehicle, enhancing the illumination of the initial image by the vehicle detection module (1100), enhancing the edges within the initial image by the vehicle detection module (1100), and finding vehicle based on homogenous properties of the body of the vehicle by the vehicle detection module (1100). The step of finding vehicle based on homogenous property of the body of the vehicle by the vehicle detection module (1100) further comprising the sub-steps of closing open edges, inverting the binary image, segmenting an inverted binary image, filtering the noise based on geometric feature, and filtering the noise based on relation. [https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf) ### Books 1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394) 2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/) ### Journal Papers * Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21) * * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf) * GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6) * * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf) * Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9). * * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM) * * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf) * Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2) * * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf) * Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52 * * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf) ### Conference Papers * Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017) * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342) * Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015) * [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf) * Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015) * [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336) * 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012) * [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf) * Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics * [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918) * License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011) * [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627) * Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011) * [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf) * [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649) * Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85. * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532) * Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010) * [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125) * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050 * [http://waset.org/publications/3636](http://waset.org/publications/3636) * An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010) * [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en) ### Articles on LinkedIn * [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/)) * [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) ) * [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/)) * [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/) * [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/) ### Slides slideshare * Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation) * Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) ) * tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) ) * Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) ) * Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) ) * Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) ) * Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) ) * How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) ) * Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) ) * Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) ) ### Online Courses * [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact) * [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life) * [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2) * [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends) * [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business) * [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity) * [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2) * [Understanding Business ](https://www.linkedin.com/learning/understanding-business) * [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea) * [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship) * [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations) * [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations) * [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations) * [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips) * [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation) * [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments) * [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow) * [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families) * [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances) * [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2) * [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2) * [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv) * [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers) * [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea) * [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital) * [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving) * [Professional Networking ](https://www.linkedin.com/learning/professional-networking) * [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home) * [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital) * [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling) * [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence) * [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers) ### Learning path * [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance) * [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner) # Completed ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link of best Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Books * [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y) * [Mathematics for Machine Learning](https://mml-book.github.io/) * Principles of Economics (6th edition) * The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It * Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) * Reinventing Your Life: The Breakthough Program to End Negative Behavior * Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence * [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf) * * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/) * [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/) * [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/) * Multiple view geometry in computer vision * Learning OpenCV Book by Adrian Kaehler and Gary Bradski * Digital Image Processing, Global Edition by Rafael C. Gonzalez * Introduction to Algorithms, 3rd Edition (The MIT Press) * The Mythical Man-Month: Essays on Software Engineering * [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition) ### Videos * [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning) * [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos) * [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/) * [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY) ### Papers * CALTag: High Precision Fiducial Markers for Camera * Diatom Autofocusing in Brightfield Microscopy: a Comparative Study * Analysis of focus measure operators in shape-from-focus * Optical flow modeling and computation: A survey * Toward general type 2 fuzzy logic systems based on zSlices ### Site * [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#)) ![](https://www.google.com/images/icons/product/drive-32.png)Reference- ComputerVisionDeepLearning.pdf * [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w) * [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679) * [https://www.freertos.org/index.html](https://www.freertos.org/index.html) * [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation) * [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/) * [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413) * [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917) * [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf) * [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/) * [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833) * [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238) * [https://koinly.io/](https://koinly.io/) site map: octopus.do [https://www.startupgermany.nrw/startup- contest/](https://www.startupgermany.nrw/startup-contest/) [https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth- camera-4k-cv-edge-object- detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai- kit-oak-depth-camera-4k-cv-edge-object-detection/description) [HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube in 2021! - YouTube ](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg) [محمد رضا شجریان، آلبوم کامل در خیال - YouTube](https://www.youtube.com/watch?v=cFdApmqlllg) [دوره جامع گوگل آنالیتیکس(UA) | آنالیتیپس](https://analytips.io/product/google-analyticsua/) [رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا - YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc) [Social Selling Index | Sales Navigator](https://www.linkedin.com/sales/ssi?src=or- search&veh=www.google.com) [https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/EvERVn15AzJZ7aNloDEKv1byWxFboB4Sbe_aPtX2ng9-SLNTjTIPJnwKRUBCWPFdXAOrqOFYlZeEgYJkKq7lPbM=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/EvERVn15AzJZ7aNloDEKv1byWxFboB4Sbe_aPtX2ng9-SLNTjTIPJnwKRUBCWPFdXAOrqOFYlZeEgYJkKq7lPbM=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Links pirahansiah, Personal, Roadmap, Books, Online Courses [](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze- oXQexwY "Open Spreadsheet, Links in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/sheets_32dp.png)Links Links from my research and interest AI in RISC-V Mixed reality Old Links Reference Link Groups Top website Python Computer Vision Documents Business Tools Patents Books Journal Papers Conference Papers Articles on LinkedIn Slides slideshare Online Courses Learning path Completed Reference Link of best Groups Books Videos Papers Site Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning training loops. GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp) [https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to- install-remmina/) [https://readme.so/](https://readme.so/) # Links from my research and interest [Build Better Generative Adversarial Networks (GANs) - Andrew Ng - 2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_- VnF) [https://www.oreilly.com/library/view/flow- architectures/9781492075882/](https://www.oreilly.com/library/view/flow- architectures/9781492075882/) ## AI in RISC-V * [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020) ## Mixed reality * [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera) * Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU. # Old Links ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Top website * [https://paperswithcode.com/](https://paperswithcode.com/) * [https://www.learnthepart.com/](https://www.learnthepart.com/) * [https://hackaday.com/](https://hackaday.com/) * [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762) * hbrew install youtube-dl * [https://www.masterclass.com/](https://www.masterclass.com/) * [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp) * [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona) * [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/) * [https://explainshell.com/](https://explainshell.com/) * [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20) * [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4) * [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4) ### Python * [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/) * [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/) * [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594) ### Computer Vision * [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/) * [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md) * [https://github.com/google/mediapipe](https://github.com/google/mediapipe) ### Documents * [https://book.keybase.io/docs](https://book.keybase.io/docs) * [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind) * [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/) * [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/) * [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/) * [https://kepler.gl/demo](https://kepler.gl/demo) * [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/) * [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE) ### Business * [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006) * [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5) ### Tools * Free convert video for mac opensource * [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake) * [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg) * [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c) * [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises * [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap * [Create and share beautiful images of your source code.](https://carbon.now.sh/) * [Web IDE](https://replit.com/templates) * [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2) ### Patents [https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) 2. A method for augmenting a plurality of face images (WO2021060971A1) 3. A method for detecting a moving vehicle (WO2021107761A1) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) The present invention relates to a system (100) for providing advertisement contents based on facial analysis comprising an image acquisition device (10) to acquire an image of a user, a face detection module (20) to detect face of the user in the image, an analysis module (40) to analyse the facial features statistically using classification models retrieved from a classification module (30), a database (60) to store matching rules, weighted advertisements and a plurality of advertisement contents; and a display device (80) to display the advertisement contents. The system (100) further comprises a computation module (50) to compute weighted image of the user and a matching module (70) to match the weighted image of the user with the weighted advertisement to select an advertisement content based on facial analysis of the user. A method of providing the advertisement contents based on facial analysis is also provided thereof. [https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf) 2. A method for augmenting a plurality of face images (WO2021060971A1) The present invention relates to a method for increasing data for face analysis in video surveillance. The method comprises the steps of acquiring at least one face image from an image acquisition module (102), acquiring a plurality of face images available on the internet using a data input module (104), increasing face images by at least one data augmentation module (106 and 107), generating a plurality of face images based on a trained Generative Adversarial Network, GAN technique by using a GAN module, selecting proper images based on quality of the face images using a fuzzy logic module (111), saving the selected images into a fifth database, and training the deep learning module (113). [https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf) 3. A method for detecting a moving vehicle (WO2021107761A1) The present invention relates to a method for detecting a moving vehicle. The method comprises the steps of grabbing an initial image from a video stream by a vehicle detection module (1100), wherein the vehicle detection module (1100) is a part of a system (1000) to identify moving vehicle, enhancing the illumination of the initial image by the vehicle detection module (1100), enhancing the edges within the initial image by the vehicle detection module (1100), and finding vehicle based on homogenous properties of the body of the vehicle by the vehicle detection module (1100). The step of finding vehicle based on homogenous property of the body of the vehicle by the vehicle detection module (1100) further comprising the sub-steps of closing open edges, inverting the binary image, segmenting an inverted binary image, filtering the noise based on geometric feature, and filtering the noise based on relation. [https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf) ### Books 1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394) 2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/) ### Journal Papers * Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21) * * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf) * GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6) * * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf) * Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9). * * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM) * * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf) * Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2) * * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf) * Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52 * * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf) ### Conference Papers * Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017) * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342) * Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015) * [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf) * Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015) * [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336) * 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012) * [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf) * Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics * [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918) * License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011) * [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627) * Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011) * [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf) * [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649) * Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85. * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532) * Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010) * [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125) * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050 * [http://waset.org/publications/3636](http://waset.org/publications/3636) * An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010) * [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en) ### Articles on LinkedIn * [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/)) * [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) ) * [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/)) * [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/) * [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/) ### Slides slideshare * Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation) * Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) ) * tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) ) * Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) ) * Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) ) * Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) ) * Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) ) * How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) ) * Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) ) * Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) ) ### Online Courses * [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact) * [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life) * [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2) * [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends) * [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business) * [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity) * [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2) * [Understanding Business ](https://www.linkedin.com/learning/understanding-business) * [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea) * [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship) * [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations) * [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations) * [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations) * [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips) * [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation) * [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments) * [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow) * [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families) * [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances) * [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2) * [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2) * [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv) * [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers) * [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea) * [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital) * [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving) * [Professional Networking ](https://www.linkedin.com/learning/professional-networking) * [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home) * [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital) * [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling) * [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence) * [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers) ### Learning path * [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance) * [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner) # Completed ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link of best Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Books * [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y) * [Mathematics for Machine Learning](https://mml-book.github.io/) * Principles of Economics (6th edition) * The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It * Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) * Reinventing Your Life: The Breakthough Program to End Negative Behavior * Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence * [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf) * * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/) * [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/) * [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/) * Multiple view geometry in computer vision * Learning OpenCV Book by Adrian Kaehler and Gary Bradski * Digital Image Processing, Global Edition by Rafael C. Gonzalez * Introduction to Algorithms, 3rd Edition (The MIT Press) * The Mythical Man-Month: Essays on Software Engineering * [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition) ### Videos * [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning) * [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos) * [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/) * [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY) ### Papers * CALTag: High Precision Fiducial Markers for Camera * Diatom Autofocusing in Brightfield Microscopy: a Comparative Study * Analysis of focus measure operators in shape-from-focus * Optical flow modeling and computation: A survey * Toward general type 2 fuzzy logic systems based on zSlices ### Site * [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#)) ![](https://www.google.com/images/icons/product/drive-32.png)Reference- ComputerVisionDeepLearning.pdf * [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w) * [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679) * [https://www.freertos.org/index.html](https://www.freertos.org/index.html) * [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation) * [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/) * [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413) * [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917) * [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf) * [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/) * [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833) * [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238) * [https://koinly.io/](https://koinly.io/) site map: octopus.do [https://www.startupgermany.nrw/startup- contest/](https://www.startupgermany.nrw/startup-contest/) [https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth- camera-4k-cv-edge-object- detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai- kit-oak-depth-camera-4k-cv-edge-object-detection/description) [HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube in 2021! - YouTube ](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg) [محمد رضا شجریان، آلبوم کامل در خیال - YouTube](https://www.youtube.com/watch?v=cFdApmqlllg) [دوره جامع گوگل آنالیتیکس(UA) | آنالیتیپس](https://analytips.io/product/google-analyticsua/) [رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا - YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc) [Social Selling Index | Sales Navigator](https://www.linkedin.com/sales/ssi?src=or- search&veh=www.google.com) [https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/EvERVn15AzJZ7aNloDEKv1byWxFboB4Sbe_aPtX2ng9-SLNTjTIPJnwKRUBCWPFdXAOrqOFYlZeEgYJkKq7lPbM=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/EvERVn15AzJZ7aNloDEKv1byWxFboB4Sbe_aPtX2ng9-SLNTjTIPJnwKRUBCWPFdXAOrqOFYlZeEgYJkKq7lPbM=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Links pirahansiah, Personal, Roadmap, Books, Online Courses [](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze- oXQexwY "Open Spreadsheet, Links in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/sheets_32dp.png)Links Links from my research and interest AI in RISC-V Mixed reality Old Links Reference Link Groups Top website Python Computer Vision Documents Business Tools Patents Books Journal Papers Conference Papers Articles on LinkedIn Slides slideshare Online Courses Learning path Completed Reference Link of best Groups Books Videos Papers Site Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning training loops. GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp) [https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to- install-remmina/) [https://readme.so/](https://readme.so/) # Links from my research and interest [Build Better Generative Adversarial Networks (GANs) - Andrew Ng - 2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_- VnF) [https://www.oreilly.com/library/view/flow- architectures/9781492075882/](https://www.oreilly.com/library/view/flow- architectures/9781492075882/) ## AI in RISC-V * [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020) ## Mixed reality * [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera) * Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU. # Old Links ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Top website * [https://paperswithcode.com/](https://paperswithcode.com/) * [https://www.learnthepart.com/](https://www.learnthepart.com/) * [https://hackaday.com/](https://hackaday.com/) * [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762) * hbrew install youtube-dl * [https://www.masterclass.com/](https://www.masterclass.com/) * [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp) * [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona) * [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/) * [https://explainshell.com/](https://explainshell.com/) * [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20) * [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4) * [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4) ### Python * [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/) * [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/) * [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594) ### Computer Vision * [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/) * [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md) * [https://github.com/google/mediapipe](https://github.com/google/mediapipe) ### Documents * [https://book.keybase.io/docs](https://book.keybase.io/docs) * [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind) * [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/) * [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/) * [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/) * [https://kepler.gl/demo](https://kepler.gl/demo) * [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/) * [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE) ### Business * [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006) * [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5) ### Tools * Free convert video for mac opensource * [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake) * [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg) * [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c) * [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises * [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap * [Create and share beautiful images of your source code.](https://carbon.now.sh/) * [Web IDE](https://replit.com/templates) * [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2) ### Patents [https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) 2. A method for augmenting a plurality of face images (WO2021060971A1) 3. A method for detecting a moving vehicle (WO2021107761A1) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) The present invention relates to a system (100) for providing advertisement contents based on facial analysis comprising an image acquisition device (10) to acquire an image of a user, a face detection module (20) to detect face of the user in the image, an analysis module (40) to analyse the facial features statistically using classification models retrieved from a classification module (30), a database (60) to store matching rules, weighted advertisements and a plurality of advertisement contents; and a display device (80) to display the advertisement contents. The system (100) further comprises a computation module (50) to compute weighted image of the user and a matching module (70) to match the weighted image of the user with the weighted advertisement to select an advertisement content based on facial analysis of the user. A method of providing the advertisement contents based on facial analysis is also provided thereof. [https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf) 2. A method for augmenting a plurality of face images (WO2021060971A1) The present invention relates to a method for increasing data for face analysis in video surveillance. The method comprises the steps of acquiring at least one face image from an image acquisition module (102), acquiring a plurality of face images available on the internet using a data input module (104), increasing face images by at least one data augmentation module (106 and 107), generating a plurality of face images based on a trained Generative Adversarial Network, GAN technique by using a GAN module, selecting proper images based on quality of the face images using a fuzzy logic module (111), saving the selected images into a fifth database, and training the deep learning module (113). [https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf) 3. A method for detecting a moving vehicle (WO2021107761A1) The present invention relates to a method for detecting a moving vehicle. The method comprises the steps of grabbing an initial image from a video stream by a vehicle detection module (1100), wherein the vehicle detection module (1100) is a part of a system (1000) to identify moving vehicle, enhancing the illumination of the initial image by the vehicle detection module (1100), enhancing the edges within the initial image by the vehicle detection module (1100), and finding vehicle based on homogenous properties of the body of the vehicle by the vehicle detection module (1100). The step of finding vehicle based on homogenous property of the body of the vehicle by the vehicle detection module (1100) further comprising the sub-steps of closing open edges, inverting the binary image, segmenting an inverted binary image, filtering the noise based on geometric feature, and filtering the noise based on relation. [https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf) ### Books 1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394) 2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/) ### Journal Papers * Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21) * * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf) * GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6) * * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf) * Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9). * * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM) * * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf) * Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2) * * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf) * Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52 * * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf) ### Conference Papers * Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017) * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342) * Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015) * [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf) * Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015) * [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336) * 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012) * [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf) * Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics * [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918) * License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011) * [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627) * Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011) * [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf) * [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649) * Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85. * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532) * Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010) * [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125) * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050 * [http://waset.org/publications/3636](http://waset.org/publications/3636) * An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010) * [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en) ### Articles on LinkedIn * [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/)) * [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) ) * [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/)) * [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/) * [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/) ### Slides slideshare * Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation) * Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) ) * tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) ) * Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) ) * Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) ) * Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) ) * Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) ) * How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) ) * Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) ) * Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) ) ### Online Courses * [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact) * [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life) * [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2) * [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends) * [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business) * [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity) * [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2) * [Understanding Business ](https://www.linkedin.com/learning/understanding-business) * [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea) * [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship) * [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations) * [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations) * [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations) * [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips) * [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation) * [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments) * [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow) * [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families) * [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances) * [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2) * [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2) * [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv) * [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers) * [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea) * [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital) * [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving) * [Professional Networking ](https://www.linkedin.com/learning/professional-networking) * [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home) * [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital) * [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling) * [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence) * [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers) ### Learning path * [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance) * [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner) # Completed ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link of best Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Books * [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y) * [Mathematics for Machine Learning](https://mml-book.github.io/) * Principles of Economics (6th edition) * The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It * Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) * Reinventing Your Life: The Breakthough Program to End Negative Behavior * Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence * [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf) * * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/) * [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/) * [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/) * Multiple view geometry in computer vision * Learning OpenCV Book by Adrian Kaehler and Gary Bradski * Digital Image Processing, Global Edition by Rafael C. Gonzalez * Introduction to Algorithms, 3rd Edition (The MIT Press) * The Mythical Man-Month: Essays on Software Engineering * [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition) ### Videos * [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning) * [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos) * [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/) * [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY) ### Papers * CALTag: High Precision Fiducial Markers for Camera * Diatom Autofocusing in Brightfield Microscopy: a Comparative Study * Analysis of focus measure operators in shape-from-focus * Optical flow modeling and computation: A survey * Toward general type 2 fuzzy logic systems based on zSlices ### Site * [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#)) ![](https://www.google.com/images/icons/product/drive-32.png)Reference- ComputerVisionDeepLearning.pdf * [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w) * [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679) * [https://www.freertos.org/index.html](https://www.freertos.org/index.html) * [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation) * [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/) * [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413) * [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917) * [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf) * [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/) * [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833) * [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238) * [https://koinly.io/](https://koinly.io/) site map: octopus.do [https://www.startupgermany.nrw/startup- contest/](https://www.startupgermany.nrw/startup-contest/) [https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth- camera-4k-cv-edge-object- detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai- kit-oak-depth-camera-4k-cv-edge-object-detection/description) [HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube in 2021! - YouTube ](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg) [محمد رضا شجریان، آلبوم کامل در خیال - YouTube](https://www.youtube.com/watch?v=cFdApmqlllg) [دوره جامع گوگل آنالیتیکس(UA) | آنالیتیپس](https://analytips.io/product/google-analyticsua/) [رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا - YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc) [Social Selling Index | Sales Navigator](https://www.linkedin.com/sales/ssi?src=or- search&veh=www.google.com) [https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/EvERVn15AzJZ7aNloDEKv1byWxFboB4Sbe_aPtX2ng9-SLNTjTIPJnwKRUBCWPFdXAOrqOFYlZeEgYJkKq7lPbM=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/EvERVn15AzJZ7aNloDEKv1byWxFboB4Sbe_aPtX2ng9-SLNTjTIPJnwKRUBCWPFdXAOrqOFYlZeEgYJkKq7lPbM=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Links pirahansiah, Personal, Roadmap, Books, Online Courses [](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze- oXQexwY "Open Spreadsheet, Links in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/sheets_32dp.png)Links Links from my research and interest AI in RISC-V Mixed reality Old Links Reference Link Groups Top website Python Computer Vision Documents Business Tools Patents Books Journal Papers Conference Papers Articles on LinkedIn Slides slideshare Online Courses Learning path Completed Reference Link of best Groups Books Videos Papers Site Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning training loops. GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp) [https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to- install-remmina/) [https://readme.so/](https://readme.so/) # Links from my research and interest [Build Better Generative Adversarial Networks (GANs) - Andrew Ng - 2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_- VnF) [https://www.oreilly.com/library/view/flow- architectures/9781492075882/](https://www.oreilly.com/library/view/flow- architectures/9781492075882/) ## AI in RISC-V * [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020) ## Mixed reality * [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera) * Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU. # Old Links ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Top website * [https://paperswithcode.com/](https://paperswithcode.com/) * [https://www.learnthepart.com/](https://www.learnthepart.com/) * [https://hackaday.com/](https://hackaday.com/) * [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762) * hbrew install youtube-dl * [https://www.masterclass.com/](https://www.masterclass.com/) * [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp) * [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona) * [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/) * [https://explainshell.com/](https://explainshell.com/) * [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20) * [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4) * [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4) ### Python * [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/) * [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/) * [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594) ### Computer Vision * [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/) * [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md) * [https://github.com/google/mediapipe](https://github.com/google/mediapipe) ### Documents * [https://book.keybase.io/docs](https://book.keybase.io/docs) * [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind) * [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/) * [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/) * [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/) * [https://kepler.gl/demo](https://kepler.gl/demo) * [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/) * [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE) ### Business * [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006) * [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5) ### Tools * Free convert video for mac opensource * [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake) * [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg) * [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c) * [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises * [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap * [Create and share beautiful images of your source code.](https://carbon.now.sh/) * [Web IDE](https://replit.com/templates) * [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2) ### Patents [https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) 2. A method for augmenting a plurality of face images (WO2021060971A1) 3. A method for detecting a moving vehicle (WO2021107761A1) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) The present invention relates to a system (100) for providing advertisement contents based on facial analysis comprising an image acquisition device (10) to acquire an image of a user, a face detection module (20) to detect face of the user in the image, an analysis module (40) to analyse the facial features statistically using classification models retrieved from a classification module (30), a database (60) to store matching rules, weighted advertisements and a plurality of advertisement contents; and a display device (80) to display the advertisement contents. The system (100) further comprises a computation module (50) to compute weighted image of the user and a matching module (70) to match the weighted image of the user with the weighted advertisement to select an advertisement content based on facial analysis of the user. A method of providing the advertisement contents based on facial analysis is also provided thereof. [https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf) 2. A method for augmenting a plurality of face images (WO2021060971A1) The present invention relates to a method for increasing data for face analysis in video surveillance. The method comprises the steps of acquiring at least one face image from an image acquisition module (102), acquiring a plurality of face images available on the internet using a data input module (104), increasing face images by at least one data augmentation module (106 and 107), generating a plurality of face images based on a trained Generative Adversarial Network, GAN technique by using a GAN module, selecting proper images based on quality of the face images using a fuzzy logic module (111), saving the selected images into a fifth database, and training the deep learning module (113). [https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf) 3. A method for detecting a moving vehicle (WO2021107761A1) The present invention relates to a method for detecting a moving vehicle. The method comprises the steps of grabbing an initial image from a video stream by a vehicle detection module (1100), wherein the vehicle detection module (1100) is a part of a system (1000) to identify moving vehicle, enhancing the illumination of the initial image by the vehicle detection module (1100), enhancing the edges within the initial image by the vehicle detection module (1100), and finding vehicle based on homogenous properties of the body of the vehicle by the vehicle detection module (1100). The step of finding vehicle based on homogenous property of the body of the vehicle by the vehicle detection module (1100) further comprising the sub-steps of closing open edges, inverting the binary image, segmenting an inverted binary image, filtering the noise based on geometric feature, and filtering the noise based on relation. [https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf) ### Books 1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394) 2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/) ### Journal Papers * Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21) * * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf) * GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6) * * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf) * Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9). * * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM) * * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf) * Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2) * * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf) * Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52 * * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf) ### Conference Papers * Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017) * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342) * Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015) * [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf) * Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015) * [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336) * 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012) * [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf) * Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics * [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918) * License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011) * [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627) * Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011) * [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf) * [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649) * Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85. * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532) * Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010) * [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125) * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050 * [http://waset.org/publications/3636](http://waset.org/publications/3636) * An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010) * [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en) ### Articles on LinkedIn * [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/)) * [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) ) * [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/)) * [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/) * [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/) ### Slides slideshare * Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation) * Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) ) * tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) ) * Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) ) * Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) ) * Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) ) * Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) ) * How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) ) * Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) ) * Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) ) ### Online Courses * [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact) * [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life) * [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2) * [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends) * [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business) * [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity) * [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2) * [Understanding Business ](https://www.linkedin.com/learning/understanding-business) * [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea) * [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship) * [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations) * [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations) * [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations) * [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips) * [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation) * [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments) * [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow) * [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families) * [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances) * [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2) * [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2) * [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv) * [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers) * [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea) * [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital) * [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving) * [Professional Networking ](https://www.linkedin.com/learning/professional-networking) * [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home) * [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital) * [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling) * [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence) * [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers) ### Learning path * [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance) * [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner) # Completed ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link of best Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Books * [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y) * [Mathematics for Machine Learning](https://mml-book.github.io/) * Principles of Economics (6th edition) * The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It * Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) * Reinventing Your Life: The Breakthough Program to End Negative Behavior * Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence * [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf) * * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/) * [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/) * [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/) * Multiple view geometry in computer vision * Learning OpenCV Book by Adrian Kaehler and Gary Bradski * Digital Image Processing, Global Edition by Rafael C. Gonzalez * Introduction to Algorithms, 3rd Edition (The MIT Press) * The Mythical Man-Month: Essays on Software Engineering * [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition) ### Videos * [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning) * [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos) * [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/) * [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY) ### Papers * CALTag: High Precision Fiducial Markers for Camera * Diatom Autofocusing in Brightfield Microscopy: a Comparative Study * Analysis of focus measure operators in shape-from-focus * Optical flow modeling and computation: A survey * Toward general type 2 fuzzy logic systems based on zSlices ### Site * [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#)) ![](https://www.google.com/images/icons/product/drive-32.png)Reference- ComputerVisionDeepLearning.pdf * [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w) * [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679) * [https://www.freertos.org/index.html](https://www.freertos.org/index.html) * [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation) * [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/) * [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413) * [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917) * [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf) * [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/) * [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833) * [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238) * [https://koinly.io/](https://koinly.io/) site map: octopus.do [https://www.startupgermany.nrw/startup- contest/](https://www.startupgermany.nrw/startup-contest/) [https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth- camera-4k-cv-edge-object- detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai- kit-oak-depth-camera-4k-cv-edge-object-detection/description) [HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube in 2021! - YouTube ](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg) [محمد رضا شجریان، آلبوم کامل در خیال - YouTube](https://www.youtube.com/watch?v=cFdApmqlllg) [دوره جامع گوگل آنالیتیکس(UA) | آنالیتیپس](https://analytips.io/product/google-analyticsua/) [رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا - YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc) [Social Selling Index | Sales Navigator](https://www.linkedin.com/sales/ssi?src=or- search&veh=www.google.com) [https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/D7V2ImRWpG00ZwtqcglAiaZQbZisCcJYW0i47NNLl2KPtrNgWne1pMRlqdchhpySycR5GkO8UmHpykqzjoV2Ia8=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/D7V2ImRWpG00ZwtqcglAiaZQbZisCcJYW0i47NNLl2KPtrNgWne1pMRlqdchhpySycR5GkO8UmHpykqzjoV2Ia8=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Links pirahansiah, Personal, Roadmap, Books, Online Courses [](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze- oXQexwY "Open Spreadsheet, Links in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/sheets_32dp.png)Links Links from my research and interest AI in RISC-V Mixed reality Old Links Reference Link Groups Top website Python Computer Vision Documents Business Tools Patents Books Journal Papers Conference Papers Articles on LinkedIn Slides slideshare Online Courses Learning path Completed Reference Link of best Groups Books Videos Papers Site Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning training loops. GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp) [https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to- install-remmina/) [https://readme.so/](https://readme.so/) # Links from my research and interest [Build Better Generative Adversarial Networks (GANs) - Andrew Ng - 2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_- VnF) [https://www.oreilly.com/library/view/flow- architectures/9781492075882/](https://www.oreilly.com/library/view/flow- architectures/9781492075882/) ## AI in RISC-V * [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020) ## Mixed reality * [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera) * Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU. # Old Links ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Top website * [https://paperswithcode.com/](https://paperswithcode.com/) * [https://www.learnthepart.com/](https://www.learnthepart.com/) * [https://hackaday.com/](https://hackaday.com/) * [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762) * hbrew install youtube-dl * [https://www.masterclass.com/](https://www.masterclass.com/) * [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp) * [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona) * [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/) * [https://explainshell.com/](https://explainshell.com/) * [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20) * [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4) * [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4) ### Python * [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/) * [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/) * [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594) ### Computer Vision * [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/) * [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md) * [https://github.com/google/mediapipe](https://github.com/google/mediapipe) ### Documents * [https://book.keybase.io/docs](https://book.keybase.io/docs) * [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind) * [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/) * [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/) * [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/) * [https://kepler.gl/demo](https://kepler.gl/demo) * [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/) * [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE) ### Business * [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006) * [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5) ### Tools * Free convert video for mac opensource * [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake) * [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg) * [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c) * [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises * [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap * [Create and share beautiful images of your source code.](https://carbon.now.sh/) * [Web IDE](https://replit.com/templates) * [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2) ### Patents [https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) 2. A method for augmenting a plurality of face images (WO2021060971A1) 3. A method for detecting a moving vehicle (WO2021107761A1) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) The present invention relates to a system (100) for providing advertisement contents based on facial analysis comprising an image acquisition device (10) to acquire an image of a user, a face detection module (20) to detect face of the user in the image, an analysis module (40) to analyse the facial features statistically using classification models retrieved from a classification module (30), a database (60) to store matching rules, weighted advertisements and a plurality of advertisement contents; and a display device (80) to display the advertisement contents. The system (100) further comprises a computation module (50) to compute weighted image of the user and a matching module (70) to match the weighted image of the user with the weighted advertisement to select an advertisement content based on facial analysis of the user. A method of providing the advertisement contents based on facial analysis is also provided thereof. [https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf) 2. A method for augmenting a plurality of face images (WO2021060971A1) The present invention relates to a method for increasing data for face analysis in video surveillance. The method comprises the steps of acquiring at least one face image from an image acquisition module (102), acquiring a plurality of face images available on the internet using a data input module (104), increasing face images by at least one data augmentation module (106 and 107), generating a plurality of face images based on a trained Generative Adversarial Network, GAN technique by using a GAN module, selecting proper images based on quality of the face images using a fuzzy logic module (111), saving the selected images into a fifth database, and training the deep learning module (113). [https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf) 3. A method for detecting a moving vehicle (WO2021107761A1) The present invention relates to a method for detecting a moving vehicle. The method comprises the steps of grabbing an initial image from a video stream by a vehicle detection module (1100), wherein the vehicle detection module (1100) is a part of a system (1000) to identify moving vehicle, enhancing the illumination of the initial image by the vehicle detection module (1100), enhancing the edges within the initial image by the vehicle detection module (1100), and finding vehicle based on homogenous properties of the body of the vehicle by the vehicle detection module (1100). The step of finding vehicle based on homogenous property of the body of the vehicle by the vehicle detection module (1100) further comprising the sub-steps of closing open edges, inverting the binary image, segmenting an inverted binary image, filtering the noise based on geometric feature, and filtering the noise based on relation. [https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf) ### Books 1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394) 2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/) ### Journal Papers * Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21) * * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf) * GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6) * * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf) * Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9). * * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM) * * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf) * Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2) * * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf) * Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52 * * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf) ### Conference Papers * Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017) * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342) * Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015) * [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf) * Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015) * [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336) * 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012) * [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf) * Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics * [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918) * License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011) * [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627) * Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011) * [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf) * [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649) * Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85. * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532) * Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010) * [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125) * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050 * [http://waset.org/publications/3636](http://waset.org/publications/3636) * An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010) * [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en) ### Articles on LinkedIn * [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/)) * [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) ) * [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/)) * [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/) * [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/) ### Slides slideshare * Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation) * Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) ) * tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) ) * Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) ) * Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) ) * Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) ) * Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) ) * How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) ) * Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) ) * Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) ) ### Online Courses * [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact) * [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life) * [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2) * [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends) * [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business) * [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity) * [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2) * [Understanding Business ](https://www.linkedin.com/learning/understanding-business) * [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea) * [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship) * [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations) * [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations) * [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations) * [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips) * [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation) * [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments) * [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow) * [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families) * [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances) * [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2) * [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2) * [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv) * [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers) * [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea) * [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital) * [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving) * [Professional Networking ](https://www.linkedin.com/learning/professional-networking) * [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home) * [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital) * [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling) * [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence) * [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers) ### Learning path * [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance) * [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner) # Completed ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link of best Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Books * [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y) * [Mathematics for Machine Learning](https://mml-book.github.io/) * Principles of Economics (6th edition) * The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It * Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) * Reinventing Your Life: The Breakthough Program to End Negative Behavior * Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence * [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf) * * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/) * [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/) * [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/) * Multiple view geometry in computer vision * Learning OpenCV Book by Adrian Kaehler and Gary Bradski * Digital Image Processing, Global Edition by Rafael C. Gonzalez * Introduction to Algorithms, 3rd Edition (The MIT Press) * The Mythical Man-Month: Essays on Software Engineering * [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition) ### Videos * [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning) * [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos) * [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/) * [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY) ### Papers * CALTag: High Precision Fiducial Markers for Camera * Diatom Autofocusing in Brightfield Microscopy: a Comparative Study * Analysis of focus measure operators in shape-from-focus * Optical flow modeling and computation: A survey * Toward general type 2 fuzzy logic systems based on zSlices ### Site * [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#)) ![](https://www.google.com/images/icons/product/drive-32.png)Reference- ComputerVisionDeepLearning.pdf * [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w) * [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679) * [https://www.freertos.org/index.html](https://www.freertos.org/index.html) * [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation) * [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/) * [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413) * [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917) * [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf) * [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/) * [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833) * [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238) * [https://koinly.io/](https://koinly.io/) site map: octopus.do [https://www.startupgermany.nrw/startup- contest/](https://www.startupgermany.nrw/startup-contest/) [https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth- camera-4k-cv-edge-object- detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai- kit-oak-depth-camera-4k-cv-edge-object-detection/description) [HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube in 2021! - YouTube ](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg) [محمد رضا شجریان، آلبوم کامل در خیال - YouTube](https://www.youtube.com/watch?v=cFdApmqlllg) [دوره جامع گوگل آنالیتیکس(UA) | آنالیتیپس](https://analytips.io/product/google-analyticsua/) [رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا - YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc) [Social Selling Index | Sales Navigator](https://www.linkedin.com/sales/ssi?src=or- search&veh=www.google.com) [https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/D7V2ImRWpG00ZwtqcglAiaZQbZisCcJYW0i47NNLl2KPtrNgWne1pMRlqdchhpySycR5GkO8UmHpykqzjoV2Ia8=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/D7V2ImRWpG00ZwtqcglAiaZQbZisCcJYW0i47NNLl2KPtrNgWne1pMRlqdchhpySycR5GkO8UmHpykqzjoV2Ia8=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Links pirahansiah, Personal, Roadmap, Books, Online Courses [](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze- oXQexwY "Open Spreadsheet, Links in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/sheets_32dp.png)Links Links from my research and interest AI in RISC-V Mixed reality Old Links Reference Link Groups Top website Python Computer Vision Documents Business Tools Patents Books Journal Papers Conference Papers Articles on LinkedIn Slides slideshare Online Courses Learning path Completed Reference Link of best Groups Books Videos Papers Site Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning training loops. GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp) [https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to- install-remmina/) [https://readme.so/](https://readme.so/) # Links from my research and interest [Build Better Generative Adversarial Networks (GANs) - Andrew Ng - 2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_- VnF) [https://www.oreilly.com/library/view/flow- architectures/9781492075882/](https://www.oreilly.com/library/view/flow- architectures/9781492075882/) ## AI in RISC-V * [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020) ## Mixed reality * [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera) * Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU. # Old Links ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Top website * [https://paperswithcode.com/](https://paperswithcode.com/) * [https://www.learnthepart.com/](https://www.learnthepart.com/) * [https://hackaday.com/](https://hackaday.com/) * [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762) * hbrew install youtube-dl * [https://www.masterclass.com/](https://www.masterclass.com/) * [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp) * [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona) * [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/) * [https://explainshell.com/](https://explainshell.com/) * [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20) * [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4) * [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4) ### Python * [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/) * [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/) * [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594) ### Computer Vision * [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/) * [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md) * [https://github.com/google/mediapipe](https://github.com/google/mediapipe) ### Documents * [https://book.keybase.io/docs](https://book.keybase.io/docs) * [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind) * [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/) * [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/) * [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/) * [https://kepler.gl/demo](https://kepler.gl/demo) * [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/) * [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE) ### Business * [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006) * [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5) ### Tools * Free convert video for mac opensource * [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake) * [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg) * [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c) * [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises * [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap * [Create and share beautiful images of your source code.](https://carbon.now.sh/) * [Web IDE](https://replit.com/templates) * [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2) ### Patents [https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) 2. A method for augmenting a plurality of face images (WO2021060971A1) 3. A method for detecting a moving vehicle (WO2021107761A1) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) The present invention relates to a system (100) for providing advertisement contents based on facial analysis comprising an image acquisition device (10) to acquire an image of a user, a face detection module (20) to detect face of the user in the image, an analysis module (40) to analyse the facial features statistically using classification models retrieved from a classification module (30), a database (60) to store matching rules, weighted advertisements and a plurality of advertisement contents; and a display device (80) to display the advertisement contents. The system (100) further comprises a computation module (50) to compute weighted image of the user and a matching module (70) to match the weighted image of the user with the weighted advertisement to select an advertisement content based on facial analysis of the user. A method of providing the advertisement contents based on facial analysis is also provided thereof. [https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf) 2. A method for augmenting a plurality of face images (WO2021060971A1) The present invention relates to a method for increasing data for face analysis in video surveillance. The method comprises the steps of acquiring at least one face image from an image acquisition module (102), acquiring a plurality of face images available on the internet using a data input module (104), increasing face images by at least one data augmentation module (106 and 107), generating a plurality of face images based on a trained Generative Adversarial Network, GAN technique by using a GAN module, selecting proper images based on quality of the face images using a fuzzy logic module (111), saving the selected images into a fifth database, and training the deep learning module (113). [https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf) 3. A method for detecting a moving vehicle (WO2021107761A1) The present invention relates to a method for detecting a moving vehicle. The method comprises the steps of grabbing an initial image from a video stream by a vehicle detection module (1100), wherein the vehicle detection module (1100) is a part of a system (1000) to identify moving vehicle, enhancing the illumination of the initial image by the vehicle detection module (1100), enhancing the edges within the initial image by the vehicle detection module (1100), and finding vehicle based on homogenous properties of the body of the vehicle by the vehicle detection module (1100). The step of finding vehicle based on homogenous property of the body of the vehicle by the vehicle detection module (1100) further comprising the sub-steps of closing open edges, inverting the binary image, segmenting an inverted binary image, filtering the noise based on geometric feature, and filtering the noise based on relation. [https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf) ### Books 1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394) 2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/) ### Journal Papers * Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21) * * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf) * GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6) * * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf) * Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9). * * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM) * * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf) * Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2) * * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf) * Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52 * * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf) ### Conference Papers * Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017) * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342) * Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015) * [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf) * Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015) * [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336) * 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012) * [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf) * Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics * [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918) * License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011) * [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627) * Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011) * [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf) * [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649) * Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85. * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532) * Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010) * [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125) * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050 * [http://waset.org/publications/3636](http://waset.org/publications/3636) * An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010) * [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en) ### Articles on LinkedIn * [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/)) * [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) ) * [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/)) * [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/) * [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/) ### Slides slideshare * Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation) * Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) ) * tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) ) * Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) ) * Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) ) * Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) ) * Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) ) * How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) ) * Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) ) * Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) ) ### Online Courses * [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact) * [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life) * [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2) * [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends) * [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business) * [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity) * [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2) * [Understanding Business ](https://www.linkedin.com/learning/understanding-business) * [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea) * [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship) * [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations) * [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations) * [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations) * [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips) * [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation) * [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments) * [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow) * [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families) * [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances) * [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2) * [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2) * [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv) * [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers) * [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea) * [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital) * [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving) * [Professional Networking ](https://www.linkedin.com/learning/professional-networking) * [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home) * [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital) * [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling) * [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence) * [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers) ### Learning path * [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance) * [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner) # Completed ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link of best Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Books * [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y) * [Mathematics for Machine Learning](https://mml-book.github.io/) * Principles of Economics (6th edition) * The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It * Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) * Reinventing Your Life: The Breakthough Program to End Negative Behavior * Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence * [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf) * * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/) * [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/) * [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/) * Multiple view geometry in computer vision * Learning OpenCV Book by Adrian Kaehler and Gary Bradski * Digital Image Processing, Global Edition by Rafael C. Gonzalez * Introduction to Algorithms, 3rd Edition (The MIT Press) * The Mythical Man-Month: Essays on Software Engineering * [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition) ### Videos * [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning) * [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos) * [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/) * [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY) ### Papers * CALTag: High Precision Fiducial Markers for Camera * Diatom Autofocusing in Brightfield Microscopy: a Comparative Study * Analysis of focus measure operators in shape-from-focus * Optical flow modeling and computation: A survey * Toward general type 2 fuzzy logic systems based on zSlices ### Site * [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#)) ![](https://www.google.com/images/icons/product/drive-32.png)Reference- ComputerVisionDeepLearning.pdf * [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w) * [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679) * [https://www.freertos.org/index.html](https://www.freertos.org/index.html) * [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation) * [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/) * [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413) * [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917) * [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf) * [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/) * [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833) * [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238) * [https://koinly.io/](https://koinly.io/) site map: octopus.do [https://www.startupgermany.nrw/startup- contest/](https://www.startupgermany.nrw/startup-contest/) [https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth- camera-4k-cv-edge-object- detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai- kit-oak-depth-camera-4k-cv-edge-object-detection/description) [HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube in 2021! - YouTube ](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg) [محمد رضا شجریان، آلبوم کامل در خیال - YouTube](https://www.youtube.com/watch?v=cFdApmqlllg) [دوره جامع گوگل آنالیتیکس(UA) | آنالیتیپس](https://analytips.io/product/google-analyticsua/) [رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا - YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc) [Social Selling Index | Sales Navigator](https://www.linkedin.com/sales/ssi?src=or- search&veh=www.google.com) [https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/D7V2ImRWpG00ZwtqcglAiaZQbZisCcJYW0i47NNLl2KPtrNgWne1pMRlqdchhpySycR5GkO8UmHpykqzjoV2Ia8=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/D7V2ImRWpG00ZwtqcglAiaZQbZisCcJYW0i47NNLl2KPtrNgWne1pMRlqdchhpySycR5GkO8UmHpykqzjoV2Ia8=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Links pirahansiah, Personal, Roadmap, Books, Online Courses [](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze- oXQexwY "Open Spreadsheet, Links in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/sheets_32dp.png)Links Links from my research and interest AI in RISC-V Mixed reality Old Links Reference Link Groups Top website Python Computer Vision Documents Business Tools Patents Books Journal Papers Conference Papers Articles on LinkedIn Slides slideshare Online Courses Learning path Completed Reference Link of best Groups Books Videos Papers Site Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning training loops. GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp) [https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to- install-remmina/) [https://readme.so/](https://readme.so/) # Links from my research and interest [Build Better Generative Adversarial Networks (GANs) - Andrew Ng - 2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_- VnF) [https://www.oreilly.com/library/view/flow- architectures/9781492075882/](https://www.oreilly.com/library/view/flow- architectures/9781492075882/) ## AI in RISC-V * [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020) ## Mixed reality * [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera) * Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU. # Old Links ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Top website * [https://paperswithcode.com/](https://paperswithcode.com/) * [https://www.learnthepart.com/](https://www.learnthepart.com/) * [https://hackaday.com/](https://hackaday.com/) * [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762) * hbrew install youtube-dl * [https://www.masterclass.com/](https://www.masterclass.com/) * [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp) * [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona) * [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/) * [https://explainshell.com/](https://explainshell.com/) * [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20) * [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4) * [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4) ### Python * [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/) * [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/) * [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594) ### Computer Vision * [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/) * [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md) * [https://github.com/google/mediapipe](https://github.com/google/mediapipe) ### Documents * [https://book.keybase.io/docs](https://book.keybase.io/docs) * [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind) * [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/) * [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/) * [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/) * [https://kepler.gl/demo](https://kepler.gl/demo) * [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/) * [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE) ### Business * [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006) * [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5) ### Tools * Free convert video for mac opensource * [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake) * [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg) * [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c) * [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises * [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap * [Create and share beautiful images of your source code.](https://carbon.now.sh/) * [Web IDE](https://replit.com/templates) * [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2) ### Patents [https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) 2. A method for augmenting a plurality of face images (WO2021060971A1) 3. A method for detecting a moving vehicle (WO2021107761A1) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) The present invention relates to a system (100) for providing advertisement contents based on facial analysis comprising an image acquisition device (10) to acquire an image of a user, a face detection module (20) to detect face of the user in the image, an analysis module (40) to analyse the facial features statistically using classification models retrieved from a classification module (30), a database (60) to store matching rules, weighted advertisements and a plurality of advertisement contents; and a display device (80) to display the advertisement contents. The system (100) further comprises a computation module (50) to compute weighted image of the user and a matching module (70) to match the weighted image of the user with the weighted advertisement to select an advertisement content based on facial analysis of the user. A method of providing the advertisement contents based on facial analysis is also provided thereof. [https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf) 2. A method for augmenting a plurality of face images (WO2021060971A1) The present invention relates to a method for increasing data for face analysis in video surveillance. The method comprises the steps of acquiring at least one face image from an image acquisition module (102), acquiring a plurality of face images available on the internet using a data input module (104), increasing face images by at least one data augmentation module (106 and 107), generating a plurality of face images based on a trained Generative Adversarial Network, GAN technique by using a GAN module, selecting proper images based on quality of the face images using a fuzzy logic module (111), saving the selected images into a fifth database, and training the deep learning module (113). [https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf) 3. A method for detecting a moving vehicle (WO2021107761A1) The present invention relates to a method for detecting a moving vehicle. The method comprises the steps of grabbing an initial image from a video stream by a vehicle detection module (1100), wherein the vehicle detection module (1100) is a part of a system (1000) to identify moving vehicle, enhancing the illumination of the initial image by the vehicle detection module (1100), enhancing the edges within the initial image by the vehicle detection module (1100), and finding vehicle based on homogenous properties of the body of the vehicle by the vehicle detection module (1100). The step of finding vehicle based on homogenous property of the body of the vehicle by the vehicle detection module (1100) further comprising the sub-steps of closing open edges, inverting the binary image, segmenting an inverted binary image, filtering the noise based on geometric feature, and filtering the noise based on relation. [https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf) ### Books 1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394) 2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/) ### Journal Papers * Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21) * * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf) * GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6) * * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf) * Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9). * * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM) * * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf) * Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2) * * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf) * Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52 * * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf) ### Conference Papers * Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017) * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342) * Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015) * [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf) * Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015) * [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336) * 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012) * [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf) * Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics * [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918) * License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011) * [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627) * Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011) * [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf) * [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649) * Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85. * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532) * Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010) * [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125) * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050 * [http://waset.org/publications/3636](http://waset.org/publications/3636) * An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010) * [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en) ### Articles on LinkedIn * [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/)) * [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) ) * [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/)) * [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/) * [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/) ### Slides slideshare * Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation) * Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) ) * tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) ) * Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) ) * Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) ) * Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) ) * Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) ) * How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) ) * Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) ) * Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) ) ### Online Courses * [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact) * [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life) * [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2) * [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends) * [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business) * [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity) * [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2) * [Understanding Business ](https://www.linkedin.com/learning/understanding-business) * [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea) * [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship) * [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations) * [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations) * [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations) * [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips) * [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation) * [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments) * [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow) * [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families) * [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances) * [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2) * [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2) * [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv) * [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers) * [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea) * [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital) * [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving) * [Professional Networking ](https://www.linkedin.com/learning/professional-networking) * [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home) * [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital) * [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling) * [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence) * [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers) ### Learning path * [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance) * [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner) # Completed ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link of best Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Books * [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y) * [Mathematics for Machine Learning](https://mml-book.github.io/) * Principles of Economics (6th edition) * The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It * Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) * Reinventing Your Life: The Breakthough Program to End Negative Behavior * Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence * [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf) * * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/) * [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/) * [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/) * Multiple view geometry in computer vision * Learning OpenCV Book by Adrian Kaehler and Gary Bradski * Digital Image Processing, Global Edition by Rafael C. Gonzalez * Introduction to Algorithms, 3rd Edition (The MIT Press) * The Mythical Man-Month: Essays on Software Engineering * [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition) ### Videos * [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning) * [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos) * [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/) * [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY) ### Papers * CALTag: High Precision Fiducial Markers for Camera * Diatom Autofocusing in Brightfield Microscopy: a Comparative Study * Analysis of focus measure operators in shape-from-focus * Optical flow modeling and computation: A survey * Toward general type 2 fuzzy logic systems based on zSlices ### Site * [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#)) ![](https://www.google.com/images/icons/product/drive-32.png)Reference- ComputerVisionDeepLearning.pdf * [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w) * [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679) * [https://www.freertos.org/index.html](https://www.freertos.org/index.html) * [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation) * [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/) * [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413) * [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917) * [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf) * [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/) * [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833) * [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238) * [https://koinly.io/](https://koinly.io/) site map: octopus.do [https://www.startupgermany.nrw/startup- contest/](https://www.startupgermany.nrw/startup-contest/) [https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth- camera-4k-cv-edge-object- detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai- kit-oak-depth-camera-4k-cv-edge-object-detection/description) [HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube in 2021! - YouTube ](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg) [محمد رضا شجریان، آلبوم کامل در خیال - YouTube](https://www.youtube.com/watch?v=cFdApmqlllg) [دوره جامع گوگل آنالیتیکس(UA) | آنالیتیپس](https://analytips.io/product/google-analyticsua/) [رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا - YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc) [Social Selling Index | Sales Navigator](https://www.linkedin.com/sales/ssi?src=or- search&veh=www.google.com) [https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/D7V2ImRWpG00ZwtqcglAiaZQbZisCcJYW0i47NNLl2KPtrNgWne1pMRlqdchhpySycR5GkO8UmHpykqzjoV2Ia8=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/D7V2ImRWpG00ZwtqcglAiaZQbZisCcJYW0i47NNLl2KPtrNgWne1pMRlqdchhpySycR5GkO8UmHpykqzjoV2Ia8=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Links pirahansiah, Personal, Roadmap, Books, Online Courses [](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze- oXQexwY "Open Spreadsheet, Links in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/sheets_32dp.png)Links Links from my research and interest AI in RISC-V Mixed reality Old Links Reference Link Groups Top website Python Computer Vision Documents Business Tools Patents Books Journal Papers Conference Papers Articles on LinkedIn Slides slideshare Online Courses Learning path Completed Reference Link of best Groups Books Videos Papers Site Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning training loops. GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp) [https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to- install-remmina/) [https://readme.so/](https://readme.so/) # Links from my research and interest [Build Better Generative Adversarial Networks (GANs) - Andrew Ng - 2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_- VnF) [https://www.oreilly.com/library/view/flow- architectures/9781492075882/](https://www.oreilly.com/library/view/flow- architectures/9781492075882/) ## AI in RISC-V * [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020) ## Mixed reality * [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera) * Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU. # Old Links ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Top website * [https://paperswithcode.com/](https://paperswithcode.com/) * [https://www.learnthepart.com/](https://www.learnthepart.com/) * [https://hackaday.com/](https://hackaday.com/) * [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762) * hbrew install youtube-dl * [https://www.masterclass.com/](https://www.masterclass.com/) * [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp) * [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona) * [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/) * [https://explainshell.com/](https://explainshell.com/) * [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20) * [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4) * [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4) ### Python * [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/) * [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/) * [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594) ### Computer Vision * [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/) * [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md) * [https://github.com/google/mediapipe](https://github.com/google/mediapipe) ### Documents * [https://book.keybase.io/docs](https://book.keybase.io/docs) * [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind) * [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/) * [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/) * [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/) * [https://kepler.gl/demo](https://kepler.gl/demo) * [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/) * [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE) ### Business * [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006) * [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5) ### Tools * Free convert video for mac opensource * [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake) * [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg) * [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c) * [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises * [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap * [Create and share beautiful images of your source code.](https://carbon.now.sh/) * [Web IDE](https://replit.com/templates) * [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2) ### Patents [https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) 2. A method for augmenting a plurality of face images (WO2021060971A1) 3. A method for detecting a moving vehicle (WO2021107761A1) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) The present invention relates to a system (100) for providing advertisement contents based on facial analysis comprising an image acquisition device (10) to acquire an image of a user, a face detection module (20) to detect face of the user in the image, an analysis module (40) to analyse the facial features statistically using classification models retrieved from a classification module (30), a database (60) to store matching rules, weighted advertisements and a plurality of advertisement contents; and a display device (80) to display the advertisement contents. The system (100) further comprises a computation module (50) to compute weighted image of the user and a matching module (70) to match the weighted image of the user with the weighted advertisement to select an advertisement content based on facial analysis of the user. A method of providing the advertisement contents based on facial analysis is also provided thereof. [https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf) 2. A method for augmenting a plurality of face images (WO2021060971A1) The present invention relates to a method for increasing data for face analysis in video surveillance. The method comprises the steps of acquiring at least one face image from an image acquisition module (102), acquiring a plurality of face images available on the internet using a data input module (104), increasing face images by at least one data augmentation module (106 and 107), generating a plurality of face images based on a trained Generative Adversarial Network, GAN technique by using a GAN module, selecting proper images based on quality of the face images using a fuzzy logic module (111), saving the selected images into a fifth database, and training the deep learning module (113). [https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf) 3. A method for detecting a moving vehicle (WO2021107761A1) The present invention relates to a method for detecting a moving vehicle. The method comprises the steps of grabbing an initial image from a video stream by a vehicle detection module (1100), wherein the vehicle detection module (1100) is a part of a system (1000) to identify moving vehicle, enhancing the illumination of the initial image by the vehicle detection module (1100), enhancing the edges within the initial image by the vehicle detection module (1100), and finding vehicle based on homogenous properties of the body of the vehicle by the vehicle detection module (1100). The step of finding vehicle based on homogenous property of the body of the vehicle by the vehicle detection module (1100) further comprising the sub-steps of closing open edges, inverting the binary image, segmenting an inverted binary image, filtering the noise based on geometric feature, and filtering the noise based on relation. [https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf) ### Books 1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394) 2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/) ### Journal Papers * Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21) * * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf) * GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6) * * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf) * Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9). * * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM) * * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf) * Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2) * * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf) * Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52 * * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf) ### Conference Papers * Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017) * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342) * Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015) * [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf) * Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015) * [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336) * 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012) * [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf) * Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics * [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918) * License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011) * [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627) * Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011) * [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf) * [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649) * Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85. * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532) * Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010) * [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125) * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050 * [http://waset.org/publications/3636](http://waset.org/publications/3636) * An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010) * [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en) ### Articles on LinkedIn * [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/)) * [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) ) * [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/)) * [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/) * [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/) ### Slides slideshare * Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation) * Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) ) * tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) ) * Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) ) * Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) ) * Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) ) * Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) ) * How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) ) * Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) ) * Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) ) ### Online Courses * [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact) * [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life) * [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2) * [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends) * [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business) * [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity) * [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2) * [Understanding Business ](https://www.linkedin.com/learning/understanding-business) * [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea) * [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship) * [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations) * [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations) * [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations) * [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips) * [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation) * [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments) * [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow) * [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families) * [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances) * [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2) * [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2) * [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv) * [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers) * [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea) * [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital) * [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving) * [Professional Networking ](https://www.linkedin.com/learning/professional-networking) * [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home) * [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital) * [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling) * [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence) * [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers) ### Learning path * [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance) * [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner) # Completed ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link of best Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Books * [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y) * [Mathematics for Machine Learning](https://mml-book.github.io/) * Principles of Economics (6th edition) * The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It * Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) * Reinventing Your Life: The Breakthough Program to End Negative Behavior * Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence * [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf) * * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/) * [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/) * [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/) * Multiple view geometry in computer vision * Learning OpenCV Book by Adrian Kaehler and Gary Bradski * Digital Image Processing, Global Edition by Rafael C. Gonzalez * Introduction to Algorithms, 3rd Edition (The MIT Press) * The Mythical Man-Month: Essays on Software Engineering * [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition) ### Videos * [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning) * [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos) * [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/) * [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY) ### Papers * CALTag: High Precision Fiducial Markers for Camera * Diatom Autofocusing in Brightfield Microscopy: a Comparative Study * Analysis of focus measure operators in shape-from-focus * Optical flow modeling and computation: A survey * Toward general type 2 fuzzy logic systems based on zSlices ### Site * [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#)) ![](https://www.google.com/images/icons/product/drive-32.png)Reference- ComputerVisionDeepLearning.pdf * [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w) * [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679) * [https://www.freertos.org/index.html](https://www.freertos.org/index.html) * [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation) * [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/) * [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413) * [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917) * [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf) * [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/) * [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833) * [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238) * [https://koinly.io/](https://koinly.io/) site map: octopus.do [https://www.startupgermany.nrw/startup- contest/](https://www.startupgermany.nrw/startup-contest/) [https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth- camera-4k-cv-edge-object- detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai- kit-oak-depth-camera-4k-cv-edge-object-detection/description) [HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube in 2021! - YouTube ](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg) [محمد رضا شجریان، آلبوم کامل در خیال - YouTube](https://www.youtube.com/watch?v=cFdApmqlllg) [دوره جامع گوگل آنالیتیکس(UA) | آنالیتیپس](https://analytips.io/product/google-analyticsua/) [رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا - YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc) [Social Selling Index | Sales Navigator](https://www.linkedin.com/sales/ssi?src=or- search&veh=www.google.com) [https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/D7V2ImRWpG00ZwtqcglAiaZQbZisCcJYW0i47NNLl2KPtrNgWne1pMRlqdchhpySycR5GkO8UmHpykqzjoV2Ia8=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/D7V2ImRWpG00ZwtqcglAiaZQbZisCcJYW0i47NNLl2KPtrNgWne1pMRlqdchhpySycR5GkO8UmHpykqzjoV2Ia8=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Links pirahansiah, Personal, Roadmap, Books, Online Courses [](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze- oXQexwY "Open Spreadsheet, Links in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/sheets_32dp.png)Links Links from my research and interest AI in RISC-V Mixed reality Old Links Reference Link Groups Top website Python Computer Vision Documents Business Tools Patents Books Journal Papers Conference Papers Articles on LinkedIn Slides slideshare Online Courses Learning path Completed Reference Link of best Groups Books Videos Papers Site Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning training loops. GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp) [https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to- install-remmina/) [https://readme.so/](https://readme.so/) # Links from my research and interest [Build Better Generative Adversarial Networks (GANs) - Andrew Ng - 2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_- VnF) [https://www.oreilly.com/library/view/flow- architectures/9781492075882/](https://www.oreilly.com/library/view/flow- architectures/9781492075882/) ## AI in RISC-V * [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020) ## Mixed reality * [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera) * Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU. # Old Links ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Top website * [https://paperswithcode.com/](https://paperswithcode.com/) * [https://www.learnthepart.com/](https://www.learnthepart.com/) * [https://hackaday.com/](https://hackaday.com/) * [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762) * hbrew install youtube-dl * [https://www.masterclass.com/](https://www.masterclass.com/) * [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp) * [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona) * [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/) * [https://explainshell.com/](https://explainshell.com/) * [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20) * [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4) * [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4) ### Python * [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/) * [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/) * [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594) ### Computer Vision * [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/) * [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md) * [https://github.com/google/mediapipe](https://github.com/google/mediapipe) ### Documents * [https://book.keybase.io/docs](https://book.keybase.io/docs) * [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind) * [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/) * [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/) * [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/) * [https://kepler.gl/demo](https://kepler.gl/demo) * [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/) * [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE) ### Business * [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006) * [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5) ### Tools * Free convert video for mac opensource * [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake) * [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg) * [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c) * [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises * [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap * [Create and share beautiful images of your source code.](https://carbon.now.sh/) * [Web IDE](https://replit.com/templates) * [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2) ### Patents [https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) 2. A method for augmenting a plurality of face images (WO2021060971A1) 3. A method for detecting a moving vehicle (WO2021107761A1) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) The present invention relates to a system (100) for providing advertisement contents based on facial analysis comprising an image acquisition device (10) to acquire an image of a user, a face detection module (20) to detect face of the user in the image, an analysis module (40) to analyse the facial features statistically using classification models retrieved from a classification module (30), a database (60) to store matching rules, weighted advertisements and a plurality of advertisement contents; and a display device (80) to display the advertisement contents. The system (100) further comprises a computation module (50) to compute weighted image of the user and a matching module (70) to match the weighted image of the user with the weighted advertisement to select an advertisement content based on facial analysis of the user. A method of providing the advertisement contents based on facial analysis is also provided thereof. [https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf) 2. A method for augmenting a plurality of face images (WO2021060971A1) The present invention relates to a method for increasing data for face analysis in video surveillance. The method comprises the steps of acquiring at least one face image from an image acquisition module (102), acquiring a plurality of face images available on the internet using a data input module (104), increasing face images by at least one data augmentation module (106 and 107), generating a plurality of face images based on a trained Generative Adversarial Network, GAN technique by using a GAN module, selecting proper images based on quality of the face images using a fuzzy logic module (111), saving the selected images into a fifth database, and training the deep learning module (113). [https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf) 3. A method for detecting a moving vehicle (WO2021107761A1) The present invention relates to a method for detecting a moving vehicle. The method comprises the steps of grabbing an initial image from a video stream by a vehicle detection module (1100), wherein the vehicle detection module (1100) is a part of a system (1000) to identify moving vehicle, enhancing the illumination of the initial image by the vehicle detection module (1100), enhancing the edges within the initial image by the vehicle detection module (1100), and finding vehicle based on homogenous properties of the body of the vehicle by the vehicle detection module (1100). The step of finding vehicle based on homogenous property of the body of the vehicle by the vehicle detection module (1100) further comprising the sub-steps of closing open edges, inverting the binary image, segmenting an inverted binary image, filtering the noise based on geometric feature, and filtering the noise based on relation. [https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf) ### Books 1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394) 2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/) ### Journal Papers * Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21) * * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf) * GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6) * * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf) * Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9). * * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM) * * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf) * Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2) * * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf) * Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52 * * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf) ### Conference Papers * Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017) * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342) * Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015) * [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf) * Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015) * [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336) * 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012) * [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf) * Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics * [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918) * License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011) * [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627) * Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011) * [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf) * [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649) * Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85. * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532) * Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010) * [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125) * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050 * [http://waset.org/publications/3636](http://waset.org/publications/3636) * An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010) * [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en) ### Articles on LinkedIn * [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/)) * [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) ) * [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/)) * [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/) * [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/) ### Slides slideshare * Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation) * Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) ) * tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) ) * Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) ) * Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) ) * Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) ) * Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) ) * How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) ) * Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) ) * Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) ) ### Online Courses * [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact) * [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life) * [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2) * [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends) * [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business) * [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity) * [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2) * [Understanding Business ](https://www.linkedin.com/learning/understanding-business) * [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea) * [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship) * [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations) * [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations) * [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations) * [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips) * [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation) * [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments) * [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow) * [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families) * [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances) * [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2) * [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2) * [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv) * [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers) * [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea) * [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital) * [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving) * [Professional Networking ](https://www.linkedin.com/learning/professional-networking) * [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home) * [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital) * [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling) * [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence) * [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers) ### Learning path * [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance) * [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner) # Completed ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link of best Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Books * [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y) * [Mathematics for Machine Learning](https://mml-book.github.io/) * Principles of Economics (6th edition) * The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It * Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) * Reinventing Your Life: The Breakthough Program to End Negative Behavior * Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence * [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf) * * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/) * [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/) * [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/) * Multiple view geometry in computer vision * Learning OpenCV Book by Adrian Kaehler and Gary Bradski * Digital Image Processing, Global Edition by Rafael C. Gonzalez * Introduction to Algorithms, 3rd Edition (The MIT Press) * The Mythical Man-Month: Essays on Software Engineering * [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition) ### Videos * [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning) * [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos) * [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/) * [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY) ### Papers * CALTag: High Precision Fiducial Markers for Camera * Diatom Autofocusing in Brightfield Microscopy: a Comparative Study * Analysis of focus measure operators in shape-from-focus * Optical flow modeling and computation: A survey * Toward general type 2 fuzzy logic systems based on zSlices ### Site * [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#)) ![](https://www.google.com/images/icons/product/drive-32.png)Reference- ComputerVisionDeepLearning.pdf * [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w) * [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679) * [https://www.freertos.org/index.html](https://www.freertos.org/index.html) * [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation) * [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/) * [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413) * [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917) * [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf) * [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/) * [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833) * [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238) * [https://koinly.io/](https://koinly.io/) site map: octopus.do [https://www.startupgermany.nrw/startup- contest/](https://www.startupgermany.nrw/startup-contest/) [https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth- camera-4k-cv-edge-object- detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai- kit-oak-depth-camera-4k-cv-edge-object-detection/description) [HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube in 2021! - YouTube ](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg) [محمد رضا شجریان، آلبوم کامل در خیال - YouTube](https://www.youtube.com/watch?v=cFdApmqlllg) [دوره جامع گوگل آنالیتیکس(UA) | آنالیتیپس](https://analytips.io/product/google-analyticsua/) [رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا - YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc) [Social Selling Index | Sales Navigator](https://www.linkedin.com/sales/ssi?src=or- search&veh=www.google.com) [https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/D7V2ImRWpG00ZwtqcglAiaZQbZisCcJYW0i47NNLl2KPtrNgWne1pMRlqdchhpySycR5GkO8UmHpykqzjoV2Ia8=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/D7V2ImRWpG00ZwtqcglAiaZQbZisCcJYW0i47NNLl2KPtrNgWne1pMRlqdchhpySycR5GkO8UmHpykqzjoV2Ia8=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Links pirahansiah, Personal, Roadmap, Books, Online Courses [](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze- oXQexwY "Open Spreadsheet, Links in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/sheets_32dp.png)Links Links from my research and interest AI in RISC-V Mixed reality Old Links Reference Link Groups Top website Python Computer Vision Documents Business Tools Patents Books Journal Papers Conference Papers Articles on LinkedIn Slides slideshare Online Courses Learning path Completed Reference Link of best Groups Books Videos Papers Site Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning training loops. GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp) [https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to- install-remmina/) [https://readme.so/](https://readme.so/) # Links from my research and interest [Build Better Generative Adversarial Networks (GANs) - Andrew Ng - 2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_- VnF) [https://www.oreilly.com/library/view/flow- architectures/9781492075882/](https://www.oreilly.com/library/view/flow- architectures/9781492075882/) ## AI in RISC-V * [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020) ## Mixed reality * [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera) * Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU. # Old Links ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Top website * [https://paperswithcode.com/](https://paperswithcode.com/) * [https://www.learnthepart.com/](https://www.learnthepart.com/) * [https://hackaday.com/](https://hackaday.com/) * [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762) * hbrew install youtube-dl * [https://www.masterclass.com/](https://www.masterclass.com/) * [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp) * [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona) * [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/) * [https://explainshell.com/](https://explainshell.com/) * [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20) * [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4) * [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4) ### Python * [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/) * [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/) * [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594) ### Computer Vision * [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/) * [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md) * [https://github.com/google/mediapipe](https://github.com/google/mediapipe) ### Documents * [https://book.keybase.io/docs](https://book.keybase.io/docs) * [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind) * [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/) * [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/) * [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/) * [https://kepler.gl/demo](https://kepler.gl/demo) * [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/) * [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE) ### Business * [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006) * [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5) ### Tools * Free convert video for mac opensource * [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake) * [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg) * [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c) * [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises * [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap * [Create and share beautiful images of your source code.](https://carbon.now.sh/) * [Web IDE](https://replit.com/templates) * [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2) ### Patents [https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) 2. A method for augmenting a plurality of face images (WO2021060971A1) 3. A method for detecting a moving vehicle (WO2021107761A1) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) The present invention relates to a system (100) for providing advertisement contents based on facial analysis comprising an image acquisition device (10) to acquire an image of a user, a face detection module (20) to detect face of the user in the image, an analysis module (40) to analyse the facial features statistically using classification models retrieved from a classification module (30), a database (60) to store matching rules, weighted advertisements and a plurality of advertisement contents; and a display device (80) to display the advertisement contents. The system (100) further comprises a computation module (50) to compute weighted image of the user and a matching module (70) to match the weighted image of the user with the weighted advertisement to select an advertisement content based on facial analysis of the user. A method of providing the advertisement contents based on facial analysis is also provided thereof. [https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf) 2. A method for augmenting a plurality of face images (WO2021060971A1) The present invention relates to a method for increasing data for face analysis in video surveillance. The method comprises the steps of acquiring at least one face image from an image acquisition module (102), acquiring a plurality of face images available on the internet using a data input module (104), increasing face images by at least one data augmentation module (106 and 107), generating a plurality of face images based on a trained Generative Adversarial Network, GAN technique by using a GAN module, selecting proper images based on quality of the face images using a fuzzy logic module (111), saving the selected images into a fifth database, and training the deep learning module (113). [https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf) 3. A method for detecting a moving vehicle (WO2021107761A1) The present invention relates to a method for detecting a moving vehicle. The method comprises the steps of grabbing an initial image from a video stream by a vehicle detection module (1100), wherein the vehicle detection module (1100) is a part of a system (1000) to identify moving vehicle, enhancing the illumination of the initial image by the vehicle detection module (1100), enhancing the edges within the initial image by the vehicle detection module (1100), and finding vehicle based on homogenous properties of the body of the vehicle by the vehicle detection module (1100). The step of finding vehicle based on homogenous property of the body of the vehicle by the vehicle detection module (1100) further comprising the sub-steps of closing open edges, inverting the binary image, segmenting an inverted binary image, filtering the noise based on geometric feature, and filtering the noise based on relation. [https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf) ### Books 1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394) 2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/) ### Journal Papers * Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21) * * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf) * GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6) * * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf) * Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9). * * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM) * * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf) * Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2) * * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf) * Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52 * * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf) ### Conference Papers * Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017) * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342) * Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015) * [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf) * Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015) * [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336) * 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012) * [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf) * Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics * [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918) * License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011) * [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627) * Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011) * [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf) * [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649) * Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85. * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532) * Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010) * [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125) * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050 * [http://waset.org/publications/3636](http://waset.org/publications/3636) * An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010) * [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en) ### Articles on LinkedIn * [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/)) * [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) ) * [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/)) * [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/) * [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/) ### Slides slideshare * Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation) * Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) ) * tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) ) * Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) ) * Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) ) * Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) ) * Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) ) * How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) ) * Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) ) * Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) ) ### Online Courses * [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact) * [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life) * [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2) * [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends) * [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business) * [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity) * [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2) * [Understanding Business ](https://www.linkedin.com/learning/understanding-business) * [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea) * [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship) * [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations) * [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations) * [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations) * [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips) * [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation) * [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments) * [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow) * [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families) * [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances) * [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2) * [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2) * [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv) * [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers) * [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea) * [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital) * [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving) * [Professional Networking ](https://www.linkedin.com/learning/professional-networking) * [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home) * [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital) * [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling) * [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence) * [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers) ### Learning path * [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance) * [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner) # Completed ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link of best Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Books * [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y) * [Mathematics for Machine Learning](https://mml-book.github.io/) * Principles of Economics (6th edition) * The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It * Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) * Reinventing Your Life: The Breakthough Program to End Negative Behavior * Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence * [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf) * * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/) * [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/) * [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/) * Multiple view geometry in computer vision * Learning OpenCV Book by Adrian Kaehler and Gary Bradski * Digital Image Processing, Global Edition by Rafael C. Gonzalez * Introduction to Algorithms, 3rd Edition (The MIT Press) * The Mythical Man-Month: Essays on Software Engineering * [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition) ### Videos * [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning) * [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos) * [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/) * [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY) ### Papers * CALTag: High Precision Fiducial Markers for Camera * Diatom Autofocusing in Brightfield Microscopy: a Comparative Study * Analysis of focus measure operators in shape-from-focus * Optical flow modeling and computation: A survey * Toward general type 2 fuzzy logic systems based on zSlices ### Site * [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#)) ![](https://www.google.com/images/icons/product/drive-32.png)Reference- ComputerVisionDeepLearning.pdf * [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w) * [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679) * [https://www.freertos.org/index.html](https://www.freertos.org/index.html) * [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation) * [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/) * [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413) * [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917) * [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf) * [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/) * [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833) * [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238) * [https://koinly.io/](https://koinly.io/) site map: octopus.do [https://www.startupgermany.nrw/startup- contest/](https://www.startupgermany.nrw/startup-contest/) [https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth- camera-4k-cv-edge-object- detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai- kit-oak-depth-camera-4k-cv-edge-object-detection/description) [HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube in 2021! - YouTube ](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg) [محمد رضا شجریان، آلبوم کامل در خیال - YouTube](https://www.youtube.com/watch?v=cFdApmqlllg) [دوره جامع گوگل آنالیتیکس(UA) | آنالیتیپس](https://analytips.io/product/google-analyticsua/) [رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا - YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc) [Social Selling Index | Sales Navigator](https://www.linkedin.com/sales/ssi?src=or- search&veh=www.google.com) [https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/D7V2ImRWpG00ZwtqcglAiaZQbZisCcJYW0i47NNLl2KPtrNgWne1pMRlqdchhpySycR5GkO8UmHpykqzjoV2Ia8=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/D7V2ImRWpG00ZwtqcglAiaZQbZisCcJYW0i47NNLl2KPtrNgWne1pMRlqdchhpySycR5GkO8UmHpykqzjoV2Ia8=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Links pirahansiah, Personal, Roadmap, Books, Online Courses [](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze- oXQexwY "Open Spreadsheet, Links in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/sheets_32dp.png)Links Links from my research and interest AI in RISC-V Mixed reality Old Links Reference Link Groups Top website Python Computer Vision Documents Business Tools Patents Books Journal Papers Conference Papers Articles on LinkedIn Slides slideshare Online Courses Learning path Completed Reference Link of best Groups Books Videos Papers Site Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning training loops. GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp) [https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to- install-remmina/) [https://readme.so/](https://readme.so/) # Links from my research and interest [Build Better Generative Adversarial Networks (GANs) - Andrew Ng - 2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_- VnF) [https://www.oreilly.com/library/view/flow- architectures/9781492075882/](https://www.oreilly.com/library/view/flow- architectures/9781492075882/) ## AI in RISC-V * [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020) ## Mixed reality * [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera) * Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU. # Old Links ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Top website * [https://paperswithcode.com/](https://paperswithcode.com/) * [https://www.learnthepart.com/](https://www.learnthepart.com/) * [https://hackaday.com/](https://hackaday.com/) * [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762) * hbrew install youtube-dl * [https://www.masterclass.com/](https://www.masterclass.com/) * [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp) * [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona) * [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/) * [https://explainshell.com/](https://explainshell.com/) * [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20) * [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4) * [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4) ### Python * [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/) * [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/) * [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594) ### Computer Vision * [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/) * [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md) * [https://github.com/google/mediapipe](https://github.com/google/mediapipe) ### Documents * [https://book.keybase.io/docs](https://book.keybase.io/docs) * [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind) * [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/) * [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/) * [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/) * [https://kepler.gl/demo](https://kepler.gl/demo) * [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/) * [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE) ### Business * [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006) * [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5) ### Tools * Free convert video for mac opensource * [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake) * [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg) * [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c) * [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises * [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap * [Create and share beautiful images of your source code.](https://carbon.now.sh/) * [Web IDE](https://replit.com/templates) * [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2) ### Patents [https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) 2. A method for augmenting a plurality of face images (WO2021060971A1) 3. A method for detecting a moving vehicle (WO2021107761A1) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) The present invention relates to a system (100) for providing advertisement contents based on facial analysis comprising an image acquisition device (10) to acquire an image of a user, a face detection module (20) to detect face of the user in the image, an analysis module (40) to analyse the facial features statistically using classification models retrieved from a classification module (30), a database (60) to store matching rules, weighted advertisements and a plurality of advertisement contents; and a display device (80) to display the advertisement contents. The system (100) further comprises a computation module (50) to compute weighted image of the user and a matching module (70) to match the weighted image of the user with the weighted advertisement to select an advertisement content based on facial analysis of the user. A method of providing the advertisement contents based on facial analysis is also provided thereof. [https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf) 2. A method for augmenting a plurality of face images (WO2021060971A1) The present invention relates to a method for increasing data for face analysis in video surveillance. The method comprises the steps of acquiring at least one face image from an image acquisition module (102), acquiring a plurality of face images available on the internet using a data input module (104), increasing face images by at least one data augmentation module (106 and 107), generating a plurality of face images based on a trained Generative Adversarial Network, GAN technique by using a GAN module, selecting proper images based on quality of the face images using a fuzzy logic module (111), saving the selected images into a fifth database, and training the deep learning module (113). [https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf) 3. A method for detecting a moving vehicle (WO2021107761A1) The present invention relates to a method for detecting a moving vehicle. The method comprises the steps of grabbing an initial image from a video stream by a vehicle detection module (1100), wherein the vehicle detection module (1100) is a part of a system (1000) to identify moving vehicle, enhancing the illumination of the initial image by the vehicle detection module (1100), enhancing the edges within the initial image by the vehicle detection module (1100), and finding vehicle based on homogenous properties of the body of the vehicle by the vehicle detection module (1100). The step of finding vehicle based on homogenous property of the body of the vehicle by the vehicle detection module (1100) further comprising the sub-steps of closing open edges, inverting the binary image, segmenting an inverted binary image, filtering the noise based on geometric feature, and filtering the noise based on relation. [https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf) ### Books 1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394) 2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/) ### Journal Papers * Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21) * * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf) * GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6) * * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf) * Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9). * * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM) * * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf) * Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2) * * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf) * Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52 * * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf) ### Conference Papers * Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017) * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342) * Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015) * [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf) * Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015) * [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336) * 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012) * [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf) * Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics * [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918) * License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011) * [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627) * Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011) * [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf) * [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649) * Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85. * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532) * Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010) * [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125) * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050 * [http://waset.org/publications/3636](http://waset.org/publications/3636) * An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010) * [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en) ### Articles on LinkedIn * [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/)) * [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) ) * [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/)) * [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/) * [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/) ### Slides slideshare * Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation) * Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) ) * tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) ) * Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) ) * Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) ) * Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) ) * Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) ) * How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) ) * Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) ) * Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) ) ### Online Courses * [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact) * [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life) * [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2) * [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends) * [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business) * [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity) * [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2) * [Understanding Business ](https://www.linkedin.com/learning/understanding-business) * [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea) * [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship) * [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations) * [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations) * [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations) * [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips) * [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation) * [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments) * [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow) * [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families) * [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances) * [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2) * [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2) * [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv) * [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers) * [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea) * [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital) * [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving) * [Professional Networking ](https://www.linkedin.com/learning/professional-networking) * [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home) * [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital) * [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling) * [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence) * [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers) ### Learning path * [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance) * [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner) # Completed ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link of best Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Books * [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y) * [Mathematics for Machine Learning](https://mml-book.github.io/) * Principles of Economics (6th edition) * The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It * Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) * Reinventing Your Life: The Breakthough Program to End Negative Behavior * Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence * [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf) * * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/) * [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/) * [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/) * Multiple view geometry in computer vision * Learning OpenCV Book by Adrian Kaehler and Gary Bradski * Digital Image Processing, Global Edition by Rafael C. Gonzalez * Introduction to Algorithms, 3rd Edition (The MIT Press) * The Mythical Man-Month: Essays on Software Engineering * [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition) ### Videos * [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning) * [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos) * [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/) * [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY) ### Papers * CALTag: High Precision Fiducial Markers for Camera * Diatom Autofocusing in Brightfield Microscopy: a Comparative Study * Analysis of focus measure operators in shape-from-focus * Optical flow modeling and computation: A survey * Toward general type 2 fuzzy logic systems based on zSlices ### Site * [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#)) ![](https://www.google.com/images/icons/product/drive-32.png)Reference- ComputerVisionDeepLearning.pdf * [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w) * [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679) * [https://www.freertos.org/index.html](https://www.freertos.org/index.html) * [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation) * [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/) * [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413) * [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917) * [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf) * [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/) * [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833) * [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238) * [https://koinly.io/](https://koinly.io/) site map: octopus.do [https://www.startupgermany.nrw/startup- contest/](https://www.startupgermany.nrw/startup-contest/) [https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth- camera-4k-cv-edge-object- detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai- kit-oak-depth-camera-4k-cv-edge-object-detection/description) [HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube in 2021! - YouTube ](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg) [محمد رضا شجریان، آلبوم کامل در خیال - YouTube](https://www.youtube.com/watch?v=cFdApmqlllg) [دوره جامع گوگل آنالیتیکس(UA) | آنالیتیپس](https://analytips.io/product/google-analyticsua/) [رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا - YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc) [Social Selling Index | Sales Navigator](https://www.linkedin.com/sales/ssi?src=or- search&veh=www.google.com) [https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/D7V2ImRWpG00ZwtqcglAiaZQbZisCcJYW0i47NNLl2KPtrNgWne1pMRlqdchhpySycR5GkO8UmHpykqzjoV2Ia8=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/D7V2ImRWpG00ZwtqcglAiaZQbZisCcJYW0i47NNLl2KPtrNgWne1pMRlqdchhpySycR5GkO8UmHpykqzjoV2Ia8=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Links pirahansiah, Personal, Roadmap, Books, Online Courses [](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze- oXQexwY "Open Spreadsheet, Links in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/sheets_32dp.png)Links Links from my research and interest AI in RISC-V Mixed reality Old Links Reference Link Groups Top website Python Computer Vision Documents Business Tools Patents Books Journal Papers Conference Papers Articles on LinkedIn Slides slideshare Online Courses Learning path Completed Reference Link of best Groups Books Videos Papers Site Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning training loops. GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp) [https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to- install-remmina/) [https://readme.so/](https://readme.so/) # Links from my research and interest [Build Better Generative Adversarial Networks (GANs) - Andrew Ng - 2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_- VnF) [https://www.oreilly.com/library/view/flow- architectures/9781492075882/](https://www.oreilly.com/library/view/flow- architectures/9781492075882/) ## AI in RISC-V * [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020) ## Mixed reality * [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera) * Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU. # Old Links ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Top website * [https://paperswithcode.com/](https://paperswithcode.com/) * [https://www.learnthepart.com/](https://www.learnthepart.com/) * [https://hackaday.com/](https://hackaday.com/) * [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762) * hbrew install youtube-dl * [https://www.masterclass.com/](https://www.masterclass.com/) * [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp) * [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona) * [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/) * [https://explainshell.com/](https://explainshell.com/) * [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20) * [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4) * [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4) ### Python * [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/) * [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/) * [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594) ### Computer Vision * [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/) * [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md) * [https://github.com/google/mediapipe](https://github.com/google/mediapipe) ### Documents * [https://book.keybase.io/docs](https://book.keybase.io/docs) * [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind) * [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/) * [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/) * [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/) * [https://kepler.gl/demo](https://kepler.gl/demo) * [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/) * [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE) ### Business * [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006) * [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5) ### Tools * Free convert video for mac opensource * [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake) * [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg) * [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c) * [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises * [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap * [Create and share beautiful images of your source code.](https://carbon.now.sh/) * [Web IDE](https://replit.com/templates) * [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2) ### Patents [https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) 2. A method for augmenting a plurality of face images (WO2021060971A1) 3. A method for detecting a moving vehicle (WO2021107761A1) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) The present invention relates to a system (100) for providing advertisement contents based on facial analysis comprising an image acquisition device (10) to acquire an image of a user, a face detection module (20) to detect face of the user in the image, an analysis module (40) to analyse the facial features statistically using classification models retrieved from a classification module (30), a database (60) to store matching rules, weighted advertisements and a plurality of advertisement contents; and a display device (80) to display the advertisement contents. The system (100) further comprises a computation module (50) to compute weighted image of the user and a matching module (70) to match the weighted image of the user with the weighted advertisement to select an advertisement content based on facial analysis of the user. A method of providing the advertisement contents based on facial analysis is also provided thereof. [https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf) 2. A method for augmenting a plurality of face images (WO2021060971A1) The present invention relates to a method for increasing data for face analysis in video surveillance. The method comprises the steps of acquiring at least one face image from an image acquisition module (102), acquiring a plurality of face images available on the internet using a data input module (104), increasing face images by at least one data augmentation module (106 and 107), generating a plurality of face images based on a trained Generative Adversarial Network, GAN technique by using a GAN module, selecting proper images based on quality of the face images using a fuzzy logic module (111), saving the selected images into a fifth database, and training the deep learning module (113). [https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf) 3. A method for detecting a moving vehicle (WO2021107761A1) The present invention relates to a method for detecting a moving vehicle. The method comprises the steps of grabbing an initial image from a video stream by a vehicle detection module (1100), wherein the vehicle detection module (1100) is a part of a system (1000) to identify moving vehicle, enhancing the illumination of the initial image by the vehicle detection module (1100), enhancing the edges within the initial image by the vehicle detection module (1100), and finding vehicle based on homogenous properties of the body of the vehicle by the vehicle detection module (1100). The step of finding vehicle based on homogenous property of the body of the vehicle by the vehicle detection module (1100) further comprising the sub-steps of closing open edges, inverting the binary image, segmenting an inverted binary image, filtering the noise based on geometric feature, and filtering the noise based on relation. [https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf) ### Books 1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394) 2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/) ### Journal Papers * Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21) * * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf) * GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6) * * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf) * Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9). * * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM) * * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf) * Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2) * * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf) * Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52 * * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf) ### Conference Papers * Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017) * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342) * Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015) * [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf) * Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015) * [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336) * 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012) * [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf) * Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics * [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918) * License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011) * [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627) * Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011) * [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf) * [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649) * Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85. * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532) * Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010) * [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125) * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050 * [http://waset.org/publications/3636](http://waset.org/publications/3636) * An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010) * [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en) ### Articles on LinkedIn * [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/)) * [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) ) * [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/)) * [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/) * [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/) ### Slides slideshare * Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation) * Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) ) * tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) ) * Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) ) * Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) ) * Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) ) * Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) ) * How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) ) * Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) ) * Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) ) ### Online Courses * [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact) * [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life) * [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2) * [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends) * [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business) * [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity) * [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2) * [Understanding Business ](https://www.linkedin.com/learning/understanding-business) * [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea) * [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship) * [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations) * [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations) * [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations) * [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips) * [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation) * [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments) * [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow) * [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families) * [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances) * [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2) * [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2) * [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv) * [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers) * [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea) * [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital) * [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving) * [Professional Networking ](https://www.linkedin.com/learning/professional-networking) * [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home) * [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital) * [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling) * [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence) * [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers) ### Learning path * [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance) * [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner) # Completed ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link of best Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Books * [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y) * [Mathematics for Machine Learning](https://mml-book.github.io/) * Principles of Economics (6th edition) * The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It * Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) * Reinventing Your Life: The Breakthough Program to End Negative Behavior * Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence * [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf) * * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/) * [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/) * [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/) * Multiple view geometry in computer vision * Learning OpenCV Book by Adrian Kaehler and Gary Bradski * Digital Image Processing, Global Edition by Rafael C. Gonzalez * Introduction to Algorithms, 3rd Edition (The MIT Press) * The Mythical Man-Month: Essays on Software Engineering * [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition) ### Videos * [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning) * [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos) * [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/) * [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY) ### Papers * CALTag: High Precision Fiducial Markers for Camera * Diatom Autofocusing in Brightfield Microscopy: a Comparative Study * Analysis of focus measure operators in shape-from-focus * Optical flow modeling and computation: A survey * Toward general type 2 fuzzy logic systems based on zSlices ### Site * [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#)) ![](https://www.google.com/images/icons/product/drive-32.png)Reference- ComputerVisionDeepLearning.pdf * [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w) * [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679) * [https://www.freertos.org/index.html](https://www.freertos.org/index.html) * [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation) * [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/) * [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413) * [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917) * [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf) * [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/) * [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833) * [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238) * [https://koinly.io/](https://koinly.io/) site map: octopus.do [https://www.startupgermany.nrw/startup- contest/](https://www.startupgermany.nrw/startup-contest/) [https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth- camera-4k-cv-edge-object- detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai- kit-oak-depth-camera-4k-cv-edge-object-detection/description) [HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube in 2021! - YouTube ](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg) [محمد رضا شجریان، آلبوم کامل در خیال - YouTube](https://www.youtube.com/watch?v=cFdApmqlllg) [دوره جامع گوگل آنالیتیکس(UA) | آنالیتیپس](https://analytips.io/product/google-analyticsua/) [رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا - YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc) [Social Selling Index | Sales Navigator](https://www.linkedin.com/sales/ssi?src=or- search&veh=www.google.com) [https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/VnELnzCZElXe9gLxGYU00_xF7qju2MljSVlgUMwWsc50I88T6vB5ahQjH2kGA --o3hIeJYu2N--BO_uidCis2Ow=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/VnELnzCZElXe9gLxGYU00_xF7qju2MljSVlgUMwWsc50I88T6vB5ahQjH2kGA --o3hIeJYu2N--BO_uidCis2Ow=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Links pirahansiah, Personal, Roadmap, Books, Online Courses [](https://drive.google.com/open?id=1ELXvmqcSciCK8-TUVsyVRnYTuxnKta-_xze- oXQexwY "Open Spreadsheet, Links in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/sheets_32dp.png)Links Links from my research and interest AI in RISC-V Mixed reality Old Links Reference Link Groups Top website Python Computer Vision Documents Business Tools Patents Books Journal Papers Conference Papers Articles on LinkedIn Slides slideshare Online Courses Learning path Completed Reference Link of best Groups Books Videos Papers Site Efficient Video Dataset Loading and Augmentation in PyTorch for deep learning training loops. GitHub[ https://lnkd.in/gpR8idsp](https://lnkd.in/gpR8idsp) [https://remmina.org/how-to-install-remmina/](https://remmina.org/how-to- install-remmina/) [https://readme.so/](https://readme.so/) # Links from my research and interest [Build Better Generative Adversarial Networks (GANs) - Andrew Ng - 2021](https://www.youtube.com/playlist?list=PLuv1FSpHurUfl44GWqaCzEhGP-aP_- VnF) [https://www.oreilly.com/library/view/flow- architectures/9781492075882/](https://www.oreilly.com/library/view/flow- architectures/9781492075882/) ## AI in RISC-V * [RISC-V hardware & software ecosystem highlights in 2020](https://www.cnx-software.com/2020/12/10/risc-v-hardware-software-ecosystem-highlights-in-2020) ## Mixed reality * [https://github.com/MIT-SPARK/Kimera](https://github.com/MIT-SPARK/Kimera) * Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU. # Old Links ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Top website * [https://paperswithcode.com/](https://paperswithcode.com/) * [https://www.learnthepart.com/](https://www.learnthepart.com/) * [https://hackaday.com/](https://hackaday.com/) * [https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762](https://medium.com/@pirahansiah/timesaver-626019137fa9?sk=cde3334919d6c16ba0964fe021a24762) * hbrew install youtube-dl * [https://www.masterclass.com/](https://www.masterclass.com/) * [Deep Learning for coders (2020)](https://www.youtube.com/user/howardjeremyp) * [Deep Learning for Video](https://mcv-m6-video.github.io/deepvideo-2019/) (Master in Computer Vision Barcelona) * [Augmented Reality using ArUco Markers in OpenCV (C++ / Python)](https://www.learnopencv.com/augmented-reality-using-aruco-markers-in-opencv-c-python/) * [https://explainshell.com/](https://explainshell.com/) * [https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/](https://stackoverflow.blog/2020/02/12/when-laziness-is-efficient-make-the-most-of-your-command-line/?utm_source=Iterable&utm_medium=email&utm_campaign=the_overflow_newsletter&utm_content=02-19-20) * [https://link.medium.com/XA49Ke0Jt4](https://link.medium.com/XA49Ke0Jt4) * [https://link.medium.com/Np0YEy1Jt4](https://link.medium.com/Np0YEy1Jt4) ### Python * [How to Write Beautiful Python Code With PEP 8](https://realpython.com/python-pep8/) * [PEP 622 -- Structural Pattern Matching](https://www.python.org/dev/peps/pep-0622/) * [Monitor Your Dependencies! Stop Being A Blind Data-Scientist.](https://towardsdatascience.com/monitor-your-dependencies-stop-being-a-blind-data-scientist-a3150bd64594) ### Computer Vision * [Hybrid CV/DL pipelines with OpenCV 4.4 G-API](https://opencv.org/hybrid-cv-dl-pipelines-with-opencv-4-4-g-api/) * [PaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. ](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/README_en.md) * [https://github.com/google/mediapipe](https://github.com/google/mediapipe) ### Documents * [https://book.keybase.io/docs](https://book.keybase.io/docs) * [kind is a tool for running local Kubernetes clusters using Docker container "nodes".](https://github.com/kubernetes-sigs/kind) * [FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. ](https://xilinx.github.io/finn/) * [Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.](https://gitter.im/) * [Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services.](https://mlperf.org/) * [https://kepler.gl/demo](https://kepler.gl/demo) * [Open source, highly available Prometheus setup with long term storage capabilities.](https://thanos.io/) * [Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity Patterns](https://www.youtube.com/watch?v=JI-MButCqUE) ### Business * [5 Ways to Make More Money as a Coach or Consultant](https://www.entrepreneur.com/article/283006) * [https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5](https://www.youtube.com/watch?v=njx09wXb9o0&list=PLnhdsL4kFmVYMNq6F8k8UiFkc6fwIdk3o&index=5) ### Tools * Free convert video for mac opensource * [https://github.com/HandBrake/HandBrake](https://github.com/HandBrake/HandBrake) * [https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg](https://handbrake.fr/rotation.php?file=HandBrake-1.3.3.dmg) * [global-gitignore.md](https://gist.github.com/subfuzion/db7f57fff2fb6998a16c) * [https://nextbillion.ai/](https://nextbillion.ai/) Mapping platform built for enterprises * [https://github.com/ondras/my-mind](https://github.com/ondras/my-mind) MindMap * [Create and share beautiful images of your source code.](https://carbon.now.sh/) * [Web IDE](https://replit.com/templates) * [https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2](https://github.com/jely2002/youtube-dl-gui/releases/tag/v2.2.2) ### Patents [https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah](https://patents.google.com/?inventor=pirahansiah&oq=pirahansiah) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) 2. A method for augmenting a plurality of face images (WO2021060971A1) 3. A method for detecting a moving vehicle (WO2021107761A1) 1. System and method for providing advertisement contents based on facial analysis (WO2020141969A2) The present invention relates to a system (100) for providing advertisement contents based on facial analysis comprising an image acquisition device (10) to acquire an image of a user, a face detection module (20) to detect face of the user in the image, an analysis module (40) to analyse the facial features statistically using classification models retrieved from a classification module (30), a database (60) to store matching rules, weighted advertisements and a plurality of advertisement contents; and a display device (80) to display the advertisement contents. The system (100) further comprises a computation module (50) to compute weighted image of the user and a matching module (70) to match the weighted image of the user with the weighted advertisement to select an advertisement content based on facial analysis of the user. A method of providing the advertisement contents based on facial analysis is also provided thereof. [https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf](https://patentimages.storage.googleapis.com/ff/3f/a5/e7b42ef58d8a03/WO2020141969A2.pdf) 2. A method for augmenting a plurality of face images (WO2021060971A1) The present invention relates to a method for increasing data for face analysis in video surveillance. The method comprises the steps of acquiring at least one face image from an image acquisition module (102), acquiring a plurality of face images available on the internet using a data input module (104), increasing face images by at least one data augmentation module (106 and 107), generating a plurality of face images based on a trained Generative Adversarial Network, GAN technique by using a GAN module, selecting proper images based on quality of the face images using a fuzzy logic module (111), saving the selected images into a fifth database, and training the deep learning module (113). [https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf](https://patentimages.storage.googleapis.com/b8/d9/7f/ea8a5b789e1dad/WO2021060971A1.pdf) 3. A method for detecting a moving vehicle (WO2021107761A1) The present invention relates to a method for detecting a moving vehicle. The method comprises the steps of grabbing an initial image from a video stream by a vehicle detection module (1100), wherein the vehicle detection module (1100) is a part of a system (1000) to identify moving vehicle, enhancing the illumination of the initial image by the vehicle detection module (1100), enhancing the edges within the initial image by the vehicle detection module (1100), and finding vehicle based on homogenous properties of the body of the vehicle by the vehicle detection module (1100). The step of finding vehicle based on homogenous property of the body of the vehicle by the vehicle detection module (1100) further comprising the sub-steps of closing open edges, inverting the binary image, segmenting an inverted binary image, filtering the noise based on geometric feature, and filtering the noise based on relation. [https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf](https://patentimages.storage.googleapis.com/16/41/83/2576e20c4a0af5/WO2021107761A1.pdf) ### Books 1. [book chapter titled “Camera Calibration and Video Stabilization Framework for Robot Localization” in the Book entitled “Control Engineering in Robotics and Industrial Automation" which will be published (24/07/2021) in Springer. ](https://www.waterstones.com/book/control-engineering-in-robotics-and-industrial-automation/muralindran-mariappan/mohd-rizal-arshad/9783030745394) 2. [book chapter titled “Augmented Optical Flow Methods for Video Stabilization", In Computational Intelligence: from theory to application. (2017) (p18). ](http://www.ukm.my/penerbit/penerbitan-2017/) ### Journal Papers * Using An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21) * * [http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf](http://www.jatit.org/volumes/Vol95No21/1Vol95No21.pdf) * GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6) * * [https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf](https://www.ijcsns.com/June.2016-Volume.4-No.6/Article01.pdf) * Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9). * * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM) * * [http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf](http://journalarticle.ukm.my/6644/1/4429-10302-1-SM.pdf) * Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2) * * [http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf](http://www.jatit.org/volumes/Vol57No2/4Vol57No2.pdf) * Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52 * * [http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf](http://www.jatit.org/volumes/Vol52No2/6Vol52No2.pdf) ### Conference Papers * Pattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017) * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8305440&isnumber=8305342) * Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015) * [http://www.academia.edu/download/45596617/farshid.pdf](http://www.academia.edu/download/45596617/farshid.pdf) * Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015) * [https://doi.org/10.1109/ASCC.2015.7360336](https://doi.org/10.1109/ASCC.2015.7360336) * 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012) * [http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf](http://www.ftsm.ukm.my/cait2012/images/Program%20Schedule%20CAIT2012.pdf) * Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics * [https://doi.org/10.1109/ICPAIR.2011.5976918](https://doi.org/10.1109/ICPAIR.2011.5976918) * License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011) * [https://doi.org/10.1109/ICEEI.2011.6021627](https://doi.org/10.1109/ICEEI.2011.6021627) * Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011) * [https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf](https://pdfs.semanticscholar.org/2d22/66bfb2f5ba5cb40b5946b82c620dc713d793.pdf) * [https://doi.org/10.1109/ICEEI.2011.6021649](https://doi.org/10.1109/ICEEI.2011.6021649) * Tafresh Grid: Grid computing in Tafresh university," 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi'an, 2011, pp. 83-85. * [http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6014009&isnumber=6013532) * Adaptive image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010) * [https://doi.org/10.1109/ICCAIE.2010.5735125](https://doi.org/10.1109/ICCAIE.2010.5735125) * [http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf](http://www.academia.edu/download/44161592/Adaptive_Image_Thresholding_Based_on_the20160328-31366-1wyb1jc.pdf) * Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050 * [http://waset.org/publications/3636](http://waset.org/publications/3636) * An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010) * [https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en](https://scholar.google.com/scholar?oi=bibs&cluster=3038184255311332521&btnI=1&hl=en) ### Articles on LinkedIn * [ADRL: Advanced Deep Reinforcement Learning {Patents and papers; Main point summary on recently published research on DRL} (draft version Feb 2019) ]([https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/](https://www.linkedin.com/pulse/patents-papers-adrl-advanced-deep-reinforcement-feb-pirahansiah-phd/)) * [The most important research papers for deep learning (updated December, 2017)]([https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/](https://www.linkedin.com/pulse/most-important-research-papers-deep-learning-updated-2017-farshid/) ) * [List of useful links (Videos, Slides, Articles) for Deep Learning ]([https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/](https://www.linkedin.com/pulse/list-useful-links-videos-slides-articles-deep-farshid-pirahansiah/)) * [Autonomous driving vehicles (Computer Vision, Deep Learning)]([https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/](https://www.linkedin.com/pulse/autonomous-driving-vehicles-computer-vision-deep-farshid-pirahansiah-1/) * [Thresholding]([https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/](https://www.linkedin.com/pulse/thresholding-farshid-pirahansiah/) ### Slides slideshare * Deep Learning for Computer Vision in Ubuntu 19; Part 1 installation [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation](https://www.slideshare.net/pirahansiah/deep-learning-for-computer-vision-in-ubuntu-19-part-1-installation) * Using Deep Learning for Computer Vision Applications [Link]([https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications](https://www.slideshare.net/pirahansiah/using-deep-learning-for-computer-vision-applications) ) * tensorflow1.3 digits6.0 Caffe [Link]([https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe](https://www.slideshare.net/pirahansiah/farshid-tensorflow13-digits60caffe) ) * Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) [Link]([https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017](https://www.slideshare.net/pirahansiah/install-tensorflow-12-on-macos-sierra-on-macbook-june-2017) ) * Best Deep Learning Post from LinkedIn Group [Link]([https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group](https://www.slideshare.net/pirahansiah/best-deep-learning-post-from-linkedin-group) ) * Install, Compile, Setup, Setting OpenCV 3.2, Visual C++ 2015, Win 64bit [Link]([https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit](https://www.slideshare.net/pirahansiah/install-compile-setup-setting-opencv-32-visual-c-2015-win-64bit) ) * Layers in Deep Learning&Caffe layers (model architecture ) [Link]([https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture](https://www.slideshare.net/pirahansiah/layers-in-deep-learningcaffe-layers-model-architecture) ) * How to install Digits 5.1 on Ubuntu 14 [Link]([https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14](https://www.slideshare.net/pirahansiah/how-to-install-digits-51-on-ubuntu-14) ) * Deep Learning for Video Analysis – part 1 (DeepStream SDK) [NVIDIA TensorRT || NVIDIA GPU Inference Engine (GIE)] [Link]([https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie](https://www.slideshare.net/pirahansiah/deep-learning-for-video-analysis-part-1-deepstream-sdk-nvidia-tensorrt-nvidia-gpu-inference-engine-gie) ) * Computer Vision, Deep Learning, OpenCV [Link]([https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv](https://www.slideshare.net/pirahansiah/computer-vision-deep-learning-opencv) ) ### Online Courses * [Writing with Impact ](https://www.linkedin.com/learning/writing-with-impact) * [Balancing Work and Life ](https://www.linkedin.com/learning/balancing-work-and-life) * [Finance Foundations: Income Taxes ](https://www.linkedin.com/learning/finance-foundations-income-taxes-2) * [Starting a Business with Family and Friends ](https://www.linkedin.com/learning/starting-a-business-with-family-and-friends) * [Finance Essentials for Small Business ](https://www.linkedin.com/learning/finance-essentials-for-small-business) * [Setting Up Your Small Business as a Legal Entity ](https://www.linkedin.com/learning/setting-up-your-small-business-as-a-legal-entity) * [Creating a Business Plan ](https://www.linkedin.com/learning/creating-a-business-plan-2) * [Understanding Business ](https://www.linkedin.com/learning/understanding-business) * [Entrepreneurship: Finding and Testing Your Business Idea ](https://www.linkedin.com/learning/entrepreneurship-finding-and-testing-your-business-idea) * [Guy Kawasaki on Entrepreneurship ](https://www.linkedin.com/learning/guy-kawasaki-on-entrepreneurship) * [Financial Modeling Foundations ](https://www.linkedin.com/learning/financial-modeling-foundations) * [Financial Accounting Foundations ](https://www.linkedin.com/learning/financial-accounting-foundations) * [Strategic Planning Foundations ](https://www.linkedin.com/learning/strategic-planning-foundations) * [5 Personal Finance Tips ](https://www.linkedin.com/learning/5-personal-finance-tips) * [Investment Evaluation ](https://www.linkedin.com/learning/investment-evaluation) * [Managing Your Personal Investments ](https://www.linkedin.com/learning/managing-your-personal-investments) * [Financial Wellness: Managing Personal Cash Flow ](https://www.linkedin.com/learning/financial-wellness-managing-personal-cash-flow) * [Financial Wellness for Couples and Families ](https://www.linkedin.com/learning/financial-wellness-for-couples-and-families) * [Managing Your Personal Finances ](https://www.linkedin.com/learning/managing-your-personal-finances) * [Finance Foundations ](https://www.linkedin.com/learning/finance-foundations-2) * [Entrepreneurship Foundations ](https://www.linkedin.com/learning/entrepreneurship-foundations-2) * [Introduction to Deep Learning with OpenCV ](https://www.linkedin.com/learning/introduction-to-deep-learning-with-opencv) * [OpenCV for Python Developers ](https://www.linkedin.com/learning/opencv-for-python-developers) * [Brad Feld on Validating Your Startup Idea ](https://www.linkedin.com/learning/brad-feld-on-validating-your-startup-idea) * [Brad Feld on Raising Capital ](https://www.linkedin.com/learning/brad-feld-on-raising-capital) * [Take a More Creative Approach to Problem-Solving ](https://www.linkedin.com/learning/take-a-more-creative-approach-to-problem-solving) * [Professional Networking ](https://www.linkedin.com/learning/professional-networking) * [Building Relationships While Working from Home ](https://www.linkedin.com/learning/building-relationships-while-working-from-home) * [Entrepreneurship: Raising Startup Capital ](https://www.linkedin.com/learning/entrepreneurship-raising-startup-capital) * [Business Analysis Foundations: Business Process Modeling ](https://www.linkedin.com/learning/business-analysis-foundations-business-process-modeling) * [Communicating with Confidence ](https://www.linkedin.com/learning/communicating-with-confidence) * [Machine Learning for iOS Developers ](https://www.linkedin.com/learning/machine-learning-for-ios-developers) ### Learning path * [Stay Ahead in Personal Finance ](https://www.linkedin.com/learning/paths/stay-ahead-in-personal-finance) * [Become a Small Business Owner ](https://www.linkedin.com/learning/paths/become-a-small-business-owner) # Completed ### Reference [https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing](https://drive.google.com/file/d/1xd5TZlEZnUUieXzw3Lxn3Sv7MwIajU_W/view?usp=sharing) ### Link of best Groups * [LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) * [Source code](https://gitlab.com/pirahansiah) * [Facebook : Deep Learning, Computer Vision, Robotics ](https://web.facebook.com/groups/185926728115336/) * [The link to one-note notebook ](https://1drv.ms/u/s!AhG15hrdT9JOgR5GZgPjve65F-e5) * [GitHub](https://github.com/pirahansiah) ### Books * [The Art of Startup Fundraising](https://www.amazon.de/-/en/Alejandro-Cremades-ebook/dp/B01DQ3GY3Y) * [Mathematics for Machine Learning](https://mml-book.github.io/) * Principles of Economics (6th edition) * The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It * Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) * Reinventing Your Life: The Breakthough Program to End Negative Behavior * Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence * [https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf](https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf) * * [Computer Vision: Algorithms and Applications](http://szeliski.org/Book/) * [Multiple View Geometry in Computer Vision Second Edition](https://www.robots.ox.ac.uk/~vgg/hzbook/) * [Complete to download the latest draft of Machine Learning Yearning](https://www.deeplearning.ai/machine-learning-yearning/) * Multiple view geometry in computer vision * Learning OpenCV Book by Adrian Kaehler and Gary Bradski * Digital Image Processing, Global Edition by Rafael C. Gonzalez * Introduction to Algorithms, 3rd Edition (The MIT Press) * The Mythical Man-Month: Essays on Software Engineering * [OpenCV-4-with-Python-Blueprints-Second-Edition](https://github.com/PacktPublishing/OpenCV-4-with-Python-Blueprints-Second-Edition) ### Videos * [https://www.youtube.com/computervisiondeeplearning](https://www.youtube.com/computervisiondeeplearning) * [Two Minute Papers](https://www.youtube.com/c/K%C3%A1rolyZsolnai/videos) * [Conference on Pattern Recognition](https://www.gcpr-vmv-vcbm-2020.uni-tuebingen.de/) * [https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY](https://researchseminars.org/?fbclid=IwAR0Ly9WLONBMBteYI7IhlRaMYR_rQR6YrpdxLG_sUxPxGYKQhB0SOTmcjgY) ### Papers * CALTag: High Precision Fiducial Markers for Camera * Diatom Autofocusing in Brightfield Microscopy: a Comparative Study * Analysis of focus measure operators in shape-from-focus * Optical flow modeling and computation: A survey * Toward general type 2 fuzzy logic systems based on zSlices ### Site * [Research Tools By: Nader Ale Ebrahim](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?t=HMmnzlA4zN) ([MindMap](https://www.mindmeister.com/39583892/research-tools-by-nader-ale-ebrahim?fullscreen=1#)) ![](https://www.google.com/images/icons/product/drive-32.png)Reference- ComputerVisionDeepLearning.pdf * [https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w](https://virgool.io/@r_alimorady/easy-install-cuda-document-hhk6nuylvl1w) * [https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679](https://techcommunity.microsoft.com/t5/azure-ai/introducing-multivariate-anomaly-detection/ba-p/2260679) * [https://www.freertos.org/index.html](https://www.freertos.org/index.html) * [https://en.wikipedia.org/wiki/Knowledge_distillation](https://en.wikipedia.org/wiki/Knowledge_distillation) * [https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/](https://80000hours.org/2015/10/startup-salaries-and-equity-compensation/) * [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413) * [https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917](https://levelup.gitconnected.com/s-o-l-i-d-principles-explained-in-python-with-examples-83b2b43bdcde?gi=a1fee170c917) * [https://arxiv.org/pdf/2104.13921.pdf](https://arxiv.org/pdf/2104.13921.pdf) * [https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/](https://blog.paperspace.com/object-detection-directed-mask-r-cnn-keras/) * [https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833](https://pub.towardsai.net/will-transformers-replace-cnns-in-computer-vision-55657a196833) * [https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238](https://medium.com/@daviddelaiglesiacastro/3d-point-cloud-generation-from-3d-triangular-mesh-bbb602ecf238) * [https://koinly.io/](https://koinly.io/) site map: octopus.do [https://www.startupgermany.nrw/startup- contest/](https://www.startupgermany.nrw/startup-contest/) [https://www.kickstarter.com/projects/opencv/opencv-ai-kit-oak-depth- camera-4k-cv-edge-object- detection/description](https://www.kickstarter.com/projects/opencv/opencv-ai- kit-oak-depth-camera-4k-cv-edge-object-detection/description) [HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! | My Strategy to Grow on YouTube in 2021! - YouTube ](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z](https://www.youtube.com/watch?v=bwd1dQr33K8&list=PLxC08P2Oexy2EJKezV02N9cp3-qOEh79Z) [https://www.youtube.com/watch?v=cFdApmqlllg](https://www.youtube.com/watch?v=cFdApmqlllg) [محمد رضا شجریان، آلبوم کامل در خیال - YouTube](https://www.youtube.com/watch?v=cFdApmqlllg) [دوره جامع گوگل آنالیتیکس(UA) | آنالیتیپس](https://analytips.io/product/google-analyticsua/) [رادیوگیک - شماره ۱۲۱ - ما این لارادیوگیک - شماره ۱۲۱ - ما این لا - YouTube](https://www.youtube.com/watch?v=dzBLhio-DIc) [Social Selling Index | Sales Navigator](https://www.linkedin.com/sales/ssi?src=or- search&veh=www.google.com) [https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827](https://rschu.me/list-a-directory-with-tree-command-on-mac- os-x-3b2d4c4a4827) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/VBNzuBGkaD3wt3TzoFwX0mTIj70nSDd3lgPVXQioi93Rci- RfkU7Cych8xXEVlNVPXCewW1vMbWtk8T6z_JVwm8=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/VBNzuBGkaD3wt3TzoFwX0mTIj70nSDd3lgPVXQioi93Rci- RfkU7Cych8xXEVlNVPXCewW1vMbWtk8T6z_JVwm8=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # startup Accelerators and Incubators - These are programs that provide funding, mentorship, and resources to help startups grow and scale. * Startup Labs * Launchpads * Innovation Centers * Venture Studios * Seed Funds * Innovation Hubs * Entrepreneurship Centers * Co-creation Spaces * Innovation Workshops * Startup Communities Germany: * Top * * Other * Rocket Internet * Wayra - A startup accelerator backed by Telefónica that provides funding, mentorship, and access to a global network of investors. * Axel Springer Plug and Play - A startup accelerator focused on media, advertising, and digital content. * High-Tech Gründerfonds - A seed fund that invests in technology startups in various sectors, including software, hardware, and engineering. * Berlin Startup Academy - A startup accelerator that offers a 3-month program of mentorship, workshops, and networking opportunities. * Startupbootcamp Berlin - A startup accelerator that offers a 3-month program focused on fintech, e-commerce, and smart transportation. * Factory Berlin - A co-working and innovation hub that provides resources and support for startups in various industries. * Founders Factory - A startup accelerator and incubator that offers mentorship, funding, and access to a global network of investors. * Next Big Thing AG - A startup incubator that focuses on the Internet of Things (IoT) and connected devices. * German Tech Entrepreneurship Center (GTEC) - A startup incubator and accelerator that offers programs and resources for entrepreneurs in various sectors, including fintech, healthtech, and mobility. * Factory Berlin: Factory Berlin is a co-working space that provides startups and entrepreneurs with access to a supportive community, events, and resources. * Berlin Startup Incubator: Berlin Startup Incubator offers startups in the technology sector access to mentorship, funding, and office space. * Betahaus: Betahaus is a co-working space that offers startups and entrepreneurs access to a community of like-minded individuals, events, and resources. * The Family: The Family is a startup accelerator that offers entrepreneurs mentorship, funding, and resources to help them build successful businesses. Venture Capital Firms - These firms provide funding and support to startups in exchange for equity in the company. Co-Working Spaces - These spaces provide a physical location for startups to work and collaborate with other entrepreneurs. Startup Consulting Firms - These firms provide guidance and advice to startups on a range of topics, such as business strategy, marketing, and operations. Business Plan Writers - These professionals can help startups create a comprehensive business plan that outlines their goals, strategies, and financial projections. ## Profit: Accelerators and incubators can earn money in a variety of ways, depending on their business model. Some common revenue streams for accelerators and incubators include: * Sponsorship - Many accelerators and incubators are sponsored by corporations, foundations, or government agencies, who provide funding in exchange for branding, marketing, or other benefits. * Equity Investment - Some accelerators and incubators do take equity in the startups they support, which allows them to earn a return on their investment if the startup is successful. * Program Fees - Some accelerators and incubators charge startups a fee to participate in their programs, which may include access to mentorship, resources, or networking opportunities. * Consulting or Advisory Services - Some accelerators and incubators may offer consulting or advisory services to startups for a fee. * Event or Conference Revenue - Some accelerators and incubators may host events or conferences, which can generate revenue through ticket sales, sponsorships, or exhibitor fees. ## grant / loan A grant or a loan can be an attractive option for startups that are looking for funding but do not want to give up equity in their company. A grant is a sum of money that is given to a startup with no obligation to pay it back. Grants are often offered by government agencies, non-profit organizations, or foundations that want to support innovation and entrepreneurship in a particular sector or industry. Grants can be highly competitive, and startups typically have to submit a detailed proposal outlining their business plan, goals, and how they plan to use the funds. A loan, on the other hand, is a sum of money that is borrowed from a lender with the obligation to pay it back over a specified period of time, usually with interest. Loans can be offered by banks, government agencies, or private investors. Startups typically have to submit a detailed business plan and financial projections to qualify for a loan. Loans can be a good option for startups that have a solid plan for revenue generation but need some initial capital to get started. Both grants and loans can provide startups with much-needed funding to help them get off the ground. However, it's important to carefully consider the terms and conditions of any funding agreement before accepting it. Startups should make sure that they understand the repayment terms, interest rates, and any other fees or requirements associated with the grant or loan. sole proprietorship (Einzelunternehmen) Investor Database 0\. fundraising content 1\. alternative datases accelerator/ incubator business angels competitions / conferences investors investor matching / public funding startups 2\. alternative funding options crowd investments europe family offices europepubluic funding germany 3\. programs accelerator / incubator DACH-Region Company builder DACH-Region innovation labs DACH-Region 4\. business angels business angels germany business angels europe carta 5\. VCs corporate venture capital europe venture capital europe US venture capital invested in europe 6\. Networks coaching & mentoring different industries / verticals female founder / diversity founderinvestor investor reviews 7\. specials company setup agencies dealflow agencies europe fundraising agencies DACH-Region venture capital law firms germany Accelerators and Incubators - These are programs that provide funding, mentorship, and resources to help startups grow and scale. Venture Capital Firms - These firms provide funding and support to startups in exchange for equity in the company. Co-Working Spaces - These spaces provide a physical location for startups to work and collaborate with other entrepreneurs. Startup Consulting Firms - These firms provide guidance and advice to startups on a range of topics, such as business strategy, marketing, and operations. Business Plan Writers - These professionals can help startups create a comprehensive business plan that outlines their goals, strategies, and financial projections. Accelerators and Incubators - These are programs that provide funding, mentorship, and resources to help startups grow and scale. Venture Capital Firms - These firms provide funding and support to startups in exchange for equity in the company. Co-Working Spaces - These spaces provide a physical location for startups to work and collaborate with other entrepreneurs. Startup Consulting Firms - These firms provide guidance and advice to startups on a range of topics, such as business strategy, marketing, and operations. Business Plan Writers - These professionals can help startups create a comprehensive business plan that outlines their goals, strategies, and financial projections. Accelerators and Incubators - These are programs that provide funding, mentorship, and resources to help startups grow and scale. Venture Capital Firms - These firms provide funding and support to startups in exchange for equity in the company. Co-Working Spaces - These spaces provide a physical location for startups to work and collaborate with other entrepreneurs. Startup Consulting Firms - These firms provide guidance and advice to startups on a range of topics, such as business strategy, marketing, and operations. Business Plan Writers - These professionals can help startups create a comprehensive business plan that outlines their goals, strategies, and financial projections. Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/VBNzuBGkaD3wt3TzoFwX0mTIj70nSDd3lgPVXQioi93Rci- RfkU7Cych8xXEVlNVPXCewW1vMbWtk8T6z_JVwm8=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/VBNzuBGkaD3wt3TzoFwX0mTIj70nSDd3lgPVXQioi93Rci- RfkU7Cych8xXEVlNVPXCewW1vMbWtk8T6z_JVwm8=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # startup Accelerators and Incubators - These are programs that provide funding, mentorship, and resources to help startups grow and scale. * Startup Labs * Launchpads * Innovation Centers * Venture Studios * Seed Funds * Innovation Hubs * Entrepreneurship Centers * Co-creation Spaces * Innovation Workshops * Startup Communities Germany: * Top * * Other * Rocket Internet * Wayra - A startup accelerator backed by Telefónica that provides funding, mentorship, and access to a global network of investors. * Axel Springer Plug and Play - A startup accelerator focused on media, advertising, and digital content. * High-Tech Gründerfonds - A seed fund that invests in technology startups in various sectors, including software, hardware, and engineering. * Berlin Startup Academy - A startup accelerator that offers a 3-month program of mentorship, workshops, and networking opportunities. * Startupbootcamp Berlin - A startup accelerator that offers a 3-month program focused on fintech, e-commerce, and smart transportation. * Factory Berlin - A co-working and innovation hub that provides resources and support for startups in various industries. * Founders Factory - A startup accelerator and incubator that offers mentorship, funding, and access to a global network of investors. * Next Big Thing AG - A startup incubator that focuses on the Internet of Things (IoT) and connected devices. * German Tech Entrepreneurship Center (GTEC) - A startup incubator and accelerator that offers programs and resources for entrepreneurs in various sectors, including fintech, healthtech, and mobility. * Factory Berlin: Factory Berlin is a co-working space that provides startups and entrepreneurs with access to a supportive community, events, and resources. * Berlin Startup Incubator: Berlin Startup Incubator offers startups in the technology sector access to mentorship, funding, and office space. * Betahaus: Betahaus is a co-working space that offers startups and entrepreneurs access to a community of like-minded individuals, events, and resources. * The Family: The Family is a startup accelerator that offers entrepreneurs mentorship, funding, and resources to help them build successful businesses. Venture Capital Firms - These firms provide funding and support to startups in exchange for equity in the company. Co-Working Spaces - These spaces provide a physical location for startups to work and collaborate with other entrepreneurs. Startup Consulting Firms - These firms provide guidance and advice to startups on a range of topics, such as business strategy, marketing, and operations. Business Plan Writers - These professionals can help startups create a comprehensive business plan that outlines their goals, strategies, and financial projections. ## Profit: Accelerators and incubators can earn money in a variety of ways, depending on their business model. Some common revenue streams for accelerators and incubators include: * Sponsorship - Many accelerators and incubators are sponsored by corporations, foundations, or government agencies, who provide funding in exchange for branding, marketing, or other benefits. * Equity Investment - Some accelerators and incubators do take equity in the startups they support, which allows them to earn a return on their investment if the startup is successful. * Program Fees - Some accelerators and incubators charge startups a fee to participate in their programs, which may include access to mentorship, resources, or networking opportunities. * Consulting or Advisory Services - Some accelerators and incubators may offer consulting or advisory services to startups for a fee. * Event or Conference Revenue - Some accelerators and incubators may host events or conferences, which can generate revenue through ticket sales, sponsorships, or exhibitor fees. ## grant / loan A grant or a loan can be an attractive option for startups that are looking for funding but do not want to give up equity in their company. A grant is a sum of money that is given to a startup with no obligation to pay it back. Grants are often offered by government agencies, non-profit organizations, or foundations that want to support innovation and entrepreneurship in a particular sector or industry. Grants can be highly competitive, and startups typically have to submit a detailed proposal outlining their business plan, goals, and how they plan to use the funds. A loan, on the other hand, is a sum of money that is borrowed from a lender with the obligation to pay it back over a specified period of time, usually with interest. Loans can be offered by banks, government agencies, or private investors. Startups typically have to submit a detailed business plan and financial projections to qualify for a loan. Loans can be a good option for startups that have a solid plan for revenue generation but need some initial capital to get started. Both grants and loans can provide startups with much-needed funding to help them get off the ground. However, it's important to carefully consider the terms and conditions of any funding agreement before accepting it. Startups should make sure that they understand the repayment terms, interest rates, and any other fees or requirements associated with the grant or loan. sole proprietorship (Einzelunternehmen) Investor Database 0\. fundraising content 1\. alternative datases accelerator/ incubator business angels competitions / conferences investors investor matching / public funding startups 2\. alternative funding options crowd investments europe family offices europepubluic funding germany 3\. programs accelerator / incubator DACH-Region Company builder DACH-Region innovation labs DACH-Region 4\. business angels business angels germany business angels europe carta 5\. VCs corporate venture capital europe venture capital europe US venture capital invested in europe 6\. Networks coaching & mentoring different industries / verticals female founder / diversity founderinvestor investor reviews 7\. specials company setup agencies dealflow agencies europe fundraising agencies DACH-Region venture capital law firms germany Accelerators and Incubators - These are programs that provide funding, mentorship, and resources to help startups grow and scale. Venture Capital Firms - These firms provide funding and support to startups in exchange for equity in the company. Co-Working Spaces - These spaces provide a physical location for startups to work and collaborate with other entrepreneurs. Startup Consulting Firms - These firms provide guidance and advice to startups on a range of topics, such as business strategy, marketing, and operations. Business Plan Writers - These professionals can help startups create a comprehensive business plan that outlines their goals, strategies, and financial projections. Accelerators and Incubators - These are programs that provide funding, mentorship, and resources to help startups grow and scale. Venture Capital Firms - These firms provide funding and support to startups in exchange for equity in the company. Co-Working Spaces - These spaces provide a physical location for startups to work and collaborate with other entrepreneurs. Startup Consulting Firms - These firms provide guidance and advice to startups on a range of topics, such as business strategy, marketing, and operations. Business Plan Writers - These professionals can help startups create a comprehensive business plan that outlines their goals, strategies, and financial projections. Accelerators and Incubators - These are programs that provide funding, mentorship, and resources to help startups grow and scale. Venture Capital Firms - These firms provide funding and support to startups in exchange for equity in the company. Co-Working Spaces - These spaces provide a physical location for startups to work and collaborate with other entrepreneurs. Startup Consulting Firms - These firms provide guidance and advice to startups on a range of topics, such as business strategy, marketing, and operations. Business Plan Writers - These professionals can help startups create a comprehensive business plan that outlines their goals, strategies, and financial projections. Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/VnELnzCZElXe9gLxGYU00_xF7qju2MljSVlgUMwWsc50I88T6vB5ahQjH2kGA --o3hIeJYu2N--BO_uidCis2Ow=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/VnELnzCZElXe9gLxGYU00_xF7qju2MljSVlgUMwWsc50I88T6vB5ahQjH2kGA --o3hIeJYu2N--BO_uidCis2Ow=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # startup Accelerators and Incubators - These are programs that provide funding, mentorship, and resources to help startups grow and scale. * Startup Labs * Launchpads * Innovation Centers * Venture Studios * Seed Funds * Innovation Hubs * Entrepreneurship Centers * Co-creation Spaces * Innovation Workshops * Startup Communities Germany: * Top * * Other * Rocket Internet * Wayra - A startup accelerator backed by Telefónica that provides funding, mentorship, and access to a global network of investors. * Axel Springer Plug and Play - A startup accelerator focused on media, advertising, and digital content. * High-Tech Gründerfonds - A seed fund that invests in technology startups in various sectors, including software, hardware, and engineering. * Berlin Startup Academy - A startup accelerator that offers a 3-month program of mentorship, workshops, and networking opportunities. * Startupbootcamp Berlin - A startup accelerator that offers a 3-month program focused on fintech, e-commerce, and smart transportation. * Factory Berlin - A co-working and innovation hub that provides resources and support for startups in various industries. * Founders Factory - A startup accelerator and incubator that offers mentorship, funding, and access to a global network of investors. * Next Big Thing AG - A startup incubator that focuses on the Internet of Things (IoT) and connected devices. * German Tech Entrepreneurship Center (GTEC) - A startup incubator and accelerator that offers programs and resources for entrepreneurs in various sectors, including fintech, healthtech, and mobility. * Factory Berlin: Factory Berlin is a co-working space that provides startups and entrepreneurs with access to a supportive community, events, and resources. * Berlin Startup Incubator: Berlin Startup Incubator offers startups in the technology sector access to mentorship, funding, and office space. * Betahaus: Betahaus is a co-working space that offers startups and entrepreneurs access to a community of like-minded individuals, events, and resources. * The Family: The Family is a startup accelerator that offers entrepreneurs mentorship, funding, and resources to help them build successful businesses. Venture Capital Firms - These firms provide funding and support to startups in exchange for equity in the company. Co-Working Spaces - These spaces provide a physical location for startups to work and collaborate with other entrepreneurs. Startup Consulting Firms - These firms provide guidance and advice to startups on a range of topics, such as business strategy, marketing, and operations. Business Plan Writers - These professionals can help startups create a comprehensive business plan that outlines their goals, strategies, and financial projections. ## Profit: Accelerators and incubators can earn money in a variety of ways, depending on their business model. Some common revenue streams for accelerators and incubators include: * Sponsorship - Many accelerators and incubators are sponsored by corporations, foundations, or government agencies, who provide funding in exchange for branding, marketing, or other benefits. * Equity Investment - Some accelerators and incubators do take equity in the startups they support, which allows them to earn a return on their investment if the startup is successful. * Program Fees - Some accelerators and incubators charge startups a fee to participate in their programs, which may include access to mentorship, resources, or networking opportunities. * Consulting or Advisory Services - Some accelerators and incubators may offer consulting or advisory services to startups for a fee. * Event or Conference Revenue - Some accelerators and incubators may host events or conferences, which can generate revenue through ticket sales, sponsorships, or exhibitor fees. ## grant / loan A grant or a loan can be an attractive option for startups that are looking for funding but do not want to give up equity in their company. A grant is a sum of money that is given to a startup with no obligation to pay it back. Grants are often offered by government agencies, non-profit organizations, or foundations that want to support innovation and entrepreneurship in a particular sector or industry. Grants can be highly competitive, and startups typically have to submit a detailed proposal outlining their business plan, goals, and how they plan to use the funds. A loan, on the other hand, is a sum of money that is borrowed from a lender with the obligation to pay it back over a specified period of time, usually with interest. Loans can be offered by banks, government agencies, or private investors. Startups typically have to submit a detailed business plan and financial projections to qualify for a loan. Loans can be a good option for startups that have a solid plan for revenue generation but need some initial capital to get started. Both grants and loans can provide startups with much-needed funding to help them get off the ground. However, it's important to carefully consider the terms and conditions of any funding agreement before accepting it. Startups should make sure that they understand the repayment terms, interest rates, and any other fees or requirements associated with the grant or loan. sole proprietorship (Einzelunternehmen) Investor Database 0\. fundraising content 1\. alternative datases accelerator/ incubator business angels competitions / conferences investors investor matching / public funding startups 2\. alternative funding options crowd investments europe family offices europepubluic funding germany 3\. programs accelerator / incubator DACH-Region Company builder DACH-Region innovation labs DACH-Region 4\. business angels business angels germany business angels europe carta 5\. VCs corporate venture capital europe venture capital europe US venture capital invested in europe 6\. Networks coaching & mentoring different industries / verticals female founder / diversity founderinvestor investor reviews 7\. specials company setup agencies dealflow agencies europe fundraising agencies DACH-Region venture capital law firms germany Accelerators and Incubators - These are programs that provide funding, mentorship, and resources to help startups grow and scale. Venture Capital Firms - These firms provide funding and support to startups in exchange for equity in the company. Co-Working Spaces - These spaces provide a physical location for startups to work and collaborate with other entrepreneurs. Startup Consulting Firms - These firms provide guidance and advice to startups on a range of topics, such as business strategy, marketing, and operations. Business Plan Writers - These professionals can help startups create a comprehensive business plan that outlines their goals, strategies, and financial projections. Accelerators and Incubators - These are programs that provide funding, mentorship, and resources to help startups grow and scale. Venture Capital Firms - These firms provide funding and support to startups in exchange for equity in the company. Co-Working Spaces - These spaces provide a physical location for startups to work and collaborate with other entrepreneurs. Startup Consulting Firms - These firms provide guidance and advice to startups on a range of topics, such as business strategy, marketing, and operations. Business Plan Writers - These professionals can help startups create a comprehensive business plan that outlines their goals, strategies, and financial projections. Accelerators and Incubators - These are programs that provide funding, mentorship, and resources to help startups grow and scale. Venture Capital Firms - These firms provide funding and support to startups in exchange for equity in the company. Co-Working Spaces - These spaces provide a physical location for startups to work and collaborate with other entrepreneurs. Startup Consulting Firms - These firms provide guidance and advice to startups on a range of topics, such as business strategy, marketing, and operations. Business Plan Writers - These professionals can help startups create a comprehensive business plan that outlines their goals, strategies, and financial projections. Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/VnELnzCZElXe9gLxGYU00_xF7qju2MljSVlgUMwWsc50I88T6vB5ahQjH2kGA --o3hIeJYu2N--BO_uidCis2Ow=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/VnELnzCZElXe9gLxGYU00_xF7qju2MljSVlgUMwWsc50I88T6vB5ahQjH2kGA --o3hIeJYu2N--BO_uidCis2Ow=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # فارسی بعد از خواندن تعداد زیادی کتاب و مقاله و ویدیوهای آموزشی که لیستشان در انتهای این متن آمده است مطالب زیر را از تجربیاتم نوشتم \- مهمترین قسمت نوشتن است. باید همیشه به هر صورتی که راحتید امکانات نوشتن هر چیزی برایتان فراهم باشد. کاغذ و خودکار و یا نوت برداری در گوشی موبایل و غیره \- نگران طبقه بندی و مرتب کردن و موضوعات مرتبط به هم نباشید . در اینجا ما روش پایین به بالا را داریم. به اینها یادداشتهای زودگذر می گویند و حداکثر بعد از 2 روز از بین می روند. حتما منابع مورد استفاده در ایجاد این یادداشتها را باید در انتها یا پشت کاغذ بنویسید. (می توانید اگر کتاب یا مقاله هست از برنامه های رایگانی که رفرنسها و منابع را مدیریت می کند استفاده کنید و اینجا لینک یا کلید اصل اون منبع را بیاورید) \- در انتهای هر روز و یا شروع روز بعد باید تمامی یادداشتهای زودگذر نوشته شده را بررسی کنید. در این قسمت ایده و نظر و فهم خودتان را از یادداشتها می نویسید. هر یادداشت فقط یک ایده یا نظر باید باشد با حداقل جملات (کمتر از یک پاراگراف). به این یادداشتهای اصلی می گویند و برای همیشه می مانند. \- طبقه بندی: مهمترین قسمت و اصلی ترین کاری که باید انجام بدهید در این قسمت است. ممکن است خواندن یک مقاله و نوت برداری از آن یک روز طول بکشد و مرتب کردن و ارتباط پیدا کردن یادداشتها هم یک روز طول بکشد و حتی بیشتر از زمانی که برای خواندن می گذارید ولی در نهایت این بخش از کار است که نتیجه اش را در آینده خواهید دید. \- در ابتدا باید بگردید در تمامی عناوین اصلی که دارید و مرتبط ترین را پیدا کنید. مثلا متن شما مربوط به مدیریت دانش شخصی است و یکی از عنوانهایی که در جعبه های یادداشتهایتان دارید مدیریت دانش است پس این متن می تواند به این عنوان مربوط باشد \- در مرحله بعدی نقشه محتوا مربوط به آن عنوان را بررسی کنید. ( در جلوتر توضیح می دم که چی هست) و ممکن است چند مرحله نیاز باشد این کار را انجام دهید \- در نقشه محتوای آخری که نزدیکترین به این یادداشت هست بگردید و جایگاه مناسب برای قراردادن این نوت را پیدا کنید. سپس آن مجموعه یاداداشتهای مرتبط را بخوانید و نزدیکترین موضوع مرتبط را پیدا کنید. \- حالا نوبت به شماره دهی به یادداشتان می رسد. به یادداشت قبلی و بعدی نگان کنید. اگر این یادداشت آخری هست شماره بعد را بنویسید و در جایگاهش بگذارید. مثلا یادداشت قبلی 1234 بوده است و این یادداشت قرار است در انتها قرار بگیرد پس شماره این یادداشت می شود 1235. \- اگر این یادداشت بین دوتا یادداشت قرار گرفت شماره گذاری با اضافه کردن حروف صورت می گیرد. مثلا بین 1234 و 1235 می شود 1234a1 \- حالا شماره این یادداشت و عنوانش ( یا کلمات کلیدی که دارد) را در انتهای نقشه محتوا وارد کنید \- به زودی خواهید دید که برای هر موضوعی که مطالات زیادی داشته اید کلی ایده و نظریات و محتوای آماده استفاده داید \- در مرحله بعدی به یادداشتهای دائمی می رسیم - بیشتر مربوط به یک موضوع خاص می باشند و قرار است که منتشر بشوند. چند نکته و توضیح \- در نرم افزارها به جای چند جعبه که نوتها داخلش باشد از فولدر استفاده می کنیم \- یک یادداشت می تواند در چندین موضع مرتبط باشد که می توانیم از هشتگ و لینک استفاده کنیم \- ممکن است یک یادداشت در چندین نقشه محتوایی نوشته شده باشد \- من نقشه های محتوایی را هم بخش بندی کرده ام و شامل بخشهای خلاصه, مقدمه , تاریخچه , شرح , مقایسه , نتایج , نتیجه گیری - می شود که یادداشتهایی که اضافه می کنم مربوط به همان بخش باشند و بعدا بتوانم به راحتی از آنها استفاده کنم \- باید کاری کنید که همه یادداشتها متناوبا بارها مرور و خوانده شوند و اگر یادداشتی بهش مراجعه نشود و مدت زیادی مرور نشود ممکن است بلا استفاده شود \- خود نوت برداری شامل چندین مدل است \- Outline Note-Taking Method \- Cornell Note-Taking Method \- Boxing Note-Taking Method \- Charting Note-Taking Method \- Mapping Note-Taking Method \- Sentence Note-Taking Method \- انواع طبقه بندی ها برای قسمت یادداشتهای دائمی \- CODE \- Capture: Saving valuable information from the internet and the world around you \- Organize: Breaking that information into small chunks and preparing them for later use \- Distill: Extracting the pieces of knowledge most relevant to your current goals \- Express: Turning your knowledge into creative output that has an impact on others \- PARA \- Projects: series of tasks linked to a goal, with a deadline. \- Areas: spheres of activity with a standard to be maintained over time. \- Resources: topics or themes of ongoing interest. \- Archives: inactive items from the other three categories. \- productive \- Home \- Goals \- Writings \- Productivity \- Creativity \- Ideas \- Zettelkasten \- Fleeting Notes \- Literature Notes \- Permanent Notes / Evergreen \- (MOC (map of content \- Slip-box مهمترین منبع آموزشی کتاب title: How to take smart notes: One simple technique to boost writing, learning and thinking authors: Sönke Ahrens year: 2022 [[@ahrens2022take]] و برنامه رایگانی که ازش استفاده می کنم OBSIDIAN بخش اول رفرنسها می باشند. که من از برنامه رایگان JabRef استفاده می کنم و تمامی رفرنسهایی که ازشون استفاده کرده ام را داخلش می گذارم. این برنامه به خوبی با برنامه های دیگر ترکیب می شود و می تواند به صورت بهینه و اتوماتیک کار رفرنس دهی به متهای شما را انجام بدهد و فرمتهای مخلف را پشتیبانی می کند بعدا برای نوشتن مقاله یا کتاب نیازی به وارد کردن دستی رفرنسها و یا مرتب کردن و عوض کردن فرمت ندارید I use citation plugin 1\. add path to the JabRef database "reading notes/dh.bib" 2\. create folder "Reading notes" 3\. use Ctrl+Shift+O to select reference 4\. automatically create file based on that reference 5\. Ctrl+Shift+E to insert link to citation page منابع 1\. تیزایران دات کام www.pirahansiah.com 2\. پیراهن سیاه دات کام www.pirahansiah.com 3\. https://www.notion.so/templates/second-brain 4\. https://filipedonadio.com/6-useful-templates-for-obsidian/ 5\. https://www.youtube.com/watch?v=4aYVLpY5FYU 6\. http://luhmann.surge.sh/communicating-with-slip-boxes 7\. https://www.youtube.com/watch?v=4bxpsvcW2mc * 8\. https://www.youtube.com/channel/UC85D7ERwhke7wVqskV_DZUA 9\. https://www.youtube.com/watch?v=ftzQOkzGCLg 10\. https://writingcooperative.com/zettelkasten-how-one-german-scholar-was- so-freakishly-productive-997e4e0ca125 11\. https://writingscientist.com/slip-box/ 12\. [Zettlr is creating a Markdown editor for the 21st century | Patreon](https://www.patreon.com/zettlr) 13\. [Choosing between Zettlr and Obsidian | Aquiles Carattino](https://notes.aquiles.me/essays/choosing_between_zettlr_and_obsidian/#:~:text=Obsidian%20is%20more%20minimalistic%20than%20Zettlr.%20They%20both,see%20how%20notes%20are%20linked%20to%20each%20other.) 14\. [Zettlr vs Obsidian md detailed comparison as of 2022 - Slant](https://www.slant.co/versus/31650/37045/~zettlr_vs_obsidian-md) 15\. https://hamed.blog/ 16\. https://niklas-luhmann-archiv.de/nachlass/zettelkasten 17\. https://niklas-luhmann-archiv.de/bestand/zettelkasten/tutorial 18\. https://www.youtube.com/watch?v=D9ivU_IKO6M&ab_channel=ArtemKirsanov 19\. https://www.youtube.com/watch?v=TDhTpPIjsDg&ab_channel=JohnMavrick 20\. https://www.youtube.com/watch?v=-bdE_54UUA4&ab_channel=JohnMavrick 21\. https://forum.obsidian.md/t/example-workflows-in-obsidian/1093 22\. https://www.youtube.com/watch?v=0zrq2-06FTY&ab_channel=Aleks 23\. https://www.sloww.co/thinking-in-systems-book/ 24\. [https://www.sloww.co/zettelkasten/](https://www.google.com/url?q=https%3A%2F%2Fwww.sloww.co%2Fzettelkasten%2F&sa=D&sntz=1&usg=AOvVaw3NrwKNA-6R29660iQWnZcU) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/VnELnzCZElXe9gLxGYU00_xF7qju2MljSVlgUMwWsc50I88T6vB5ahQjH2kGA --o3hIeJYu2N--BO_uidCis2Ow=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/VnELnzCZElXe9gLxGYU00_xF7qju2MljSVlgUMwWsc50I88T6vB5ahQjH2kGA --o3hIeJYu2N--BO_uidCis2Ow=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # How to make perfect resume (CV) # Best template All secret, tips and tricks for the interview [https://vimeo.com/user118789239](https://www.google.com/url?q=https%3A%2F%2Fvimeo.com%2Fuser118789239&sa=D&sntz=1&usg=AOvVaw12_ck3O4dQG2ZMCP2VWOi3) My experience in Germany [https://europa.eu/europass/eportfolio/api/eprofile/shared- profile/cbe41589-3c74-4743-9b08-8cab86ef932b?view=html](https://www.google.com/url?q=https%3A%2F%2Feuropa.eu%2Feuropass%2Feportfolio%2Fapi%2Feprofile%2Fshared- profile%2Fcbe41589-3c74-4743-9b08-8cab86ef932b%3Fview%3Dhtml&sa=D&sntz=1&usg=AOvVaw2KTv6q596j2CEFJbGDdUuz) #Metaverse #pirahansiah #CV #resume * Very good YouTube channel about basic question in interview : * Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/VBNzuBGkaD3wt3TzoFwX0mTIj70nSDd3lgPVXQioi93Rci- RfkU7Cych8xXEVlNVPXCewW1vMbWtk8T6z_JVwm8=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/VBNzuBGkaD3wt3TzoFwX0mTIj70nSDd3lgPVXQioi93Rci- RfkU7Cych8xXEVlNVPXCewW1vMbWtk8T6z_JVwm8=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # AltCoin Disclaimer/ Risk warning: I am not a financial advisor and anything you read, see or hear in this site, podcast, video should not by any means be construed as financial advice it is purely intended for your entertainment and demonstration and illustrative purposes only. This is not financial advice and should not be taken as financial advice. the views I have in everyone of my site/post/blog/links/text/documents/powerpoint/videos are completely speculative opinions and do not guarantee any specific result. The NFT, AltCoin, Metaverse, ... is extremely volatile and has high risk. You should never act on anyone's advice or opinions, without first doing your own research, realising your own risk, and making your own decision. I recommend speaking with a licensed and qualified professional before making any financial decision. Basic Links Hands on setup:macOS 1: install nvm: Node Version Manager 2\. install hardhat: Ethereum development environment for professionals 3. METAVERSE crypto 18.12.21 - 22.April.2022 NFT Stock * ^ Purchasing managers' indexes (PMI): * A PMI index over 50 represents growth or expansion within the manufacturing sector of the economy compared with the prior month. * ^ United States Philadelphia Fed Manufacturing Index * A value greater than 0 reflects growth in the manufacturing sector, whereas a value less than 0 reflects a contraction. * ![](https://lh4.googleusercontent.com/eNrU_i5TMskU_ZA3cXB0rIVuJlCnE4YgTGpfGaSlrejV9fJMw6GLEOimP- GqHJcZAQTDhr-h1DHW3wQhxXT98zyRp1NlPhLB9s8bNWrxrBjgJlv9QFwpgAdleVPQhW37Fw=w1280) ![](https://lh4.googleusercontent.com/qARdwh01SGT2SdP9lG- PEJeMiqeNun3sP2v2QYwmDwYAunVYxdLJaesG5N5-KnTh1evxyq2hl1MXKofdGDW7PTK9nJ1GYa3nVqLckI3t6WJh2JNRCiQkw_rZC28bphtXvw=w1280) My token _**pirahansiah (TIZ**_ _ **)**_ on Solana Token name pirahansiah (TIZ) Token address FsniTTtb9GeGq1DHkipxera4bsgFkb19maBLKZwMe7in **Token** **pirahansiah** https://solscan.io/token/FsniTTtb9GeGq1DHkipxera4bsgFkb19maBLKZwMe7in My token _**pirahansiah (TIZ)**_ **Token Contract Address** 0xe30407DB873302D6AEaAB3bA619f44Bc9F924594 **Token Decimal:** 18 **Network:** BNB Smart Chain Mainnet only 100 token available to sell [https://bscscan.com/token/0xe30407db873302d6aeaab3ba619f44bc9f924594](https://www.google.com/url?q=https%3A%2F%2Fbscscan.com%2Ftoken%2F0xe30407db873302d6aeaab3ba619f44bc9f924594&sa=D&sntz=1&usg=AOvVaw3owOOObdS- gyWJtD_682hE) [https://admin.moralis.io/speedyNodes](https://www.google.com/url?q=https%3A%2F%2Fadmin.moralis.io%2FspeedyNodes&sa=D&sntz=1&usg=AOvVaw1e_X2Eutx- uPjibkv51EdW) [https://testnet.binance.org/faucet- smart](https://www.google.com/url?q=https%3A%2F%2Ftestnet.binance.org%2Ffaucet- smart&sa=D&sntz=1&usg=AOvVaw0YuT9b0CIeLmtQULao873v) [https://github.com/OpenZeppelin/openzeppelin- contracts/blob/master/contracts/token/ERC20/ERC20.sol](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FOpenZeppelin%2Fopenzeppelin- contracts%2Fblob%2Fmaster%2Fcontracts%2Ftoken%2FERC20%2FERC20.sol&sa=D&sntz=1&usg=AOvVaw3JW77-QoFjcv- DNK3whJT2) [https://remix.ethereum.org/#optimize=false&runs=200&evmVersion=null&version=soljson-v0.8.7+commit.e28d00a7.js](https://www.google.com/url?q=https%3A%2F%2Fremix.ethereum.org%2F%23optimize%3Dfalse%26runs%3D200%26evmVersion%3Dnull%26version%3Dsoljson-v0.8.7%2Bcommit.e28d00a7.js&sa=D&sntz=1&usg=AOvVaw39lC9qXtU3OwPP5mxpf9qK) [https://bscscan.com/token/0xe30407db873302d6aeaab3ba619f44bc9f924594](https://www.google.com/url?q=https%3A%2F%2Fbscscan.com%2Ftoken%2F0xe30407db873302d6aeaab3ba619f44bc9f924594&sa=D&sntz=1&usg=AOvVaw3owOOObdS- gyWJtD_682hE) [https://github.com/OpenZeppelin/openzeppelin- contracts/blob/master/contracts/token/ERC20/ERC20.sol](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FOpenZeppelin%2Fopenzeppelin- contracts%2Fblob%2Fmaster%2Fcontracts%2Ftoken%2FERC20%2FERC20.sol&sa=D&sntz=1&usg=AOvVaw3JW77-QoFjcv- DNK3whJT2) * how to modify the code * [https://www.tradingview.com](https://www.google.com/url?q=https%3A%2F%2Fwww.tradingview.com&sa=D&sntz=1&usg=AOvVaw15P-ffkNM5fCBySsWtLXHo) best tools to analysis market * [https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr](https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr) Persian Interface: minimum data and functions required to make it a standard ERC/EIP smart contract _value the _ shows it is a parameters Constant is a variable that can't be changed Mapping() function maps elements from a key to a value Constructor() function that automatically runs when a new data item is created( initialization code) Emit() function triggers an event (message to be sent out) 1. New project 1. ./geth --syncmode "light" 2. Mkdir 3. Truffle init Truffle deploy --reset Truffle console HelloWorld.deployed().then(function(instance) {return instance} ); HelloWorld.deployed().then(function(instance) {return instance.getHelloMessage()} ); npm install @truffle/hdwallet-provider 1. LinkedIn 1. Start 10.April.2022 2. [https://geth.ethereum.org/](https://www.google.com/url?q=https%3A%2F%2Fgeth.ethereum.org%2F&sa=D&sntz=1&usg=AOvVaw1XnmIZJr5e4_oBt8pPHXQK) 3. [https://trufflesuite.com/ganache/](https://www.google.com/url?q=https%3A%2F%2Ftrufflesuite.com%2Fganache%2F&sa=D&sntz=1&usg=AOvVaw3wxvy_EfDUepyc6z9ctYdc) 4. [https://trufflesuite.com/truffle/](https://www.google.com/url?q=https%3A%2F%2Ftrufflesuite.com%2Ftruffle%2F&sa=D&sntz=1&usg=AOvVaw1dbGm84hwOf1zhcx6viT9A) 1. npm install truffle -g 5. ERC-20 (500K) 6. ERC721: Non-Fungible Tokens (NFT): 70K 7. ERC1155: Multi-Token Tokens : 8K 8. dApp 9. Security:[ ](https://www.google.com/url?q=https%3A%2F%2Fconsensys.github.io%2Fsmart-contract-best-practices%2F&sa=D&sntz=1&usg=AOvVaw0ABPhCIEaExBvmeOe-D_Hm)[https://consensys.github.io/smart-contract-best-practices/](https://www.google.com/url?q=https%3A%2F%2Fconsensys.github.io%2Fsmart-contract-best-practices%2F&sa=D&sntz=1&usg=AOvVaw0ABPhCIEaExBvmeOe-D_Hm) 2. LinkedIn II 1. Hyperledger.org 2. solidity: [https://docs.soliditylang.org/en/v0.8.13/installing-solidity.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.soliditylang.org%2Fen%2Fv0.8.13%2Finstalling-solidity.html&sa=D&sntz=1&usg=AOvVaw1GDJZfpxlMTD-VyuM3EM1n) 1. docker run ethereum/solc:stable --help 2. brew update 3. brew upgrade 4. brew tap ethereum/ethereum 5. brew install solidity 6. 4\. Visual studio code: Name: solidity Id: JuanBlanco.solidity Description: Ethereum Solidity Language for Visual Studio Code Version: 0.0.139 Publisher: Juan Blanco VS Marketplace Link:[ ](https://www.google.com/url?q=https%3A%2F%2Fmarketplace.visualstudio.com%2Fitems%3FitemName%3DJuanBlanco.solidity&sa=D&sntz=1&usg=AOvVaw3KMZrAVx3FuyLTrUEtjBlI)[https://marketplace.visualstudio.com/items?itemName=JuanBlanco.solidity](https://www.google.com/url?q=https%3A%2F%2Fmarketplace.visualstudio.com%2Fitems%3FitemName%3DJuanBlanco.solidity&sa=D&sntz=1&usg=AOvVaw3KMZrAVx3FuyLTrUEtjBlI) 5\. Online editor: [https://remix.ethereum.org](https://www.google.com/url?q=https%3A%2F%2Fremix.ethereum.org&sa=D&sntz=1&usg=AOvVaw12Y9fbyx49-9qH8CSJfuQ3) اجماع majority rules validate transactions properly computer nodes 51% consensus mechanisms advantage proof of work: * anybody can attached machines and gain rewards blockchain trilemma: 1- scalable/speed 2- decentralization secure fastest: solana (arweave), [](https://drive.google.com/open?id=1iJqtIoSQ1gmMr7tleuFQ6lX_FmiFj7p4FELnCVGT1C4 "Open Spreadsheet, AltCoin in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/sheets_32dp.png)AltCoin # Basic [proof of work/stake](https://www.youtube.com/watch?v=08vnE2_cAeQ) # Links [https://koinly.io/](https://www.google.com/url?q=https%3A%2F%2Fkoinly.io%2F&sa=D&sntz=1&usg=AOvVaw1MlwUKbWcWOp2wDgfk5wTa) [https://chain.link/bootcamp/bootcamp-2021-on- demand](https://www.google.com/url?q=https%3A%2F%2Fchain.link%2Fbootcamp%2Fbootcamp-2021-on- demand&sa=D&sntz=1&usg=AOvVaw3_THOQshewlXoxNX0CKMKf) [https://software.intel.com/content/www/us/en/develop/download/download- maccpuid.html](https://www.google.com/url?q=https%3A%2F%2Fsoftware.intel.com%2Fcontent%2Fwww%2Fus%2Fen%2Fdevelop%2Fdownload%2Fdownload- maccpuid.html&sa=D&sntz=1&usg=AOvVaw3fAaHZJ9DSuYORK5STffBn) [آموزش زبان سالیدیتی(solidity) برای نوشتن اسمارت کانترکت روی شبکه اتریوم، شماره ۱](https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr) [https://cryptozombies.io/](https://www.google.com/url?q=https%3A%2F%2Fcryptozombies.io%2F&sa=D&sntz=1&usg=AOvVaw2gbwUmHX9yWMhTVmpbHEPy) # Hands on ## setup:macOS ### 1: install nvm: Node Version Manager curl -o- https://raw.githubusercontent.com/creationix/nvm/v0.35.2/install.sh | bash nvm install 12 nvm use 12 nvm alias default 12 npm install npm --global # Upgrade npm to the latest version ### 2\. install hardhat: Ethereum development environment for professionals npm install --save-dev hardhat ### 3\. # METAVERSE 1. Coin Bureau: : TOP 5 Virtual Land NFTs!! BEST Metaverse Plays?? 🚀 #metaverse #land #crypto * sandbox * metaverse group buy decentraland: 2.43 M$ * [https://nonfungible.com/](https://www.google.com/url?q=https%3A%2F%2Fnonfungible.com%2F&sa=D&sntz=1&usg=AOvVaw1d1IAffRPgXgbELGML_Zak) : cryptopunks, the sandbox, decentraland, cryptovoxels, somnium space, superworld, arcona. OVR Top 5 Land 1. * * 1. axie infinity * savannah, forest, arctic, mystic, genesis, lunas landing 2. decentraland * * 9K land * ~4500 MANA * 3$ = 13500$ * Decentraland Tutorials: * my land pirahansiah: [https://share.decentraland.org/b/scene/ed5823d2-788c-440f-8875-2614fc139c42](https://www.google.com/url?q=https%3A%2F%2Fshare.decentraland.org%2Fb%2Fscene%2Fed5823d2-788c-440f-8875-2614fc139c42&sa=D&sntz=1&usg=AOvVaw2SHZ1c0evkthfjNgJ8Mcw2) 3. the sandbox * ~2.5 Eth * 3850 = 9625 1. * * 4. bitcountry * create and personalise metaverse * 5. aavegotchi * 2. [https://www.sandbox.game/en/](https://www.google.com/url?q=https%3A%2F%2Fwww.sandbox.game%2Fen%2F&sa=D&sntz=1&usg=AOvVaw0MmdZePFR3AUuLjPjVKlIk) * it is virtual world in ethereum 2011 - 2018 - * open metaverse - the sandbox alpha - 29.11 to 20.12.21 * require the sandbox (SAND) ~ 5$ (17.12.21) ~2.73$ (22.April.2022) ~0.58 (14.11.22) * 3. learn: 4. learn: 5. AI meta: [https://ai.facebook.com/events/neurips2021](https://www.google.com/url?q=https%3A%2F%2Fai.facebook.com%2Fevents%2Fneurips2021&sa=D&sntz=1&usg=AOvVaw3OvYQKZfEVFHqbYen7VYwh) 6. Metaverse, Mesh, Open AI and more from Microsoft Ignite Fall 2021: [https://valoremreply.com/post/metaverse-mesh-openai-microsoft-ignite-fall-2021/?utm_source=social_media&utm_medium=Rene-Reshare&utm_campaign=trends2021](https://www.google.com/url?q=https%3A%2F%2Fvaloremreply.com%2Fpost%2Fmetaverse-mesh-openai-microsoft-ignite-fall-2021%2F%3Futm_source%3Dsocial_media%26utm_medium%3DRene-Reshare%26utm_campaign%3Dtrends2021&sa=D&sntz=1&usg=AOvVaw2aU3w2NugSf1HZh-Neisf5) ## crypto 18.12.21 - 22.April.2022 * Decentraland (MANA): ~3$ - ~2.03$ (22.April.2022) * sandbox (SAND): ~ 5$ - ~2.73$ (22.April.2022) * Axie Infinity (AXS): ~95$ - ~45.65$ (22.April.2022) * illuvium (ILV) : ~1136$ - ~514.98$ (22.April.2022) * star atlas price (ATLAS) base on solana : ~0.10 - ~0.023$ (22.April.2022) * wilder world (wild): ~3.67$ - ~1.15$ (22.April.2022) 1. bitCoin : revolution to currency, 2. DeFi, ethereum 3. NFT 4. Metaverse # NFT * [https://opensea.io/collection/computervision](https://www.google.com/url?q=https%3A%2F%2Fopensea.io%2Fcollection%2Fcomputervision&sa=D&sntz=1&usg=AOvVaw2kn1tyV7uv_uTI9i50hUVQ) * Links: [https://github.com/MetaMask/metamask- mobile](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FMetaMask%2Fmetamask- mobile&sa=D&sntz=1&usg=AOvVaw1YH7hABPeEsRI7ne1ErWR4) [https://readyplayer.me](https://www.google.com/url?q=https%3A%2F%2Freadyplayer.me&sa=D&sntz=1&usg=AOvVaw1q7g9_FKZoK_- FuOwYHQEN) [https://github.com/ish- app/ish](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fish- app%2Fish&sa=D&sntz=1&usg=AOvVaw12yqknRonHBDjzyVJZ543t) [https://getutm.app/install/](https://www.google.com/url?q=https%3A%2F%2Fgetutm.app%2Finstall%2F&sa=D&sntz=1&usg=AOvVaw2i4ClkKwh4GkEdZg7y5WOX) [https://github.com/rileytestut/AltStore](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Frileytestut%2FAltStore&sa=D&sntz=1&usg=AOvVaw1PXzTxhU5BDAOhM0lo3aMS) [https://altstore.io](https://www.google.com/url?q=https%3A%2F%2Faltstore.io&sa=D&sntz=1&usg=AOvVaw3cXCR8rKRUvIWq8tNFytLE) [https://vscode.dev/github/pirahansiah/pirahansiah](https://www.google.com/url?q=https%3A%2F%2Fvscode.dev%2Fgithub%2Fpirahansiah%2Fpirahansiah&sa=D&sntz=1&usg=AOvVaw3ar7dCuBaV4_X4lplrNKXP) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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This is not financial advice and should not be taken as financial advice. the views I have in everyone of my site/post/blog/links/text/documents/powerpoint/videos are completely speculative opinions and do not guarantee any specific result. The NFT, AltCoin, Metaverse, ... is extremely volatile and has high risk. You should never act on anyone's advice or opinions, without first doing your own research, realising your own risk, and making your own decision. I recommend speaking with a licensed and qualified professional before making any financial decision. Basic Links Hands on setup:macOS 1: install nvm: Node Version Manager 2\. install hardhat: Ethereum development environment for professionals 3. METAVERSE crypto 18.12.21 - 22.April.2022 NFT Stock * ^ Purchasing managers' indexes (PMI): * A PMI index over 50 represents growth or expansion within the manufacturing sector of the economy compared with the prior month. * ^ United States Philadelphia Fed Manufacturing Index * A value greater than 0 reflects growth in the manufacturing sector, whereas a value less than 0 reflects a contraction. * ![](https://lh4.googleusercontent.com/eNrU_i5TMskU_ZA3cXB0rIVuJlCnE4YgTGpfGaSlrejV9fJMw6GLEOimP- GqHJcZAQTDhr-h1DHW3wQhxXT98zyRp1NlPhLB9s8bNWrxrBjgJlv9QFwpgAdleVPQhW37Fw=w1280) ![](https://lh4.googleusercontent.com/qARdwh01SGT2SdP9lG- PEJeMiqeNun3sP2v2QYwmDwYAunVYxdLJaesG5N5-KnTh1evxyq2hl1MXKofdGDW7PTK9nJ1GYa3nVqLckI3t6WJh2JNRCiQkw_rZC28bphtXvw=w1280) My token _**pirahansiah (TIZ**_ _ **)**_ on Solana Token name pirahansiah (TIZ) Token address FsniTTtb9GeGq1DHkipxera4bsgFkb19maBLKZwMe7in **Token** **pirahansiah** https://solscan.io/token/FsniTTtb9GeGq1DHkipxera4bsgFkb19maBLKZwMe7in My token _**pirahansiah (TIZ)**_ **Token Contract Address** 0xe30407DB873302D6AEaAB3bA619f44Bc9F924594 **Token Decimal:** 18 **Network:** BNB Smart Chain Mainnet only 100 token available to sell [https://bscscan.com/token/0xe30407db873302d6aeaab3ba619f44bc9f924594](https://www.google.com/url?q=https%3A%2F%2Fbscscan.com%2Ftoken%2F0xe30407db873302d6aeaab3ba619f44bc9f924594&sa=D&sntz=1&usg=AOvVaw3owOOObdS- gyWJtD_682hE) [https://admin.moralis.io/speedyNodes](https://www.google.com/url?q=https%3A%2F%2Fadmin.moralis.io%2FspeedyNodes&sa=D&sntz=1&usg=AOvVaw1e_X2Eutx- uPjibkv51EdW) [https://testnet.binance.org/faucet- smart](https://www.google.com/url?q=https%3A%2F%2Ftestnet.binance.org%2Ffaucet- smart&sa=D&sntz=1&usg=AOvVaw0YuT9b0CIeLmtQULao873v) [https://github.com/OpenZeppelin/openzeppelin- contracts/blob/master/contracts/token/ERC20/ERC20.sol](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FOpenZeppelin%2Fopenzeppelin- contracts%2Fblob%2Fmaster%2Fcontracts%2Ftoken%2FERC20%2FERC20.sol&sa=D&sntz=1&usg=AOvVaw3JW77-QoFjcv- DNK3whJT2) [https://remix.ethereum.org/#optimize=false&runs=200&evmVersion=null&version=soljson-v0.8.7+commit.e28d00a7.js](https://www.google.com/url?q=https%3A%2F%2Fremix.ethereum.org%2F%23optimize%3Dfalse%26runs%3D200%26evmVersion%3Dnull%26version%3Dsoljson-v0.8.7%2Bcommit.e28d00a7.js&sa=D&sntz=1&usg=AOvVaw39lC9qXtU3OwPP5mxpf9qK) [https://bscscan.com/token/0xe30407db873302d6aeaab3ba619f44bc9f924594](https://www.google.com/url?q=https%3A%2F%2Fbscscan.com%2Ftoken%2F0xe30407db873302d6aeaab3ba619f44bc9f924594&sa=D&sntz=1&usg=AOvVaw3owOOObdS- gyWJtD_682hE) [https://github.com/OpenZeppelin/openzeppelin- contracts/blob/master/contracts/token/ERC20/ERC20.sol](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FOpenZeppelin%2Fopenzeppelin- contracts%2Fblob%2Fmaster%2Fcontracts%2Ftoken%2FERC20%2FERC20.sol&sa=D&sntz=1&usg=AOvVaw3JW77-QoFjcv- DNK3whJT2) * how to modify the code * [https://www.tradingview.com](https://www.google.com/url?q=https%3A%2F%2Fwww.tradingview.com&sa=D&sntz=1&usg=AOvVaw15P-ffkNM5fCBySsWtLXHo) best tools to analysis market * [https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr](https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr) Persian Interface: minimum data and functions required to make it a standard ERC/EIP smart contract _value the _ shows it is a parameters Constant is a variable that can't be changed Mapping() function maps elements from a key to a value Constructor() function that automatically runs when a new data item is created( initialization code) Emit() function triggers an event (message to be sent out) 1. New project 1. ./geth --syncmode "light" 2. Mkdir 3. Truffle init Truffle deploy --reset Truffle console HelloWorld.deployed().then(function(instance) {return instance} ); HelloWorld.deployed().then(function(instance) {return instance.getHelloMessage()} ); npm install @truffle/hdwallet-provider 1. LinkedIn 1. Start 10.April.2022 2. [https://geth.ethereum.org/](https://www.google.com/url?q=https%3A%2F%2Fgeth.ethereum.org%2F&sa=D&sntz=1&usg=AOvVaw1XnmIZJr5e4_oBt8pPHXQK) 3. [https://trufflesuite.com/ganache/](https://www.google.com/url?q=https%3A%2F%2Ftrufflesuite.com%2Fganache%2F&sa=D&sntz=1&usg=AOvVaw3wxvy_EfDUepyc6z9ctYdc) 4. [https://trufflesuite.com/truffle/](https://www.google.com/url?q=https%3A%2F%2Ftrufflesuite.com%2Ftruffle%2F&sa=D&sntz=1&usg=AOvVaw1dbGm84hwOf1zhcx6viT9A) 1. npm install truffle -g 5. ERC-20 (500K) 6. ERC721: Non-Fungible Tokens (NFT): 70K 7. ERC1155: Multi-Token Tokens : 8K 8. dApp 9. Security:[ ](https://www.google.com/url?q=https%3A%2F%2Fconsensys.github.io%2Fsmart-contract-best-practices%2F&sa=D&sntz=1&usg=AOvVaw0ABPhCIEaExBvmeOe-D_Hm)[https://consensys.github.io/smart-contract-best-practices/](https://www.google.com/url?q=https%3A%2F%2Fconsensys.github.io%2Fsmart-contract-best-practices%2F&sa=D&sntz=1&usg=AOvVaw0ABPhCIEaExBvmeOe-D_Hm) 2. LinkedIn II 1. Hyperledger.org 2. solidity: [https://docs.soliditylang.org/en/v0.8.13/installing-solidity.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.soliditylang.org%2Fen%2Fv0.8.13%2Finstalling-solidity.html&sa=D&sntz=1&usg=AOvVaw1GDJZfpxlMTD-VyuM3EM1n) 1. docker run ethereum/solc:stable --help 2. brew update 3. brew upgrade 4. brew tap ethereum/ethereum 5. brew install solidity 6. 4\. Visual studio code: Name: solidity Id: JuanBlanco.solidity Description: Ethereum Solidity Language for Visual Studio Code Version: 0.0.139 Publisher: Juan Blanco VS Marketplace Link:[ ](https://www.google.com/url?q=https%3A%2F%2Fmarketplace.visualstudio.com%2Fitems%3FitemName%3DJuanBlanco.solidity&sa=D&sntz=1&usg=AOvVaw3KMZrAVx3FuyLTrUEtjBlI)[https://marketplace.visualstudio.com/items?itemName=JuanBlanco.solidity](https://www.google.com/url?q=https%3A%2F%2Fmarketplace.visualstudio.com%2Fitems%3FitemName%3DJuanBlanco.solidity&sa=D&sntz=1&usg=AOvVaw3KMZrAVx3FuyLTrUEtjBlI) 5\. Online editor: [https://remix.ethereum.org](https://www.google.com/url?q=https%3A%2F%2Fremix.ethereum.org&sa=D&sntz=1&usg=AOvVaw12Y9fbyx49-9qH8CSJfuQ3) اجماع majority rules validate transactions properly computer nodes 51% consensus mechanisms advantage proof of work: * anybody can attached machines and gain rewards blockchain trilemma: 1- scalable/speed 2- decentralization secure fastest: solana (arweave), [](https://drive.google.com/open?id=1iJqtIoSQ1gmMr7tleuFQ6lX_FmiFj7p4FELnCVGT1C4 "Open Spreadsheet, AltCoin in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/sheets_32dp.png)AltCoin # Basic [proof of work/stake](https://www.youtube.com/watch?v=08vnE2_cAeQ) # Links [https://koinly.io/](https://www.google.com/url?q=https%3A%2F%2Fkoinly.io%2F&sa=D&sntz=1&usg=AOvVaw1MlwUKbWcWOp2wDgfk5wTa) [https://chain.link/bootcamp/bootcamp-2021-on- demand](https://www.google.com/url?q=https%3A%2F%2Fchain.link%2Fbootcamp%2Fbootcamp-2021-on- demand&sa=D&sntz=1&usg=AOvVaw3_THOQshewlXoxNX0CKMKf) [https://software.intel.com/content/www/us/en/develop/download/download- maccpuid.html](https://www.google.com/url?q=https%3A%2F%2Fsoftware.intel.com%2Fcontent%2Fwww%2Fus%2Fen%2Fdevelop%2Fdownload%2Fdownload- maccpuid.html&sa=D&sntz=1&usg=AOvVaw3fAaHZJ9DSuYORK5STffBn) [آموزش زبان سالیدیتی(solidity) برای نوشتن اسمارت کانترکت روی شبکه اتریوم، شماره ۱](https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr) [https://cryptozombies.io/](https://www.google.com/url?q=https%3A%2F%2Fcryptozombies.io%2F&sa=D&sntz=1&usg=AOvVaw2gbwUmHX9yWMhTVmpbHEPy) # Hands on ## setup:macOS ### 1: install nvm: Node Version Manager curl -o- https://raw.githubusercontent.com/creationix/nvm/v0.35.2/install.sh | bash nvm install 12 nvm use 12 nvm alias default 12 npm install npm --global # Upgrade npm to the latest version ### 2\. install hardhat: Ethereum development environment for professionals npm install --save-dev hardhat ### 3\. # METAVERSE 1. Coin Bureau: : TOP 5 Virtual Land NFTs!! BEST Metaverse Plays?? 🚀 #metaverse #land #crypto * sandbox * metaverse group buy decentraland: 2.43 M$ * [https://nonfungible.com/](https://www.google.com/url?q=https%3A%2F%2Fnonfungible.com%2F&sa=D&sntz=1&usg=AOvVaw1d1IAffRPgXgbELGML_Zak) : cryptopunks, the sandbox, decentraland, cryptovoxels, somnium space, superworld, arcona. OVR Top 5 Land 1. * * 1. axie infinity * savannah, forest, arctic, mystic, genesis, lunas landing 2. decentraland * * 9K land * ~4500 MANA * 3$ = 13500$ * Decentraland Tutorials: * my land pirahansiah: [https://share.decentraland.org/b/scene/ed5823d2-788c-440f-8875-2614fc139c42](https://www.google.com/url?q=https%3A%2F%2Fshare.decentraland.org%2Fb%2Fscene%2Fed5823d2-788c-440f-8875-2614fc139c42&sa=D&sntz=1&usg=AOvVaw2SHZ1c0evkthfjNgJ8Mcw2) 3. the sandbox * ~2.5 Eth * 3850 = 9625 1. * * 4. bitcountry * create and personalise metaverse * 5. aavegotchi * 2. [https://www.sandbox.game/en/](https://www.google.com/url?q=https%3A%2F%2Fwww.sandbox.game%2Fen%2F&sa=D&sntz=1&usg=AOvVaw0MmdZePFR3AUuLjPjVKlIk) * it is virtual world in ethereum 2011 - 2018 - * open metaverse - the sandbox alpha - 29.11 to 20.12.21 * require the sandbox (SAND) ~ 5$ (17.12.21) ~2.73$ (22.April.2022) ~0.58 (14.11.22) * 3. learn: 4. learn: 5. AI meta: [https://ai.facebook.com/events/neurips2021](https://www.google.com/url?q=https%3A%2F%2Fai.facebook.com%2Fevents%2Fneurips2021&sa=D&sntz=1&usg=AOvVaw3OvYQKZfEVFHqbYen7VYwh) 6. Metaverse, Mesh, Open AI and more from Microsoft Ignite Fall 2021: [https://valoremreply.com/post/metaverse-mesh-openai-microsoft-ignite-fall-2021/?utm_source=social_media&utm_medium=Rene-Reshare&utm_campaign=trends2021](https://www.google.com/url?q=https%3A%2F%2Fvaloremreply.com%2Fpost%2Fmetaverse-mesh-openai-microsoft-ignite-fall-2021%2F%3Futm_source%3Dsocial_media%26utm_medium%3DRene-Reshare%26utm_campaign%3Dtrends2021&sa=D&sntz=1&usg=AOvVaw2aU3w2NugSf1HZh-Neisf5) ## crypto 18.12.21 - 22.April.2022 * Decentraland (MANA): ~3$ - ~2.03$ (22.April.2022) * sandbox (SAND): ~ 5$ - ~2.73$ (22.April.2022) * Axie Infinity (AXS): ~95$ - ~45.65$ (22.April.2022) * illuvium (ILV) : ~1136$ - ~514.98$ (22.April.2022) * star atlas price (ATLAS) base on solana : ~0.10 - ~0.023$ (22.April.2022) * wilder world (wild): ~3.67$ - ~1.15$ (22.April.2022) 1. bitCoin : revolution to currency, 2. DeFi, ethereum 3. NFT 4. Metaverse # NFT * [https://opensea.io/collection/computervision](https://www.google.com/url?q=https%3A%2F%2Fopensea.io%2Fcollection%2Fcomputervision&sa=D&sntz=1&usg=AOvVaw2kn1tyV7uv_uTI9i50hUVQ) * Links: [https://github.com/MetaMask/metamask- mobile](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FMetaMask%2Fmetamask- mobile&sa=D&sntz=1&usg=AOvVaw1YH7hABPeEsRI7ne1ErWR4) [https://readyplayer.me](https://www.google.com/url?q=https%3A%2F%2Freadyplayer.me&sa=D&sntz=1&usg=AOvVaw1q7g9_FKZoK_- FuOwYHQEN) [https://github.com/ish- app/ish](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fish- app%2Fish&sa=D&sntz=1&usg=AOvVaw12yqknRonHBDjzyVJZ543t) [https://getutm.app/install/](https://www.google.com/url?q=https%3A%2F%2Fgetutm.app%2Finstall%2F&sa=D&sntz=1&usg=AOvVaw2i4ClkKwh4GkEdZg7y5WOX) [https://github.com/rileytestut/AltStore](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Frileytestut%2FAltStore&sa=D&sntz=1&usg=AOvVaw1PXzTxhU5BDAOhM0lo3aMS) [https://altstore.io](https://www.google.com/url?q=https%3A%2F%2Faltstore.io&sa=D&sntz=1&usg=AOvVaw3cXCR8rKRUvIWq8tNFytLE) [https://vscode.dev/github/pirahansiah/pirahansiah](https://www.google.com/url?q=https%3A%2F%2Fvscode.dev%2Fgithub%2Fpirahansiah%2Fpirahansiah&sa=D&sntz=1&usg=AOvVaw3ar7dCuBaV4_X4lplrNKXP) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/VBNzuBGkaD3wt3TzoFwX0mTIj70nSDd3lgPVXQioi93Rci- RfkU7Cych8xXEVlNVPXCewW1vMbWtk8T6z_JVwm8=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/VBNzuBGkaD3wt3TzoFwX0mTIj70nSDd3lgPVXQioi93Rci- RfkU7Cych8xXEVlNVPXCewW1vMbWtk8T6z_JVwm8=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # AltCoin Disclaimer/ Risk warning: I am not a financial advisor and anything you read, see or hear in this site, podcast, video should not by any means be construed as financial advice it is purely intended for your entertainment and demonstration and illustrative purposes only. This is not financial advice and should not be taken as financial advice. the views I have in everyone of my site/post/blog/links/text/documents/powerpoint/videos are completely speculative opinions and do not guarantee any specific result. The NFT, AltCoin, Metaverse, ... is extremely volatile and has high risk. You should never act on anyone's advice or opinions, without first doing your own research, realising your own risk, and making your own decision. I recommend speaking with a licensed and qualified professional before making any financial decision. Basic Links Hands on setup:macOS 1: install nvm: Node Version Manager 2\. install hardhat: Ethereum development environment for professionals 3. METAVERSE crypto 18.12.21 - 22.April.2022 NFT Stock * ^ Purchasing managers' indexes (PMI): * A PMI index over 50 represents growth or expansion within the manufacturing sector of the economy compared with the prior month. * ^ United States Philadelphia Fed Manufacturing Index * A value greater than 0 reflects growth in the manufacturing sector, whereas a value less than 0 reflects a contraction. * ![](https://lh4.googleusercontent.com/eNrU_i5TMskU_ZA3cXB0rIVuJlCnE4YgTGpfGaSlrejV9fJMw6GLEOimP- GqHJcZAQTDhr-h1DHW3wQhxXT98zyRp1NlPhLB9s8bNWrxrBjgJlv9QFwpgAdleVPQhW37Fw=w1280) ![](https://lh4.googleusercontent.com/qARdwh01SGT2SdP9lG- PEJeMiqeNun3sP2v2QYwmDwYAunVYxdLJaesG5N5-KnTh1evxyq2hl1MXKofdGDW7PTK9nJ1GYa3nVqLckI3t6WJh2JNRCiQkw_rZC28bphtXvw=w1280) My token _**pirahansiah (TIZ**_ _ **)**_ on Solana Token name pirahansiah (TIZ) Token address FsniTTtb9GeGq1DHkipxera4bsgFkb19maBLKZwMe7in **Token** **pirahansiah** https://solscan.io/token/FsniTTtb9GeGq1DHkipxera4bsgFkb19maBLKZwMe7in My token _**pirahansiah (TIZ)**_ **Token Contract Address** 0xe30407DB873302D6AEaAB3bA619f44Bc9F924594 **Token Decimal:** 18 **Network:** BNB Smart Chain Mainnet only 100 token available to sell [https://bscscan.com/token/0xe30407db873302d6aeaab3ba619f44bc9f924594](https://www.google.com/url?q=https%3A%2F%2Fbscscan.com%2Ftoken%2F0xe30407db873302d6aeaab3ba619f44bc9f924594&sa=D&sntz=1&usg=AOvVaw3owOOObdS- gyWJtD_682hE) [https://admin.moralis.io/speedyNodes](https://www.google.com/url?q=https%3A%2F%2Fadmin.moralis.io%2FspeedyNodes&sa=D&sntz=1&usg=AOvVaw1e_X2Eutx- uPjibkv51EdW) [https://testnet.binance.org/faucet- smart](https://www.google.com/url?q=https%3A%2F%2Ftestnet.binance.org%2Ffaucet- smart&sa=D&sntz=1&usg=AOvVaw0YuT9b0CIeLmtQULao873v) [https://github.com/OpenZeppelin/openzeppelin- contracts/blob/master/contracts/token/ERC20/ERC20.sol](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FOpenZeppelin%2Fopenzeppelin- contracts%2Fblob%2Fmaster%2Fcontracts%2Ftoken%2FERC20%2FERC20.sol&sa=D&sntz=1&usg=AOvVaw3JW77-QoFjcv- DNK3whJT2) [https://remix.ethereum.org/#optimize=false&runs=200&evmVersion=null&version=soljson-v0.8.7+commit.e28d00a7.js](https://www.google.com/url?q=https%3A%2F%2Fremix.ethereum.org%2F%23optimize%3Dfalse%26runs%3D200%26evmVersion%3Dnull%26version%3Dsoljson-v0.8.7%2Bcommit.e28d00a7.js&sa=D&sntz=1&usg=AOvVaw39lC9qXtU3OwPP5mxpf9qK) [https://bscscan.com/token/0xe30407db873302d6aeaab3ba619f44bc9f924594](https://www.google.com/url?q=https%3A%2F%2Fbscscan.com%2Ftoken%2F0xe30407db873302d6aeaab3ba619f44bc9f924594&sa=D&sntz=1&usg=AOvVaw3owOOObdS- gyWJtD_682hE) [https://github.com/OpenZeppelin/openzeppelin- contracts/blob/master/contracts/token/ERC20/ERC20.sol](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FOpenZeppelin%2Fopenzeppelin- contracts%2Fblob%2Fmaster%2Fcontracts%2Ftoken%2FERC20%2FERC20.sol&sa=D&sntz=1&usg=AOvVaw3JW77-QoFjcv- DNK3whJT2) * how to modify the code * [https://www.tradingview.com](https://www.google.com/url?q=https%3A%2F%2Fwww.tradingview.com&sa=D&sntz=1&usg=AOvVaw15P-ffkNM5fCBySsWtLXHo) best tools to analysis market * [https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr](https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr) Persian Interface: minimum data and functions required to make it a standard ERC/EIP smart contract _value the _ shows it is a parameters Constant is a variable that can't be changed Mapping() function maps elements from a key to a value Constructor() function that automatically runs when a new data item is created( initialization code) Emit() function triggers an event (message to be sent out) 1. New project 1. ./geth --syncmode "light" 2. Mkdir 3. Truffle init Truffle deploy --reset Truffle console HelloWorld.deployed().then(function(instance) {return instance} ); HelloWorld.deployed().then(function(instance) {return instance.getHelloMessage()} ); npm install @truffle/hdwallet-provider 1. LinkedIn 1. Start 10.April.2022 2. [https://geth.ethereum.org/](https://www.google.com/url?q=https%3A%2F%2Fgeth.ethereum.org%2F&sa=D&sntz=1&usg=AOvVaw1XnmIZJr5e4_oBt8pPHXQK) 3. [https://trufflesuite.com/ganache/](https://www.google.com/url?q=https%3A%2F%2Ftrufflesuite.com%2Fganache%2F&sa=D&sntz=1&usg=AOvVaw3wxvy_EfDUepyc6z9ctYdc) 4. [https://trufflesuite.com/truffle/](https://www.google.com/url?q=https%3A%2F%2Ftrufflesuite.com%2Ftruffle%2F&sa=D&sntz=1&usg=AOvVaw1dbGm84hwOf1zhcx6viT9A) 1. npm install truffle -g 5. ERC-20 (500K) 6. ERC721: Non-Fungible Tokens (NFT): 70K 7. ERC1155: Multi-Token Tokens : 8K 8. dApp 9. Security:[ ](https://www.google.com/url?q=https%3A%2F%2Fconsensys.github.io%2Fsmart-contract-best-practices%2F&sa=D&sntz=1&usg=AOvVaw0ABPhCIEaExBvmeOe-D_Hm)[https://consensys.github.io/smart-contract-best-practices/](https://www.google.com/url?q=https%3A%2F%2Fconsensys.github.io%2Fsmart-contract-best-practices%2F&sa=D&sntz=1&usg=AOvVaw0ABPhCIEaExBvmeOe-D_Hm) 2. LinkedIn II 1. Hyperledger.org 2. solidity: [https://docs.soliditylang.org/en/v0.8.13/installing-solidity.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.soliditylang.org%2Fen%2Fv0.8.13%2Finstalling-solidity.html&sa=D&sntz=1&usg=AOvVaw1GDJZfpxlMTD-VyuM3EM1n) 1. docker run ethereum/solc:stable --help 2. brew update 3. brew upgrade 4. brew tap ethereum/ethereum 5. brew install solidity 6. 4\. Visual studio code: Name: solidity Id: JuanBlanco.solidity Description: Ethereum Solidity Language for Visual Studio Code Version: 0.0.139 Publisher: Juan Blanco VS Marketplace Link:[ ](https://www.google.com/url?q=https%3A%2F%2Fmarketplace.visualstudio.com%2Fitems%3FitemName%3DJuanBlanco.solidity&sa=D&sntz=1&usg=AOvVaw3KMZrAVx3FuyLTrUEtjBlI)[https://marketplace.visualstudio.com/items?itemName=JuanBlanco.solidity](https://www.google.com/url?q=https%3A%2F%2Fmarketplace.visualstudio.com%2Fitems%3FitemName%3DJuanBlanco.solidity&sa=D&sntz=1&usg=AOvVaw3KMZrAVx3FuyLTrUEtjBlI) 5\. Online editor: [https://remix.ethereum.org](https://www.google.com/url?q=https%3A%2F%2Fremix.ethereum.org&sa=D&sntz=1&usg=AOvVaw12Y9fbyx49-9qH8CSJfuQ3) اجماع majority rules validate transactions properly computer nodes 51% consensus mechanisms advantage proof of work: * anybody can attached machines and gain rewards blockchain trilemma: 1- scalable/speed 2- decentralization secure fastest: solana (arweave), [](https://drive.google.com/open?id=1iJqtIoSQ1gmMr7tleuFQ6lX_FmiFj7p4FELnCVGT1C4 "Open Spreadsheet, AltCoin in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/sheets_32dp.png)AltCoin # Basic [proof of work/stake](https://www.youtube.com/watch?v=08vnE2_cAeQ) # Links [https://koinly.io/](https://www.google.com/url?q=https%3A%2F%2Fkoinly.io%2F&sa=D&sntz=1&usg=AOvVaw1MlwUKbWcWOp2wDgfk5wTa) [https://chain.link/bootcamp/bootcamp-2021-on- demand](https://www.google.com/url?q=https%3A%2F%2Fchain.link%2Fbootcamp%2Fbootcamp-2021-on- demand&sa=D&sntz=1&usg=AOvVaw3_THOQshewlXoxNX0CKMKf) [https://software.intel.com/content/www/us/en/develop/download/download- maccpuid.html](https://www.google.com/url?q=https%3A%2F%2Fsoftware.intel.com%2Fcontent%2Fwww%2Fus%2Fen%2Fdevelop%2Fdownload%2Fdownload- maccpuid.html&sa=D&sntz=1&usg=AOvVaw3fAaHZJ9DSuYORK5STffBn) [آموزش زبان سالیدیتی(solidity) برای نوشتن اسمارت کانترکت روی شبکه اتریوم، شماره ۱](https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr) [https://cryptozombies.io/](https://www.google.com/url?q=https%3A%2F%2Fcryptozombies.io%2F&sa=D&sntz=1&usg=AOvVaw2gbwUmHX9yWMhTVmpbHEPy) # Hands on ## setup:macOS ### 1: install nvm: Node Version Manager curl -o- https://raw.githubusercontent.com/creationix/nvm/v0.35.2/install.sh | bash nvm install 12 nvm use 12 nvm alias default 12 npm install npm --global # Upgrade npm to the latest version ### 2\. install hardhat: Ethereum development environment for professionals npm install --save-dev hardhat ### 3\. # METAVERSE 1. Coin Bureau: : TOP 5 Virtual Land NFTs!! BEST Metaverse Plays?? 🚀 #metaverse #land #crypto * sandbox * metaverse group buy decentraland: 2.43 M$ * [https://nonfungible.com/](https://www.google.com/url?q=https%3A%2F%2Fnonfungible.com%2F&sa=D&sntz=1&usg=AOvVaw1d1IAffRPgXgbELGML_Zak) : cryptopunks, the sandbox, decentraland, cryptovoxels, somnium space, superworld, arcona. OVR Top 5 Land 1. * * 1. axie infinity * savannah, forest, arctic, mystic, genesis, lunas landing 2. decentraland * * 9K land * ~4500 MANA * 3$ = 13500$ * Decentraland Tutorials: * my land pirahansiah: [https://share.decentraland.org/b/scene/ed5823d2-788c-440f-8875-2614fc139c42](https://www.google.com/url?q=https%3A%2F%2Fshare.decentraland.org%2Fb%2Fscene%2Fed5823d2-788c-440f-8875-2614fc139c42&sa=D&sntz=1&usg=AOvVaw2SHZ1c0evkthfjNgJ8Mcw2) 3. the sandbox * ~2.5 Eth * 3850 = 9625 1. * * 4. bitcountry * create and personalise metaverse * 5. aavegotchi * 2. [https://www.sandbox.game/en/](https://www.google.com/url?q=https%3A%2F%2Fwww.sandbox.game%2Fen%2F&sa=D&sntz=1&usg=AOvVaw0MmdZePFR3AUuLjPjVKlIk) * it is virtual world in ethereum 2011 - 2018 - * open metaverse - the sandbox alpha - 29.11 to 20.12.21 * require the sandbox (SAND) ~ 5$ (17.12.21) ~2.73$ (22.April.2022) ~0.58 (14.11.22) * 3. learn: 4. learn: 5. AI meta: [https://ai.facebook.com/events/neurips2021](https://www.google.com/url?q=https%3A%2F%2Fai.facebook.com%2Fevents%2Fneurips2021&sa=D&sntz=1&usg=AOvVaw3OvYQKZfEVFHqbYen7VYwh) 6. Metaverse, Mesh, Open AI and more from Microsoft Ignite Fall 2021: [https://valoremreply.com/post/metaverse-mesh-openai-microsoft-ignite-fall-2021/?utm_source=social_media&utm_medium=Rene-Reshare&utm_campaign=trends2021](https://www.google.com/url?q=https%3A%2F%2Fvaloremreply.com%2Fpost%2Fmetaverse-mesh-openai-microsoft-ignite-fall-2021%2F%3Futm_source%3Dsocial_media%26utm_medium%3DRene-Reshare%26utm_campaign%3Dtrends2021&sa=D&sntz=1&usg=AOvVaw2aU3w2NugSf1HZh-Neisf5) ## crypto 18.12.21 - 22.April.2022 * Decentraland (MANA): ~3$ - ~2.03$ (22.April.2022) * sandbox (SAND): ~ 5$ - ~2.73$ (22.April.2022) * Axie Infinity (AXS): ~95$ - ~45.65$ (22.April.2022) * illuvium (ILV) : ~1136$ - ~514.98$ (22.April.2022) * star atlas price (ATLAS) base on solana : ~0.10 - ~0.023$ (22.April.2022) * wilder world (wild): ~3.67$ - ~1.15$ (22.April.2022) 1. bitCoin : revolution to currency, 2. DeFi, ethereum 3. NFT 4. Metaverse # NFT * [https://opensea.io/collection/computervision](https://www.google.com/url?q=https%3A%2F%2Fopensea.io%2Fcollection%2Fcomputervision&sa=D&sntz=1&usg=AOvVaw2kn1tyV7uv_uTI9i50hUVQ) * Links: [https://github.com/MetaMask/metamask- mobile](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FMetaMask%2Fmetamask- mobile&sa=D&sntz=1&usg=AOvVaw1YH7hABPeEsRI7ne1ErWR4) [https://readyplayer.me](https://www.google.com/url?q=https%3A%2F%2Freadyplayer.me&sa=D&sntz=1&usg=AOvVaw1q7g9_FKZoK_- FuOwYHQEN) [https://github.com/ish- app/ish](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fish- app%2Fish&sa=D&sntz=1&usg=AOvVaw12yqknRonHBDjzyVJZ543t) [https://getutm.app/install/](https://www.google.com/url?q=https%3A%2F%2Fgetutm.app%2Finstall%2F&sa=D&sntz=1&usg=AOvVaw2i4ClkKwh4GkEdZg7y5WOX) [https://github.com/rileytestut/AltStore](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Frileytestut%2FAltStore&sa=D&sntz=1&usg=AOvVaw1PXzTxhU5BDAOhM0lo3aMS) [https://altstore.io](https://www.google.com/url?q=https%3A%2F%2Faltstore.io&sa=D&sntz=1&usg=AOvVaw3cXCR8rKRUvIWq8tNFytLE) [https://vscode.dev/github/pirahansiah/pirahansiah](https://www.google.com/url?q=https%3A%2F%2Fvscode.dev%2Fgithub%2Fpirahansiah%2Fpirahansiah&sa=D&sntz=1&usg=AOvVaw3ar7dCuBaV4_X4lplrNKXP) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/VBNzuBGkaD3wt3TzoFwX0mTIj70nSDd3lgPVXQioi93Rci- RfkU7Cych8xXEVlNVPXCewW1vMbWtk8T6z_JVwm8=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/VBNzuBGkaD3wt3TzoFwX0mTIj70nSDd3lgPVXQioi93Rci- RfkU7Cych8xXEVlNVPXCewW1vMbWtk8T6z_JVwm8=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # AltCoin Disclaimer/ Risk warning: I am not a financial advisor and anything you read, see or hear in this site, podcast, video should not by any means be construed as financial advice it is purely intended for your entertainment and demonstration and illustrative purposes only. This is not financial advice and should not be taken as financial advice. the views I have in everyone of my site/post/blog/links/text/documents/powerpoint/videos are completely speculative opinions and do not guarantee any specific result. The NFT, AltCoin, Metaverse, ... is extremely volatile and has high risk. You should never act on anyone's advice or opinions, without first doing your own research, realising your own risk, and making your own decision. I recommend speaking with a licensed and qualified professional before making any financial decision. Basic Links Hands on setup:macOS 1: install nvm: Node Version Manager 2\. install hardhat: Ethereum development environment for professionals 3. METAVERSE crypto 18.12.21 - 22.April.2022 NFT Stock * ^ Purchasing managers' indexes (PMI): * A PMI index over 50 represents growth or expansion within the manufacturing sector of the economy compared with the prior month. * ^ United States Philadelphia Fed Manufacturing Index * A value greater than 0 reflects growth in the manufacturing sector, whereas a value less than 0 reflects a contraction. * ![](https://lh4.googleusercontent.com/eNrU_i5TMskU_ZA3cXB0rIVuJlCnE4YgTGpfGaSlrejV9fJMw6GLEOimP- GqHJcZAQTDhr-h1DHW3wQhxXT98zyRp1NlPhLB9s8bNWrxrBjgJlv9QFwpgAdleVPQhW37Fw=w1280) ![](https://lh4.googleusercontent.com/qARdwh01SGT2SdP9lG- PEJeMiqeNun3sP2v2QYwmDwYAunVYxdLJaesG5N5-KnTh1evxyq2hl1MXKofdGDW7PTK9nJ1GYa3nVqLckI3t6WJh2JNRCiQkw_rZC28bphtXvw=w1280) My token _**pirahansiah (TIZ**_ _ **)**_ on Solana Token name pirahansiah (TIZ) Token address FsniTTtb9GeGq1DHkipxera4bsgFkb19maBLKZwMe7in **Token** **pirahansiah** https://solscan.io/token/FsniTTtb9GeGq1DHkipxera4bsgFkb19maBLKZwMe7in My token _**pirahansiah (TIZ)**_ **Token Contract Address** 0xe30407DB873302D6AEaAB3bA619f44Bc9F924594 **Token Decimal:** 18 **Network:** BNB Smart Chain Mainnet only 100 token available to sell [https://bscscan.com/token/0xe30407db873302d6aeaab3ba619f44bc9f924594](https://www.google.com/url?q=https%3A%2F%2Fbscscan.com%2Ftoken%2F0xe30407db873302d6aeaab3ba619f44bc9f924594&sa=D&sntz=1&usg=AOvVaw3owOOObdS- gyWJtD_682hE) [https://admin.moralis.io/speedyNodes](https://www.google.com/url?q=https%3A%2F%2Fadmin.moralis.io%2FspeedyNodes&sa=D&sntz=1&usg=AOvVaw1e_X2Eutx- uPjibkv51EdW) [https://testnet.binance.org/faucet- smart](https://www.google.com/url?q=https%3A%2F%2Ftestnet.binance.org%2Ffaucet- smart&sa=D&sntz=1&usg=AOvVaw0YuT9b0CIeLmtQULao873v) [https://github.com/OpenZeppelin/openzeppelin- contracts/blob/master/contracts/token/ERC20/ERC20.sol](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FOpenZeppelin%2Fopenzeppelin- contracts%2Fblob%2Fmaster%2Fcontracts%2Ftoken%2FERC20%2FERC20.sol&sa=D&sntz=1&usg=AOvVaw3JW77-QoFjcv- DNK3whJT2) [https://remix.ethereum.org/#optimize=false&runs=200&evmVersion=null&version=soljson-v0.8.7+commit.e28d00a7.js](https://www.google.com/url?q=https%3A%2F%2Fremix.ethereum.org%2F%23optimize%3Dfalse%26runs%3D200%26evmVersion%3Dnull%26version%3Dsoljson-v0.8.7%2Bcommit.e28d00a7.js&sa=D&sntz=1&usg=AOvVaw39lC9qXtU3OwPP5mxpf9qK) [https://bscscan.com/token/0xe30407db873302d6aeaab3ba619f44bc9f924594](https://www.google.com/url?q=https%3A%2F%2Fbscscan.com%2Ftoken%2F0xe30407db873302d6aeaab3ba619f44bc9f924594&sa=D&sntz=1&usg=AOvVaw3owOOObdS- gyWJtD_682hE) [https://github.com/OpenZeppelin/openzeppelin- contracts/blob/master/contracts/token/ERC20/ERC20.sol](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FOpenZeppelin%2Fopenzeppelin- contracts%2Fblob%2Fmaster%2Fcontracts%2Ftoken%2FERC20%2FERC20.sol&sa=D&sntz=1&usg=AOvVaw3JW77-QoFjcv- DNK3whJT2) * how to modify the code * [https://www.tradingview.com](https://www.google.com/url?q=https%3A%2F%2Fwww.tradingview.com&sa=D&sntz=1&usg=AOvVaw15P-ffkNM5fCBySsWtLXHo) best tools to analysis market * [https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr](https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr) Persian Interface: minimum data and functions required to make it a standard ERC/EIP smart contract _value the _ shows it is a parameters Constant is a variable that can't be changed Mapping() function maps elements from a key to a value Constructor() function that automatically runs when a new data item is created( initialization code) Emit() function triggers an event (message to be sent out) 1. New project 1. ./geth --syncmode "light" 2. Mkdir 3. Truffle init Truffle deploy --reset Truffle console HelloWorld.deployed().then(function(instance) {return instance} ); HelloWorld.deployed().then(function(instance) {return instance.getHelloMessage()} ); npm install @truffle/hdwallet-provider 1. LinkedIn 1. Start 10.April.2022 2. [https://geth.ethereum.org/](https://www.google.com/url?q=https%3A%2F%2Fgeth.ethereum.org%2F&sa=D&sntz=1&usg=AOvVaw1XnmIZJr5e4_oBt8pPHXQK) 3. [https://trufflesuite.com/ganache/](https://www.google.com/url?q=https%3A%2F%2Ftrufflesuite.com%2Fganache%2F&sa=D&sntz=1&usg=AOvVaw3wxvy_EfDUepyc6z9ctYdc) 4. [https://trufflesuite.com/truffle/](https://www.google.com/url?q=https%3A%2F%2Ftrufflesuite.com%2Ftruffle%2F&sa=D&sntz=1&usg=AOvVaw1dbGm84hwOf1zhcx6viT9A) 1. npm install truffle -g 5. ERC-20 (500K) 6. ERC721: Non-Fungible Tokens (NFT): 70K 7. ERC1155: Multi-Token Tokens : 8K 8. dApp 9. Security:[ ](https://www.google.com/url?q=https%3A%2F%2Fconsensys.github.io%2Fsmart-contract-best-practices%2F&sa=D&sntz=1&usg=AOvVaw0ABPhCIEaExBvmeOe-D_Hm)[https://consensys.github.io/smart-contract-best-practices/](https://www.google.com/url?q=https%3A%2F%2Fconsensys.github.io%2Fsmart-contract-best-practices%2F&sa=D&sntz=1&usg=AOvVaw0ABPhCIEaExBvmeOe-D_Hm) 2. LinkedIn II 1. Hyperledger.org 2. solidity: [https://docs.soliditylang.org/en/v0.8.13/installing-solidity.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.soliditylang.org%2Fen%2Fv0.8.13%2Finstalling-solidity.html&sa=D&sntz=1&usg=AOvVaw1GDJZfpxlMTD-VyuM3EM1n) 1. docker run ethereum/solc:stable --help 2. brew update 3. brew upgrade 4. brew tap ethereum/ethereum 5. brew install solidity 6. 4\. Visual studio code: Name: solidity Id: JuanBlanco.solidity Description: Ethereum Solidity Language for Visual Studio Code Version: 0.0.139 Publisher: Juan Blanco VS Marketplace Link:[ ](https://www.google.com/url?q=https%3A%2F%2Fmarketplace.visualstudio.com%2Fitems%3FitemName%3DJuanBlanco.solidity&sa=D&sntz=1&usg=AOvVaw3KMZrAVx3FuyLTrUEtjBlI)[https://marketplace.visualstudio.com/items?itemName=JuanBlanco.solidity](https://www.google.com/url?q=https%3A%2F%2Fmarketplace.visualstudio.com%2Fitems%3FitemName%3DJuanBlanco.solidity&sa=D&sntz=1&usg=AOvVaw3KMZrAVx3FuyLTrUEtjBlI) 5\. Online editor: [https://remix.ethereum.org](https://www.google.com/url?q=https%3A%2F%2Fremix.ethereum.org&sa=D&sntz=1&usg=AOvVaw12Y9fbyx49-9qH8CSJfuQ3) اجماع majority rules validate transactions properly computer nodes 51% consensus mechanisms advantage proof of work: * anybody can attached machines and gain rewards blockchain trilemma: 1- scalable/speed 2- decentralization secure fastest: solana (arweave), [](https://drive.google.com/open?id=1iJqtIoSQ1gmMr7tleuFQ6lX_FmiFj7p4FELnCVGT1C4 "Open Spreadsheet, AltCoin in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/sheets_32dp.png)AltCoin # Basic [proof of work/stake](https://www.youtube.com/watch?v=08vnE2_cAeQ) # Links [https://koinly.io/](https://www.google.com/url?q=https%3A%2F%2Fkoinly.io%2F&sa=D&sntz=1&usg=AOvVaw1MlwUKbWcWOp2wDgfk5wTa) [https://chain.link/bootcamp/bootcamp-2021-on- demand](https://www.google.com/url?q=https%3A%2F%2Fchain.link%2Fbootcamp%2Fbootcamp-2021-on- demand&sa=D&sntz=1&usg=AOvVaw3_THOQshewlXoxNX0CKMKf) [https://software.intel.com/content/www/us/en/develop/download/download- maccpuid.html](https://www.google.com/url?q=https%3A%2F%2Fsoftware.intel.com%2Fcontent%2Fwww%2Fus%2Fen%2Fdevelop%2Fdownload%2Fdownload- maccpuid.html&sa=D&sntz=1&usg=AOvVaw3fAaHZJ9DSuYORK5STffBn) [آموزش زبان سالیدیتی(solidity) برای نوشتن اسمارت کانترکت روی شبکه اتریوم، شماره ۱](https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr) [https://cryptozombies.io/](https://www.google.com/url?q=https%3A%2F%2Fcryptozombies.io%2F&sa=D&sntz=1&usg=AOvVaw2gbwUmHX9yWMhTVmpbHEPy) # Hands on ## setup:macOS ### 1: install nvm: Node Version Manager curl -o- https://raw.githubusercontent.com/creationix/nvm/v0.35.2/install.sh | bash nvm install 12 nvm use 12 nvm alias default 12 npm install npm --global # Upgrade npm to the latest version ### 2\. install hardhat: Ethereum development environment for professionals npm install --save-dev hardhat ### 3\. # METAVERSE 1. Coin Bureau: : TOP 5 Virtual Land NFTs!! BEST Metaverse Plays?? 🚀 #metaverse #land #crypto * sandbox * metaverse group buy decentraland: 2.43 M$ * [https://nonfungible.com/](https://www.google.com/url?q=https%3A%2F%2Fnonfungible.com%2F&sa=D&sntz=1&usg=AOvVaw1d1IAffRPgXgbELGML_Zak) : cryptopunks, the sandbox, decentraland, cryptovoxels, somnium space, superworld, arcona. OVR Top 5 Land 1. * * 1. axie infinity * savannah, forest, arctic, mystic, genesis, lunas landing 2. decentraland * * 9K land * ~4500 MANA * 3$ = 13500$ * Decentraland Tutorials: * my land pirahansiah: [https://share.decentraland.org/b/scene/ed5823d2-788c-440f-8875-2614fc139c42](https://www.google.com/url?q=https%3A%2F%2Fshare.decentraland.org%2Fb%2Fscene%2Fed5823d2-788c-440f-8875-2614fc139c42&sa=D&sntz=1&usg=AOvVaw2SHZ1c0evkthfjNgJ8Mcw2) 3. the sandbox * ~2.5 Eth * 3850 = 9625 1. * * 4. bitcountry * create and personalise metaverse * 5. aavegotchi * 2. [https://www.sandbox.game/en/](https://www.google.com/url?q=https%3A%2F%2Fwww.sandbox.game%2Fen%2F&sa=D&sntz=1&usg=AOvVaw0MmdZePFR3AUuLjPjVKlIk) * it is virtual world in ethereum 2011 - 2018 - * open metaverse - the sandbox alpha - 29.11 to 20.12.21 * require the sandbox (SAND) ~ 5$ (17.12.21) ~2.73$ (22.April.2022) ~0.58 (14.11.22) * 3. learn: 4. learn: 5. AI meta: [https://ai.facebook.com/events/neurips2021](https://www.google.com/url?q=https%3A%2F%2Fai.facebook.com%2Fevents%2Fneurips2021&sa=D&sntz=1&usg=AOvVaw3OvYQKZfEVFHqbYen7VYwh) 6. Metaverse, Mesh, Open AI and more from Microsoft Ignite Fall 2021: [https://valoremreply.com/post/metaverse-mesh-openai-microsoft-ignite-fall-2021/?utm_source=social_media&utm_medium=Rene-Reshare&utm_campaign=trends2021](https://www.google.com/url?q=https%3A%2F%2Fvaloremreply.com%2Fpost%2Fmetaverse-mesh-openai-microsoft-ignite-fall-2021%2F%3Futm_source%3Dsocial_media%26utm_medium%3DRene-Reshare%26utm_campaign%3Dtrends2021&sa=D&sntz=1&usg=AOvVaw2aU3w2NugSf1HZh-Neisf5) ## crypto 18.12.21 - 22.April.2022 * Decentraland (MANA): ~3$ - ~2.03$ (22.April.2022) * sandbox (SAND): ~ 5$ - ~2.73$ (22.April.2022) * Axie Infinity (AXS): ~95$ - ~45.65$ (22.April.2022) * illuvium (ILV) : ~1136$ - ~514.98$ (22.April.2022) * star atlas price (ATLAS) base on solana : ~0.10 - ~0.023$ (22.April.2022) * wilder world (wild): ~3.67$ - ~1.15$ (22.April.2022) 1. bitCoin : revolution to currency, 2. DeFi, ethereum 3. NFT 4. Metaverse # NFT * [https://opensea.io/collection/computervision](https://www.google.com/url?q=https%3A%2F%2Fopensea.io%2Fcollection%2Fcomputervision&sa=D&sntz=1&usg=AOvVaw2kn1tyV7uv_uTI9i50hUVQ) * Links: [https://github.com/MetaMask/metamask- mobile](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FMetaMask%2Fmetamask- mobile&sa=D&sntz=1&usg=AOvVaw1YH7hABPeEsRI7ne1ErWR4) [https://readyplayer.me](https://www.google.com/url?q=https%3A%2F%2Freadyplayer.me&sa=D&sntz=1&usg=AOvVaw1q7g9_FKZoK_- FuOwYHQEN) [https://github.com/ish- app/ish](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fish- app%2Fish&sa=D&sntz=1&usg=AOvVaw12yqknRonHBDjzyVJZ543t) [https://getutm.app/install/](https://www.google.com/url?q=https%3A%2F%2Fgetutm.app%2Finstall%2F&sa=D&sntz=1&usg=AOvVaw2i4ClkKwh4GkEdZg7y5WOX) [https://github.com/rileytestut/AltStore](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Frileytestut%2FAltStore&sa=D&sntz=1&usg=AOvVaw1PXzTxhU5BDAOhM0lo3aMS) [https://altstore.io](https://www.google.com/url?q=https%3A%2F%2Faltstore.io&sa=D&sntz=1&usg=AOvVaw3cXCR8rKRUvIWq8tNFytLE) [https://vscode.dev/github/pirahansiah/pirahansiah](https://www.google.com/url?q=https%3A%2F%2Fvscode.dev%2Fgithub%2Fpirahansiah%2Fpirahansiah&sa=D&sntz=1&usg=AOvVaw3ar7dCuBaV4_X4lplrNKXP) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/VBNzuBGkaD3wt3TzoFwX0mTIj70nSDd3lgPVXQioi93Rci- RfkU7Cych8xXEVlNVPXCewW1vMbWtk8T6z_JVwm8=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/VBNzuBGkaD3wt3TzoFwX0mTIj70nSDd3lgPVXQioi93Rci- RfkU7Cych8xXEVlNVPXCewW1vMbWtk8T6z_JVwm8=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # AltCoin Disclaimer/ Risk warning: I am not a financial advisor and anything you read, see or hear in this site, podcast, video should not by any means be construed as financial advice it is purely intended for your entertainment and demonstration and illustrative purposes only. This is not financial advice and should not be taken as financial advice. the views I have in everyone of my site/post/blog/links/text/documents/powerpoint/videos are completely speculative opinions and do not guarantee any specific result. The NFT, AltCoin, Metaverse, ... is extremely volatile and has high risk. You should never act on anyone's advice or opinions, without first doing your own research, realising your own risk, and making your own decision. I recommend speaking with a licensed and qualified professional before making any financial decision. Basic Links Hands on setup:macOS 1: install nvm: Node Version Manager 2\. install hardhat: Ethereum development environment for professionals 3. METAVERSE crypto 18.12.21 - 22.April.2022 NFT Stock * ^ Purchasing managers' indexes (PMI): * A PMI index over 50 represents growth or expansion within the manufacturing sector of the economy compared with the prior month. * ^ United States Philadelphia Fed Manufacturing Index * A value greater than 0 reflects growth in the manufacturing sector, whereas a value less than 0 reflects a contraction. * ![](https://lh4.googleusercontent.com/eNrU_i5TMskU_ZA3cXB0rIVuJlCnE4YgTGpfGaSlrejV9fJMw6GLEOimP- GqHJcZAQTDhr-h1DHW3wQhxXT98zyRp1NlPhLB9s8bNWrxrBjgJlv9QFwpgAdleVPQhW37Fw=w1280) ![](https://lh4.googleusercontent.com/qARdwh01SGT2SdP9lG- PEJeMiqeNun3sP2v2QYwmDwYAunVYxdLJaesG5N5-KnTh1evxyq2hl1MXKofdGDW7PTK9nJ1GYa3nVqLckI3t6WJh2JNRCiQkw_rZC28bphtXvw=w1280) My token _**pirahansiah (TIZ**_ _ **)**_ on Solana Token name pirahansiah (TIZ) Token address FsniTTtb9GeGq1DHkipxera4bsgFkb19maBLKZwMe7in **Token** **pirahansiah** https://solscan.io/token/FsniTTtb9GeGq1DHkipxera4bsgFkb19maBLKZwMe7in My token _**pirahansiah (TIZ)**_ **Token Contract Address** 0xe30407DB873302D6AEaAB3bA619f44Bc9F924594 **Token Decimal:** 18 **Network:** BNB Smart Chain Mainnet only 100 token available to sell [https://bscscan.com/token/0xe30407db873302d6aeaab3ba619f44bc9f924594](https://www.google.com/url?q=https%3A%2F%2Fbscscan.com%2Ftoken%2F0xe30407db873302d6aeaab3ba619f44bc9f924594&sa=D&sntz=1&usg=AOvVaw3owOOObdS- gyWJtD_682hE) [https://admin.moralis.io/speedyNodes](https://www.google.com/url?q=https%3A%2F%2Fadmin.moralis.io%2FspeedyNodes&sa=D&sntz=1&usg=AOvVaw1e_X2Eutx- uPjibkv51EdW) [https://testnet.binance.org/faucet- smart](https://www.google.com/url?q=https%3A%2F%2Ftestnet.binance.org%2Ffaucet- smart&sa=D&sntz=1&usg=AOvVaw0YuT9b0CIeLmtQULao873v) [https://github.com/OpenZeppelin/openzeppelin- contracts/blob/master/contracts/token/ERC20/ERC20.sol](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FOpenZeppelin%2Fopenzeppelin- contracts%2Fblob%2Fmaster%2Fcontracts%2Ftoken%2FERC20%2FERC20.sol&sa=D&sntz=1&usg=AOvVaw3JW77-QoFjcv- DNK3whJT2) [https://remix.ethereum.org/#optimize=false&runs=200&evmVersion=null&version=soljson-v0.8.7+commit.e28d00a7.js](https://www.google.com/url?q=https%3A%2F%2Fremix.ethereum.org%2F%23optimize%3Dfalse%26runs%3D200%26evmVersion%3Dnull%26version%3Dsoljson-v0.8.7%2Bcommit.e28d00a7.js&sa=D&sntz=1&usg=AOvVaw39lC9qXtU3OwPP5mxpf9qK) [https://bscscan.com/token/0xe30407db873302d6aeaab3ba619f44bc9f924594](https://www.google.com/url?q=https%3A%2F%2Fbscscan.com%2Ftoken%2F0xe30407db873302d6aeaab3ba619f44bc9f924594&sa=D&sntz=1&usg=AOvVaw3owOOObdS- gyWJtD_682hE) [https://github.com/OpenZeppelin/openzeppelin- contracts/blob/master/contracts/token/ERC20/ERC20.sol](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FOpenZeppelin%2Fopenzeppelin- contracts%2Fblob%2Fmaster%2Fcontracts%2Ftoken%2FERC20%2FERC20.sol&sa=D&sntz=1&usg=AOvVaw3JW77-QoFjcv- DNK3whJT2) * how to modify the code * [https://www.tradingview.com](https://www.google.com/url?q=https%3A%2F%2Fwww.tradingview.com&sa=D&sntz=1&usg=AOvVaw15P-ffkNM5fCBySsWtLXHo) best tools to analysis market * [https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr](https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr) Persian Interface: minimum data and functions required to make it a standard ERC/EIP smart contract _value the _ shows it is a parameters Constant is a variable that can't be changed Mapping() function maps elements from a key to a value Constructor() function that automatically runs when a new data item is created( initialization code) Emit() function triggers an event (message to be sent out) 1. New project 1. ./geth --syncmode "light" 2. Mkdir 3. Truffle init Truffle deploy --reset Truffle console HelloWorld.deployed().then(function(instance) {return instance} ); HelloWorld.deployed().then(function(instance) {return instance.getHelloMessage()} ); npm install @truffle/hdwallet-provider 1. LinkedIn 1. Start 10.April.2022 2. [https://geth.ethereum.org/](https://www.google.com/url?q=https%3A%2F%2Fgeth.ethereum.org%2F&sa=D&sntz=1&usg=AOvVaw1XnmIZJr5e4_oBt8pPHXQK) 3. [https://trufflesuite.com/ganache/](https://www.google.com/url?q=https%3A%2F%2Ftrufflesuite.com%2Fganache%2F&sa=D&sntz=1&usg=AOvVaw3wxvy_EfDUepyc6z9ctYdc) 4. [https://trufflesuite.com/truffle/](https://www.google.com/url?q=https%3A%2F%2Ftrufflesuite.com%2Ftruffle%2F&sa=D&sntz=1&usg=AOvVaw1dbGm84hwOf1zhcx6viT9A) 1. npm install truffle -g 5. ERC-20 (500K) 6. ERC721: Non-Fungible Tokens (NFT): 70K 7. ERC1155: Multi-Token Tokens : 8K 8. dApp 9. Security:[ ](https://www.google.com/url?q=https%3A%2F%2Fconsensys.github.io%2Fsmart-contract-best-practices%2F&sa=D&sntz=1&usg=AOvVaw0ABPhCIEaExBvmeOe-D_Hm)[https://consensys.github.io/smart-contract-best-practices/](https://www.google.com/url?q=https%3A%2F%2Fconsensys.github.io%2Fsmart-contract-best-practices%2F&sa=D&sntz=1&usg=AOvVaw0ABPhCIEaExBvmeOe-D_Hm) 2. LinkedIn II 1. Hyperledger.org 2. solidity: [https://docs.soliditylang.org/en/v0.8.13/installing-solidity.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.soliditylang.org%2Fen%2Fv0.8.13%2Finstalling-solidity.html&sa=D&sntz=1&usg=AOvVaw1GDJZfpxlMTD-VyuM3EM1n) 1. docker run ethereum/solc:stable --help 2. brew update 3. brew upgrade 4. brew tap ethereum/ethereum 5. brew install solidity 6. 4\. Visual studio code: Name: solidity Id: JuanBlanco.solidity Description: Ethereum Solidity Language for Visual Studio Code Version: 0.0.139 Publisher: Juan Blanco VS Marketplace Link:[ ](https://www.google.com/url?q=https%3A%2F%2Fmarketplace.visualstudio.com%2Fitems%3FitemName%3DJuanBlanco.solidity&sa=D&sntz=1&usg=AOvVaw3KMZrAVx3FuyLTrUEtjBlI)[https://marketplace.visualstudio.com/items?itemName=JuanBlanco.solidity](https://www.google.com/url?q=https%3A%2F%2Fmarketplace.visualstudio.com%2Fitems%3FitemName%3DJuanBlanco.solidity&sa=D&sntz=1&usg=AOvVaw3KMZrAVx3FuyLTrUEtjBlI) 5\. Online editor: [https://remix.ethereum.org](https://www.google.com/url?q=https%3A%2F%2Fremix.ethereum.org&sa=D&sntz=1&usg=AOvVaw12Y9fbyx49-9qH8CSJfuQ3) اجماع majority rules validate transactions properly computer nodes 51% consensus mechanisms advantage proof of work: * anybody can attached machines and gain rewards blockchain trilemma: 1- scalable/speed 2- decentralization secure fastest: solana (arweave), [](https://drive.google.com/open?id=1iJqtIoSQ1gmMr7tleuFQ6lX_FmiFj7p4FELnCVGT1C4 "Open Spreadsheet, AltCoin in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/sheets_32dp.png)AltCoin # Basic [proof of work/stake](https://www.youtube.com/watch?v=08vnE2_cAeQ) # Links [https://koinly.io/](https://www.google.com/url?q=https%3A%2F%2Fkoinly.io%2F&sa=D&sntz=1&usg=AOvVaw1MlwUKbWcWOp2wDgfk5wTa) [https://chain.link/bootcamp/bootcamp-2021-on- demand](https://www.google.com/url?q=https%3A%2F%2Fchain.link%2Fbootcamp%2Fbootcamp-2021-on- demand&sa=D&sntz=1&usg=AOvVaw3_THOQshewlXoxNX0CKMKf) [https://software.intel.com/content/www/us/en/develop/download/download- maccpuid.html](https://www.google.com/url?q=https%3A%2F%2Fsoftware.intel.com%2Fcontent%2Fwww%2Fus%2Fen%2Fdevelop%2Fdownload%2Fdownload- maccpuid.html&sa=D&sntz=1&usg=AOvVaw3fAaHZJ9DSuYORK5STffBn) [آموزش زبان سالیدیتی(solidity) برای نوشتن اسمارت کانترکت روی شبکه اتریوم، شماره ۱](https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr) [https://cryptozombies.io/](https://www.google.com/url?q=https%3A%2F%2Fcryptozombies.io%2F&sa=D&sntz=1&usg=AOvVaw2gbwUmHX9yWMhTVmpbHEPy) # Hands on ## setup:macOS ### 1: install nvm: Node Version Manager curl -o- https://raw.githubusercontent.com/creationix/nvm/v0.35.2/install.sh | bash nvm install 12 nvm use 12 nvm alias default 12 npm install npm --global # Upgrade npm to the latest version ### 2\. install hardhat: Ethereum development environment for professionals npm install --save-dev hardhat ### 3\. # METAVERSE 1. Coin Bureau: : TOP 5 Virtual Land NFTs!! BEST Metaverse Plays?? 🚀 #metaverse #land #crypto * sandbox * metaverse group buy decentraland: 2.43 M$ * [https://nonfungible.com/](https://www.google.com/url?q=https%3A%2F%2Fnonfungible.com%2F&sa=D&sntz=1&usg=AOvVaw1d1IAffRPgXgbELGML_Zak) : cryptopunks, the sandbox, decentraland, cryptovoxels, somnium space, superworld, arcona. OVR Top 5 Land 1. * * 1. axie infinity * savannah, forest, arctic, mystic, genesis, lunas landing 2. decentraland * * 9K land * ~4500 MANA * 3$ = 13500$ * Decentraland Tutorials: * my land pirahansiah: [https://share.decentraland.org/b/scene/ed5823d2-788c-440f-8875-2614fc139c42](https://www.google.com/url?q=https%3A%2F%2Fshare.decentraland.org%2Fb%2Fscene%2Fed5823d2-788c-440f-8875-2614fc139c42&sa=D&sntz=1&usg=AOvVaw2SHZ1c0evkthfjNgJ8Mcw2) 3. the sandbox * ~2.5 Eth * 3850 = 9625 1. * * 4. bitcountry * create and personalise metaverse * 5. aavegotchi * 2. [https://www.sandbox.game/en/](https://www.google.com/url?q=https%3A%2F%2Fwww.sandbox.game%2Fen%2F&sa=D&sntz=1&usg=AOvVaw0MmdZePFR3AUuLjPjVKlIk) * it is virtual world in ethereum 2011 - 2018 - * open metaverse - the sandbox alpha - 29.11 to 20.12.21 * require the sandbox (SAND) ~ 5$ (17.12.21) ~2.73$ (22.April.2022) ~0.58 (14.11.22) * 3. learn: 4. learn: 5. AI meta: [https://ai.facebook.com/events/neurips2021](https://www.google.com/url?q=https%3A%2F%2Fai.facebook.com%2Fevents%2Fneurips2021&sa=D&sntz=1&usg=AOvVaw3OvYQKZfEVFHqbYen7VYwh) 6. Metaverse, Mesh, Open AI and more from Microsoft Ignite Fall 2021: [https://valoremreply.com/post/metaverse-mesh-openai-microsoft-ignite-fall-2021/?utm_source=social_media&utm_medium=Rene-Reshare&utm_campaign=trends2021](https://www.google.com/url?q=https%3A%2F%2Fvaloremreply.com%2Fpost%2Fmetaverse-mesh-openai-microsoft-ignite-fall-2021%2F%3Futm_source%3Dsocial_media%26utm_medium%3DRene-Reshare%26utm_campaign%3Dtrends2021&sa=D&sntz=1&usg=AOvVaw2aU3w2NugSf1HZh-Neisf5) ## crypto 18.12.21 - 22.April.2022 * Decentraland (MANA): ~3$ - ~2.03$ (22.April.2022) * sandbox (SAND): ~ 5$ - ~2.73$ (22.April.2022) * Axie Infinity (AXS): ~95$ - ~45.65$ (22.April.2022) * illuvium (ILV) : ~1136$ - ~514.98$ (22.April.2022) * star atlas price (ATLAS) base on solana : ~0.10 - ~0.023$ (22.April.2022) * wilder world (wild): ~3.67$ - ~1.15$ (22.April.2022) 1. bitCoin : revolution to currency, 2. DeFi, ethereum 3. NFT 4. Metaverse # NFT * [https://opensea.io/collection/computervision](https://www.google.com/url?q=https%3A%2F%2Fopensea.io%2Fcollection%2Fcomputervision&sa=D&sntz=1&usg=AOvVaw2kn1tyV7uv_uTI9i50hUVQ) * Links: [https://github.com/MetaMask/metamask- mobile](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FMetaMask%2Fmetamask- mobile&sa=D&sntz=1&usg=AOvVaw1YH7hABPeEsRI7ne1ErWR4) [https://readyplayer.me](https://www.google.com/url?q=https%3A%2F%2Freadyplayer.me&sa=D&sntz=1&usg=AOvVaw1q7g9_FKZoK_- FuOwYHQEN) [https://github.com/ish- app/ish](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fish- app%2Fish&sa=D&sntz=1&usg=AOvVaw12yqknRonHBDjzyVJZ543t) [https://getutm.app/install/](https://www.google.com/url?q=https%3A%2F%2Fgetutm.app%2Finstall%2F&sa=D&sntz=1&usg=AOvVaw2i4ClkKwh4GkEdZg7y5WOX) [https://github.com/rileytestut/AltStore](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Frileytestut%2FAltStore&sa=D&sntz=1&usg=AOvVaw1PXzTxhU5BDAOhM0lo3aMS) [https://altstore.io](https://www.google.com/url?q=https%3A%2F%2Faltstore.io&sa=D&sntz=1&usg=AOvVaw3cXCR8rKRUvIWq8tNFytLE) [https://vscode.dev/github/pirahansiah/pirahansiah](https://www.google.com/url?q=https%3A%2F%2Fvscode.dev%2Fgithub%2Fpirahansiah%2Fpirahansiah&sa=D&sntz=1&usg=AOvVaw3ar7dCuBaV4_X4lplrNKXP) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/VBNzuBGkaD3wt3TzoFwX0mTIj70nSDd3lgPVXQioi93Rci- RfkU7Cych8xXEVlNVPXCewW1vMbWtk8T6z_JVwm8=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/VBNzuBGkaD3wt3TzoFwX0mTIj70nSDd3lgPVXQioi93Rci- RfkU7Cych8xXEVlNVPXCewW1vMbWtk8T6z_JVwm8=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # AltCoin Disclaimer/ Risk warning: I am not a financial advisor and anything you read, see or hear in this site, podcast, video should not by any means be construed as financial advice it is purely intended for your entertainment and demonstration and illustrative purposes only. This is not financial advice and should not be taken as financial advice. the views I have in everyone of my site/post/blog/links/text/documents/powerpoint/videos are completely speculative opinions and do not guarantee any specific result. The NFT, AltCoin, Metaverse, ... is extremely volatile and has high risk. You should never act on anyone's advice or opinions, without first doing your own research, realising your own risk, and making your own decision. I recommend speaking with a licensed and qualified professional before making any financial decision. Basic Links Hands on setup:macOS 1: install nvm: Node Version Manager 2\. install hardhat: Ethereum development environment for professionals 3. METAVERSE crypto 18.12.21 - 22.April.2022 NFT Stock * ^ Purchasing managers' indexes (PMI): * A PMI index over 50 represents growth or expansion within the manufacturing sector of the economy compared with the prior month. * ^ United States Philadelphia Fed Manufacturing Index * A value greater than 0 reflects growth in the manufacturing sector, whereas a value less than 0 reflects a contraction. * ![](https://lh4.googleusercontent.com/eNrU_i5TMskU_ZA3cXB0rIVuJlCnE4YgTGpfGaSlrejV9fJMw6GLEOimP- GqHJcZAQTDhr-h1DHW3wQhxXT98zyRp1NlPhLB9s8bNWrxrBjgJlv9QFwpgAdleVPQhW37Fw=w1280) ![](https://lh4.googleusercontent.com/qARdwh01SGT2SdP9lG- PEJeMiqeNun3sP2v2QYwmDwYAunVYxdLJaesG5N5-KnTh1evxyq2hl1MXKofdGDW7PTK9nJ1GYa3nVqLckI3t6WJh2JNRCiQkw_rZC28bphtXvw=w1280) My token _**pirahansiah (TIZ**_ _ **)**_ on Solana Token name pirahansiah (TIZ) Token address FsniTTtb9GeGq1DHkipxera4bsgFkb19maBLKZwMe7in **Token** **pirahansiah** https://solscan.io/token/FsniTTtb9GeGq1DHkipxera4bsgFkb19maBLKZwMe7in My token _**pirahansiah (TIZ)**_ **Token Contract Address** 0xe30407DB873302D6AEaAB3bA619f44Bc9F924594 **Token Decimal:** 18 **Network:** BNB Smart Chain Mainnet only 100 token available to sell [https://bscscan.com/token/0xe30407db873302d6aeaab3ba619f44bc9f924594](https://www.google.com/url?q=https%3A%2F%2Fbscscan.com%2Ftoken%2F0xe30407db873302d6aeaab3ba619f44bc9f924594&sa=D&sntz=1&usg=AOvVaw3owOOObdS- gyWJtD_682hE) [https://admin.moralis.io/speedyNodes](https://www.google.com/url?q=https%3A%2F%2Fadmin.moralis.io%2FspeedyNodes&sa=D&sntz=1&usg=AOvVaw1e_X2Eutx- uPjibkv51EdW) [https://testnet.binance.org/faucet- smart](https://www.google.com/url?q=https%3A%2F%2Ftestnet.binance.org%2Ffaucet- smart&sa=D&sntz=1&usg=AOvVaw0YuT9b0CIeLmtQULao873v) [https://github.com/OpenZeppelin/openzeppelin- contracts/blob/master/contracts/token/ERC20/ERC20.sol](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FOpenZeppelin%2Fopenzeppelin- contracts%2Fblob%2Fmaster%2Fcontracts%2Ftoken%2FERC20%2FERC20.sol&sa=D&sntz=1&usg=AOvVaw3JW77-QoFjcv- DNK3whJT2) [https://remix.ethereum.org/#optimize=false&runs=200&evmVersion=null&version=soljson-v0.8.7+commit.e28d00a7.js](https://www.google.com/url?q=https%3A%2F%2Fremix.ethereum.org%2F%23optimize%3Dfalse%26runs%3D200%26evmVersion%3Dnull%26version%3Dsoljson-v0.8.7%2Bcommit.e28d00a7.js&sa=D&sntz=1&usg=AOvVaw39lC9qXtU3OwPP5mxpf9qK) [https://bscscan.com/token/0xe30407db873302d6aeaab3ba619f44bc9f924594](https://www.google.com/url?q=https%3A%2F%2Fbscscan.com%2Ftoken%2F0xe30407db873302d6aeaab3ba619f44bc9f924594&sa=D&sntz=1&usg=AOvVaw3owOOObdS- gyWJtD_682hE) [https://github.com/OpenZeppelin/openzeppelin- contracts/blob/master/contracts/token/ERC20/ERC20.sol](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FOpenZeppelin%2Fopenzeppelin- contracts%2Fblob%2Fmaster%2Fcontracts%2Ftoken%2FERC20%2FERC20.sol&sa=D&sntz=1&usg=AOvVaw3JW77-QoFjcv- DNK3whJT2) * how to modify the code * [https://www.tradingview.com](https://www.google.com/url?q=https%3A%2F%2Fwww.tradingview.com&sa=D&sntz=1&usg=AOvVaw15P-ffkNM5fCBySsWtLXHo) best tools to analysis market * [https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr](https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr) Persian Interface: minimum data and functions required to make it a standard ERC/EIP smart contract _value the _ shows it is a parameters Constant is a variable that can't be changed Mapping() function maps elements from a key to a value Constructor() function that automatically runs when a new data item is created( initialization code) Emit() function triggers an event (message to be sent out) 1. New project 1. ./geth --syncmode "light" 2. Mkdir 3. Truffle init Truffle deploy --reset Truffle console HelloWorld.deployed().then(function(instance) {return instance} ); HelloWorld.deployed().then(function(instance) {return instance.getHelloMessage()} ); npm install @truffle/hdwallet-provider 1. LinkedIn 1. Start 10.April.2022 2. [https://geth.ethereum.org/](https://www.google.com/url?q=https%3A%2F%2Fgeth.ethereum.org%2F&sa=D&sntz=1&usg=AOvVaw1XnmIZJr5e4_oBt8pPHXQK) 3. [https://trufflesuite.com/ganache/](https://www.google.com/url?q=https%3A%2F%2Ftrufflesuite.com%2Fganache%2F&sa=D&sntz=1&usg=AOvVaw3wxvy_EfDUepyc6z9ctYdc) 4. [https://trufflesuite.com/truffle/](https://www.google.com/url?q=https%3A%2F%2Ftrufflesuite.com%2Ftruffle%2F&sa=D&sntz=1&usg=AOvVaw1dbGm84hwOf1zhcx6viT9A) 1. npm install truffle -g 5. ERC-20 (500K) 6. ERC721: Non-Fungible Tokens (NFT): 70K 7. ERC1155: Multi-Token Tokens : 8K 8. dApp 9. Security:[ ](https://www.google.com/url?q=https%3A%2F%2Fconsensys.github.io%2Fsmart-contract-best-practices%2F&sa=D&sntz=1&usg=AOvVaw0ABPhCIEaExBvmeOe-D_Hm)[https://consensys.github.io/smart-contract-best-practices/](https://www.google.com/url?q=https%3A%2F%2Fconsensys.github.io%2Fsmart-contract-best-practices%2F&sa=D&sntz=1&usg=AOvVaw0ABPhCIEaExBvmeOe-D_Hm) 2. LinkedIn II 1. Hyperledger.org 2. solidity: [https://docs.soliditylang.org/en/v0.8.13/installing-solidity.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.soliditylang.org%2Fen%2Fv0.8.13%2Finstalling-solidity.html&sa=D&sntz=1&usg=AOvVaw1GDJZfpxlMTD-VyuM3EM1n) 1. docker run ethereum/solc:stable --help 2. brew update 3. brew upgrade 4. brew tap ethereum/ethereum 5. brew install solidity 6. 4\. Visual studio code: Name: solidity Id: JuanBlanco.solidity Description: Ethereum Solidity Language for Visual Studio Code Version: 0.0.139 Publisher: Juan Blanco VS Marketplace Link:[ ](https://www.google.com/url?q=https%3A%2F%2Fmarketplace.visualstudio.com%2Fitems%3FitemName%3DJuanBlanco.solidity&sa=D&sntz=1&usg=AOvVaw3KMZrAVx3FuyLTrUEtjBlI)[https://marketplace.visualstudio.com/items?itemName=JuanBlanco.solidity](https://www.google.com/url?q=https%3A%2F%2Fmarketplace.visualstudio.com%2Fitems%3FitemName%3DJuanBlanco.solidity&sa=D&sntz=1&usg=AOvVaw3KMZrAVx3FuyLTrUEtjBlI) 5\. Online editor: [https://remix.ethereum.org](https://www.google.com/url?q=https%3A%2F%2Fremix.ethereum.org&sa=D&sntz=1&usg=AOvVaw12Y9fbyx49-9qH8CSJfuQ3) اجماع majority rules validate transactions properly computer nodes 51% consensus mechanisms advantage proof of work: * anybody can attached machines and gain rewards blockchain trilemma: 1- scalable/speed 2- decentralization secure fastest: solana (arweave), [](https://drive.google.com/open?id=1iJqtIoSQ1gmMr7tleuFQ6lX_FmiFj7p4FELnCVGT1C4 "Open Spreadsheet, AltCoin in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/sheets_32dp.png)AltCoin # Basic [proof of work/stake](https://www.youtube.com/watch?v=08vnE2_cAeQ) # Links [https://koinly.io/](https://www.google.com/url?q=https%3A%2F%2Fkoinly.io%2F&sa=D&sntz=1&usg=AOvVaw1MlwUKbWcWOp2wDgfk5wTa) [https://chain.link/bootcamp/bootcamp-2021-on- demand](https://www.google.com/url?q=https%3A%2F%2Fchain.link%2Fbootcamp%2Fbootcamp-2021-on- demand&sa=D&sntz=1&usg=AOvVaw3_THOQshewlXoxNX0CKMKf) [https://software.intel.com/content/www/us/en/develop/download/download- maccpuid.html](https://www.google.com/url?q=https%3A%2F%2Fsoftware.intel.com%2Fcontent%2Fwww%2Fus%2Fen%2Fdevelop%2Fdownload%2Fdownload- maccpuid.html&sa=D&sntz=1&usg=AOvVaw3fAaHZJ9DSuYORK5STffBn) [آموزش زبان سالیدیتی(solidity) برای نوشتن اسمارت کانترکت روی شبکه اتریوم، شماره ۱](https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr) [https://cryptozombies.io/](https://www.google.com/url?q=https%3A%2F%2Fcryptozombies.io%2F&sa=D&sntz=1&usg=AOvVaw2gbwUmHX9yWMhTVmpbHEPy) # Hands on ## setup:macOS ### 1: install nvm: Node Version Manager curl -o- https://raw.githubusercontent.com/creationix/nvm/v0.35.2/install.sh | bash nvm install 12 nvm use 12 nvm alias default 12 npm install npm --global # Upgrade npm to the latest version ### 2\. install hardhat: Ethereum development environment for professionals npm install --save-dev hardhat ### 3\. # METAVERSE 1. Coin Bureau: : TOP 5 Virtual Land NFTs!! BEST Metaverse Plays?? 🚀 #metaverse #land #crypto * sandbox * metaverse group buy decentraland: 2.43 M$ * [https://nonfungible.com/](https://www.google.com/url?q=https%3A%2F%2Fnonfungible.com%2F&sa=D&sntz=1&usg=AOvVaw1d1IAffRPgXgbELGML_Zak) : cryptopunks, the sandbox, decentraland, cryptovoxels, somnium space, superworld, arcona. OVR Top 5 Land 1. * * 1. axie infinity * savannah, forest, arctic, mystic, genesis, lunas landing 2. decentraland * * 9K land * ~4500 MANA * 3$ = 13500$ * Decentraland Tutorials: * my land pirahansiah: [https://share.decentraland.org/b/scene/ed5823d2-788c-440f-8875-2614fc139c42](https://www.google.com/url?q=https%3A%2F%2Fshare.decentraland.org%2Fb%2Fscene%2Fed5823d2-788c-440f-8875-2614fc139c42&sa=D&sntz=1&usg=AOvVaw2SHZ1c0evkthfjNgJ8Mcw2) 3. the sandbox * ~2.5 Eth * 3850 = 9625 1. * * 4. bitcountry * create and personalise metaverse * 5. aavegotchi * 2. [https://www.sandbox.game/en/](https://www.google.com/url?q=https%3A%2F%2Fwww.sandbox.game%2Fen%2F&sa=D&sntz=1&usg=AOvVaw0MmdZePFR3AUuLjPjVKlIk) * it is virtual world in ethereum 2011 - 2018 - * open metaverse - the sandbox alpha - 29.11 to 20.12.21 * require the sandbox (SAND) ~ 5$ (17.12.21) ~2.73$ (22.April.2022) ~0.58 (14.11.22) * 3. learn: 4. learn: 5. AI meta: [https://ai.facebook.com/events/neurips2021](https://www.google.com/url?q=https%3A%2F%2Fai.facebook.com%2Fevents%2Fneurips2021&sa=D&sntz=1&usg=AOvVaw3OvYQKZfEVFHqbYen7VYwh) 6. Metaverse, Mesh, Open AI and more from Microsoft Ignite Fall 2021: [https://valoremreply.com/post/metaverse-mesh-openai-microsoft-ignite-fall-2021/?utm_source=social_media&utm_medium=Rene-Reshare&utm_campaign=trends2021](https://www.google.com/url?q=https%3A%2F%2Fvaloremreply.com%2Fpost%2Fmetaverse-mesh-openai-microsoft-ignite-fall-2021%2F%3Futm_source%3Dsocial_media%26utm_medium%3DRene-Reshare%26utm_campaign%3Dtrends2021&sa=D&sntz=1&usg=AOvVaw2aU3w2NugSf1HZh-Neisf5) ## crypto 18.12.21 - 22.April.2022 * Decentraland (MANA): ~3$ - ~2.03$ (22.April.2022) * sandbox (SAND): ~ 5$ - ~2.73$ (22.April.2022) * Axie Infinity (AXS): ~95$ - ~45.65$ (22.April.2022) * illuvium (ILV) : ~1136$ - ~514.98$ (22.April.2022) * star atlas price (ATLAS) base on solana : ~0.10 - ~0.023$ (22.April.2022) * wilder world (wild): ~3.67$ - ~1.15$ (22.April.2022) 1. bitCoin : revolution to currency, 2. DeFi, ethereum 3. NFT 4. Metaverse # NFT * [https://opensea.io/collection/computervision](https://www.google.com/url?q=https%3A%2F%2Fopensea.io%2Fcollection%2Fcomputervision&sa=D&sntz=1&usg=AOvVaw2kn1tyV7uv_uTI9i50hUVQ) * Links: [https://github.com/MetaMask/metamask- mobile](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FMetaMask%2Fmetamask- mobile&sa=D&sntz=1&usg=AOvVaw1YH7hABPeEsRI7ne1ErWR4) [https://readyplayer.me](https://www.google.com/url?q=https%3A%2F%2Freadyplayer.me&sa=D&sntz=1&usg=AOvVaw1q7g9_FKZoK_- FuOwYHQEN) [https://github.com/ish- app/ish](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fish- app%2Fish&sa=D&sntz=1&usg=AOvVaw12yqknRonHBDjzyVJZ543t) [https://getutm.app/install/](https://www.google.com/url?q=https%3A%2F%2Fgetutm.app%2Finstall%2F&sa=D&sntz=1&usg=AOvVaw2i4ClkKwh4GkEdZg7y5WOX) [https://github.com/rileytestut/AltStore](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Frileytestut%2FAltStore&sa=D&sntz=1&usg=AOvVaw1PXzTxhU5BDAOhM0lo3aMS) [https://altstore.io](https://www.google.com/url?q=https%3A%2F%2Faltstore.io&sa=D&sntz=1&usg=AOvVaw3cXCR8rKRUvIWq8tNFytLE) [https://vscode.dev/github/pirahansiah/pirahansiah](https://www.google.com/url?q=https%3A%2F%2Fvscode.dev%2Fgithub%2Fpirahansiah%2Fpirahansiah&sa=D&sntz=1&usg=AOvVaw3ar7dCuBaV4_X4lplrNKXP) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/VBNzuBGkaD3wt3TzoFwX0mTIj70nSDd3lgPVXQioi93Rci- RfkU7Cych8xXEVlNVPXCewW1vMbWtk8T6z_JVwm8=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/VBNzuBGkaD3wt3TzoFwX0mTIj70nSDd3lgPVXQioi93Rci- RfkU7Cych8xXEVlNVPXCewW1vMbWtk8T6z_JVwm8=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # AltCoin Disclaimer/ Risk warning: I am not a financial advisor and anything you read, see or hear in this site, podcast, video should not by any means be construed as financial advice it is purely intended for your entertainment and demonstration and illustrative purposes only. This is not financial advice and should not be taken as financial advice. the views I have in everyone of my site/post/blog/links/text/documents/powerpoint/videos are completely speculative opinions and do not guarantee any specific result. The NFT, AltCoin, Metaverse, ... is extremely volatile and has high risk. You should never act on anyone's advice or opinions, without first doing your own research, realising your own risk, and making your own decision. I recommend speaking with a licensed and qualified professional before making any financial decision. Basic Links Hands on setup:macOS 1: install nvm: Node Version Manager 2\. install hardhat: Ethereum development environment for professionals 3. METAVERSE crypto 18.12.21 - 22.April.2022 NFT Stock * ^ Purchasing managers' indexes (PMI): * A PMI index over 50 represents growth or expansion within the manufacturing sector of the economy compared with the prior month. * ^ United States Philadelphia Fed Manufacturing Index * A value greater than 0 reflects growth in the manufacturing sector, whereas a value less than 0 reflects a contraction. * ![](https://lh4.googleusercontent.com/eNrU_i5TMskU_ZA3cXB0rIVuJlCnE4YgTGpfGaSlrejV9fJMw6GLEOimP- GqHJcZAQTDhr-h1DHW3wQhxXT98zyRp1NlPhLB9s8bNWrxrBjgJlv9QFwpgAdleVPQhW37Fw=w1280) ![](https://lh4.googleusercontent.com/qARdwh01SGT2SdP9lG- PEJeMiqeNun3sP2v2QYwmDwYAunVYxdLJaesG5N5-KnTh1evxyq2hl1MXKofdGDW7PTK9nJ1GYa3nVqLckI3t6WJh2JNRCiQkw_rZC28bphtXvw=w1280) My token _**pirahansiah (TIZ**_ _ **)**_ on Solana Token name pirahansiah (TIZ) Token address FsniTTtb9GeGq1DHkipxera4bsgFkb19maBLKZwMe7in **Token** **pirahansiah** https://solscan.io/token/FsniTTtb9GeGq1DHkipxera4bsgFkb19maBLKZwMe7in My token _**pirahansiah (TIZ)**_ **Token Contract Address** 0xe30407DB873302D6AEaAB3bA619f44Bc9F924594 **Token Decimal:** 18 **Network:** BNB Smart Chain Mainnet only 100 token available to sell [https://bscscan.com/token/0xe30407db873302d6aeaab3ba619f44bc9f924594](https://www.google.com/url?q=https%3A%2F%2Fbscscan.com%2Ftoken%2F0xe30407db873302d6aeaab3ba619f44bc9f924594&sa=D&sntz=1&usg=AOvVaw3owOOObdS- gyWJtD_682hE) [https://admin.moralis.io/speedyNodes](https://www.google.com/url?q=https%3A%2F%2Fadmin.moralis.io%2FspeedyNodes&sa=D&sntz=1&usg=AOvVaw1e_X2Eutx- uPjibkv51EdW) [https://testnet.binance.org/faucet- smart](https://www.google.com/url?q=https%3A%2F%2Ftestnet.binance.org%2Ffaucet- smart&sa=D&sntz=1&usg=AOvVaw0YuT9b0CIeLmtQULao873v) [https://github.com/OpenZeppelin/openzeppelin- contracts/blob/master/contracts/token/ERC20/ERC20.sol](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FOpenZeppelin%2Fopenzeppelin- contracts%2Fblob%2Fmaster%2Fcontracts%2Ftoken%2FERC20%2FERC20.sol&sa=D&sntz=1&usg=AOvVaw3JW77-QoFjcv- DNK3whJT2) [https://remix.ethereum.org/#optimize=false&runs=200&evmVersion=null&version=soljson-v0.8.7+commit.e28d00a7.js](https://www.google.com/url?q=https%3A%2F%2Fremix.ethereum.org%2F%23optimize%3Dfalse%26runs%3D200%26evmVersion%3Dnull%26version%3Dsoljson-v0.8.7%2Bcommit.e28d00a7.js&sa=D&sntz=1&usg=AOvVaw39lC9qXtU3OwPP5mxpf9qK) [https://bscscan.com/token/0xe30407db873302d6aeaab3ba619f44bc9f924594](https://www.google.com/url?q=https%3A%2F%2Fbscscan.com%2Ftoken%2F0xe30407db873302d6aeaab3ba619f44bc9f924594&sa=D&sntz=1&usg=AOvVaw3owOOObdS- gyWJtD_682hE) [https://github.com/OpenZeppelin/openzeppelin- contracts/blob/master/contracts/token/ERC20/ERC20.sol](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FOpenZeppelin%2Fopenzeppelin- contracts%2Fblob%2Fmaster%2Fcontracts%2Ftoken%2FERC20%2FERC20.sol&sa=D&sntz=1&usg=AOvVaw3JW77-QoFjcv- DNK3whJT2) * how to modify the code * [https://www.tradingview.com](https://www.google.com/url?q=https%3A%2F%2Fwww.tradingview.com&sa=D&sntz=1&usg=AOvVaw15P-ffkNM5fCBySsWtLXHo) best tools to analysis market * [https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr](https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr) Persian Interface: minimum data and functions required to make it a standard ERC/EIP smart contract _value the _ shows it is a parameters Constant is a variable that can't be changed Mapping() function maps elements from a key to a value Constructor() function that automatically runs when a new data item is created( initialization code) Emit() function triggers an event (message to be sent out) 1. New project 1. ./geth --syncmode "light" 2. Mkdir 3. Truffle init Truffle deploy --reset Truffle console HelloWorld.deployed().then(function(instance) {return instance} ); HelloWorld.deployed().then(function(instance) {return instance.getHelloMessage()} ); npm install @truffle/hdwallet-provider 1. LinkedIn 1. Start 10.April.2022 2. [https://geth.ethereum.org/](https://www.google.com/url?q=https%3A%2F%2Fgeth.ethereum.org%2F&sa=D&sntz=1&usg=AOvVaw1XnmIZJr5e4_oBt8pPHXQK) 3. [https://trufflesuite.com/ganache/](https://www.google.com/url?q=https%3A%2F%2Ftrufflesuite.com%2Fganache%2F&sa=D&sntz=1&usg=AOvVaw3wxvy_EfDUepyc6z9ctYdc) 4. [https://trufflesuite.com/truffle/](https://www.google.com/url?q=https%3A%2F%2Ftrufflesuite.com%2Ftruffle%2F&sa=D&sntz=1&usg=AOvVaw1dbGm84hwOf1zhcx6viT9A) 1. npm install truffle -g 5. ERC-20 (500K) 6. ERC721: Non-Fungible Tokens (NFT): 70K 7. ERC1155: Multi-Token Tokens : 8K 8. dApp 9. Security:[ ](https://www.google.com/url?q=https%3A%2F%2Fconsensys.github.io%2Fsmart-contract-best-practices%2F&sa=D&sntz=1&usg=AOvVaw0ABPhCIEaExBvmeOe-D_Hm)[https://consensys.github.io/smart-contract-best-practices/](https://www.google.com/url?q=https%3A%2F%2Fconsensys.github.io%2Fsmart-contract-best-practices%2F&sa=D&sntz=1&usg=AOvVaw0ABPhCIEaExBvmeOe-D_Hm) 2. LinkedIn II 1. Hyperledger.org 2. solidity: [https://docs.soliditylang.org/en/v0.8.13/installing-solidity.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.soliditylang.org%2Fen%2Fv0.8.13%2Finstalling-solidity.html&sa=D&sntz=1&usg=AOvVaw1GDJZfpxlMTD-VyuM3EM1n) 1. docker run ethereum/solc:stable --help 2. brew update 3. brew upgrade 4. brew tap ethereum/ethereum 5. brew install solidity 6. 4\. Visual studio code: Name: solidity Id: JuanBlanco.solidity Description: Ethereum Solidity Language for Visual Studio Code Version: 0.0.139 Publisher: Juan Blanco VS Marketplace Link:[ ](https://www.google.com/url?q=https%3A%2F%2Fmarketplace.visualstudio.com%2Fitems%3FitemName%3DJuanBlanco.solidity&sa=D&sntz=1&usg=AOvVaw3KMZrAVx3FuyLTrUEtjBlI)[https://marketplace.visualstudio.com/items?itemName=JuanBlanco.solidity](https://www.google.com/url?q=https%3A%2F%2Fmarketplace.visualstudio.com%2Fitems%3FitemName%3DJuanBlanco.solidity&sa=D&sntz=1&usg=AOvVaw3KMZrAVx3FuyLTrUEtjBlI) 5\. Online editor: [https://remix.ethereum.org](https://www.google.com/url?q=https%3A%2F%2Fremix.ethereum.org&sa=D&sntz=1&usg=AOvVaw12Y9fbyx49-9qH8CSJfuQ3) اجماع majority rules validate transactions properly computer nodes 51% consensus mechanisms advantage proof of work: * anybody can attached machines and gain rewards blockchain trilemma: 1- scalable/speed 2- decentralization secure fastest: solana (arweave), [](https://drive.google.com/open?id=1iJqtIoSQ1gmMr7tleuFQ6lX_FmiFj7p4FELnCVGT1C4 "Open Spreadsheet, AltCoin in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/sheets_32dp.png)AltCoin # Basic [proof of work/stake](https://www.youtube.com/watch?v=08vnE2_cAeQ) # Links [https://koinly.io/](https://www.google.com/url?q=https%3A%2F%2Fkoinly.io%2F&sa=D&sntz=1&usg=AOvVaw1MlwUKbWcWOp2wDgfk5wTa) [https://chain.link/bootcamp/bootcamp-2021-on- demand](https://www.google.com/url?q=https%3A%2F%2Fchain.link%2Fbootcamp%2Fbootcamp-2021-on- demand&sa=D&sntz=1&usg=AOvVaw3_THOQshewlXoxNX0CKMKf) [https://software.intel.com/content/www/us/en/develop/download/download- maccpuid.html](https://www.google.com/url?q=https%3A%2F%2Fsoftware.intel.com%2Fcontent%2Fwww%2Fus%2Fen%2Fdevelop%2Fdownload%2Fdownload- maccpuid.html&sa=D&sntz=1&usg=AOvVaw3fAaHZJ9DSuYORK5STffBn) [آموزش زبان سالیدیتی(solidity) برای نوشتن اسمارت کانترکت روی شبکه اتریوم، شماره ۱](https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr) [https://cryptozombies.io/](https://www.google.com/url?q=https%3A%2F%2Fcryptozombies.io%2F&sa=D&sntz=1&usg=AOvVaw2gbwUmHX9yWMhTVmpbHEPy) # Hands on ## setup:macOS ### 1: install nvm: Node Version Manager curl -o- https://raw.githubusercontent.com/creationix/nvm/v0.35.2/install.sh | bash nvm install 12 nvm use 12 nvm alias default 12 npm install npm --global # Upgrade npm to the latest version ### 2\. install hardhat: Ethereum development environment for professionals npm install --save-dev hardhat ### 3\. # METAVERSE 1. Coin Bureau: : TOP 5 Virtual Land NFTs!! BEST Metaverse Plays?? 🚀 #metaverse #land #crypto * sandbox * metaverse group buy decentraland: 2.43 M$ * [https://nonfungible.com/](https://www.google.com/url?q=https%3A%2F%2Fnonfungible.com%2F&sa=D&sntz=1&usg=AOvVaw1d1IAffRPgXgbELGML_Zak) : cryptopunks, the sandbox, decentraland, cryptovoxels, somnium space, superworld, arcona. OVR Top 5 Land 1. * * 1. axie infinity * savannah, forest, arctic, mystic, genesis, lunas landing 2. decentraland * * 9K land * ~4500 MANA * 3$ = 13500$ * Decentraland Tutorials: * my land pirahansiah: [https://share.decentraland.org/b/scene/ed5823d2-788c-440f-8875-2614fc139c42](https://www.google.com/url?q=https%3A%2F%2Fshare.decentraland.org%2Fb%2Fscene%2Fed5823d2-788c-440f-8875-2614fc139c42&sa=D&sntz=1&usg=AOvVaw2SHZ1c0evkthfjNgJ8Mcw2) 3. the sandbox * ~2.5 Eth * 3850 = 9625 1. * * 4. bitcountry * create and personalise metaverse * 5. aavegotchi * 2. [https://www.sandbox.game/en/](https://www.google.com/url?q=https%3A%2F%2Fwww.sandbox.game%2Fen%2F&sa=D&sntz=1&usg=AOvVaw0MmdZePFR3AUuLjPjVKlIk) * it is virtual world in ethereum 2011 - 2018 - * open metaverse - the sandbox alpha - 29.11 to 20.12.21 * require the sandbox (SAND) ~ 5$ (17.12.21) ~2.73$ (22.April.2022) ~0.58 (14.11.22) * 3. learn: 4. learn: 5. AI meta: [https://ai.facebook.com/events/neurips2021](https://www.google.com/url?q=https%3A%2F%2Fai.facebook.com%2Fevents%2Fneurips2021&sa=D&sntz=1&usg=AOvVaw3OvYQKZfEVFHqbYen7VYwh) 6. Metaverse, Mesh, Open AI and more from Microsoft Ignite Fall 2021: [https://valoremreply.com/post/metaverse-mesh-openai-microsoft-ignite-fall-2021/?utm_source=social_media&utm_medium=Rene-Reshare&utm_campaign=trends2021](https://www.google.com/url?q=https%3A%2F%2Fvaloremreply.com%2Fpost%2Fmetaverse-mesh-openai-microsoft-ignite-fall-2021%2F%3Futm_source%3Dsocial_media%26utm_medium%3DRene-Reshare%26utm_campaign%3Dtrends2021&sa=D&sntz=1&usg=AOvVaw2aU3w2NugSf1HZh-Neisf5) ## crypto 18.12.21 - 22.April.2022 * Decentraland (MANA): ~3$ - ~2.03$ (22.April.2022) * sandbox (SAND): ~ 5$ - ~2.73$ (22.April.2022) * Axie Infinity (AXS): ~95$ - ~45.65$ (22.April.2022) * illuvium (ILV) : ~1136$ - ~514.98$ (22.April.2022) * star atlas price (ATLAS) base on solana : ~0.10 - ~0.023$ (22.April.2022) * wilder world (wild): ~3.67$ - ~1.15$ (22.April.2022) 1. bitCoin : revolution to currency, 2. DeFi, ethereum 3. NFT 4. Metaverse # NFT * [https://opensea.io/collection/computervision](https://www.google.com/url?q=https%3A%2F%2Fopensea.io%2Fcollection%2Fcomputervision&sa=D&sntz=1&usg=AOvVaw2kn1tyV7uv_uTI9i50hUVQ) * Links: [https://github.com/MetaMask/metamask- mobile](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FMetaMask%2Fmetamask- mobile&sa=D&sntz=1&usg=AOvVaw1YH7hABPeEsRI7ne1ErWR4) [https://readyplayer.me](https://www.google.com/url?q=https%3A%2F%2Freadyplayer.me&sa=D&sntz=1&usg=AOvVaw1q7g9_FKZoK_- FuOwYHQEN) [https://github.com/ish- app/ish](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fish- app%2Fish&sa=D&sntz=1&usg=AOvVaw12yqknRonHBDjzyVJZ543t) [https://getutm.app/install/](https://www.google.com/url?q=https%3A%2F%2Fgetutm.app%2Finstall%2F&sa=D&sntz=1&usg=AOvVaw2i4ClkKwh4GkEdZg7y5WOX) [https://github.com/rileytestut/AltStore](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Frileytestut%2FAltStore&sa=D&sntz=1&usg=AOvVaw1PXzTxhU5BDAOhM0lo3aMS) [https://altstore.io](https://www.google.com/url?q=https%3A%2F%2Faltstore.io&sa=D&sntz=1&usg=AOvVaw3cXCR8rKRUvIWq8tNFytLE) [https://vscode.dev/github/pirahansiah/pirahansiah](https://www.google.com/url?q=https%3A%2F%2Fvscode.dev%2Fgithub%2Fpirahansiah%2Fpirahansiah&sa=D&sntz=1&usg=AOvVaw3ar7dCuBaV4_X4lplrNKXP) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/VBNzuBGkaD3wt3TzoFwX0mTIj70nSDd3lgPVXQioi93Rci- RfkU7Cych8xXEVlNVPXCewW1vMbWtk8T6z_JVwm8=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/VBNzuBGkaD3wt3TzoFwX0mTIj70nSDd3lgPVXQioi93Rci- RfkU7Cych8xXEVlNVPXCewW1vMbWtk8T6z_JVwm8=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # AltCoin Disclaimer/ Risk warning: I am not a financial advisor and anything you read, see or hear in this site, podcast, video should not by any means be construed as financial advice it is purely intended for your entertainment and demonstration and illustrative purposes only. This is not financial advice and should not be taken as financial advice. the views I have in everyone of my site/post/blog/links/text/documents/powerpoint/videos are completely speculative opinions and do not guarantee any specific result. The NFT, AltCoin, Metaverse, ... is extremely volatile and has high risk. You should never act on anyone's advice or opinions, without first doing your own research, realising your own risk, and making your own decision. I recommend speaking with a licensed and qualified professional before making any financial decision. Basic Links Hands on setup:macOS 1: install nvm: Node Version Manager 2\. install hardhat: Ethereum development environment for professionals 3. METAVERSE crypto 18.12.21 - 22.April.2022 NFT Stock * ^ Purchasing managers' indexes (PMI): * A PMI index over 50 represents growth or expansion within the manufacturing sector of the economy compared with the prior month. * ^ United States Philadelphia Fed Manufacturing Index * A value greater than 0 reflects growth in the manufacturing sector, whereas a value less than 0 reflects a contraction. * ![](https://lh4.googleusercontent.com/eNrU_i5TMskU_ZA3cXB0rIVuJlCnE4YgTGpfGaSlrejV9fJMw6GLEOimP- GqHJcZAQTDhr-h1DHW3wQhxXT98zyRp1NlPhLB9s8bNWrxrBjgJlv9QFwpgAdleVPQhW37Fw=w1280) ![](https://lh4.googleusercontent.com/qARdwh01SGT2SdP9lG- PEJeMiqeNun3sP2v2QYwmDwYAunVYxdLJaesG5N5-KnTh1evxyq2hl1MXKofdGDW7PTK9nJ1GYa3nVqLckI3t6WJh2JNRCiQkw_rZC28bphtXvw=w1280) My token _**pirahansiah (TIZ**_ _ **)**_ on Solana Token name pirahansiah (TIZ) Token address FsniTTtb9GeGq1DHkipxera4bsgFkb19maBLKZwMe7in **Token** **pirahansiah** https://solscan.io/token/FsniTTtb9GeGq1DHkipxera4bsgFkb19maBLKZwMe7in My token _**pirahansiah (TIZ)**_ **Token Contract Address** 0xe30407DB873302D6AEaAB3bA619f44Bc9F924594 **Token Decimal:** 18 **Network:** BNB Smart Chain Mainnet only 100 token available to sell [https://bscscan.com/token/0xe30407db873302d6aeaab3ba619f44bc9f924594](https://www.google.com/url?q=https%3A%2F%2Fbscscan.com%2Ftoken%2F0xe30407db873302d6aeaab3ba619f44bc9f924594&sa=D&sntz=1&usg=AOvVaw3owOOObdS- gyWJtD_682hE) [https://admin.moralis.io/speedyNodes](https://www.google.com/url?q=https%3A%2F%2Fadmin.moralis.io%2FspeedyNodes&sa=D&sntz=1&usg=AOvVaw1e_X2Eutx- uPjibkv51EdW) [https://testnet.binance.org/faucet- smart](https://www.google.com/url?q=https%3A%2F%2Ftestnet.binance.org%2Ffaucet- smart&sa=D&sntz=1&usg=AOvVaw0YuT9b0CIeLmtQULao873v) [https://github.com/OpenZeppelin/openzeppelin- contracts/blob/master/contracts/token/ERC20/ERC20.sol](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FOpenZeppelin%2Fopenzeppelin- contracts%2Fblob%2Fmaster%2Fcontracts%2Ftoken%2FERC20%2FERC20.sol&sa=D&sntz=1&usg=AOvVaw3JW77-QoFjcv- DNK3whJT2) [https://remix.ethereum.org/#optimize=false&runs=200&evmVersion=null&version=soljson-v0.8.7+commit.e28d00a7.js](https://www.google.com/url?q=https%3A%2F%2Fremix.ethereum.org%2F%23optimize%3Dfalse%26runs%3D200%26evmVersion%3Dnull%26version%3Dsoljson-v0.8.7%2Bcommit.e28d00a7.js&sa=D&sntz=1&usg=AOvVaw39lC9qXtU3OwPP5mxpf9qK) [https://bscscan.com/token/0xe30407db873302d6aeaab3ba619f44bc9f924594](https://www.google.com/url?q=https%3A%2F%2Fbscscan.com%2Ftoken%2F0xe30407db873302d6aeaab3ba619f44bc9f924594&sa=D&sntz=1&usg=AOvVaw3owOOObdS- gyWJtD_682hE) [https://github.com/OpenZeppelin/openzeppelin- contracts/blob/master/contracts/token/ERC20/ERC20.sol](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FOpenZeppelin%2Fopenzeppelin- contracts%2Fblob%2Fmaster%2Fcontracts%2Ftoken%2FERC20%2FERC20.sol&sa=D&sntz=1&usg=AOvVaw3JW77-QoFjcv- DNK3whJT2) * how to modify the code * [https://www.tradingview.com](https://www.google.com/url?q=https%3A%2F%2Fwww.tradingview.com&sa=D&sntz=1&usg=AOvVaw15P-ffkNM5fCBySsWtLXHo) best tools to analysis market * [https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr](https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr) Persian Interface: minimum data and functions required to make it a standard ERC/EIP smart contract _value the _ shows it is a parameters Constant is a variable that can't be changed Mapping() function maps elements from a key to a value Constructor() function that automatically runs when a new data item is created( initialization code) Emit() function triggers an event (message to be sent out) 1. New project 1. ./geth --syncmode "light" 2. Mkdir 3. Truffle init Truffle deploy --reset Truffle console HelloWorld.deployed().then(function(instance) {return instance} ); HelloWorld.deployed().then(function(instance) {return instance.getHelloMessage()} ); npm install @truffle/hdwallet-provider 1. LinkedIn 1. Start 10.April.2022 2. [https://geth.ethereum.org/](https://www.google.com/url?q=https%3A%2F%2Fgeth.ethereum.org%2F&sa=D&sntz=1&usg=AOvVaw1XnmIZJr5e4_oBt8pPHXQK) 3. [https://trufflesuite.com/ganache/](https://www.google.com/url?q=https%3A%2F%2Ftrufflesuite.com%2Fganache%2F&sa=D&sntz=1&usg=AOvVaw3wxvy_EfDUepyc6z9ctYdc) 4. [https://trufflesuite.com/truffle/](https://www.google.com/url?q=https%3A%2F%2Ftrufflesuite.com%2Ftruffle%2F&sa=D&sntz=1&usg=AOvVaw1dbGm84hwOf1zhcx6viT9A) 1. npm install truffle -g 5. ERC-20 (500K) 6. ERC721: Non-Fungible Tokens (NFT): 70K 7. ERC1155: Multi-Token Tokens : 8K 8. dApp 9. Security:[ ](https://www.google.com/url?q=https%3A%2F%2Fconsensys.github.io%2Fsmart-contract-best-practices%2F&sa=D&sntz=1&usg=AOvVaw0ABPhCIEaExBvmeOe-D_Hm)[https://consensys.github.io/smart-contract-best-practices/](https://www.google.com/url?q=https%3A%2F%2Fconsensys.github.io%2Fsmart-contract-best-practices%2F&sa=D&sntz=1&usg=AOvVaw0ABPhCIEaExBvmeOe-D_Hm) 2. LinkedIn II 1. Hyperledger.org 2. solidity: [https://docs.soliditylang.org/en/v0.8.13/installing-solidity.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.soliditylang.org%2Fen%2Fv0.8.13%2Finstalling-solidity.html&sa=D&sntz=1&usg=AOvVaw1GDJZfpxlMTD-VyuM3EM1n) 1. docker run ethereum/solc:stable --help 2. brew update 3. brew upgrade 4. brew tap ethereum/ethereum 5. brew install solidity 6. 4\. Visual studio code: Name: solidity Id: JuanBlanco.solidity Description: Ethereum Solidity Language for Visual Studio Code Version: 0.0.139 Publisher: Juan Blanco VS Marketplace Link:[ ](https://www.google.com/url?q=https%3A%2F%2Fmarketplace.visualstudio.com%2Fitems%3FitemName%3DJuanBlanco.solidity&sa=D&sntz=1&usg=AOvVaw3KMZrAVx3FuyLTrUEtjBlI)[https://marketplace.visualstudio.com/items?itemName=JuanBlanco.solidity](https://www.google.com/url?q=https%3A%2F%2Fmarketplace.visualstudio.com%2Fitems%3FitemName%3DJuanBlanco.solidity&sa=D&sntz=1&usg=AOvVaw3KMZrAVx3FuyLTrUEtjBlI) 5\. Online editor: [https://remix.ethereum.org](https://www.google.com/url?q=https%3A%2F%2Fremix.ethereum.org&sa=D&sntz=1&usg=AOvVaw12Y9fbyx49-9qH8CSJfuQ3) اجماع majority rules validate transactions properly computer nodes 51% consensus mechanisms advantage proof of work: * anybody can attached machines and gain rewards blockchain trilemma: 1- scalable/speed 2- decentralization secure fastest: solana (arweave), [](https://drive.google.com/open?id=1iJqtIoSQ1gmMr7tleuFQ6lX_FmiFj7p4FELnCVGT1C4 "Open Spreadsheet, AltCoin in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/sheets_32dp.png)AltCoin # Basic [proof of work/stake](https://www.youtube.com/watch?v=08vnE2_cAeQ) # Links [https://koinly.io/](https://www.google.com/url?q=https%3A%2F%2Fkoinly.io%2F&sa=D&sntz=1&usg=AOvVaw1MlwUKbWcWOp2wDgfk5wTa) [https://chain.link/bootcamp/bootcamp-2021-on- demand](https://www.google.com/url?q=https%3A%2F%2Fchain.link%2Fbootcamp%2Fbootcamp-2021-on- demand&sa=D&sntz=1&usg=AOvVaw3_THOQshewlXoxNX0CKMKf) [https://software.intel.com/content/www/us/en/develop/download/download- maccpuid.html](https://www.google.com/url?q=https%3A%2F%2Fsoftware.intel.com%2Fcontent%2Fwww%2Fus%2Fen%2Fdevelop%2Fdownload%2Fdownload- maccpuid.html&sa=D&sntz=1&usg=AOvVaw3fAaHZJ9DSuYORK5STffBn) [آموزش زبان سالیدیتی(solidity) برای نوشتن اسمارت کانترکت روی شبکه اتریوم، شماره ۱](https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr) [https://cryptozombies.io/](https://www.google.com/url?q=https%3A%2F%2Fcryptozombies.io%2F&sa=D&sntz=1&usg=AOvVaw2gbwUmHX9yWMhTVmpbHEPy) # Hands on ## setup:macOS ### 1: install nvm: Node Version Manager curl -o- https://raw.githubusercontent.com/creationix/nvm/v0.35.2/install.sh | bash nvm install 12 nvm use 12 nvm alias default 12 npm install npm --global # Upgrade npm to the latest version ### 2\. install hardhat: Ethereum development environment for professionals npm install --save-dev hardhat ### 3\. # METAVERSE 1. Coin Bureau: : TOP 5 Virtual Land NFTs!! BEST Metaverse Plays?? 🚀 #metaverse #land #crypto * sandbox * metaverse group buy decentraland: 2.43 M$ * [https://nonfungible.com/](https://www.google.com/url?q=https%3A%2F%2Fnonfungible.com%2F&sa=D&sntz=1&usg=AOvVaw1d1IAffRPgXgbELGML_Zak) : cryptopunks, the sandbox, decentraland, cryptovoxels, somnium space, superworld, arcona. OVR Top 5 Land 1. * * 1. axie infinity * savannah, forest, arctic, mystic, genesis, lunas landing 2. decentraland * * 9K land * ~4500 MANA * 3$ = 13500$ * Decentraland Tutorials: * my land pirahansiah: [https://share.decentraland.org/b/scene/ed5823d2-788c-440f-8875-2614fc139c42](https://www.google.com/url?q=https%3A%2F%2Fshare.decentraland.org%2Fb%2Fscene%2Fed5823d2-788c-440f-8875-2614fc139c42&sa=D&sntz=1&usg=AOvVaw2SHZ1c0evkthfjNgJ8Mcw2) 3. the sandbox * ~2.5 Eth * 3850 = 9625 1. * * 4. bitcountry * create and personalise metaverse * 5. aavegotchi * 2. [https://www.sandbox.game/en/](https://www.google.com/url?q=https%3A%2F%2Fwww.sandbox.game%2Fen%2F&sa=D&sntz=1&usg=AOvVaw0MmdZePFR3AUuLjPjVKlIk) * it is virtual world in ethereum 2011 - 2018 - * open metaverse - the sandbox alpha - 29.11 to 20.12.21 * require the sandbox (SAND) ~ 5$ (17.12.21) ~2.73$ (22.April.2022) ~0.58 (14.11.22) * 3. learn: 4. learn: 5. AI meta: [https://ai.facebook.com/events/neurips2021](https://www.google.com/url?q=https%3A%2F%2Fai.facebook.com%2Fevents%2Fneurips2021&sa=D&sntz=1&usg=AOvVaw3OvYQKZfEVFHqbYen7VYwh) 6. Metaverse, Mesh, Open AI and more from Microsoft Ignite Fall 2021: [https://valoremreply.com/post/metaverse-mesh-openai-microsoft-ignite-fall-2021/?utm_source=social_media&utm_medium=Rene-Reshare&utm_campaign=trends2021](https://www.google.com/url?q=https%3A%2F%2Fvaloremreply.com%2Fpost%2Fmetaverse-mesh-openai-microsoft-ignite-fall-2021%2F%3Futm_source%3Dsocial_media%26utm_medium%3DRene-Reshare%26utm_campaign%3Dtrends2021&sa=D&sntz=1&usg=AOvVaw2aU3w2NugSf1HZh-Neisf5) ## crypto 18.12.21 - 22.April.2022 * Decentraland (MANA): ~3$ - ~2.03$ (22.April.2022) * sandbox (SAND): ~ 5$ - ~2.73$ (22.April.2022) * Axie Infinity (AXS): ~95$ - ~45.65$ (22.April.2022) * illuvium (ILV) : ~1136$ - ~514.98$ (22.April.2022) * star atlas price (ATLAS) base on solana : ~0.10 - ~0.023$ (22.April.2022) * wilder world (wild): ~3.67$ - ~1.15$ (22.April.2022) 1. bitCoin : revolution to currency, 2. DeFi, ethereum 3. NFT 4. Metaverse # NFT * [https://opensea.io/collection/computervision](https://www.google.com/url?q=https%3A%2F%2Fopensea.io%2Fcollection%2Fcomputervision&sa=D&sntz=1&usg=AOvVaw2kn1tyV7uv_uTI9i50hUVQ) * Links: [https://github.com/MetaMask/metamask- mobile](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FMetaMask%2Fmetamask- mobile&sa=D&sntz=1&usg=AOvVaw1YH7hABPeEsRI7ne1ErWR4) [https://readyplayer.me](https://www.google.com/url?q=https%3A%2F%2Freadyplayer.me&sa=D&sntz=1&usg=AOvVaw1q7g9_FKZoK_- FuOwYHQEN) [https://github.com/ish- app/ish](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fish- app%2Fish&sa=D&sntz=1&usg=AOvVaw12yqknRonHBDjzyVJZ543t) [https://getutm.app/install/](https://www.google.com/url?q=https%3A%2F%2Fgetutm.app%2Finstall%2F&sa=D&sntz=1&usg=AOvVaw2i4ClkKwh4GkEdZg7y5WOX) [https://github.com/rileytestut/AltStore](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Frileytestut%2FAltStore&sa=D&sntz=1&usg=AOvVaw1PXzTxhU5BDAOhM0lo3aMS) [https://altstore.io](https://www.google.com/url?q=https%3A%2F%2Faltstore.io&sa=D&sntz=1&usg=AOvVaw3cXCR8rKRUvIWq8tNFytLE) [https://vscode.dev/github/pirahansiah/pirahansiah](https://www.google.com/url?q=https%3A%2F%2Fvscode.dev%2Fgithub%2Fpirahansiah%2Fpirahansiah&sa=D&sntz=1&usg=AOvVaw3ar7dCuBaV4_X4lplrNKXP) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/VBNzuBGkaD3wt3TzoFwX0mTIj70nSDd3lgPVXQioi93Rci- RfkU7Cych8xXEVlNVPXCewW1vMbWtk8T6z_JVwm8=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/VBNzuBGkaD3wt3TzoFwX0mTIj70nSDd3lgPVXQioi93Rci- RfkU7Cych8xXEVlNVPXCewW1vMbWtk8T6z_JVwm8=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # AltCoin Disclaimer/ Risk warning: I am not a financial advisor and anything you read, see or hear in this site, podcast, video should not by any means be construed as financial advice it is purely intended for your entertainment and demonstration and illustrative purposes only. This is not financial advice and should not be taken as financial advice. the views I have in everyone of my site/post/blog/links/text/documents/powerpoint/videos are completely speculative opinions and do not guarantee any specific result. The NFT, AltCoin, Metaverse, ... is extremely volatile and has high risk. You should never act on anyone's advice or opinions, without first doing your own research, realising your own risk, and making your own decision. I recommend speaking with a licensed and qualified professional before making any financial decision. Basic Links Hands on setup:macOS 1: install nvm: Node Version Manager 2\. install hardhat: Ethereum development environment for professionals 3. METAVERSE crypto 18.12.21 - 22.April.2022 NFT Stock * ^ Purchasing managers' indexes (PMI): * A PMI index over 50 represents growth or expansion within the manufacturing sector of the economy compared with the prior month. * ^ United States Philadelphia Fed Manufacturing Index * A value greater than 0 reflects growth in the manufacturing sector, whereas a value less than 0 reflects a contraction. * ![](https://lh4.googleusercontent.com/eNrU_i5TMskU_ZA3cXB0rIVuJlCnE4YgTGpfGaSlrejV9fJMw6GLEOimP- GqHJcZAQTDhr-h1DHW3wQhxXT98zyRp1NlPhLB9s8bNWrxrBjgJlv9QFwpgAdleVPQhW37Fw=w1280) ![](https://lh4.googleusercontent.com/qARdwh01SGT2SdP9lG- PEJeMiqeNun3sP2v2QYwmDwYAunVYxdLJaesG5N5-KnTh1evxyq2hl1MXKofdGDW7PTK9nJ1GYa3nVqLckI3t6WJh2JNRCiQkw_rZC28bphtXvw=w1280) My token _**pirahansiah (TIZ**_ _ **)**_ on Solana Token name pirahansiah (TIZ) Token address FsniTTtb9GeGq1DHkipxera4bsgFkb19maBLKZwMe7in **Token** **pirahansiah** https://solscan.io/token/FsniTTtb9GeGq1DHkipxera4bsgFkb19maBLKZwMe7in My token _**pirahansiah (TIZ)**_ **Token Contract Address** 0xe30407DB873302D6AEaAB3bA619f44Bc9F924594 **Token Decimal:** 18 **Network:** BNB Smart Chain Mainnet only 100 token available to sell [https://bscscan.com/token/0xe30407db873302d6aeaab3ba619f44bc9f924594](https://www.google.com/url?q=https%3A%2F%2Fbscscan.com%2Ftoken%2F0xe30407db873302d6aeaab3ba619f44bc9f924594&sa=D&sntz=1&usg=AOvVaw3owOOObdS- gyWJtD_682hE) [https://admin.moralis.io/speedyNodes](https://www.google.com/url?q=https%3A%2F%2Fadmin.moralis.io%2FspeedyNodes&sa=D&sntz=1&usg=AOvVaw1e_X2Eutx- uPjibkv51EdW) [https://testnet.binance.org/faucet- smart](https://www.google.com/url?q=https%3A%2F%2Ftestnet.binance.org%2Ffaucet- smart&sa=D&sntz=1&usg=AOvVaw0YuT9b0CIeLmtQULao873v) [https://github.com/OpenZeppelin/openzeppelin- contracts/blob/master/contracts/token/ERC20/ERC20.sol](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FOpenZeppelin%2Fopenzeppelin- contracts%2Fblob%2Fmaster%2Fcontracts%2Ftoken%2FERC20%2FERC20.sol&sa=D&sntz=1&usg=AOvVaw3JW77-QoFjcv- DNK3whJT2) [https://remix.ethereum.org/#optimize=false&runs=200&evmVersion=null&version=soljson-v0.8.7+commit.e28d00a7.js](https://www.google.com/url?q=https%3A%2F%2Fremix.ethereum.org%2F%23optimize%3Dfalse%26runs%3D200%26evmVersion%3Dnull%26version%3Dsoljson-v0.8.7%2Bcommit.e28d00a7.js&sa=D&sntz=1&usg=AOvVaw39lC9qXtU3OwPP5mxpf9qK) [https://bscscan.com/token/0xe30407db873302d6aeaab3ba619f44bc9f924594](https://www.google.com/url?q=https%3A%2F%2Fbscscan.com%2Ftoken%2F0xe30407db873302d6aeaab3ba619f44bc9f924594&sa=D&sntz=1&usg=AOvVaw3owOOObdS- gyWJtD_682hE) [https://github.com/OpenZeppelin/openzeppelin- contracts/blob/master/contracts/token/ERC20/ERC20.sol](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FOpenZeppelin%2Fopenzeppelin- contracts%2Fblob%2Fmaster%2Fcontracts%2Ftoken%2FERC20%2FERC20.sol&sa=D&sntz=1&usg=AOvVaw3JW77-QoFjcv- DNK3whJT2) * how to modify the code * [https://www.tradingview.com](https://www.google.com/url?q=https%3A%2F%2Fwww.tradingview.com&sa=D&sntz=1&usg=AOvVaw15P-ffkNM5fCBySsWtLXHo) best tools to analysis market * [https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr](https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr) Persian Interface: minimum data and functions required to make it a standard ERC/EIP smart contract _value the _ shows it is a parameters Constant is a variable that can't be changed Mapping() function maps elements from a key to a value Constructor() function that automatically runs when a new data item is created( initialization code) Emit() function triggers an event (message to be sent out) 1. New project 1. ./geth --syncmode "light" 2. Mkdir 3. Truffle init Truffle deploy --reset Truffle console HelloWorld.deployed().then(function(instance) {return instance} ); HelloWorld.deployed().then(function(instance) {return instance.getHelloMessage()} ); npm install @truffle/hdwallet-provider 1. LinkedIn 1. Start 10.April.2022 2. [https://geth.ethereum.org/](https://www.google.com/url?q=https%3A%2F%2Fgeth.ethereum.org%2F&sa=D&sntz=1&usg=AOvVaw1XnmIZJr5e4_oBt8pPHXQK) 3. [https://trufflesuite.com/ganache/](https://www.google.com/url?q=https%3A%2F%2Ftrufflesuite.com%2Fganache%2F&sa=D&sntz=1&usg=AOvVaw3wxvy_EfDUepyc6z9ctYdc) 4. [https://trufflesuite.com/truffle/](https://www.google.com/url?q=https%3A%2F%2Ftrufflesuite.com%2Ftruffle%2F&sa=D&sntz=1&usg=AOvVaw1dbGm84hwOf1zhcx6viT9A) 1. npm install truffle -g 5. ERC-20 (500K) 6. ERC721: Non-Fungible Tokens (NFT): 70K 7. ERC1155: Multi-Token Tokens : 8K 8. dApp 9. Security:[ ](https://www.google.com/url?q=https%3A%2F%2Fconsensys.github.io%2Fsmart-contract-best-practices%2F&sa=D&sntz=1&usg=AOvVaw0ABPhCIEaExBvmeOe-D_Hm)[https://consensys.github.io/smart-contract-best-practices/](https://www.google.com/url?q=https%3A%2F%2Fconsensys.github.io%2Fsmart-contract-best-practices%2F&sa=D&sntz=1&usg=AOvVaw0ABPhCIEaExBvmeOe-D_Hm) 2. LinkedIn II 1. Hyperledger.org 2. solidity: [https://docs.soliditylang.org/en/v0.8.13/installing-solidity.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.soliditylang.org%2Fen%2Fv0.8.13%2Finstalling-solidity.html&sa=D&sntz=1&usg=AOvVaw1GDJZfpxlMTD-VyuM3EM1n) 1. docker run ethereum/solc:stable --help 2. brew update 3. brew upgrade 4. brew tap ethereum/ethereum 5. brew install solidity 6. 4\. Visual studio code: Name: solidity Id: JuanBlanco.solidity Description: Ethereum Solidity Language for Visual Studio Code Version: 0.0.139 Publisher: Juan Blanco VS Marketplace Link:[ ](https://www.google.com/url?q=https%3A%2F%2Fmarketplace.visualstudio.com%2Fitems%3FitemName%3DJuanBlanco.solidity&sa=D&sntz=1&usg=AOvVaw3KMZrAVx3FuyLTrUEtjBlI)[https://marketplace.visualstudio.com/items?itemName=JuanBlanco.solidity](https://www.google.com/url?q=https%3A%2F%2Fmarketplace.visualstudio.com%2Fitems%3FitemName%3DJuanBlanco.solidity&sa=D&sntz=1&usg=AOvVaw3KMZrAVx3FuyLTrUEtjBlI) 5\. Online editor: [https://remix.ethereum.org](https://www.google.com/url?q=https%3A%2F%2Fremix.ethereum.org&sa=D&sntz=1&usg=AOvVaw12Y9fbyx49-9qH8CSJfuQ3) اجماع majority rules validate transactions properly computer nodes 51% consensus mechanisms advantage proof of work: * anybody can attached machines and gain rewards blockchain trilemma: 1- scalable/speed 2- decentralization secure fastest: solana (arweave), [](https://drive.google.com/open?id=1iJqtIoSQ1gmMr7tleuFQ6lX_FmiFj7p4FELnCVGT1C4 "Open Spreadsheet, AltCoin in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/sheets_32dp.png)AltCoin # Basic [proof of work/stake](https://www.youtube.com/watch?v=08vnE2_cAeQ) # Links [https://koinly.io/](https://www.google.com/url?q=https%3A%2F%2Fkoinly.io%2F&sa=D&sntz=1&usg=AOvVaw1MlwUKbWcWOp2wDgfk5wTa) [https://chain.link/bootcamp/bootcamp-2021-on- demand](https://www.google.com/url?q=https%3A%2F%2Fchain.link%2Fbootcamp%2Fbootcamp-2021-on- demand&sa=D&sntz=1&usg=AOvVaw3_THOQshewlXoxNX0CKMKf) [https://software.intel.com/content/www/us/en/develop/download/download- maccpuid.html](https://www.google.com/url?q=https%3A%2F%2Fsoftware.intel.com%2Fcontent%2Fwww%2Fus%2Fen%2Fdevelop%2Fdownload%2Fdownload- maccpuid.html&sa=D&sntz=1&usg=AOvVaw3fAaHZJ9DSuYORK5STffBn) [آموزش زبان سالیدیتی(solidity) برای نوشتن اسمارت کانترکت روی شبکه اتریوم، شماره ۱](https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr) [https://cryptozombies.io/](https://www.google.com/url?q=https%3A%2F%2Fcryptozombies.io%2F&sa=D&sntz=1&usg=AOvVaw2gbwUmHX9yWMhTVmpbHEPy) # Hands on ## setup:macOS ### 1: install nvm: Node Version Manager curl -o- https://raw.githubusercontent.com/creationix/nvm/v0.35.2/install.sh | bash nvm install 12 nvm use 12 nvm alias default 12 npm install npm --global # Upgrade npm to the latest version ### 2\. install hardhat: Ethereum development environment for professionals npm install --save-dev hardhat ### 3\. # METAVERSE 1. Coin Bureau: : TOP 5 Virtual Land NFTs!! BEST Metaverse Plays?? 🚀 #metaverse #land #crypto * sandbox * metaverse group buy decentraland: 2.43 M$ * [https://nonfungible.com/](https://www.google.com/url?q=https%3A%2F%2Fnonfungible.com%2F&sa=D&sntz=1&usg=AOvVaw1d1IAffRPgXgbELGML_Zak) : cryptopunks, the sandbox, decentraland, cryptovoxels, somnium space, superworld, arcona. OVR Top 5 Land 1. * * 1. axie infinity * savannah, forest, arctic, mystic, genesis, lunas landing 2. decentraland * * 9K land * ~4500 MANA * 3$ = 13500$ * Decentraland Tutorials: * my land pirahansiah: [https://share.decentraland.org/b/scene/ed5823d2-788c-440f-8875-2614fc139c42](https://www.google.com/url?q=https%3A%2F%2Fshare.decentraland.org%2Fb%2Fscene%2Fed5823d2-788c-440f-8875-2614fc139c42&sa=D&sntz=1&usg=AOvVaw2SHZ1c0evkthfjNgJ8Mcw2) 3. the sandbox * ~2.5 Eth * 3850 = 9625 1. * * 4. bitcountry * create and personalise metaverse * 5. aavegotchi * 2. [https://www.sandbox.game/en/](https://www.google.com/url?q=https%3A%2F%2Fwww.sandbox.game%2Fen%2F&sa=D&sntz=1&usg=AOvVaw0MmdZePFR3AUuLjPjVKlIk) * it is virtual world in ethereum 2011 - 2018 - * open metaverse - the sandbox alpha - 29.11 to 20.12.21 * require the sandbox (SAND) ~ 5$ (17.12.21) ~2.73$ (22.April.2022) ~0.58 (14.11.22) * 3. learn: 4. learn: 5. AI meta: [https://ai.facebook.com/events/neurips2021](https://www.google.com/url?q=https%3A%2F%2Fai.facebook.com%2Fevents%2Fneurips2021&sa=D&sntz=1&usg=AOvVaw3OvYQKZfEVFHqbYen7VYwh) 6. Metaverse, Mesh, Open AI and more from Microsoft Ignite Fall 2021: [https://valoremreply.com/post/metaverse-mesh-openai-microsoft-ignite-fall-2021/?utm_source=social_media&utm_medium=Rene-Reshare&utm_campaign=trends2021](https://www.google.com/url?q=https%3A%2F%2Fvaloremreply.com%2Fpost%2Fmetaverse-mesh-openai-microsoft-ignite-fall-2021%2F%3Futm_source%3Dsocial_media%26utm_medium%3DRene-Reshare%26utm_campaign%3Dtrends2021&sa=D&sntz=1&usg=AOvVaw2aU3w2NugSf1HZh-Neisf5) ## crypto 18.12.21 - 22.April.2022 * Decentraland (MANA): ~3$ - ~2.03$ (22.April.2022) * sandbox (SAND): ~ 5$ - ~2.73$ (22.April.2022) * Axie Infinity (AXS): ~95$ - ~45.65$ (22.April.2022) * illuvium (ILV) : ~1136$ - ~514.98$ (22.April.2022) * star atlas price (ATLAS) base on solana : ~0.10 - ~0.023$ (22.April.2022) * wilder world (wild): ~3.67$ - ~1.15$ (22.April.2022) 1. bitCoin : revolution to currency, 2. DeFi, ethereum 3. NFT 4. Metaverse # NFT * [https://opensea.io/collection/computervision](https://www.google.com/url?q=https%3A%2F%2Fopensea.io%2Fcollection%2Fcomputervision&sa=D&sntz=1&usg=AOvVaw2kn1tyV7uv_uTI9i50hUVQ) * Links: [https://github.com/MetaMask/metamask- mobile](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FMetaMask%2Fmetamask- mobile&sa=D&sntz=1&usg=AOvVaw1YH7hABPeEsRI7ne1ErWR4) [https://readyplayer.me](https://www.google.com/url?q=https%3A%2F%2Freadyplayer.me&sa=D&sntz=1&usg=AOvVaw1q7g9_FKZoK_- FuOwYHQEN) [https://github.com/ish- app/ish](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fish- app%2Fish&sa=D&sntz=1&usg=AOvVaw12yqknRonHBDjzyVJZ543t) [https://getutm.app/install/](https://www.google.com/url?q=https%3A%2F%2Fgetutm.app%2Finstall%2F&sa=D&sntz=1&usg=AOvVaw2i4ClkKwh4GkEdZg7y5WOX) [https://github.com/rileytestut/AltStore](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Frileytestut%2FAltStore&sa=D&sntz=1&usg=AOvVaw1PXzTxhU5BDAOhM0lo3aMS) [https://altstore.io](https://www.google.com/url?q=https%3A%2F%2Faltstore.io&sa=D&sntz=1&usg=AOvVaw3cXCR8rKRUvIWq8tNFytLE) [https://vscode.dev/github/pirahansiah/pirahansiah](https://www.google.com/url?q=https%3A%2F%2Fvscode.dev%2Fgithub%2Fpirahansiah%2Fpirahansiah&sa=D&sntz=1&usg=AOvVaw3ar7dCuBaV4_X4lplrNKXP) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/VBNzuBGkaD3wt3TzoFwX0mTIj70nSDd3lgPVXQioi93Rci- RfkU7Cych8xXEVlNVPXCewW1vMbWtk8T6z_JVwm8=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/VBNzuBGkaD3wt3TzoFwX0mTIj70nSDd3lgPVXQioi93Rci- RfkU7Cych8xXEVlNVPXCewW1vMbWtk8T6z_JVwm8=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # AltCoin Disclaimer/ Risk warning: I am not a financial advisor and anything you read, see or hear in this site, podcast, video should not by any means be construed as financial advice it is purely intended for your entertainment and demonstration and illustrative purposes only. This is not financial advice and should not be taken as financial advice. the views I have in everyone of my site/post/blog/links/text/documents/powerpoint/videos are completely speculative opinions and do not guarantee any specific result. The NFT, AltCoin, Metaverse, ... is extremely volatile and has high risk. You should never act on anyone's advice or opinions, without first doing your own research, realising your own risk, and making your own decision. I recommend speaking with a licensed and qualified professional before making any financial decision. Basic Links Hands on setup:macOS 1: install nvm: Node Version Manager 2\. install hardhat: Ethereum development environment for professionals 3. METAVERSE crypto 18.12.21 - 22.April.2022 NFT Stock * ^ Purchasing managers' indexes (PMI): * A PMI index over 50 represents growth or expansion within the manufacturing sector of the economy compared with the prior month. * ^ United States Philadelphia Fed Manufacturing Index * A value greater than 0 reflects growth in the manufacturing sector, whereas a value less than 0 reflects a contraction. * ![](https://lh4.googleusercontent.com/eNrU_i5TMskU_ZA3cXB0rIVuJlCnE4YgTGpfGaSlrejV9fJMw6GLEOimP- GqHJcZAQTDhr-h1DHW3wQhxXT98zyRp1NlPhLB9s8bNWrxrBjgJlv9QFwpgAdleVPQhW37Fw=w1280) ![](https://lh4.googleusercontent.com/qARdwh01SGT2SdP9lG- PEJeMiqeNun3sP2v2QYwmDwYAunVYxdLJaesG5N5-KnTh1evxyq2hl1MXKofdGDW7PTK9nJ1GYa3nVqLckI3t6WJh2JNRCiQkw_rZC28bphtXvw=w1280) My token _**pirahansiah (TIZ**_ _ **)**_ on Solana Token name pirahansiah (TIZ) Token address FsniTTtb9GeGq1DHkipxera4bsgFkb19maBLKZwMe7in **Token** **pirahansiah** https://solscan.io/token/FsniTTtb9GeGq1DHkipxera4bsgFkb19maBLKZwMe7in My token _**pirahansiah (TIZ)**_ **Token Contract Address** 0xe30407DB873302D6AEaAB3bA619f44Bc9F924594 **Token Decimal:** 18 **Network:** BNB Smart Chain Mainnet only 100 token available to sell [https://bscscan.com/token/0xe30407db873302d6aeaab3ba619f44bc9f924594](https://www.google.com/url?q=https%3A%2F%2Fbscscan.com%2Ftoken%2F0xe30407db873302d6aeaab3ba619f44bc9f924594&sa=D&sntz=1&usg=AOvVaw3owOOObdS- gyWJtD_682hE) [https://admin.moralis.io/speedyNodes](https://www.google.com/url?q=https%3A%2F%2Fadmin.moralis.io%2FspeedyNodes&sa=D&sntz=1&usg=AOvVaw1e_X2Eutx- uPjibkv51EdW) [https://testnet.binance.org/faucet- smart](https://www.google.com/url?q=https%3A%2F%2Ftestnet.binance.org%2Ffaucet- smart&sa=D&sntz=1&usg=AOvVaw0YuT9b0CIeLmtQULao873v) [https://github.com/OpenZeppelin/openzeppelin- contracts/blob/master/contracts/token/ERC20/ERC20.sol](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FOpenZeppelin%2Fopenzeppelin- contracts%2Fblob%2Fmaster%2Fcontracts%2Ftoken%2FERC20%2FERC20.sol&sa=D&sntz=1&usg=AOvVaw3JW77-QoFjcv- DNK3whJT2) [https://remix.ethereum.org/#optimize=false&runs=200&evmVersion=null&version=soljson-v0.8.7+commit.e28d00a7.js](https://www.google.com/url?q=https%3A%2F%2Fremix.ethereum.org%2F%23optimize%3Dfalse%26runs%3D200%26evmVersion%3Dnull%26version%3Dsoljson-v0.8.7%2Bcommit.e28d00a7.js&sa=D&sntz=1&usg=AOvVaw39lC9qXtU3OwPP5mxpf9qK) [https://bscscan.com/token/0xe30407db873302d6aeaab3ba619f44bc9f924594](https://www.google.com/url?q=https%3A%2F%2Fbscscan.com%2Ftoken%2F0xe30407db873302d6aeaab3ba619f44bc9f924594&sa=D&sntz=1&usg=AOvVaw3owOOObdS- gyWJtD_682hE) [https://github.com/OpenZeppelin/openzeppelin- contracts/blob/master/contracts/token/ERC20/ERC20.sol](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FOpenZeppelin%2Fopenzeppelin- contracts%2Fblob%2Fmaster%2Fcontracts%2Ftoken%2FERC20%2FERC20.sol&sa=D&sntz=1&usg=AOvVaw3JW77-QoFjcv- DNK3whJT2) * how to modify the code * [https://www.tradingview.com](https://www.google.com/url?q=https%3A%2F%2Fwww.tradingview.com&sa=D&sntz=1&usg=AOvVaw15P-ffkNM5fCBySsWtLXHo) best tools to analysis market * [https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr](https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr) Persian Interface: minimum data and functions required to make it a standard ERC/EIP smart contract _value the _ shows it is a parameters Constant is a variable that can't be changed Mapping() function maps elements from a key to a value Constructor() function that automatically runs when a new data item is created( initialization code) Emit() function triggers an event (message to be sent out) 1. New project 1. ./geth --syncmode "light" 2. Mkdir 3. Truffle init Truffle deploy --reset Truffle console HelloWorld.deployed().then(function(instance) {return instance} ); HelloWorld.deployed().then(function(instance) {return instance.getHelloMessage()} ); npm install @truffle/hdwallet-provider 1. LinkedIn 1. Start 10.April.2022 2. [https://geth.ethereum.org/](https://www.google.com/url?q=https%3A%2F%2Fgeth.ethereum.org%2F&sa=D&sntz=1&usg=AOvVaw1XnmIZJr5e4_oBt8pPHXQK) 3. [https://trufflesuite.com/ganache/](https://www.google.com/url?q=https%3A%2F%2Ftrufflesuite.com%2Fganache%2F&sa=D&sntz=1&usg=AOvVaw3wxvy_EfDUepyc6z9ctYdc) 4. [https://trufflesuite.com/truffle/](https://www.google.com/url?q=https%3A%2F%2Ftrufflesuite.com%2Ftruffle%2F&sa=D&sntz=1&usg=AOvVaw1dbGm84hwOf1zhcx6viT9A) 1. npm install truffle -g 5. ERC-20 (500K) 6. ERC721: Non-Fungible Tokens (NFT): 70K 7. ERC1155: Multi-Token Tokens : 8K 8. dApp 9. Security:[ ](https://www.google.com/url?q=https%3A%2F%2Fconsensys.github.io%2Fsmart-contract-best-practices%2F&sa=D&sntz=1&usg=AOvVaw0ABPhCIEaExBvmeOe-D_Hm)[https://consensys.github.io/smart-contract-best-practices/](https://www.google.com/url?q=https%3A%2F%2Fconsensys.github.io%2Fsmart-contract-best-practices%2F&sa=D&sntz=1&usg=AOvVaw0ABPhCIEaExBvmeOe-D_Hm) 2. LinkedIn II 1. Hyperledger.org 2. solidity: [https://docs.soliditylang.org/en/v0.8.13/installing-solidity.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.soliditylang.org%2Fen%2Fv0.8.13%2Finstalling-solidity.html&sa=D&sntz=1&usg=AOvVaw1GDJZfpxlMTD-VyuM3EM1n) 1. docker run ethereum/solc:stable --help 2. brew update 3. brew upgrade 4. brew tap ethereum/ethereum 5. brew install solidity 6. 4\. Visual studio code: Name: solidity Id: JuanBlanco.solidity Description: Ethereum Solidity Language for Visual Studio Code Version: 0.0.139 Publisher: Juan Blanco VS Marketplace Link:[ ](https://www.google.com/url?q=https%3A%2F%2Fmarketplace.visualstudio.com%2Fitems%3FitemName%3DJuanBlanco.solidity&sa=D&sntz=1&usg=AOvVaw3KMZrAVx3FuyLTrUEtjBlI)[https://marketplace.visualstudio.com/items?itemName=JuanBlanco.solidity](https://www.google.com/url?q=https%3A%2F%2Fmarketplace.visualstudio.com%2Fitems%3FitemName%3DJuanBlanco.solidity&sa=D&sntz=1&usg=AOvVaw3KMZrAVx3FuyLTrUEtjBlI) 5\. Online editor: [https://remix.ethereum.org](https://www.google.com/url?q=https%3A%2F%2Fremix.ethereum.org&sa=D&sntz=1&usg=AOvVaw12Y9fbyx49-9qH8CSJfuQ3) اجماع majority rules validate transactions properly computer nodes 51% consensus mechanisms advantage proof of work: * anybody can attached machines and gain rewards blockchain trilemma: 1- scalable/speed 2- decentralization secure fastest: solana (arweave), [](https://drive.google.com/open?id=1iJqtIoSQ1gmMr7tleuFQ6lX_FmiFj7p4FELnCVGT1C4 "Open Spreadsheet, AltCoin in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/sheets_32dp.png)AltCoin # Basic [proof of work/stake](https://www.youtube.com/watch?v=08vnE2_cAeQ) # Links [https://koinly.io/](https://www.google.com/url?q=https%3A%2F%2Fkoinly.io%2F&sa=D&sntz=1&usg=AOvVaw1MlwUKbWcWOp2wDgfk5wTa) [https://chain.link/bootcamp/bootcamp-2021-on- demand](https://www.google.com/url?q=https%3A%2F%2Fchain.link%2Fbootcamp%2Fbootcamp-2021-on- demand&sa=D&sntz=1&usg=AOvVaw3_THOQshewlXoxNX0CKMKf) [https://software.intel.com/content/www/us/en/develop/download/download- maccpuid.html](https://www.google.com/url?q=https%3A%2F%2Fsoftware.intel.com%2Fcontent%2Fwww%2Fus%2Fen%2Fdevelop%2Fdownload%2Fdownload- maccpuid.html&sa=D&sntz=1&usg=AOvVaw3fAaHZJ9DSuYORK5STffBn) [آموزش زبان سالیدیتی(solidity) برای نوشتن اسمارت کانترکت روی شبکه اتریوم، شماره ۱](https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr) [https://cryptozombies.io/](https://www.google.com/url?q=https%3A%2F%2Fcryptozombies.io%2F&sa=D&sntz=1&usg=AOvVaw2gbwUmHX9yWMhTVmpbHEPy) # Hands on ## setup:macOS ### 1: install nvm: Node Version Manager curl -o- https://raw.githubusercontent.com/creationix/nvm/v0.35.2/install.sh | bash nvm install 12 nvm use 12 nvm alias default 12 npm install npm --global # Upgrade npm to the latest version ### 2\. install hardhat: Ethereum development environment for professionals npm install --save-dev hardhat ### 3\. # METAVERSE 1. Coin Bureau: : TOP 5 Virtual Land NFTs!! BEST Metaverse Plays?? 🚀 #metaverse #land #crypto * sandbox * metaverse group buy decentraland: 2.43 M$ * [https://nonfungible.com/](https://www.google.com/url?q=https%3A%2F%2Fnonfungible.com%2F&sa=D&sntz=1&usg=AOvVaw1d1IAffRPgXgbELGML_Zak) : cryptopunks, the sandbox, decentraland, cryptovoxels, somnium space, superworld, arcona. OVR Top 5 Land 1. * * 1. axie infinity * savannah, forest, arctic, mystic, genesis, lunas landing 2. decentraland * * 9K land * ~4500 MANA * 3$ = 13500$ * Decentraland Tutorials: * my land pirahansiah: [https://share.decentraland.org/b/scene/ed5823d2-788c-440f-8875-2614fc139c42](https://www.google.com/url?q=https%3A%2F%2Fshare.decentraland.org%2Fb%2Fscene%2Fed5823d2-788c-440f-8875-2614fc139c42&sa=D&sntz=1&usg=AOvVaw2SHZ1c0evkthfjNgJ8Mcw2) 3. the sandbox * ~2.5 Eth * 3850 = 9625 1. * * 4. bitcountry * create and personalise metaverse * 5. aavegotchi * 2. [https://www.sandbox.game/en/](https://www.google.com/url?q=https%3A%2F%2Fwww.sandbox.game%2Fen%2F&sa=D&sntz=1&usg=AOvVaw0MmdZePFR3AUuLjPjVKlIk) * it is virtual world in ethereum 2011 - 2018 - * open metaverse - the sandbox alpha - 29.11 to 20.12.21 * require the sandbox (SAND) ~ 5$ (17.12.21) ~2.73$ (22.April.2022) ~0.58 (14.11.22) * 3. learn: 4. learn: 5. AI meta: [https://ai.facebook.com/events/neurips2021](https://www.google.com/url?q=https%3A%2F%2Fai.facebook.com%2Fevents%2Fneurips2021&sa=D&sntz=1&usg=AOvVaw3OvYQKZfEVFHqbYen7VYwh) 6. Metaverse, Mesh, Open AI and more from Microsoft Ignite Fall 2021: [https://valoremreply.com/post/metaverse-mesh-openai-microsoft-ignite-fall-2021/?utm_source=social_media&utm_medium=Rene-Reshare&utm_campaign=trends2021](https://www.google.com/url?q=https%3A%2F%2Fvaloremreply.com%2Fpost%2Fmetaverse-mesh-openai-microsoft-ignite-fall-2021%2F%3Futm_source%3Dsocial_media%26utm_medium%3DRene-Reshare%26utm_campaign%3Dtrends2021&sa=D&sntz=1&usg=AOvVaw2aU3w2NugSf1HZh-Neisf5) ## crypto 18.12.21 - 22.April.2022 * Decentraland (MANA): ~3$ - ~2.03$ (22.April.2022) * sandbox (SAND): ~ 5$ - ~2.73$ (22.April.2022) * Axie Infinity (AXS): ~95$ - ~45.65$ (22.April.2022) * illuvium (ILV) : ~1136$ - ~514.98$ (22.April.2022) * star atlas price (ATLAS) base on solana : ~0.10 - ~0.023$ (22.April.2022) * wilder world (wild): ~3.67$ - ~1.15$ (22.April.2022) 1. bitCoin : revolution to currency, 2. DeFi, ethereum 3. NFT 4. Metaverse # NFT * [https://opensea.io/collection/computervision](https://www.google.com/url?q=https%3A%2F%2Fopensea.io%2Fcollection%2Fcomputervision&sa=D&sntz=1&usg=AOvVaw2kn1tyV7uv_uTI9i50hUVQ) * Links: [https://github.com/MetaMask/metamask- mobile](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FMetaMask%2Fmetamask- mobile&sa=D&sntz=1&usg=AOvVaw1YH7hABPeEsRI7ne1ErWR4) [https://readyplayer.me](https://www.google.com/url?q=https%3A%2F%2Freadyplayer.me&sa=D&sntz=1&usg=AOvVaw1q7g9_FKZoK_- FuOwYHQEN) [https://github.com/ish- app/ish](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fish- app%2Fish&sa=D&sntz=1&usg=AOvVaw12yqknRonHBDjzyVJZ543t) [https://getutm.app/install/](https://www.google.com/url?q=https%3A%2F%2Fgetutm.app%2Finstall%2F&sa=D&sntz=1&usg=AOvVaw2i4ClkKwh4GkEdZg7y5WOX) [https://github.com/rileytestut/AltStore](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Frileytestut%2FAltStore&sa=D&sntz=1&usg=AOvVaw1PXzTxhU5BDAOhM0lo3aMS) [https://altstore.io](https://www.google.com/url?q=https%3A%2F%2Faltstore.io&sa=D&sntz=1&usg=AOvVaw3cXCR8rKRUvIWq8tNFytLE) [https://vscode.dev/github/pirahansiah/pirahansiah](https://www.google.com/url?q=https%3A%2F%2Fvscode.dev%2Fgithub%2Fpirahansiah%2Fpirahansiah&sa=D&sntz=1&usg=AOvVaw3ar7dCuBaV4_X4lplrNKXP) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/VnELnzCZElXe9gLxGYU00_xF7qju2MljSVlgUMwWsc50I88T6vB5ahQjH2kGA --o3hIeJYu2N--BO_uidCis2Ow=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/VnELnzCZElXe9gLxGYU00_xF7qju2MljSVlgUMwWsc50I88T6vB5ahQjH2kGA --o3hIeJYu2N--BO_uidCis2Ow=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # AltCoin Disclaimer/ Risk warning: I am not a financial advisor and anything you read, see or hear in this site, podcast, video should not by any means be construed as financial advice it is purely intended for your entertainment and demonstration and illustrative purposes only. This is not financial advice and should not be taken as financial advice. the views I have in everyone of my site/post/blog/links/text/documents/powerpoint/videos are completely speculative opinions and do not guarantee any specific result. The NFT, AltCoin, Metaverse, ... is extremely volatile and has high risk. You should never act on anyone's advice or opinions, without first doing your own research, realising your own risk, and making your own decision. I recommend speaking with a licensed and qualified professional before making any financial decision. Basic Links Hands on setup:macOS 1: install nvm: Node Version Manager 2\. install hardhat: Ethereum development environment for professionals 3. METAVERSE crypto 18.12.21 - 22.April.2022 NFT Stock * ^ Purchasing managers' indexes (PMI): * A PMI index over 50 represents growth or expansion within the manufacturing sector of the economy compared with the prior month. * ^ United States Philadelphia Fed Manufacturing Index * A value greater than 0 reflects growth in the manufacturing sector, whereas a value less than 0 reflects a contraction. * ![](https://lh5.googleusercontent.com/HAu7WxJFQt3YaCbmBKFoT-b3r6RIw_cekbjW4GNGUnf5-Es3PRRvVZM9APADabkVGYuqRU8sJ6AJ5MRF7cn5cUCI-N8jYVhHBGPCF2qd-6obI2lH7QD8lWG2gDXr2GOaUg=w1280) ![](https://lh5.googleusercontent.com/5ndnR_746Z63ncGkagkVGe5Mn1OY0OVmckSQLJJy_- tkReAg14C5pmbNOoPJ21Jje- MqLHp2XkclQKi1-tFHYeA2VBcd54W7HiAATGLmYMAKDht3Qab1vJOtiWOx_wXQBw=w1280) My token _**pirahansiah (TIZ**_ _ **)**_ on Solana Token name pirahansiah (TIZ) Token address FsniTTtb9GeGq1DHkipxera4bsgFkb19maBLKZwMe7in **Token** **pirahansiah** https://solscan.io/token/FsniTTtb9GeGq1DHkipxera4bsgFkb19maBLKZwMe7in My token _**pirahansiah (TIZ)**_ **Token Contract Address** 0xe30407DB873302D6AEaAB3bA619f44Bc9F924594 **Token Decimal:** 18 **Network:** BNB Smart Chain Mainnet only 100 token available to sell [https://bscscan.com/token/0xe30407db873302d6aeaab3ba619f44bc9f924594](https://www.google.com/url?q=https%3A%2F%2Fbscscan.com%2Ftoken%2F0xe30407db873302d6aeaab3ba619f44bc9f924594&sa=D&sntz=1&usg=AOvVaw3owOOObdS- gyWJtD_682hE) [https://admin.moralis.io/speedyNodes](https://www.google.com/url?q=https%3A%2F%2Fadmin.moralis.io%2FspeedyNodes&sa=D&sntz=1&usg=AOvVaw1e_X2Eutx- uPjibkv51EdW) [https://testnet.binance.org/faucet- smart](https://www.google.com/url?q=https%3A%2F%2Ftestnet.binance.org%2Ffaucet- smart&sa=D&sntz=1&usg=AOvVaw0YuT9b0CIeLmtQULao873v) [https://github.com/OpenZeppelin/openzeppelin- contracts/blob/master/contracts/token/ERC20/ERC20.sol](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FOpenZeppelin%2Fopenzeppelin- contracts%2Fblob%2Fmaster%2Fcontracts%2Ftoken%2FERC20%2FERC20.sol&sa=D&sntz=1&usg=AOvVaw3JW77-QoFjcv- DNK3whJT2) [https://remix.ethereum.org/#optimize=false&runs=200&evmVersion=null&version=soljson-v0.8.7+commit.e28d00a7.js](https://www.google.com/url?q=https%3A%2F%2Fremix.ethereum.org%2F%23optimize%3Dfalse%26runs%3D200%26evmVersion%3Dnull%26version%3Dsoljson-v0.8.7%2Bcommit.e28d00a7.js&sa=D&sntz=1&usg=AOvVaw39lC9qXtU3OwPP5mxpf9qK) [https://bscscan.com/token/0xe30407db873302d6aeaab3ba619f44bc9f924594](https://www.google.com/url?q=https%3A%2F%2Fbscscan.com%2Ftoken%2F0xe30407db873302d6aeaab3ba619f44bc9f924594&sa=D&sntz=1&usg=AOvVaw3owOOObdS- gyWJtD_682hE) [https://github.com/OpenZeppelin/openzeppelin- contracts/blob/master/contracts/token/ERC20/ERC20.sol](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FOpenZeppelin%2Fopenzeppelin- contracts%2Fblob%2Fmaster%2Fcontracts%2Ftoken%2FERC20%2FERC20.sol&sa=D&sntz=1&usg=AOvVaw3JW77-QoFjcv- DNK3whJT2) * how to modify the code * [https://www.tradingview.com](https://www.google.com/url?q=https%3A%2F%2Fwww.tradingview.com&sa=D&sntz=1&usg=AOvVaw15P-ffkNM5fCBySsWtLXHo) best tools to analysis market * [https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr](https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr) Persian Interface: minimum data and functions required to make it a standard ERC/EIP smart contract _value the _ shows it is a parameters Constant is a variable that can't be changed Mapping() function maps elements from a key to a value Constructor() function that automatically runs when a new data item is created( initialization code) Emit() function triggers an event (message to be sent out) 1. New project 1. ./geth --syncmode "light" 2. Mkdir 3. Truffle init Truffle deploy --reset Truffle console HelloWorld.deployed().then(function(instance) {return instance} ); HelloWorld.deployed().then(function(instance) {return instance.getHelloMessage()} ); npm install @truffle/hdwallet-provider 1. LinkedIn 1. Start 10.April.2022 2. [https://geth.ethereum.org/](https://www.google.com/url?q=https%3A%2F%2Fgeth.ethereum.org%2F&sa=D&sntz=1&usg=AOvVaw1XnmIZJr5e4_oBt8pPHXQK) 3. [https://trufflesuite.com/ganache/](https://www.google.com/url?q=https%3A%2F%2Ftrufflesuite.com%2Fganache%2F&sa=D&sntz=1&usg=AOvVaw3wxvy_EfDUepyc6z9ctYdc) 4. [https://trufflesuite.com/truffle/](https://www.google.com/url?q=https%3A%2F%2Ftrufflesuite.com%2Ftruffle%2F&sa=D&sntz=1&usg=AOvVaw1dbGm84hwOf1zhcx6viT9A) 1. npm install truffle -g 5. ERC-20 (500K) 6. ERC721: Non-Fungible Tokens (NFT): 70K 7. ERC1155: Multi-Token Tokens : 8K 8. dApp 9. Security:[ ](https://www.google.com/url?q=https%3A%2F%2Fconsensys.github.io%2Fsmart-contract-best-practices%2F&sa=D&sntz=1&usg=AOvVaw0ABPhCIEaExBvmeOe-D_Hm)[https://consensys.github.io/smart-contract-best-practices/](https://www.google.com/url?q=https%3A%2F%2Fconsensys.github.io%2Fsmart-contract-best-practices%2F&sa=D&sntz=1&usg=AOvVaw0ABPhCIEaExBvmeOe-D_Hm) 2. LinkedIn II 1. Hyperledger.org 2. solidity: [https://docs.soliditylang.org/en/v0.8.13/installing-solidity.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.soliditylang.org%2Fen%2Fv0.8.13%2Finstalling-solidity.html&sa=D&sntz=1&usg=AOvVaw1GDJZfpxlMTD-VyuM3EM1n) 1. docker run ethereum/solc:stable --help 2. brew update 3. brew upgrade 4. brew tap ethereum/ethereum 5. brew install solidity 6. 4\. Visual studio code: Name: solidity Id: JuanBlanco.solidity Description: Ethereum Solidity Language for Visual Studio Code Version: 0.0.139 Publisher: Juan Blanco VS Marketplace Link:[ ](https://www.google.com/url?q=https%3A%2F%2Fmarketplace.visualstudio.com%2Fitems%3FitemName%3DJuanBlanco.solidity&sa=D&sntz=1&usg=AOvVaw3KMZrAVx3FuyLTrUEtjBlI)[https://marketplace.visualstudio.com/items?itemName=JuanBlanco.solidity](https://www.google.com/url?q=https%3A%2F%2Fmarketplace.visualstudio.com%2Fitems%3FitemName%3DJuanBlanco.solidity&sa=D&sntz=1&usg=AOvVaw3KMZrAVx3FuyLTrUEtjBlI) 5\. Online editor: [https://remix.ethereum.org](https://www.google.com/url?q=https%3A%2F%2Fremix.ethereum.org&sa=D&sntz=1&usg=AOvVaw12Y9fbyx49-9qH8CSJfuQ3) اجماع majority rules validate transactions properly computer nodes 51% consensus mechanisms advantage proof of work: * anybody can attached machines and gain rewards blockchain trilemma: 1- scalable/speed 2- decentralization secure fastest: solana (arweave), [](https://drive.google.com/open?id=1iJqtIoSQ1gmMr7tleuFQ6lX_FmiFj7p4FELnCVGT1C4 "Open Spreadsheet, AltCoin in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/sheets_32dp.png)AltCoin # Basic [proof of work/stake](https://www.youtube.com/watch?v=08vnE2_cAeQ) # Links [https://koinly.io/](https://www.google.com/url?q=https%3A%2F%2Fkoinly.io%2F&sa=D&sntz=1&usg=AOvVaw1MlwUKbWcWOp2wDgfk5wTa) [https://chain.link/bootcamp/bootcamp-2021-on- demand](https://www.google.com/url?q=https%3A%2F%2Fchain.link%2Fbootcamp%2Fbootcamp-2021-on- demand&sa=D&sntz=1&usg=AOvVaw3_THOQshewlXoxNX0CKMKf) [https://software.intel.com/content/www/us/en/develop/download/download- maccpuid.html](https://www.google.com/url?q=https%3A%2F%2Fsoftware.intel.com%2Fcontent%2Fwww%2Fus%2Fen%2Fdevelop%2Fdownload%2Fdownload- maccpuid.html&sa=D&sntz=1&usg=AOvVaw3fAaHZJ9DSuYORK5STffBn) [آموزش زبان سالیدیتی(solidity) برای نوشتن اسمارت کانترکت روی شبکه اتریوم، شماره ۱](https://www.youtube.com/watch?v=Ds0BNTt3zEc&list=PLeDqzoyH3js8dsTH6MN4qWiym0TcxWmBr) [https://cryptozombies.io/](https://www.google.com/url?q=https%3A%2F%2Fcryptozombies.io%2F&sa=D&sntz=1&usg=AOvVaw2gbwUmHX9yWMhTVmpbHEPy) # Hands on ## setup:macOS ### 1: install nvm: Node Version Manager curl -o- https://raw.githubusercontent.com/creationix/nvm/v0.35.2/install.sh | bash nvm install 12 nvm use 12 nvm alias default 12 npm install npm --global # Upgrade npm to the latest version ### 2\. install hardhat: Ethereum development environment for professionals npm install --save-dev hardhat ### 3\. # METAVERSE 1. Coin Bureau: : TOP 5 Virtual Land NFTs!! BEST Metaverse Plays?? 🚀 #metaverse #land #crypto * sandbox * metaverse group buy decentraland: 2.43 M$ * [https://nonfungible.com/](https://www.google.com/url?q=https%3A%2F%2Fnonfungible.com%2F&sa=D&sntz=1&usg=AOvVaw1d1IAffRPgXgbELGML_Zak) : cryptopunks, the sandbox, decentraland, cryptovoxels, somnium space, superworld, arcona. OVR Top 5 Land 1. * * 1. axie infinity * savannah, forest, arctic, mystic, genesis, lunas landing 2. decentraland * * 9K land * ~4500 MANA * 3$ = 13500$ * Decentraland Tutorials: * my land pirahansiah: [https://share.decentraland.org/b/scene/ed5823d2-788c-440f-8875-2614fc139c42](https://www.google.com/url?q=https%3A%2F%2Fshare.decentraland.org%2Fb%2Fscene%2Fed5823d2-788c-440f-8875-2614fc139c42&sa=D&sntz=1&usg=AOvVaw2SHZ1c0evkthfjNgJ8Mcw2) 3. the sandbox * ~2.5 Eth * 3850 = 9625 1. * * 4. bitcountry * create and personalise metaverse * 5. aavegotchi * 2. [https://www.sandbox.game/en/](https://www.google.com/url?q=https%3A%2F%2Fwww.sandbox.game%2Fen%2F&sa=D&sntz=1&usg=AOvVaw0MmdZePFR3AUuLjPjVKlIk) * it is virtual world in ethereum 2011 - 2018 - * open metaverse - the sandbox alpha - 29.11 to 20.12.21 * require the sandbox (SAND) ~ 5$ (17.12.21) ~2.73$ (22.April.2022) ~0.58 (14.11.22) * 3. learn: 4. learn: 5. AI meta: [https://ai.facebook.com/events/neurips2021](https://www.google.com/url?q=https%3A%2F%2Fai.facebook.com%2Fevents%2Fneurips2021&sa=D&sntz=1&usg=AOvVaw3OvYQKZfEVFHqbYen7VYwh) 6. Metaverse, Mesh, Open AI and more from Microsoft Ignite Fall 2021: [https://valoremreply.com/post/metaverse-mesh-openai-microsoft-ignite-fall-2021/?utm_source=social_media&utm_medium=Rene-Reshare&utm_campaign=trends2021](https://www.google.com/url?q=https%3A%2F%2Fvaloremreply.com%2Fpost%2Fmetaverse-mesh-openai-microsoft-ignite-fall-2021%2F%3Futm_source%3Dsocial_media%26utm_medium%3DRene-Reshare%26utm_campaign%3Dtrends2021&sa=D&sntz=1&usg=AOvVaw2aU3w2NugSf1HZh-Neisf5) ## crypto 18.12.21 - 22.April.2022 * Decentraland (MANA): ~3$ - ~2.03$ (22.April.2022) * sandbox (SAND): ~ 5$ - ~2.73$ (22.April.2022) * Axie Infinity (AXS): ~95$ - ~45.65$ (22.April.2022) * illuvium (ILV) : ~1136$ - ~514.98$ (22.April.2022) * star atlas price (ATLAS) base on solana : ~0.10 - ~0.023$ (22.April.2022) * wilder world (wild): ~3.67$ - ~1.15$ (22.April.2022) 1. bitCoin : revolution to currency, 2. DeFi, ethereum 3. NFT 4. Metaverse # NFT * [https://opensea.io/collection/computervision](https://www.google.com/url?q=https%3A%2F%2Fopensea.io%2Fcollection%2Fcomputervision&sa=D&sntz=1&usg=AOvVaw2kn1tyV7uv_uTI9i50hUVQ) * Links: [https://github.com/MetaMask/metamask- mobile](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FMetaMask%2Fmetamask- mobile&sa=D&sntz=1&usg=AOvVaw1YH7hABPeEsRI7ne1ErWR4) [https://readyplayer.me](https://www.google.com/url?q=https%3A%2F%2Freadyplayer.me&sa=D&sntz=1&usg=AOvVaw1q7g9_FKZoK_- FuOwYHQEN) [https://github.com/ish- app/ish](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fish- app%2Fish&sa=D&sntz=1&usg=AOvVaw12yqknRonHBDjzyVJZ543t) [https://getutm.app/install/](https://www.google.com/url?q=https%3A%2F%2Fgetutm.app%2Finstall%2F&sa=D&sntz=1&usg=AOvVaw2i4ClkKwh4GkEdZg7y5WOX) [https://github.com/rileytestut/AltStore](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Frileytestut%2FAltStore&sa=D&sntz=1&usg=AOvVaw1PXzTxhU5BDAOhM0lo3aMS) [https://altstore.io](https://www.google.com/url?q=https%3A%2F%2Faltstore.io&sa=D&sntz=1&usg=AOvVaw3cXCR8rKRUvIWq8tNFytLE) [https://vscode.dev/github/pirahansiah/pirahansiah](https://www.google.com/url?q=https%3A%2F%2Fvscode.dev%2Fgithub%2Fpirahansiah%2Fpirahansiah&sa=D&sntz=1&usg=AOvVaw3ar7dCuBaV4_X4lplrNKXP) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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Scaled-YOLOv4:scaling model based on hardware Cost How to use computer vision with deep learning in IoT devices. Inference machine learning on Edge require some extra steps. Use special frameworks or library for edge devices: In some case you need to enhance model for inference. There are many techniques to use such as, How # Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera * Camera * * Camera Specs: Color camera, Stereo pair * [IMX214](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/imx214.html#imx214) (PY014 AF, PY114 FF), [OV7251](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/ov7251.html#ov7251) (PY013) * DFOV / HFOV / VFOV= 81° / 69° / 54° , 86° / 73° / 58° * Resolution: 13MP (4208x3120), 480P (640x480) * Focus: AF: 8cm - ∞ OR FF: 50cm - ∞ , Fixed-Focus 6.5cm - ∞ * Max Framerate: 35 FPS, 120 FPS * Width: 91 mm , Height: 28 mm, Length: 17.5 mm, Baseline: 75 mm, Weight: 61 g * chips: * * Robotics Vision Core 2 (RVC2 in short) Myriad X are integrated into the Robotics Vision Core 2 * Speed ML * * Model name, Size, FPS, Latency [ms], * MobileOne S0 224x224, 165.5, 11.1 * YoloV8n, 416x416, 31.3, 56.9, * YoloV8n, 640x640, 14.3, 123.6 * YoloV8s, 416x416, 15.2, 111.9 * YoloV8m, 416x416, 6.0, 273.8 # Hardware for Deep Learning (machine learning) [https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware) I experiment with many different hardware to train and run deep learning application. The below list shows my suggestion, comparison, expectation of using different hardware. Embedded AI, implementing distributed data parallel, distributed model parallel solutions. [https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware) #hardware #deep_learning #IoT #training_machine_learning_model #pirahansiah Laptop: * NVIDIA Geforce RTX 3080 Ti * Razer Blade 17 - 17.3 inch gaming laptop (NVIDIA Geforce RTX 3080 Ti, Intel i9-12900H, 4K UHD 144Hz display 32GB DDR5 RAM, 1TB SSD, Desktop * eGPU * Razer RC21-01310100-R351 Core X External Graphics Card Case = ~ 300 Euro + GPU * Cooler Master MasterCase EG200 External GPU Enclosure - Thunderbolt 3 Compatible eGPU Enclosure, 1 PWM 92mm Fan, V550 SFX Gold Fully Modular PSU, USB Hub, Vertical Laptop Support - EU Plug = ~ 300 Euro + GPU * GPU * Geforce RTX 3090 24G 384Bit Gddr6x Nvidia Geforce * MSI GeForce RTX 3090 GAMING TRIO 24G Gaming Graphics Card - NVIDIA RTX 3090, GPU 1740MHz, 24GB GDDR6X memory = ~ 2800 Euro IoT: * Raspberry pi 3 (you need accelerator ) * Raspberry pi 4 (you need accelerator ) * Intel® Neural Compute Stick 2 * Intel® Distribution of OpenVINO™ Toolkit * I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models * Google Coral * I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models * Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used * NVIDIA Jetson Nano ( 2GB and 4GB ram) * I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes * NVIDIA JETSON AGX XAVIER * NVIDIA AGX Orin = ~ 1900 Euro * [Compare NVIDIA Jetson AGX Orin with AGX Xavier: 8x AI performance, in-advance Ampere GPU, CPU, Memory & Storage](https://www.seeedstudio.com/blog/2022/03/17/compare-nvidia-jetson-agx-orin-with-agx-xavier-6x-ai-performance-in-advance-ampere-gpu-cpu-memory-storage/) * OpenCV AI Kit * OAK = ~ 100 Euro * OAK—D = ~ 200 Euro * OAK—D + Wifi = ~ 250 Euro * OpenCV AI Kit: OAK—D-PoE = ~ 250 Euro * OAK—D lite = ~ 100 Euro # My experience I tested many different hardware for 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[#GraphQL](https://www.linkedin.com/feed/hashtag/?keywords=graphql&highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6787871627586625536) #imageprocessing #patternrecognition ![](https://lh6.googleusercontent.com/2LsypDJMlRYl1XY38HLkB4EqHyVq3MAMl0CqC9xFAgMvmOLmRkF3rE8Y4i2mu6mB86bYaUQlQfHxSbuWw226YBfXULnaYcPKEm- RR5EIIxqLe1r2k2LNhlWy5xJUcUp1vQ=w1280) # Raspberry Pi 4 How to upgrade Raspberry Pi 4 EEPROM boot recovery; Released 2020-09-14; to install and boot from USB 3 (SSD) 1. update Raspberry Pi 4 EEPROM boot recovery 2. install Ubuntu 20 on SSD 3. change the config.txt and add "program_usb_boot_mode=1" at the end of file 4. remove and micro sd card and boot from ssd ![](https://lh4.googleusercontent.com/z2IpzkJPiuclHKvJ- JZL7E1I45Rx1OUPRngOni40LUX2i8gHt7IBREr0XcOSUcUYa9pi__BZhFiVmikq52ruGu- DONp8cq6bHcRExsm0QnJ4D2DSGqosZUliItD4EyZfnQ=w1280) ![](https://lh3.googleusercontent.com/LmYId4__AqpSmBCXbovkUPT0EopUELf4GMwxk- zrVvhU9UPuYaxxNXyHeblpdqHEqmI5nBsdfPwuGdTE3aSPz03AYgR2RT-0WwSvhOxTYO0WzHtBF- sc32-gdgwY-yWjsw=w1280) ![](https://lh6.googleusercontent.com/wGm7IWs1g2fQHaCOcBXUiLh3qU-4lOkoqiCLF9YpmfgSd9ZHjaTnIk4A2EXDXo6cMLnNA0xTRC3R7r5-HCCgzjkbC1xbSxOJfNQbUv8Pxg97rE5Fhr_OCs2AUVZKZv2QPw=w1280) ![](https://lh3.googleusercontent.com/Hx33mCHy_K3B-j6iXuaCUjuFK22St5r8zIkuSoJ9sBAJxW2-D9ZdTZC0QeNlJkU4vY6yV71uEA0slFCnNZPKkfizwKmUcXtDUOC9FZNyzMH4r5dfCun2phaOQpxvz07baw=w1280) ![](https://lh5.googleusercontent.com/U5FKWGlELP_9GoIwhn4MXh_QPBgvP0I_4rh- Mve1CrOzGeK7bST3U4u8XQzgPxCmZxmo5LYXv37LrnHo35gIiIqQt-eNZN0E9Hmi4g5MdbuNr2fcv- SN0uygv9GV9FwrHw=w1280) ![](https://lh5.googleusercontent.com/Y8uq6FrJOzxjjQ2KpAIaHJf6frFFOFCmgmPRkV-T6dBean13GBUKY84JKlW3_hntVm2mq70DgsLJzM6dPo9xSdq9B326nJHN39St0vsFCQ2LbRH2ErpqzR- MsRumRMeTmg=w1280) ![](https://lh3.googleusercontent.com/4Vwcq2eyzYmYjsm_qs81CZrnYnK553NfseggYjVFHu2cdpFhqkk3-8GId- PrvhR98-xPRsOuI-eDY63wjFJ- Mzg55wRMfaGXejnTmFeWFnqlyrX1vA6uEk_qhAOENuv_Fw=w1280) # Smart AI IoT, Robotic, 3D SLAM, AR, VR * [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2) # [RISC-V](/workshops-and-events/risc-v) # I worked with many different hardware such as * Raspberry pi 3 * Raspberry pi 4 * Intel® Neural Compute Stick 2 * Intel® Distribution of OpenVINO™ Toolkit * I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models * Google Coral * I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models * Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used * NVIDIA Jetson Nano * I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes * NVIDIA JETSON AGX XAVIER * The best hardware * I attended in may conferences and summits in area of Hardware for deep learning such as: * * * AI Hardware Europe Summit (July 2020) * [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit) * Apache TVM And Deep Learning Compilation Conference (December 2020) * RISC-V Summit (December 2020) * OpenCV AI Kit ## Camera I worked with many different cameras such as: * Camera Module V1 * Camera Module V2 * Camera Module V2.1 * multispectral camera * USB webcam * IP camera * high resolution camera > 8K * depth camera * stereo camera ### What is important? * camera calibration is important * Quantum efficiency [%] (spectral response) * Sensor size [inches or mm] and pixel size [micro meter] * Dynamic Range [dB] * Image noise and signal to noise ratio (SNR), PSNR, SSIM, : greater SNR yields better contrast and clarity, as well as improved low light performance * inter face, cable length in m, bandwidth max in MB/s , multi camera, cable costs, real time, plug and play * * firewire, 4.5 , 64, *, *, **, ** * gige, 100, 100, **, **, *, * * usb, 8, 350, *, *, **, ** * link, 10, 850, -, -, **, - * usb-c, 10, 40 GB,,,, * distortions, scaling factors, quality is important, calculate minimum sensor resolution *, determine your sensor size, focal length, * * sensor resolution= image resolution = 2 * ( field of view (FOV) / smallest feature ) * some online tools: baslerweb.com, edmundoptics.com, flir.com * to sum up * use USB-C camera. it will help you in the future upgrades in hardware and easy to use with less issues * find your best trade-off between WD and FOV * sometimes you cannot have everything in life! * your lens aperture (f/#) is your friend, use it! * a larger DOF requires a larger f/# * lens performance curves are the ultimate documentation to read when selecting a lens * understanding them properly requires good knowledge in optics, but it totally worth it. ## Scaled-YOLOv4:scaling model based on hardware # Cost * [How much does a patent cost?](https://www.fu-berlin.de/en/forschung/service/patente-und-lizenzen/faq/kosten.html) * [Mobile, Open Hardware, RISC-V System-on-Chip (SoC) Development Kit](https://www.crowdsupply.com/sutajio-kosagi/precursor) * Hardware * NVIDIA Jetson Xavier NX Developer Kit * WIFI * SparkFun GPS-RTK Dead Reckoning pHAT * Micro Sd card * Mophie Powerstation USB C 20000 * ZED 2 Stereo Camera * 3D-printed box * AWS * AWS S3 * AWS xml.p2.xlarge EC2 instances * AWS Sagemaker * [Hackboard 2 with Ubuntu Linux (99$) Intel CPU](https://www.crowdsupply.com/hackboard/hb2) * [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2) * Post Product to customer by * * * * * * * [easyship](https://www.easyship.com/) * [fulfillmentcrowd](https://www.fulfilmentcrowd.com/) * [ChinaDivision](https://www.chinadivision.com/) * [ORQA FPV](https://orqafpv.com/) * [floship](https://www.floship.com/) Update 26.April.2021 # How to use computer vision with deep learning in IoT devices. Inference machine learning on Edge require some extra steps. I tested several hardware such as Raspberry pi 3, Raspberry pi 4, Intel® Neural Compute Stick 2, OpenCV AI Kit, Google Coral, NVIDIA Jetson Nano, etc. Different OS: real-time operating system (RTOS), Nasa cFS (core Flight System), Real-Time Executive for Multiprocessor Systems (RTEMS), anomaly detection, object detection, object tracking, ... ## Use special frameworks or library for edge devices: * NVIDIA TensorRT * TensorFlow Lite: TensorFlow Lite on Microcontroller Gesture Recognition OpenMV/Tensorflow/ studio.edgeimpulse.com * TensorFlow.js * PyTorch Lightning * PyTorch Mobile * Intel® Distribution of OpenVINO Toolkit * CoreML * ML kit * FRITZ * MediaPipe * Apache TVM * TinyML: enabling ultra-low power machine learning at the edge tiny machine learning with Arduino * Libraries: ffmpeg, GStreamer, celery, * GPU library for python: PyCUDA, NumbaPro, PyOpenCL, CuPy Moreover, think about deep learning model for your specific hardware at first stage. ## In some case you need to enhance model for inference. There are many techniques to use such as, * Pruning * Quantization * Distillation Techniques * Binarized Neural Networks (BNNs) * Apache TVM (incubating) is a compiler stack for deep learning systems * Distributed machine learning and load balancing strategy * Low rank matrix factorization (LRMF) * Compact convolutional filters (Video/CNN) * Knowledge distillation * Neural Networks Compression Framework (NNCF) * Parallel programming ## How Distributed machine learning and load balancing strategy Pruning model pruning: reducing redundant parameters which are not sensitive to the performance. aim: remove all connections with absolute weights below a threshold. 🤔go for bigger size of network with many layers then pruning much better and faster Quantization The best way is using Google library which support most comprehensive methods compresses by reducing the number of bits used to represent the weights quantization effectively constraints the number of different weights we can use inside our kernels per channel quantization for weights, which improves performance by model compression and latency reduction. training a compact neural network with distilled knowledge of a large model distillation (knowledge transfer) from an ensemble of big networks into a much smaller network which learns directly from the cumbersome model's outputs, that is lighter to deploy Distillation Techniques Distill-Net: Application-Specific Distillation of Deep Convolutional Neural Networks for Resource-Constrained IoT Platforms Binarized Neural Networks (BNNs) It is not support by GPU hardware such as Jetson Nano. mostly based on CPU Apache TVM (incubating) is a compiler stack for deep learning systems challenges with large scale models deep neural networks are: expensive computationally expensive memory intensive hindering their deployment in:devices with low memory resources applications with strict latency requirements other issues:data security: tend to memorize everything including PII bias e.g. profanity: trained on large scale public datas elf discovering: instead of manually configuring conversational flows, automatically discover them from your data self training: let your system train itself with new example s self managing: let your system optimize by itself knowledge distillation Distributed machine learning and load balancing strategy run models which use all processing power like CPU,GPU,DSP,AI chip together to enhance inference performance. dynamic pruning of kernels which aims to the parsimonious inference by learning to exploit and dynamically remove the redundant capacity of a CNN architecture. partitioning techniques through convolution layer fusion to dynamically select the optimal partition according to the availability of computational resources and network conditions. Low rank matrix factorization (LRMF) there exists latent structures in the data, by uncovering which we can obtain a compressed representation of the dataLRMF factorizes the original matrix into lower rank matrices while preserving latent structures and addressing the issue of sparseness Compact convolutional filters (Video/CNN) designing special structural convolutional filters to save parameters replace over parametric filters with compact filters to achieve overall speedup while maintaining comparable accuracy Knowledge distillation Neural Networks Compression Framework (NNCF) AI Edge: How to inference deep learning models on edge/IoT Enabling efficient high-performance Accelerators/Optimization on Deep Learning if the object is large and we do not need small anchor in mobileNet we can remove small part of network which related to small objects. in YOLO reduce number of anchor. decrease size of image input but reduce the accuracy Parallel programming and clean code, design pattern, Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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Scaled-YOLOv4:scaling model based on hardware Cost How to use computer vision with deep learning in IoT devices. Inference machine learning on Edge require some extra steps. Use special frameworks or library for edge devices: In some case you need to enhance model for inference. There are many techniques to use such as, How # Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera * Camera * * Camera Specs: Color camera, Stereo pair * [IMX214](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/imx214.html#imx214) (PY014 AF, PY114 FF), [OV7251](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/ov7251.html#ov7251) (PY013) * DFOV / HFOV / VFOV= 81° / 69° / 54° , 86° / 73° / 58° * Resolution: 13MP (4208x3120), 480P (640x480) * Focus: AF: 8cm - ∞ OR FF: 50cm - ∞ , Fixed-Focus 6.5cm - ∞ * Max Framerate: 35 FPS, 120 FPS * Width: 91 mm , Height: 28 mm, Length: 17.5 mm, Baseline: 75 mm, Weight: 61 g * chips: * * Robotics Vision Core 2 (RVC2 in short) Myriad X are integrated into the Robotics Vision Core 2 * Speed ML * * Model name, Size, FPS, Latency [ms], * MobileOne S0 224x224, 165.5, 11.1 * YoloV8n, 416x416, 31.3, 56.9, * YoloV8n, 640x640, 14.3, 123.6 * YoloV8s, 416x416, 15.2, 111.9 * YoloV8m, 416x416, 6.0, 273.8 # Hardware for Deep Learning (machine learning) [https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware) I experiment with many different hardware to train and run deep learning application. The below list shows my suggestion, comparison, expectation of using different hardware. Embedded AI, implementing distributed data parallel, distributed model parallel solutions. [https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware) #hardware #deep_learning #IoT #training_machine_learning_model #pirahansiah Laptop: * NVIDIA Geforce RTX 3080 Ti * Razer Blade 17 - 17.3 inch gaming laptop (NVIDIA Geforce RTX 3080 Ti, Intel i9-12900H, 4K UHD 144Hz display 32GB DDR5 RAM, 1TB SSD, Desktop * eGPU * Razer RC21-01310100-R351 Core X External Graphics Card Case = ~ 300 Euro + GPU * Cooler Master MasterCase EG200 External GPU Enclosure - Thunderbolt 3 Compatible eGPU Enclosure, 1 PWM 92mm Fan, V550 SFX Gold Fully Modular PSU, USB Hub, Vertical Laptop Support - EU Plug = ~ 300 Euro + GPU * GPU * Geforce RTX 3090 24G 384Bit Gddr6x Nvidia Geforce * MSI GeForce RTX 3090 GAMING TRIO 24G Gaming Graphics Card - NVIDIA RTX 3090, GPU 1740MHz, 24GB GDDR6X memory = ~ 2800 Euro IoT: * Raspberry pi 3 (you need accelerator ) * Raspberry pi 4 (you need accelerator ) * Intel® Neural Compute Stick 2 * Intel® Distribution of OpenVINO™ Toolkit * I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models * Google Coral * I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models * Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used * NVIDIA Jetson Nano ( 2GB and 4GB ram) * I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes * NVIDIA JETSON AGX XAVIER * NVIDIA AGX Orin = ~ 1900 Euro * [Compare NVIDIA Jetson AGX Orin with AGX Xavier: 8x AI performance, in-advance Ampere GPU, CPU, Memory & Storage](https://www.seeedstudio.com/blog/2022/03/17/compare-nvidia-jetson-agx-orin-with-agx-xavier-6x-ai-performance-in-advance-ampere-gpu-cpu-memory-storage/) * OpenCV AI Kit * OAK = ~ 100 Euro * OAK—D = ~ 200 Euro * OAK—D + Wifi = ~ 250 Euro * OpenCV AI Kit: OAK—D-PoE = ~ 250 Euro * OAK—D lite = ~ 100 Euro # My experience I tested many different hardware for 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[#GraphQL](https://www.linkedin.com/feed/hashtag/?keywords=graphql&highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6787871627586625536) #imageprocessing #patternrecognition ![](https://lh6.googleusercontent.com/2LsypDJMlRYl1XY38HLkB4EqHyVq3MAMl0CqC9xFAgMvmOLmRkF3rE8Y4i2mu6mB86bYaUQlQfHxSbuWw226YBfXULnaYcPKEm- RR5EIIxqLe1r2k2LNhlWy5xJUcUp1vQ=w1280) # Raspberry Pi 4 How to upgrade Raspberry Pi 4 EEPROM boot recovery; Released 2020-09-14; to install and boot from USB 3 (SSD) 1. update Raspberry Pi 4 EEPROM boot recovery 2. install Ubuntu 20 on SSD 3. change the config.txt and add "program_usb_boot_mode=1" at the end of file 4. remove and micro sd card and boot from ssd ![](https://lh4.googleusercontent.com/z2IpzkJPiuclHKvJ- JZL7E1I45Rx1OUPRngOni40LUX2i8gHt7IBREr0XcOSUcUYa9pi__BZhFiVmikq52ruGu- DONp8cq6bHcRExsm0QnJ4D2DSGqosZUliItD4EyZfnQ=w1280) ![](https://lh3.googleusercontent.com/LmYId4__AqpSmBCXbovkUPT0EopUELf4GMwxk- zrVvhU9UPuYaxxNXyHeblpdqHEqmI5nBsdfPwuGdTE3aSPz03AYgR2RT-0WwSvhOxTYO0WzHtBF- sc32-gdgwY-yWjsw=w1280) ![](https://lh6.googleusercontent.com/wGm7IWs1g2fQHaCOcBXUiLh3qU-4lOkoqiCLF9YpmfgSd9ZHjaTnIk4A2EXDXo6cMLnNA0xTRC3R7r5-HCCgzjkbC1xbSxOJfNQbUv8Pxg97rE5Fhr_OCs2AUVZKZv2QPw=w1280) ![](https://lh3.googleusercontent.com/Hx33mCHy_K3B-j6iXuaCUjuFK22St5r8zIkuSoJ9sBAJxW2-D9ZdTZC0QeNlJkU4vY6yV71uEA0slFCnNZPKkfizwKmUcXtDUOC9FZNyzMH4r5dfCun2phaOQpxvz07baw=w1280) ![](https://lh5.googleusercontent.com/U5FKWGlELP_9GoIwhn4MXh_QPBgvP0I_4rh- Mve1CrOzGeK7bST3U4u8XQzgPxCmZxmo5LYXv37LrnHo35gIiIqQt-eNZN0E9Hmi4g5MdbuNr2fcv- SN0uygv9GV9FwrHw=w1280) ![](https://lh5.googleusercontent.com/Y8uq6FrJOzxjjQ2KpAIaHJf6frFFOFCmgmPRkV-T6dBean13GBUKY84JKlW3_hntVm2mq70DgsLJzM6dPo9xSdq9B326nJHN39St0vsFCQ2LbRH2ErpqzR- MsRumRMeTmg=w1280) ![](https://lh3.googleusercontent.com/4Vwcq2eyzYmYjsm_qs81CZrnYnK553NfseggYjVFHu2cdpFhqkk3-8GId- PrvhR98-xPRsOuI-eDY63wjFJ- Mzg55wRMfaGXejnTmFeWFnqlyrX1vA6uEk_qhAOENuv_Fw=w1280) # Smart AI IoT, Robotic, 3D SLAM, AR, VR * [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2) # [RISC-V](/workshops-and-events/risc-v) # I worked with many different hardware such as * Raspberry pi 3 * Raspberry pi 4 * Intel® Neural Compute Stick 2 * Intel® Distribution of OpenVINO™ Toolkit * I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models * Google Coral * I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models * Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used * NVIDIA Jetson Nano * I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes * NVIDIA JETSON AGX XAVIER * The best hardware * I attended in may conferences and summits in area of Hardware for deep learning such as: * * * AI Hardware Europe Summit (July 2020) * [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit) * Apache TVM And Deep Learning Compilation Conference (December 2020) * RISC-V Summit (December 2020) * OpenCV AI Kit ## Camera I worked with many different cameras such as: * Camera Module V1 * Camera Module V2 * Camera Module V2.1 * multispectral camera * USB webcam * IP camera * high resolution camera > 8K * depth camera * stereo camera ### What is important? * camera calibration is important * Quantum efficiency [%] (spectral response) * Sensor size [inches or mm] and pixel size [micro meter] * Dynamic Range [dB] * Image noise and signal to noise ratio (SNR), PSNR, SSIM, : greater SNR yields better contrast and clarity, as well as improved low light performance * inter face, cable length in m, bandwidth max in MB/s , multi camera, cable costs, real time, plug and play * * firewire, 4.5 , 64, *, *, **, ** * gige, 100, 100, **, **, *, * * usb, 8, 350, *, *, **, ** * link, 10, 850, -, -, **, - * usb-c, 10, 40 GB,,,, * distortions, scaling factors, quality is important, calculate minimum sensor resolution *, determine your sensor size, focal length, * * sensor resolution= image resolution = 2 * ( field of view (FOV) / smallest feature ) * some online tools: baslerweb.com, edmundoptics.com, flir.com * to sum up * use USB-C camera. it will help you in the future upgrades in hardware and easy to use with less issues * find your best trade-off between WD and FOV * sometimes you cannot have everything in life! * your lens aperture (f/#) is your friend, use it! * a larger DOF requires a larger f/# * lens performance curves are the ultimate documentation to read when selecting a lens * understanding them properly requires good knowledge in optics, but it totally worth it. ## Scaled-YOLOv4:scaling model based on hardware # Cost * [How much does a patent cost?](https://www.fu-berlin.de/en/forschung/service/patente-und-lizenzen/faq/kosten.html) * [Mobile, Open Hardware, RISC-V System-on-Chip (SoC) Development Kit](https://www.crowdsupply.com/sutajio-kosagi/precursor) * Hardware * NVIDIA Jetson Xavier NX Developer Kit * WIFI * SparkFun GPS-RTK Dead Reckoning pHAT * Micro Sd card * Mophie Powerstation USB C 20000 * ZED 2 Stereo Camera * 3D-printed box * AWS * AWS S3 * AWS xml.p2.xlarge EC2 instances * AWS Sagemaker * [Hackboard 2 with Ubuntu Linux (99$) Intel CPU](https://www.crowdsupply.com/hackboard/hb2) * [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2) * Post Product to customer by * * * * * * * [easyship](https://www.easyship.com/) * [fulfillmentcrowd](https://www.fulfilmentcrowd.com/) * [ChinaDivision](https://www.chinadivision.com/) * [ORQA FPV](https://orqafpv.com/) * [floship](https://www.floship.com/) Update 26.April.2021 # How to use computer vision with deep learning in IoT devices. Inference machine learning on Edge require some extra steps. I tested several hardware such as Raspberry pi 3, Raspberry pi 4, Intel® Neural Compute Stick 2, OpenCV AI Kit, Google Coral, NVIDIA Jetson Nano, etc. Different OS: real-time operating system (RTOS), Nasa cFS (core Flight System), Real-Time Executive for Multiprocessor Systems (RTEMS), anomaly detection, object detection, object tracking, ... ## Use special frameworks or library for edge devices: * NVIDIA TensorRT * TensorFlow Lite: TensorFlow Lite on Microcontroller Gesture Recognition OpenMV/Tensorflow/ studio.edgeimpulse.com * TensorFlow.js * PyTorch Lightning * PyTorch Mobile * Intel® Distribution of OpenVINO Toolkit * CoreML * ML kit * FRITZ * MediaPipe * Apache TVM * TinyML: enabling ultra-low power machine learning at the edge tiny machine learning with Arduino * Libraries: ffmpeg, GStreamer, celery, * GPU library for python: PyCUDA, NumbaPro, PyOpenCL, CuPy Moreover, think about deep learning model for your specific hardware at first stage. ## In some case you need to enhance model for inference. There are many techniques to use such as, * Pruning * Quantization * Distillation Techniques * Binarized Neural Networks (BNNs) * Apache TVM (incubating) is a compiler stack for deep learning systems * Distributed machine learning and load balancing strategy * Low rank matrix factorization (LRMF) * Compact convolutional filters (Video/CNN) * Knowledge distillation * Neural Networks Compression Framework (NNCF) * Parallel programming ## How Distributed machine learning and load balancing strategy Pruning model pruning: reducing redundant parameters which are not sensitive to the performance. aim: remove all connections with absolute weights below a threshold. 🤔go for bigger size of network with many layers then pruning much better and faster Quantization The best way is using Google library which support most comprehensive methods compresses by reducing the number of bits used to represent the weights quantization effectively constraints the number of different weights we can use inside our kernels per channel quantization for weights, which improves performance by model compression and latency reduction. training a compact neural network with distilled knowledge of a large model distillation (knowledge transfer) from an ensemble of big networks into a much smaller network which learns directly from the cumbersome model's outputs, that is lighter to deploy Distillation Techniques Distill-Net: Application-Specific Distillation of Deep Convolutional Neural Networks for Resource-Constrained IoT Platforms Binarized Neural Networks (BNNs) It is not support by GPU hardware such as Jetson Nano. mostly based on CPU Apache TVM (incubating) is a compiler stack for deep learning systems challenges with large scale models deep neural networks are: expensive computationally expensive memory intensive hindering their deployment in:devices with low memory resources applications with strict latency requirements other issues:data security: tend to memorize everything including PII bias e.g. profanity: trained on large scale public datas elf discovering: instead of manually configuring conversational flows, automatically discover them from your data self training: let your system train itself with new example s self managing: let your system optimize by itself knowledge distillation Distributed machine learning and load balancing strategy run models which use all processing power like CPU,GPU,DSP,AI chip together to enhance inference performance. dynamic pruning of kernels which aims to the parsimonious inference by learning to exploit and dynamically remove the redundant capacity of a CNN architecture. partitioning techniques through convolution layer fusion to dynamically select the optimal partition according to the availability of computational resources and network conditions. Low rank matrix factorization (LRMF) there exists latent structures in the data, by uncovering which we can obtain a compressed representation of the dataLRMF factorizes the original matrix into lower rank matrices while preserving latent structures and addressing the issue of sparseness Compact convolutional filters (Video/CNN) designing special structural convolutional filters to save parameters replace over parametric filters with compact filters to achieve overall speedup while maintaining comparable accuracy Knowledge distillation Neural Networks Compression Framework (NNCF) AI Edge: How to inference deep learning models on edge/IoT Enabling efficient high-performance Accelerators/Optimization on Deep Learning if the object is large and we do not need small anchor in mobileNet we can remove small part of network which related to small objects. in YOLO reduce number of anchor. decrease size of image input but reduce the accuracy Parallel programming and clean code, design pattern, Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/XgaKEt2FLCAUFKSVB7bWm_daXvBUuQ- IMFaLMazoeqc9v81q9tB-xdRfUwaMvMXAPNtdRJ- erMVMYoDarnLyYNw=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/XgaKEt2FLCAUFKSVB7bWm_daXvBUuQ- IMFaLMazoeqc9v81q9tB-xdRfUwaMvMXAPNtdRJ-erMVMYoDarnLyYNw=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Hardware Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera Hardware for Deep Learning (machine learning) My experience Raspberry Pi 4 Smart AI IoT, Robotic, 3D SLAM, AR, VR RISC-V I worked with many different hardware such as Camera What is important? Scaled-YOLOv4:scaling model based on hardware Cost How to use computer vision with deep learning in IoT devices. Inference machine learning on Edge require some extra steps. Use special frameworks or library for edge devices: In some case you need to enhance model for inference. There are many techniques to use such as, How # Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera * Camera * * Camera Specs: Color camera, Stereo pair * [IMX214](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/imx214.html#imx214) (PY014 AF, PY114 FF), [OV7251](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/ov7251.html#ov7251) (PY013) * DFOV / HFOV / VFOV= 81° / 69° / 54° , 86° / 73° / 58° * Resolution: 13MP (4208x3120), 480P (640x480) * Focus: AF: 8cm - ∞ OR FF: 50cm - ∞ , Fixed-Focus 6.5cm - ∞ * Max Framerate: 35 FPS, 120 FPS * Width: 91 mm , Height: 28 mm, Length: 17.5 mm, Baseline: 75 mm, Weight: 61 g * chips: * * Robotics Vision Core 2 (RVC2 in short) Myriad X are integrated into the Robotics Vision Core 2 * Speed ML * * Model name, Size, FPS, Latency [ms], * MobileOne S0 224x224, 165.5, 11.1 * YoloV8n, 416x416, 31.3, 56.9, * YoloV8n, 640x640, 14.3, 123.6 * YoloV8s, 416x416, 15.2, 111.9 * YoloV8m, 416x416, 6.0, 273.8 # Hardware for Deep Learning (machine learning) [https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware) I experiment with many different hardware to train and run deep learning application. The below list shows my suggestion, comparison, expectation of using different hardware. Embedded AI, implementing distributed data parallel, distributed model parallel solutions. [https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware) #hardware #deep_learning #IoT #training_machine_learning_model #pirahansiah Laptop: * NVIDIA Geforce RTX 3080 Ti * Razer Blade 17 - 17.3 inch gaming laptop (NVIDIA Geforce RTX 3080 Ti, Intel i9-12900H, 4K UHD 144Hz display 32GB DDR5 RAM, 1TB SSD, Desktop * eGPU * Razer RC21-01310100-R351 Core X External Graphics Card Case = ~ 300 Euro + GPU * Cooler Master MasterCase EG200 External GPU Enclosure - Thunderbolt 3 Compatible eGPU Enclosure, 1 PWM 92mm Fan, V550 SFX Gold Fully Modular PSU, USB Hub, Vertical Laptop Support - EU Plug = ~ 300 Euro + GPU * GPU * Geforce RTX 3090 24G 384Bit Gddr6x Nvidia Geforce * MSI GeForce RTX 3090 GAMING TRIO 24G Gaming Graphics Card - NVIDIA RTX 3090, GPU 1740MHz, 24GB GDDR6X memory = ~ 2800 Euro IoT: * Raspberry pi 3 (you need accelerator ) * Raspberry pi 4 (you need accelerator ) * Intel® Neural Compute Stick 2 * Intel® Distribution of OpenVINO™ Toolkit * I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models * Google Coral * I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models * Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used * NVIDIA Jetson Nano ( 2GB and 4GB ram) * I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes * NVIDIA JETSON AGX XAVIER * NVIDIA AGX Orin = ~ 1900 Euro * [Compare NVIDIA Jetson AGX Orin with AGX Xavier: 8x AI performance, in-advance Ampere GPU, CPU, Memory & Storage](https://www.seeedstudio.com/blog/2022/03/17/compare-nvidia-jetson-agx-orin-with-agx-xavier-6x-ai-performance-in-advance-ampere-gpu-cpu-memory-storage/) * OpenCV AI Kit * OAK = ~ 100 Euro * OAK—D = ~ 200 Euro * OAK—D + Wifi = ~ 250 Euro * OpenCV AI Kit: OAK—D-PoE = ~ 250 Euro * OAK—D lite = ~ 100 Euro # My experience I tested many different hardware for 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[#GraphQL](https://www.linkedin.com/feed/hashtag/?keywords=graphql&highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6787871627586625536) #imageprocessing #patternrecognition ![](https://lh6.googleusercontent.com/2LsypDJMlRYl1XY38HLkB4EqHyVq3MAMl0CqC9xFAgMvmOLmRkF3rE8Y4i2mu6mB86bYaUQlQfHxSbuWw226YBfXULnaYcPKEm- RR5EIIxqLe1r2k2LNhlWy5xJUcUp1vQ=w1280) # Raspberry Pi 4 How to upgrade Raspberry Pi 4 EEPROM boot recovery; Released 2020-09-14; to install and boot from USB 3 (SSD) 1. update Raspberry Pi 4 EEPROM boot recovery 2. install Ubuntu 20 on SSD 3. change the config.txt and add "program_usb_boot_mode=1" at the end of file 4. remove and micro sd card and boot from ssd ![](https://lh4.googleusercontent.com/z2IpzkJPiuclHKvJ- JZL7E1I45Rx1OUPRngOni40LUX2i8gHt7IBREr0XcOSUcUYa9pi__BZhFiVmikq52ruGu- DONp8cq6bHcRExsm0QnJ4D2DSGqosZUliItD4EyZfnQ=w1280) ![](https://lh3.googleusercontent.com/LmYId4__AqpSmBCXbovkUPT0EopUELf4GMwxk- zrVvhU9UPuYaxxNXyHeblpdqHEqmI5nBsdfPwuGdTE3aSPz03AYgR2RT-0WwSvhOxTYO0WzHtBF- sc32-gdgwY-yWjsw=w1280) ![](https://lh6.googleusercontent.com/wGm7IWs1g2fQHaCOcBXUiLh3qU-4lOkoqiCLF9YpmfgSd9ZHjaTnIk4A2EXDXo6cMLnNA0xTRC3R7r5-HCCgzjkbC1xbSxOJfNQbUv8Pxg97rE5Fhr_OCs2AUVZKZv2QPw=w1280) ![](https://lh3.googleusercontent.com/Hx33mCHy_K3B-j6iXuaCUjuFK22St5r8zIkuSoJ9sBAJxW2-D9ZdTZC0QeNlJkU4vY6yV71uEA0slFCnNZPKkfizwKmUcXtDUOC9FZNyzMH4r5dfCun2phaOQpxvz07baw=w1280) ![](https://lh5.googleusercontent.com/U5FKWGlELP_9GoIwhn4MXh_QPBgvP0I_4rh- Mve1CrOzGeK7bST3U4u8XQzgPxCmZxmo5LYXv37LrnHo35gIiIqQt-eNZN0E9Hmi4g5MdbuNr2fcv- SN0uygv9GV9FwrHw=w1280) ![](https://lh5.googleusercontent.com/Y8uq6FrJOzxjjQ2KpAIaHJf6frFFOFCmgmPRkV-T6dBean13GBUKY84JKlW3_hntVm2mq70DgsLJzM6dPo9xSdq9B326nJHN39St0vsFCQ2LbRH2ErpqzR- MsRumRMeTmg=w1280) ![](https://lh3.googleusercontent.com/4Vwcq2eyzYmYjsm_qs81CZrnYnK553NfseggYjVFHu2cdpFhqkk3-8GId- PrvhR98-xPRsOuI-eDY63wjFJ- Mzg55wRMfaGXejnTmFeWFnqlyrX1vA6uEk_qhAOENuv_Fw=w1280) # Smart AI IoT, Robotic, 3D SLAM, AR, VR * [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2) # [RISC-V](/workshops-and-events/risc-v) # I worked with many different hardware such as * Raspberry pi 3 * Raspberry pi 4 * Intel® Neural Compute Stick 2 * Intel® Distribution of OpenVINO™ Toolkit * I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models * Google Coral * I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models * Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used * NVIDIA Jetson Nano * I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes * NVIDIA JETSON AGX XAVIER * The best hardware * I attended in may conferences and summits in area of Hardware for deep learning such as: * * * AI Hardware Europe Summit (July 2020) * [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit) * Apache TVM And Deep Learning Compilation Conference (December 2020) * RISC-V Summit (December 2020) * OpenCV AI Kit ## Camera I worked with many different cameras such as: * Camera Module V1 * Camera Module V2 * Camera Module V2.1 * multispectral camera * USB webcam * IP camera * high resolution camera > 8K * depth camera * stereo camera ### What is important? * camera calibration is important * Quantum efficiency [%] (spectral response) * Sensor size [inches or mm] and pixel size [micro meter] * Dynamic Range [dB] * Image noise and signal to noise ratio (SNR), PSNR, SSIM, : greater SNR yields better contrast and clarity, as well as improved low light performance * inter face, cable length in m, bandwidth max in MB/s , multi camera, cable costs, real time, plug and play * * firewire, 4.5 , 64, *, *, **, ** * gige, 100, 100, **, **, *, * * usb, 8, 350, *, *, **, ** * link, 10, 850, -, -, **, - * usb-c, 10, 40 GB,,,, * distortions, scaling factors, quality is important, calculate minimum sensor resolution *, determine your sensor size, focal length, * * sensor resolution= image resolution = 2 * ( field of view (FOV) / smallest feature ) * some online tools: baslerweb.com, edmundoptics.com, flir.com * to sum up * use USB-C camera. it will help you in the future upgrades in hardware and easy to use with less issues * find your best trade-off between WD and FOV * sometimes you cannot have everything in life! * your lens aperture (f/#) is your friend, use it! * a larger DOF requires a larger f/# * lens performance curves are the ultimate documentation to read when selecting a lens * understanding them properly requires good knowledge in optics, but it totally worth it. ## Scaled-YOLOv4:scaling model based on hardware # Cost * [How much does a patent cost?](https://www.fu-berlin.de/en/forschung/service/patente-und-lizenzen/faq/kosten.html) * [Mobile, Open Hardware, RISC-V System-on-Chip (SoC) Development Kit](https://www.crowdsupply.com/sutajio-kosagi/precursor) * Hardware * NVIDIA Jetson Xavier NX Developer Kit * WIFI * SparkFun GPS-RTK Dead Reckoning pHAT * Micro Sd card * Mophie Powerstation USB C 20000 * ZED 2 Stereo Camera * 3D-printed box * AWS * AWS S3 * AWS xml.p2.xlarge EC2 instances * AWS Sagemaker * [Hackboard 2 with Ubuntu Linux (99$) Intel CPU](https://www.crowdsupply.com/hackboard/hb2) * [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2) * Post Product to customer by * * * * * * * [easyship](https://www.easyship.com/) * [fulfillmentcrowd](https://www.fulfilmentcrowd.com/) * [ChinaDivision](https://www.chinadivision.com/) * [ORQA FPV](https://orqafpv.com/) * [floship](https://www.floship.com/) Update 26.April.2021 # How to use computer vision with deep learning in IoT devices. Inference machine learning on Edge require some extra steps. I tested several hardware such as Raspberry pi 3, Raspberry pi 4, Intel® Neural Compute Stick 2, OpenCV AI Kit, Google Coral, NVIDIA Jetson Nano, etc. Different OS: real-time operating system (RTOS), Nasa cFS (core Flight System), Real-Time Executive for Multiprocessor Systems (RTEMS), anomaly detection, object detection, object tracking, ... ## Use special frameworks or library for edge devices: * NVIDIA TensorRT * TensorFlow Lite: TensorFlow Lite on Microcontroller Gesture Recognition OpenMV/Tensorflow/ studio.edgeimpulse.com * TensorFlow.js * PyTorch Lightning * PyTorch Mobile * Intel® Distribution of OpenVINO Toolkit * CoreML * ML kit * FRITZ * MediaPipe * Apache TVM * TinyML: enabling ultra-low power machine learning at the edge tiny machine learning with Arduino * Libraries: ffmpeg, GStreamer, celery, * GPU library for python: PyCUDA, NumbaPro, PyOpenCL, CuPy Moreover, think about deep learning model for your specific hardware at first stage. ## In some case you need to enhance model for inference. There are many techniques to use such as, * Pruning * Quantization * Distillation Techniques * Binarized Neural Networks (BNNs) * Apache TVM (incubating) is a compiler stack for deep learning systems * Distributed machine learning and load balancing strategy * Low rank matrix factorization (LRMF) * Compact convolutional filters (Video/CNN) * Knowledge distillation * Neural Networks Compression Framework (NNCF) * Parallel programming ## How Distributed machine learning and load balancing strategy Pruning model pruning: reducing redundant parameters which are not sensitive to the performance. aim: remove all connections with absolute weights below a threshold. 🤔go for bigger size of network with many layers then pruning much better and faster Quantization The best way is using Google library which support most comprehensive methods compresses by reducing the number of bits used to represent the weights quantization effectively constraints the number of different weights we can use inside our kernels per channel quantization for weights, which improves performance by model compression and latency reduction. training a compact neural network with distilled knowledge of a large model distillation (knowledge transfer) from an ensemble of big networks into a much smaller network which learns directly from the cumbersome model's outputs, that is lighter to deploy Distillation Techniques Distill-Net: Application-Specific Distillation of Deep Convolutional Neural Networks for Resource-Constrained IoT Platforms Binarized Neural Networks (BNNs) It is not support by GPU hardware such as Jetson Nano. mostly based on CPU Apache TVM (incubating) is a compiler stack for deep learning systems challenges with large scale models deep neural networks are: expensive computationally expensive memory intensive hindering their deployment in:devices with low memory resources applications with strict latency requirements other issues:data security: tend to memorize everything including PII bias e.g. profanity: trained on large scale public datas elf discovering: instead of manually configuring conversational flows, automatically discover them from your data self training: let your system train itself with new example s self managing: let your system optimize by itself knowledge distillation Distributed machine learning and load balancing strategy run models which use all processing power like CPU,GPU,DSP,AI chip together to enhance inference performance. dynamic pruning of kernels which aims to the parsimonious inference by learning to exploit and dynamically remove the redundant capacity of a CNN architecture. partitioning techniques through convolution layer fusion to dynamically select the optimal partition according to the availability of computational resources and network conditions. Low rank matrix factorization (LRMF) there exists latent structures in the data, by uncovering which we can obtain a compressed representation of the dataLRMF factorizes the original matrix into lower rank matrices while preserving latent structures and addressing the issue of sparseness Compact convolutional filters (Video/CNN) designing special structural convolutional filters to save parameters replace over parametric filters with compact filters to achieve overall speedup while maintaining comparable accuracy Knowledge distillation Neural Networks Compression Framework (NNCF) AI Edge: How to inference deep learning models on edge/IoT Enabling efficient high-performance Accelerators/Optimization on Deep Learning if the object is large and we do not need small anchor in mobileNet we can remove small part of network which related to small objects. in YOLO reduce number of anchor. decrease size of image input but reduce the accuracy Parallel programming and clean code, design pattern, Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/XgaKEt2FLCAUFKSVB7bWm_daXvBUuQ- IMFaLMazoeqc9v81q9tB-xdRfUwaMvMXAPNtdRJ- erMVMYoDarnLyYNw=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/XgaKEt2FLCAUFKSVB7bWm_daXvBUuQ- IMFaLMazoeqc9v81q9tB-xdRfUwaMvMXAPNtdRJ-erMVMYoDarnLyYNw=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Hardware Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera Hardware for Deep Learning (machine learning) My experience Raspberry Pi 4 Smart AI IoT, Robotic, 3D SLAM, AR, VR RISC-V I worked with many different hardware such as Camera What is important? Scaled-YOLOv4:scaling model based on hardware Cost How to use computer vision with deep learning in IoT devices. Inference machine learning on Edge require some extra steps. Use special frameworks or library for edge devices: In some case you need to enhance model for inference. There are many techniques to use such as, How # Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera * Camera * * Camera Specs: Color camera, Stereo pair * [IMX214](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/imx214.html#imx214) (PY014 AF, PY114 FF), [OV7251](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/ov7251.html#ov7251) (PY013) * DFOV / HFOV / VFOV= 81° / 69° / 54° , 86° / 73° / 58° * Resolution: 13MP (4208x3120), 480P (640x480) * Focus: AF: 8cm - ∞ OR FF: 50cm - ∞ , Fixed-Focus 6.5cm - ∞ * Max Framerate: 35 FPS, 120 FPS * Width: 91 mm , Height: 28 mm, Length: 17.5 mm, Baseline: 75 mm, Weight: 61 g * chips: * * Robotics Vision Core 2 (RVC2 in short) Myriad X are integrated into the Robotics Vision Core 2 * Speed ML * * Model name, Size, FPS, Latency [ms], * MobileOne S0 224x224, 165.5, 11.1 * YoloV8n, 416x416, 31.3, 56.9, * YoloV8n, 640x640, 14.3, 123.6 * YoloV8s, 416x416, 15.2, 111.9 * YoloV8m, 416x416, 6.0, 273.8 # Hardware for Deep Learning (machine learning) [https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware) I experiment with many different hardware to train and run deep learning application. The below list shows my suggestion, comparison, expectation of using different hardware. Embedded AI, implementing distributed data parallel, distributed model parallel solutions. [https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware) #hardware #deep_learning #IoT #training_machine_learning_model #pirahansiah Laptop: * NVIDIA Geforce RTX 3080 Ti * Razer Blade 17 - 17.3 inch gaming laptop (NVIDIA Geforce RTX 3080 Ti, Intel i9-12900H, 4K UHD 144Hz display 32GB DDR5 RAM, 1TB SSD, Desktop * eGPU * Razer RC21-01310100-R351 Core X External Graphics Card Case = ~ 300 Euro + GPU * Cooler Master MasterCase EG200 External GPU Enclosure - Thunderbolt 3 Compatible eGPU Enclosure, 1 PWM 92mm Fan, V550 SFX Gold Fully Modular PSU, USB Hub, Vertical Laptop Support - EU Plug = ~ 300 Euro + GPU * GPU * Geforce RTX 3090 24G 384Bit Gddr6x Nvidia Geforce * MSI GeForce RTX 3090 GAMING TRIO 24G Gaming Graphics Card - NVIDIA RTX 3090, GPU 1740MHz, 24GB GDDR6X memory = ~ 2800 Euro IoT: * Raspberry pi 3 (you need accelerator ) * Raspberry pi 4 (you need accelerator ) * Intel® Neural Compute Stick 2 * Intel® Distribution of OpenVINO™ Toolkit * I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models * Google Coral * I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models * Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used * NVIDIA Jetson Nano ( 2GB and 4GB ram) * I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes * NVIDIA JETSON AGX XAVIER * NVIDIA AGX Orin = ~ 1900 Euro * [Compare NVIDIA Jetson AGX Orin with AGX Xavier: 8x AI performance, in-advance Ampere GPU, CPU, Memory & Storage](https://www.seeedstudio.com/blog/2022/03/17/compare-nvidia-jetson-agx-orin-with-agx-xavier-6x-ai-performance-in-advance-ampere-gpu-cpu-memory-storage/) * OpenCV AI Kit * OAK = ~ 100 Euro * OAK—D = ~ 200 Euro * OAK—D + Wifi = ~ 250 Euro * OpenCV AI Kit: OAK—D-PoE = ~ 250 Euro * OAK—D lite = ~ 100 Euro # My experience I tested many different hardware for 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[#GraphQL](https://www.linkedin.com/feed/hashtag/?keywords=graphql&highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6787871627586625536) #imageprocessing #patternrecognition ![](https://lh6.googleusercontent.com/2LsypDJMlRYl1XY38HLkB4EqHyVq3MAMl0CqC9xFAgMvmOLmRkF3rE8Y4i2mu6mB86bYaUQlQfHxSbuWw226YBfXULnaYcPKEm- RR5EIIxqLe1r2k2LNhlWy5xJUcUp1vQ=w1280) # Raspberry Pi 4 How to upgrade Raspberry Pi 4 EEPROM boot recovery; Released 2020-09-14; to install and boot from USB 3 (SSD) 1. update Raspberry Pi 4 EEPROM boot recovery 2. install Ubuntu 20 on SSD 3. change the config.txt and add "program_usb_boot_mode=1" at the end of file 4. remove and micro sd card and boot from ssd ![](https://lh4.googleusercontent.com/z2IpzkJPiuclHKvJ- JZL7E1I45Rx1OUPRngOni40LUX2i8gHt7IBREr0XcOSUcUYa9pi__BZhFiVmikq52ruGu- DONp8cq6bHcRExsm0QnJ4D2DSGqosZUliItD4EyZfnQ=w1280) ![](https://lh3.googleusercontent.com/LmYId4__AqpSmBCXbovkUPT0EopUELf4GMwxk- zrVvhU9UPuYaxxNXyHeblpdqHEqmI5nBsdfPwuGdTE3aSPz03AYgR2RT-0WwSvhOxTYO0WzHtBF- sc32-gdgwY-yWjsw=w1280) ![](https://lh6.googleusercontent.com/wGm7IWs1g2fQHaCOcBXUiLh3qU-4lOkoqiCLF9YpmfgSd9ZHjaTnIk4A2EXDXo6cMLnNA0xTRC3R7r5-HCCgzjkbC1xbSxOJfNQbUv8Pxg97rE5Fhr_OCs2AUVZKZv2QPw=w1280) ![](https://lh3.googleusercontent.com/Hx33mCHy_K3B-j6iXuaCUjuFK22St5r8zIkuSoJ9sBAJxW2-D9ZdTZC0QeNlJkU4vY6yV71uEA0slFCnNZPKkfizwKmUcXtDUOC9FZNyzMH4r5dfCun2phaOQpxvz07baw=w1280) ![](https://lh5.googleusercontent.com/U5FKWGlELP_9GoIwhn4MXh_QPBgvP0I_4rh- Mve1CrOzGeK7bST3U4u8XQzgPxCmZxmo5LYXv37LrnHo35gIiIqQt-eNZN0E9Hmi4g5MdbuNr2fcv- SN0uygv9GV9FwrHw=w1280) ![](https://lh5.googleusercontent.com/Y8uq6FrJOzxjjQ2KpAIaHJf6frFFOFCmgmPRkV-T6dBean13GBUKY84JKlW3_hntVm2mq70DgsLJzM6dPo9xSdq9B326nJHN39St0vsFCQ2LbRH2ErpqzR- MsRumRMeTmg=w1280) ![](https://lh3.googleusercontent.com/4Vwcq2eyzYmYjsm_qs81CZrnYnK553NfseggYjVFHu2cdpFhqkk3-8GId- PrvhR98-xPRsOuI-eDY63wjFJ- Mzg55wRMfaGXejnTmFeWFnqlyrX1vA6uEk_qhAOENuv_Fw=w1280) # Smart AI IoT, Robotic, 3D SLAM, AR, VR * [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2) # [RISC-V](/workshops-and-events/risc-v) # I worked with many different hardware such as * Raspberry pi 3 * Raspberry pi 4 * Intel® Neural Compute Stick 2 * Intel® Distribution of OpenVINO™ Toolkit * I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models * Google Coral * I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models * Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used * NVIDIA Jetson Nano * I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes * NVIDIA JETSON AGX XAVIER * The best hardware * I attended in may conferences and summits in area of Hardware for deep learning such as: * * * AI Hardware Europe Summit (July 2020) * [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit) * Apache TVM And Deep Learning Compilation Conference (December 2020) * RISC-V Summit (December 2020) * OpenCV AI Kit ## Camera I worked with many different cameras such as: * Camera Module V1 * Camera Module V2 * Camera Module V2.1 * multispectral camera * USB webcam * IP camera * high resolution camera > 8K * depth camera * stereo camera ### What is important? * camera calibration is important * Quantum efficiency [%] (spectral response) * Sensor size [inches or mm] and pixel size [micro meter] * Dynamic Range [dB] * Image noise and signal to noise ratio (SNR), PSNR, SSIM, : greater SNR yields better contrast and clarity, as well as improved low light performance * inter face, cable length in m, bandwidth max in MB/s , multi camera, cable costs, real time, plug and play * * firewire, 4.5 , 64, *, *, **, ** * gige, 100, 100, **, **, *, * * usb, 8, 350, *, *, **, ** * link, 10, 850, -, -, **, - * usb-c, 10, 40 GB,,,, * distortions, scaling factors, quality is important, calculate minimum sensor resolution *, determine your sensor size, focal length, * * sensor resolution= image resolution = 2 * ( field of view (FOV) / smallest feature ) * some online tools: baslerweb.com, edmundoptics.com, flir.com * to sum up * use USB-C camera. it will help you in the future upgrades in hardware and easy to use with less issues * find your best trade-off between WD and FOV * sometimes you cannot have everything in life! * your lens aperture (f/#) is your friend, use it! * a larger DOF requires a larger f/# * lens performance curves are the ultimate documentation to read when selecting a lens * understanding them properly requires good knowledge in optics, but it totally worth it. ## Scaled-YOLOv4:scaling model based on hardware # Cost * [How much does a patent cost?](https://www.fu-berlin.de/en/forschung/service/patente-und-lizenzen/faq/kosten.html) * [Mobile, Open Hardware, RISC-V System-on-Chip (SoC) Development Kit](https://www.crowdsupply.com/sutajio-kosagi/precursor) * Hardware * NVIDIA Jetson Xavier NX Developer Kit * WIFI * SparkFun GPS-RTK Dead Reckoning pHAT * Micro Sd card * Mophie Powerstation USB C 20000 * ZED 2 Stereo Camera * 3D-printed box * AWS * AWS S3 * AWS xml.p2.xlarge EC2 instances * AWS Sagemaker * [Hackboard 2 with Ubuntu Linux (99$) Intel CPU](https://www.crowdsupply.com/hackboard/hb2) * [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2) * Post Product to customer by * * * * * * * [easyship](https://www.easyship.com/) * [fulfillmentcrowd](https://www.fulfilmentcrowd.com/) * [ChinaDivision](https://www.chinadivision.com/) * [ORQA FPV](https://orqafpv.com/) * [floship](https://www.floship.com/) Update 26.April.2021 # How to use computer vision with deep learning in IoT devices. Inference machine learning on Edge require some extra steps. I tested several hardware such as Raspberry pi 3, Raspberry pi 4, Intel® Neural Compute Stick 2, OpenCV AI Kit, Google Coral, NVIDIA Jetson Nano, etc. Different OS: real-time operating system (RTOS), Nasa cFS (core Flight System), Real-Time Executive for Multiprocessor Systems (RTEMS), anomaly detection, object detection, object tracking, ... ## Use special frameworks or library for edge devices: * NVIDIA TensorRT * TensorFlow Lite: TensorFlow Lite on Microcontroller Gesture Recognition OpenMV/Tensorflow/ studio.edgeimpulse.com * TensorFlow.js * PyTorch Lightning * PyTorch Mobile * Intel® Distribution of OpenVINO Toolkit * CoreML * ML kit * FRITZ * MediaPipe * Apache TVM * TinyML: enabling ultra-low power machine learning at the edge tiny machine learning with Arduino * Libraries: ffmpeg, GStreamer, celery, * GPU library for python: PyCUDA, NumbaPro, PyOpenCL, CuPy Moreover, think about deep learning model for your specific hardware at first stage. ## In some case you need to enhance model for inference. There are many techniques to use such as, * Pruning * Quantization * Distillation Techniques * Binarized Neural Networks (BNNs) * Apache TVM (incubating) is a compiler stack for deep learning systems * Distributed machine learning and load balancing strategy * Low rank matrix factorization (LRMF) * Compact convolutional filters (Video/CNN) * Knowledge distillation * Neural Networks Compression Framework (NNCF) * Parallel programming ## How Distributed machine learning and load balancing strategy Pruning model pruning: reducing redundant parameters which are not sensitive to the performance. aim: remove all connections with absolute weights below a threshold. 🤔go for bigger size of network with many layers then pruning much better and faster Quantization The best way is using Google library which support most comprehensive methods compresses by reducing the number of bits used to represent the weights quantization effectively constraints the number of different weights we can use inside our kernels per channel quantization for weights, which improves performance by model compression and latency reduction. training a compact neural network with distilled knowledge of a large model distillation (knowledge transfer) from an ensemble of big networks into a much smaller network which learns directly from the cumbersome model's outputs, that is lighter to deploy Distillation Techniques Distill-Net: Application-Specific Distillation of Deep Convolutional Neural Networks for Resource-Constrained IoT Platforms Binarized Neural Networks (BNNs) It is not support by GPU hardware such as Jetson Nano. mostly based on CPU Apache TVM (incubating) is a compiler stack for deep learning systems challenges with large scale models deep neural networks are: expensive computationally expensive memory intensive hindering their deployment in:devices with low memory resources applications with strict latency requirements other issues:data security: tend to memorize everything including PII bias e.g. profanity: trained on large scale public datas elf discovering: instead of manually configuring conversational flows, automatically discover them from your data self training: let your system train itself with new example s self managing: let your system optimize by itself knowledge distillation Distributed machine learning and load balancing strategy run models which use all processing power like CPU,GPU,DSP,AI chip together to enhance inference performance. dynamic pruning of kernels which aims to the parsimonious inference by learning to exploit and dynamically remove the redundant capacity of a CNN architecture. partitioning techniques through convolution layer fusion to dynamically select the optimal partition according to the availability of computational resources and network conditions. Low rank matrix factorization (LRMF) there exists latent structures in the data, by uncovering which we can obtain a compressed representation of the dataLRMF factorizes the original matrix into lower rank matrices while preserving latent structures and addressing the issue of sparseness Compact convolutional filters (Video/CNN) designing special structural convolutional filters to save parameters replace over parametric filters with compact filters to achieve overall speedup while maintaining comparable accuracy Knowledge distillation Neural Networks Compression Framework (NNCF) AI Edge: How to inference deep learning models on edge/IoT Enabling efficient high-performance Accelerators/Optimization on Deep Learning if the object is large and we do not need small anchor in mobileNet we can remove small part of network which related to small objects. in YOLO reduce number of anchor. decrease size of image input but reduce the accuracy Parallel programming and clean code, design pattern, Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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Scaled-YOLOv4:scaling model based on hardware Cost How to use computer vision with deep learning in IoT devices. Inference machine learning on Edge require some extra steps. Use special frameworks or library for edge devices: In some case you need to enhance model for inference. There are many techniques to use such as, How # Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera * Camera * * Camera Specs: Color camera, Stereo pair * [IMX214](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/imx214.html#imx214) (PY014 AF, PY114 FF), [OV7251](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/ov7251.html#ov7251) (PY013) * DFOV / HFOV / VFOV= 81° / 69° / 54° , 86° / 73° / 58° * Resolution: 13MP (4208x3120), 480P (640x480) * Focus: AF: 8cm - ∞ OR FF: 50cm - ∞ , Fixed-Focus 6.5cm - ∞ * Max Framerate: 35 FPS, 120 FPS * Width: 91 mm , Height: 28 mm, Length: 17.5 mm, Baseline: 75 mm, Weight: 61 g * chips: * * Robotics Vision Core 2 (RVC2 in short) Myriad X are integrated into the Robotics Vision Core 2 * Speed ML * * Model name, Size, FPS, Latency [ms], * MobileOne S0 224x224, 165.5, 11.1 * YoloV8n, 416x416, 31.3, 56.9, * YoloV8n, 640x640, 14.3, 123.6 * YoloV8s, 416x416, 15.2, 111.9 * YoloV8m, 416x416, 6.0, 273.8 # Hardware for Deep Learning (machine learning) [https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware) I experiment with many different hardware to train and run deep learning application. The below list shows my suggestion, comparison, expectation of using different hardware. Embedded AI, implementing distributed data parallel, distributed model parallel solutions. [https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware) #hardware #deep_learning #IoT #training_machine_learning_model #pirahansiah Laptop: * NVIDIA Geforce RTX 3080 Ti * Razer Blade 17 - 17.3 inch gaming laptop (NVIDIA Geforce RTX 3080 Ti, Intel i9-12900H, 4K UHD 144Hz display 32GB DDR5 RAM, 1TB SSD, Desktop * eGPU * Razer RC21-01310100-R351 Core X External Graphics Card Case = ~ 300 Euro + GPU * Cooler Master MasterCase EG200 External GPU Enclosure - Thunderbolt 3 Compatible eGPU Enclosure, 1 PWM 92mm Fan, V550 SFX Gold Fully Modular PSU, USB Hub, Vertical Laptop Support - EU Plug = ~ 300 Euro + GPU * GPU * Geforce RTX 3090 24G 384Bit Gddr6x Nvidia Geforce * MSI GeForce RTX 3090 GAMING TRIO 24G Gaming Graphics Card - NVIDIA RTX 3090, GPU 1740MHz, 24GB GDDR6X memory = ~ 2800 Euro IoT: * Raspberry pi 3 (you need accelerator ) * Raspberry pi 4 (you need accelerator ) * Intel® Neural Compute Stick 2 * Intel® Distribution of OpenVINO™ Toolkit * I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models * Google Coral * I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models * Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used * NVIDIA Jetson Nano ( 2GB and 4GB ram) * I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes * NVIDIA JETSON AGX XAVIER * NVIDIA AGX Orin = ~ 1900 Euro * [Compare NVIDIA Jetson AGX Orin with AGX Xavier: 8x AI performance, in-advance Ampere GPU, CPU, Memory & Storage](https://www.seeedstudio.com/blog/2022/03/17/compare-nvidia-jetson-agx-orin-with-agx-xavier-6x-ai-performance-in-advance-ampere-gpu-cpu-memory-storage/) * OpenCV AI Kit * OAK = ~ 100 Euro * OAK—D = ~ 200 Euro * OAK—D + Wifi = ~ 250 Euro * OpenCV AI Kit: OAK—D-PoE = ~ 250 Euro * OAK—D lite = ~ 100 Euro # My experience I tested many different hardware for 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[#GraphQL](https://www.linkedin.com/feed/hashtag/?keywords=graphql&highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6787871627586625536) #imageprocessing #patternrecognition ![](https://lh6.googleusercontent.com/2LsypDJMlRYl1XY38HLkB4EqHyVq3MAMl0CqC9xFAgMvmOLmRkF3rE8Y4i2mu6mB86bYaUQlQfHxSbuWw226YBfXULnaYcPKEm- RR5EIIxqLe1r2k2LNhlWy5xJUcUp1vQ=w1280) # Raspberry Pi 4 How to upgrade Raspberry Pi 4 EEPROM boot recovery; Released 2020-09-14; to install and boot from USB 3 (SSD) 1. update Raspberry Pi 4 EEPROM boot recovery 2. install Ubuntu 20 on SSD 3. change the config.txt and add "program_usb_boot_mode=1" at the end of file 4. remove and micro sd card and boot from ssd ![](https://lh4.googleusercontent.com/z2IpzkJPiuclHKvJ- JZL7E1I45Rx1OUPRngOni40LUX2i8gHt7IBREr0XcOSUcUYa9pi__BZhFiVmikq52ruGu- DONp8cq6bHcRExsm0QnJ4D2DSGqosZUliItD4EyZfnQ=w1280) ![](https://lh3.googleusercontent.com/LmYId4__AqpSmBCXbovkUPT0EopUELf4GMwxk- zrVvhU9UPuYaxxNXyHeblpdqHEqmI5nBsdfPwuGdTE3aSPz03AYgR2RT-0WwSvhOxTYO0WzHtBF- sc32-gdgwY-yWjsw=w1280) ![](https://lh6.googleusercontent.com/wGm7IWs1g2fQHaCOcBXUiLh3qU-4lOkoqiCLF9YpmfgSd9ZHjaTnIk4A2EXDXo6cMLnNA0xTRC3R7r5-HCCgzjkbC1xbSxOJfNQbUv8Pxg97rE5Fhr_OCs2AUVZKZv2QPw=w1280) ![](https://lh3.googleusercontent.com/Hx33mCHy_K3B-j6iXuaCUjuFK22St5r8zIkuSoJ9sBAJxW2-D9ZdTZC0QeNlJkU4vY6yV71uEA0slFCnNZPKkfizwKmUcXtDUOC9FZNyzMH4r5dfCun2phaOQpxvz07baw=w1280) ![](https://lh5.googleusercontent.com/U5FKWGlELP_9GoIwhn4MXh_QPBgvP0I_4rh- Mve1CrOzGeK7bST3U4u8XQzgPxCmZxmo5LYXv37LrnHo35gIiIqQt-eNZN0E9Hmi4g5MdbuNr2fcv- SN0uygv9GV9FwrHw=w1280) ![](https://lh5.googleusercontent.com/Y8uq6FrJOzxjjQ2KpAIaHJf6frFFOFCmgmPRkV-T6dBean13GBUKY84JKlW3_hntVm2mq70DgsLJzM6dPo9xSdq9B326nJHN39St0vsFCQ2LbRH2ErpqzR- MsRumRMeTmg=w1280) ![](https://lh3.googleusercontent.com/4Vwcq2eyzYmYjsm_qs81CZrnYnK553NfseggYjVFHu2cdpFhqkk3-8GId- PrvhR98-xPRsOuI-eDY63wjFJ- Mzg55wRMfaGXejnTmFeWFnqlyrX1vA6uEk_qhAOENuv_Fw=w1280) # Smart AI IoT, Robotic, 3D SLAM, AR, VR * [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2) # [RISC-V](/workshops-and-events/risc-v) # I worked with many different hardware such as * Raspberry pi 3 * Raspberry pi 4 * Intel® Neural Compute Stick 2 * Intel® Distribution of OpenVINO™ Toolkit * I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models * Google Coral * I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models * Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used * NVIDIA Jetson Nano * I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes * NVIDIA JETSON AGX XAVIER * The best hardware * I attended in may conferences and summits in area of Hardware for deep learning such as: * * * AI Hardware Europe Summit (July 2020) * [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit) * Apache TVM And Deep Learning Compilation Conference (December 2020) * RISC-V Summit (December 2020) * OpenCV AI Kit ## Camera I worked with many different cameras such as: * Camera Module V1 * Camera Module V2 * Camera Module V2.1 * multispectral camera * USB webcam * IP camera * high resolution camera > 8K * depth camera * stereo camera ### What is important? * camera calibration is important * Quantum efficiency [%] (spectral response) * Sensor size [inches or mm] and pixel size [micro meter] * Dynamic Range [dB] * Image noise and signal to noise ratio (SNR), PSNR, SSIM, : greater SNR yields better contrast and clarity, as well as improved low light performance * inter face, cable length in m, bandwidth max in MB/s , multi camera, cable costs, real time, plug and play * * firewire, 4.5 , 64, *, *, **, ** * gige, 100, 100, **, **, *, * * usb, 8, 350, *, *, **, ** * link, 10, 850, -, -, **, - * usb-c, 10, 40 GB,,,, * distortions, scaling factors, quality is important, calculate minimum sensor resolution *, determine your sensor size, focal length, * * sensor resolution= image resolution = 2 * ( field of view (FOV) / smallest feature ) * some online tools: baslerweb.com, edmundoptics.com, flir.com * to sum up * use USB-C camera. it will help you in the future upgrades in hardware and easy to use with less issues * find your best trade-off between WD and FOV * sometimes you cannot have everything in life! * your lens aperture (f/#) is your friend, use it! * a larger DOF requires a larger f/# * lens performance curves are the ultimate documentation to read when selecting a lens * understanding them properly requires good knowledge in optics, but it totally worth it. ## Scaled-YOLOv4:scaling model based on hardware # Cost * [How much does a patent cost?](https://www.fu-berlin.de/en/forschung/service/patente-und-lizenzen/faq/kosten.html) * [Mobile, Open Hardware, RISC-V System-on-Chip (SoC) Development Kit](https://www.crowdsupply.com/sutajio-kosagi/precursor) * Hardware * NVIDIA Jetson Xavier NX Developer Kit * WIFI * SparkFun GPS-RTK Dead Reckoning pHAT * Micro Sd card * Mophie Powerstation USB C 20000 * ZED 2 Stereo Camera * 3D-printed box * AWS * AWS S3 * AWS xml.p2.xlarge EC2 instances * AWS Sagemaker * [Hackboard 2 with Ubuntu Linux (99$) Intel CPU](https://www.crowdsupply.com/hackboard/hb2) * [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2) * Post Product to customer by * * * * * * * [easyship](https://www.easyship.com/) * [fulfillmentcrowd](https://www.fulfilmentcrowd.com/) * [ChinaDivision](https://www.chinadivision.com/) * [ORQA FPV](https://orqafpv.com/) * [floship](https://www.floship.com/) Update 26.April.2021 # How to use computer vision with deep learning in IoT devices. Inference machine learning on Edge require some extra steps. I tested several hardware such as Raspberry pi 3, Raspberry pi 4, Intel® Neural Compute Stick 2, OpenCV AI Kit, Google Coral, NVIDIA Jetson Nano, etc. Different OS: real-time operating system (RTOS), Nasa cFS (core Flight System), Real-Time Executive for Multiprocessor Systems (RTEMS), anomaly detection, object detection, object tracking, ... ## Use special frameworks or library for edge devices: * NVIDIA TensorRT * TensorFlow Lite: TensorFlow Lite on Microcontroller Gesture Recognition OpenMV/Tensorflow/ studio.edgeimpulse.com * TensorFlow.js * PyTorch Lightning * PyTorch Mobile * Intel® Distribution of OpenVINO Toolkit * CoreML * ML kit * FRITZ * MediaPipe * Apache TVM * TinyML: enabling ultra-low power machine learning at the edge tiny machine learning with Arduino * Libraries: ffmpeg, GStreamer, celery, * GPU library for python: PyCUDA, NumbaPro, PyOpenCL, CuPy Moreover, think about deep learning model for your specific hardware at first stage. ## In some case you need to enhance model for inference. There are many techniques to use such as, * Pruning * Quantization * Distillation Techniques * Binarized Neural Networks (BNNs) * Apache TVM (incubating) is a compiler stack for deep learning systems * Distributed machine learning and load balancing strategy * Low rank matrix factorization (LRMF) * Compact convolutional filters (Video/CNN) * Knowledge distillation * Neural Networks Compression Framework (NNCF) * Parallel programming ## How Distributed machine learning and load balancing strategy Pruning model pruning: reducing redundant parameters which are not sensitive to the performance. aim: remove all connections with absolute weights below a threshold. 🤔go for bigger size of network with many layers then pruning much better and faster Quantization The best way is using Google library which support most comprehensive methods compresses by reducing the number of bits used to represent the weights quantization effectively constraints the number of different weights we can use inside our kernels per channel quantization for weights, which improves performance by model compression and latency reduction. training a compact neural network with distilled knowledge of a large model distillation (knowledge transfer) from an ensemble of big networks into a much smaller network which learns directly from the cumbersome model's outputs, that is lighter to deploy Distillation Techniques Distill-Net: Application-Specific Distillation of Deep Convolutional Neural Networks for Resource-Constrained IoT Platforms Binarized Neural Networks (BNNs) It is not support by GPU hardware such as Jetson Nano. mostly based on CPU Apache TVM (incubating) is a compiler stack for deep learning systems challenges with large scale models deep neural networks are: expensive computationally expensive memory intensive hindering their deployment in:devices with low memory resources applications with strict latency requirements other issues:data security: tend to memorize everything including PII bias e.g. profanity: trained on large scale public datas elf discovering: instead of manually configuring conversational flows, automatically discover them from your data self training: let your system train itself with new example s self managing: let your system optimize by itself knowledge distillation Distributed machine learning and load balancing strategy run models which use all processing power like CPU,GPU,DSP,AI chip together to enhance inference performance. dynamic pruning of kernels which aims to the parsimonious inference by learning to exploit and dynamically remove the redundant capacity of a CNN architecture. partitioning techniques through convolution layer fusion to dynamically select the optimal partition according to the availability of computational resources and network conditions. Low rank matrix factorization (LRMF) there exists latent structures in the data, by uncovering which we can obtain a compressed representation of the dataLRMF factorizes the original matrix into lower rank matrices while preserving latent structures and addressing the issue of sparseness Compact convolutional filters (Video/CNN) designing special structural convolutional filters to save parameters replace over parametric filters with compact filters to achieve overall speedup while maintaining comparable accuracy Knowledge distillation Neural Networks Compression Framework (NNCF) AI Edge: How to inference deep learning models on edge/IoT Enabling efficient high-performance Accelerators/Optimization on Deep Learning if the object is large and we do not need small anchor in mobileNet we can remove small part of network which related to small objects. in YOLO reduce number of anchor. decrease size of image input but reduce the accuracy Parallel programming and clean code, design pattern, Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/XgaKEt2FLCAUFKSVB7bWm_daXvBUuQ- IMFaLMazoeqc9v81q9tB-xdRfUwaMvMXAPNtdRJ- erMVMYoDarnLyYNw=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/XgaKEt2FLCAUFKSVB7bWm_daXvBUuQ- IMFaLMazoeqc9v81q9tB-xdRfUwaMvMXAPNtdRJ-erMVMYoDarnLyYNw=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Hardware Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera Hardware for Deep Learning (machine learning) My experience Raspberry Pi 4 Smart AI IoT, Robotic, 3D SLAM, AR, VR RISC-V I worked with many different hardware such as Camera What is important? Scaled-YOLOv4:scaling model based on hardware Cost How to use computer vision with deep learning in IoT devices. Inference machine learning on Edge require some extra steps. Use special frameworks or library for edge devices: In some case you need to enhance model for inference. There are many techniques to use such as, How # Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera * Camera * * Camera Specs: Color camera, Stereo pair * [IMX214](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/imx214.html#imx214) (PY014 AF, PY114 FF), [OV7251](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/ov7251.html#ov7251) (PY013) * DFOV / HFOV / VFOV= 81° / 69° / 54° , 86° / 73° / 58° * Resolution: 13MP (4208x3120), 480P (640x480) * Focus: AF: 8cm - ∞ OR FF: 50cm - ∞ , Fixed-Focus 6.5cm - ∞ * Max Framerate: 35 FPS, 120 FPS * Width: 91 mm , Height: 28 mm, Length: 17.5 mm, Baseline: 75 mm, Weight: 61 g * chips: * * Robotics Vision Core 2 (RVC2 in short) Myriad X are integrated into the Robotics Vision Core 2 * Speed ML * * Model name, Size, FPS, Latency [ms], * MobileOne S0 224x224, 165.5, 11.1 * YoloV8n, 416x416, 31.3, 56.9, * YoloV8n, 640x640, 14.3, 123.6 * YoloV8s, 416x416, 15.2, 111.9 * YoloV8m, 416x416, 6.0, 273.8 # Hardware for Deep Learning (machine learning) [https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware) I experiment with many different hardware to train and run deep learning application. The below list shows my suggestion, comparison, expectation of using different hardware. Embedded AI, implementing distributed data parallel, distributed model parallel solutions. [https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware) #hardware #deep_learning #IoT #training_machine_learning_model #pirahansiah Laptop: * NVIDIA Geforce RTX 3080 Ti * Razer Blade 17 - 17.3 inch gaming laptop (NVIDIA Geforce RTX 3080 Ti, Intel i9-12900H, 4K UHD 144Hz display 32GB DDR5 RAM, 1TB SSD, Desktop * eGPU * Razer RC21-01310100-R351 Core X External Graphics Card Case = ~ 300 Euro + GPU * Cooler Master MasterCase EG200 External GPU Enclosure - Thunderbolt 3 Compatible eGPU Enclosure, 1 PWM 92mm Fan, V550 SFX Gold Fully Modular PSU, USB Hub, Vertical Laptop Support - EU Plug = ~ 300 Euro + GPU * GPU * Geforce RTX 3090 24G 384Bit Gddr6x Nvidia Geforce * MSI GeForce RTX 3090 GAMING TRIO 24G Gaming Graphics Card - NVIDIA RTX 3090, GPU 1740MHz, 24GB GDDR6X memory = ~ 2800 Euro IoT: * Raspberry pi 3 (you need accelerator ) * Raspberry pi 4 (you need accelerator ) * Intel® Neural Compute Stick 2 * Intel® Distribution of OpenVINO™ Toolkit * I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models * Google Coral * I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models * Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used * NVIDIA Jetson Nano ( 2GB and 4GB ram) * I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes * NVIDIA JETSON AGX XAVIER * NVIDIA AGX Orin = ~ 1900 Euro * [Compare NVIDIA Jetson AGX Orin with AGX Xavier: 8x AI performance, in-advance Ampere GPU, CPU, Memory & Storage](https://www.seeedstudio.com/blog/2022/03/17/compare-nvidia-jetson-agx-orin-with-agx-xavier-6x-ai-performance-in-advance-ampere-gpu-cpu-memory-storage/) * OpenCV AI Kit * OAK = ~ 100 Euro * OAK—D = ~ 200 Euro * OAK—D + Wifi = ~ 250 Euro * OpenCV AI Kit: OAK—D-PoE = ~ 250 Euro * OAK—D lite = ~ 100 Euro # My experience I tested many different hardware for 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[#GraphQL](https://www.linkedin.com/feed/hashtag/?keywords=graphql&highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6787871627586625536) #imageprocessing #patternrecognition ![](https://lh6.googleusercontent.com/2LsypDJMlRYl1XY38HLkB4EqHyVq3MAMl0CqC9xFAgMvmOLmRkF3rE8Y4i2mu6mB86bYaUQlQfHxSbuWw226YBfXULnaYcPKEm- RR5EIIxqLe1r2k2LNhlWy5xJUcUp1vQ=w1280) # Raspberry Pi 4 How to upgrade Raspberry Pi 4 EEPROM boot recovery; Released 2020-09-14; to install and boot from USB 3 (SSD) 1. update Raspberry Pi 4 EEPROM boot recovery 2. install Ubuntu 20 on SSD 3. change the config.txt and add "program_usb_boot_mode=1" at the end of file 4. remove and micro sd card and boot from ssd ![](https://lh4.googleusercontent.com/z2IpzkJPiuclHKvJ- JZL7E1I45Rx1OUPRngOni40LUX2i8gHt7IBREr0XcOSUcUYa9pi__BZhFiVmikq52ruGu- DONp8cq6bHcRExsm0QnJ4D2DSGqosZUliItD4EyZfnQ=w1280) ![](https://lh3.googleusercontent.com/LmYId4__AqpSmBCXbovkUPT0EopUELf4GMwxk- zrVvhU9UPuYaxxNXyHeblpdqHEqmI5nBsdfPwuGdTE3aSPz03AYgR2RT-0WwSvhOxTYO0WzHtBF- sc32-gdgwY-yWjsw=w1280) ![](https://lh6.googleusercontent.com/wGm7IWs1g2fQHaCOcBXUiLh3qU-4lOkoqiCLF9YpmfgSd9ZHjaTnIk4A2EXDXo6cMLnNA0xTRC3R7r5-HCCgzjkbC1xbSxOJfNQbUv8Pxg97rE5Fhr_OCs2AUVZKZv2QPw=w1280) ![](https://lh3.googleusercontent.com/Hx33mCHy_K3B-j6iXuaCUjuFK22St5r8zIkuSoJ9sBAJxW2-D9ZdTZC0QeNlJkU4vY6yV71uEA0slFCnNZPKkfizwKmUcXtDUOC9FZNyzMH4r5dfCun2phaOQpxvz07baw=w1280) ![](https://lh5.googleusercontent.com/U5FKWGlELP_9GoIwhn4MXh_QPBgvP0I_4rh- Mve1CrOzGeK7bST3U4u8XQzgPxCmZxmo5LYXv37LrnHo35gIiIqQt-eNZN0E9Hmi4g5MdbuNr2fcv- SN0uygv9GV9FwrHw=w1280) ![](https://lh5.googleusercontent.com/Y8uq6FrJOzxjjQ2KpAIaHJf6frFFOFCmgmPRkV-T6dBean13GBUKY84JKlW3_hntVm2mq70DgsLJzM6dPo9xSdq9B326nJHN39St0vsFCQ2LbRH2ErpqzR- MsRumRMeTmg=w1280) ![](https://lh3.googleusercontent.com/4Vwcq2eyzYmYjsm_qs81CZrnYnK553NfseggYjVFHu2cdpFhqkk3-8GId- PrvhR98-xPRsOuI-eDY63wjFJ- Mzg55wRMfaGXejnTmFeWFnqlyrX1vA6uEk_qhAOENuv_Fw=w1280) # Smart AI IoT, Robotic, 3D SLAM, AR, VR * [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2) # [RISC-V](/workshops-and-events/risc-v) # I worked with many different hardware such as * Raspberry pi 3 * Raspberry pi 4 * Intel® Neural Compute Stick 2 * Intel® Distribution of OpenVINO™ Toolkit * I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models * Google Coral * I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models * Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used * NVIDIA Jetson Nano * I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes * NVIDIA JETSON AGX XAVIER * The best hardware * I attended in may conferences and summits in area of Hardware for deep learning such as: * * * AI Hardware Europe Summit (July 2020) * [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit) * Apache TVM And Deep Learning Compilation Conference (December 2020) * RISC-V Summit (December 2020) * OpenCV AI Kit ## Camera I worked with many different cameras such as: * Camera Module V1 * Camera Module V2 * Camera Module V2.1 * multispectral camera * USB webcam * IP camera * high resolution camera > 8K * depth camera * stereo camera ### What is important? * camera calibration is important * Quantum efficiency [%] (spectral response) * Sensor size [inches or mm] and pixel size [micro meter] * Dynamic Range [dB] * Image noise and signal to noise ratio (SNR), PSNR, SSIM, : greater SNR yields better contrast and clarity, as well as improved low light performance * inter face, cable length in m, bandwidth max in MB/s , multi camera, cable costs, real time, plug and play * * firewire, 4.5 , 64, *, *, **, ** * gige, 100, 100, **, **, *, * * usb, 8, 350, *, *, **, ** * link, 10, 850, -, -, **, - * usb-c, 10, 40 GB,,,, * distortions, scaling factors, quality is important, calculate minimum sensor resolution *, determine your sensor size, focal length, * * sensor resolution= image resolution = 2 * ( field of view (FOV) / smallest feature ) * some online tools: baslerweb.com, edmundoptics.com, flir.com * to sum up * use USB-C camera. it will help you in the future upgrades in hardware and easy to use with less issues * find your best trade-off between WD and FOV * sometimes you cannot have everything in life! * your lens aperture (f/#) is your friend, use it! * a larger DOF requires a larger f/# * lens performance curves are the ultimate documentation to read when selecting a lens * understanding them properly requires good knowledge in optics, but it totally worth it. ## Scaled-YOLOv4:scaling model based on hardware # Cost * [How much does a patent cost?](https://www.fu-berlin.de/en/forschung/service/patente-und-lizenzen/faq/kosten.html) * [Mobile, Open Hardware, RISC-V System-on-Chip (SoC) Development Kit](https://www.crowdsupply.com/sutajio-kosagi/precursor) * Hardware * NVIDIA Jetson Xavier NX Developer Kit * WIFI * SparkFun GPS-RTK Dead Reckoning pHAT * Micro Sd card * Mophie Powerstation USB C 20000 * ZED 2 Stereo Camera * 3D-printed box * AWS * AWS S3 * AWS xml.p2.xlarge EC2 instances * AWS Sagemaker * [Hackboard 2 with Ubuntu Linux (99$) Intel CPU](https://www.crowdsupply.com/hackboard/hb2) * [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2) * Post Product to customer by * * * * * * * [easyship](https://www.easyship.com/) * [fulfillmentcrowd](https://www.fulfilmentcrowd.com/) * [ChinaDivision](https://www.chinadivision.com/) * [ORQA FPV](https://orqafpv.com/) * [floship](https://www.floship.com/) Update 26.April.2021 # How to use computer vision with deep learning in IoT devices. Inference machine learning on Edge require some extra steps. I tested several hardware such as Raspberry pi 3, Raspberry pi 4, Intel® Neural Compute Stick 2, OpenCV AI Kit, Google Coral, NVIDIA Jetson Nano, etc. Different OS: real-time operating system (RTOS), Nasa cFS (core Flight System), Real-Time Executive for Multiprocessor Systems (RTEMS), anomaly detection, object detection, object tracking, ... ## Use special frameworks or library for edge devices: * NVIDIA TensorRT * TensorFlow Lite: TensorFlow Lite on Microcontroller Gesture Recognition OpenMV/Tensorflow/ studio.edgeimpulse.com * TensorFlow.js * PyTorch Lightning * PyTorch Mobile * Intel® Distribution of OpenVINO Toolkit * CoreML * ML kit * FRITZ * MediaPipe * Apache TVM * TinyML: enabling ultra-low power machine learning at the edge tiny machine learning with Arduino * Libraries: ffmpeg, GStreamer, celery, * GPU library for python: PyCUDA, NumbaPro, PyOpenCL, CuPy Moreover, think about deep learning model for your specific hardware at first stage. ## In some case you need to enhance model for inference. There are many techniques to use such as, * Pruning * Quantization * Distillation Techniques * Binarized Neural Networks (BNNs) * Apache TVM (incubating) is a compiler stack for deep learning systems * Distributed machine learning and load balancing strategy * Low rank matrix factorization (LRMF) * Compact convolutional filters (Video/CNN) * Knowledge distillation * Neural Networks Compression Framework (NNCF) * Parallel programming ## How Distributed machine learning and load balancing strategy Pruning model pruning: reducing redundant parameters which are not sensitive to the performance. aim: remove all connections with absolute weights below a threshold. 🤔go for bigger size of network with many layers then pruning much better and faster Quantization The best way is using Google library which support most comprehensive methods compresses by reducing the number of bits used to represent the weights quantization effectively constraints the number of different weights we can use inside our kernels per channel quantization for weights, which improves performance by model compression and latency reduction. training a compact neural network with distilled knowledge of a large model distillation (knowledge transfer) from an ensemble of big networks into a much smaller network which learns directly from the cumbersome model's outputs, that is lighter to deploy Distillation Techniques Distill-Net: Application-Specific Distillation of Deep Convolutional Neural Networks for Resource-Constrained IoT Platforms Binarized Neural Networks (BNNs) It is not support by GPU hardware such as Jetson Nano. mostly based on CPU Apache TVM (incubating) is a compiler stack for deep learning systems challenges with large scale models deep neural networks are: expensive computationally expensive memory intensive hindering their deployment in:devices with low memory resources applications with strict latency requirements other issues:data security: tend to memorize everything including PII bias e.g. profanity: trained on large scale public datas elf discovering: instead of manually configuring conversational flows, automatically discover them from your data self training: let your system train itself with new example s self managing: let your system optimize by itself knowledge distillation Distributed machine learning and load balancing strategy run models which use all processing power like CPU,GPU,DSP,AI chip together to enhance inference performance. dynamic pruning of kernels which aims to the parsimonious inference by learning to exploit and dynamically remove the redundant capacity of a CNN architecture. partitioning techniques through convolution layer fusion to dynamically select the optimal partition according to the availability of computational resources and network conditions. Low rank matrix factorization (LRMF) there exists latent structures in the data, by uncovering which we can obtain a compressed representation of the dataLRMF factorizes the original matrix into lower rank matrices while preserving latent structures and addressing the issue of sparseness Compact convolutional filters (Video/CNN) designing special structural convolutional filters to save parameters replace over parametric filters with compact filters to achieve overall speedup while maintaining comparable accuracy Knowledge distillation Neural Networks Compression Framework (NNCF) AI Edge: How to inference deep learning models on edge/IoT Enabling efficient high-performance Accelerators/Optimization on Deep Learning if the object is large and we do not need small anchor in mobileNet we can remove small part of network which related to small objects. in YOLO reduce number of anchor. decrease size of image input but reduce the accuracy Parallel programming and clean code, design pattern, Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/XgaKEt2FLCAUFKSVB7bWm_daXvBUuQ- IMFaLMazoeqc9v81q9tB-xdRfUwaMvMXAPNtdRJ- erMVMYoDarnLyYNw=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/XgaKEt2FLCAUFKSVB7bWm_daXvBUuQ- IMFaLMazoeqc9v81q9tB-xdRfUwaMvMXAPNtdRJ-erMVMYoDarnLyYNw=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Hardware Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera Hardware for Deep Learning (machine learning) My experience Raspberry Pi 4 Smart AI IoT, Robotic, 3D SLAM, AR, VR RISC-V I worked with many different hardware such as Camera What is important? Scaled-YOLOv4:scaling model based on hardware Cost How to use computer vision with deep learning in IoT devices. Inference machine learning on Edge require some extra steps. Use special frameworks or library for edge devices: In some case you need to enhance model for inference. There are many techniques to use such as, How # Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera * Camera * * Camera Specs: Color camera, Stereo pair * [IMX214](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/imx214.html#imx214) (PY014 AF, PY114 FF), [OV7251](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/ov7251.html#ov7251) (PY013) * DFOV / HFOV / VFOV= 81° / 69° / 54° , 86° / 73° / 58° * Resolution: 13MP (4208x3120), 480P (640x480) * Focus: AF: 8cm - ∞ OR FF: 50cm - ∞ , Fixed-Focus 6.5cm - ∞ * Max Framerate: 35 FPS, 120 FPS * Width: 91 mm , Height: 28 mm, Length: 17.5 mm, Baseline: 75 mm, Weight: 61 g * chips: * * Robotics Vision Core 2 (RVC2 in short) Myriad X are integrated into the Robotics Vision Core 2 * Speed ML * * Model name, Size, FPS, Latency [ms], * MobileOne S0 224x224, 165.5, 11.1 * YoloV8n, 416x416, 31.3, 56.9, * YoloV8n, 640x640, 14.3, 123.6 * YoloV8s, 416x416, 15.2, 111.9 * YoloV8m, 416x416, 6.0, 273.8 # Hardware for Deep Learning (machine learning) [https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware) I experiment with many different hardware to train and run deep learning application. The below list shows my suggestion, comparison, expectation of using different hardware. Embedded AI, implementing distributed data parallel, distributed model parallel solutions. [https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware) #hardware #deep_learning #IoT #training_machine_learning_model #pirahansiah Laptop: * NVIDIA Geforce RTX 3080 Ti * Razer Blade 17 - 17.3 inch gaming laptop (NVIDIA Geforce RTX 3080 Ti, Intel i9-12900H, 4K UHD 144Hz display 32GB DDR5 RAM, 1TB SSD, Desktop * eGPU * Razer RC21-01310100-R351 Core X External Graphics Card Case = ~ 300 Euro + GPU * Cooler Master MasterCase EG200 External GPU Enclosure - Thunderbolt 3 Compatible eGPU Enclosure, 1 PWM 92mm Fan, V550 SFX Gold Fully Modular PSU, USB Hub, Vertical Laptop Support - EU Plug = ~ 300 Euro + GPU * GPU * Geforce RTX 3090 24G 384Bit Gddr6x Nvidia Geforce * MSI GeForce RTX 3090 GAMING TRIO 24G Gaming Graphics Card - NVIDIA RTX 3090, GPU 1740MHz, 24GB GDDR6X memory = ~ 2800 Euro IoT: * Raspberry pi 3 (you need accelerator ) * Raspberry pi 4 (you need accelerator ) * Intel® Neural Compute Stick 2 * Intel® Distribution of OpenVINO™ Toolkit * I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models * Google Coral * I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models * Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used * NVIDIA Jetson Nano ( 2GB and 4GB ram) * I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes * NVIDIA JETSON AGX XAVIER * NVIDIA AGX Orin = ~ 1900 Euro * [Compare NVIDIA Jetson AGX Orin with AGX Xavier: 8x AI performance, in-advance Ampere GPU, CPU, Memory & Storage](https://www.seeedstudio.com/blog/2022/03/17/compare-nvidia-jetson-agx-orin-with-agx-xavier-6x-ai-performance-in-advance-ampere-gpu-cpu-memory-storage/) * OpenCV AI Kit * OAK = ~ 100 Euro * OAK—D = ~ 200 Euro * OAK—D + Wifi = ~ 250 Euro * OpenCV AI Kit: OAK—D-PoE = ~ 250 Euro * OAK—D lite = ~ 100 Euro # My experience I tested many different hardware for 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[#GraphQL](https://www.linkedin.com/feed/hashtag/?keywords=graphql&highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6787871627586625536) #imageprocessing #patternrecognition ![](https://lh6.googleusercontent.com/2LsypDJMlRYl1XY38HLkB4EqHyVq3MAMl0CqC9xFAgMvmOLmRkF3rE8Y4i2mu6mB86bYaUQlQfHxSbuWw226YBfXULnaYcPKEm- RR5EIIxqLe1r2k2LNhlWy5xJUcUp1vQ=w1280) # Raspberry Pi 4 How to upgrade Raspberry Pi 4 EEPROM boot recovery; Released 2020-09-14; to install and boot from USB 3 (SSD) 1. update Raspberry Pi 4 EEPROM boot recovery 2. install Ubuntu 20 on SSD 3. change the config.txt and add "program_usb_boot_mode=1" at the end of file 4. remove and micro sd card and boot from ssd ![](https://lh4.googleusercontent.com/z2IpzkJPiuclHKvJ- JZL7E1I45Rx1OUPRngOni40LUX2i8gHt7IBREr0XcOSUcUYa9pi__BZhFiVmikq52ruGu- DONp8cq6bHcRExsm0QnJ4D2DSGqosZUliItD4EyZfnQ=w1280) ![](https://lh3.googleusercontent.com/LmYId4__AqpSmBCXbovkUPT0EopUELf4GMwxk- zrVvhU9UPuYaxxNXyHeblpdqHEqmI5nBsdfPwuGdTE3aSPz03AYgR2RT-0WwSvhOxTYO0WzHtBF- sc32-gdgwY-yWjsw=w1280) ![](https://lh6.googleusercontent.com/wGm7IWs1g2fQHaCOcBXUiLh3qU-4lOkoqiCLF9YpmfgSd9ZHjaTnIk4A2EXDXo6cMLnNA0xTRC3R7r5-HCCgzjkbC1xbSxOJfNQbUv8Pxg97rE5Fhr_OCs2AUVZKZv2QPw=w1280) ![](https://lh3.googleusercontent.com/Hx33mCHy_K3B-j6iXuaCUjuFK22St5r8zIkuSoJ9sBAJxW2-D9ZdTZC0QeNlJkU4vY6yV71uEA0slFCnNZPKkfizwKmUcXtDUOC9FZNyzMH4r5dfCun2phaOQpxvz07baw=w1280) ![](https://lh5.googleusercontent.com/U5FKWGlELP_9GoIwhn4MXh_QPBgvP0I_4rh- Mve1CrOzGeK7bST3U4u8XQzgPxCmZxmo5LYXv37LrnHo35gIiIqQt-eNZN0E9Hmi4g5MdbuNr2fcv- SN0uygv9GV9FwrHw=w1280) ![](https://lh5.googleusercontent.com/Y8uq6FrJOzxjjQ2KpAIaHJf6frFFOFCmgmPRkV-T6dBean13GBUKY84JKlW3_hntVm2mq70DgsLJzM6dPo9xSdq9B326nJHN39St0vsFCQ2LbRH2ErpqzR- MsRumRMeTmg=w1280) ![](https://lh3.googleusercontent.com/4Vwcq2eyzYmYjsm_qs81CZrnYnK553NfseggYjVFHu2cdpFhqkk3-8GId- PrvhR98-xPRsOuI-eDY63wjFJ- Mzg55wRMfaGXejnTmFeWFnqlyrX1vA6uEk_qhAOENuv_Fw=w1280) # Smart AI IoT, Robotic, 3D SLAM, AR, VR * [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2) # [RISC-V](/workshops-and-events/risc-v) # I worked with many different hardware such as * Raspberry pi 3 * Raspberry pi 4 * Intel® Neural Compute Stick 2 * Intel® Distribution of OpenVINO™ Toolkit * I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models * Google Coral * I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models * Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used * NVIDIA Jetson Nano * I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes * NVIDIA JETSON AGX XAVIER * The best hardware * I attended in may conferences and summits in area of Hardware for deep learning such as: * * * AI Hardware Europe Summit (July 2020) * [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit) * Apache TVM And Deep Learning Compilation Conference (December 2020) * RISC-V Summit (December 2020) * OpenCV AI Kit ## Camera I worked with many different cameras such as: * Camera Module V1 * Camera Module V2 * Camera Module V2.1 * multispectral camera * USB webcam * IP camera * high resolution camera > 8K * depth camera * stereo camera ### What is important? * camera calibration is important * Quantum efficiency [%] (spectral response) * Sensor size [inches or mm] and pixel size [micro meter] * Dynamic Range [dB] * Image noise and signal to noise ratio (SNR), PSNR, SSIM, : greater SNR yields better contrast and clarity, as well as improved low light performance * inter face, cable length in m, bandwidth max in MB/s , multi camera, cable costs, real time, plug and play * * firewire, 4.5 , 64, *, *, **, ** * gige, 100, 100, **, **, *, * * usb, 8, 350, *, *, **, ** * link, 10, 850, -, -, **, - * usb-c, 10, 40 GB,,,, * distortions, scaling factors, quality is important, calculate minimum sensor resolution *, determine your sensor size, focal length, * * sensor resolution= image resolution = 2 * ( field of view (FOV) / smallest feature ) * some online tools: baslerweb.com, edmundoptics.com, flir.com * to sum up * use USB-C camera. it will help you in the future upgrades in hardware and easy to use with less issues * find your best trade-off between WD and FOV * sometimes you cannot have everything in life! * your lens aperture (f/#) is your friend, use it! * a larger DOF requires a larger f/# * lens performance curves are the ultimate documentation to read when selecting a lens * understanding them properly requires good knowledge in optics, but it totally worth it. ## Scaled-YOLOv4:scaling model based on hardware # Cost * [How much does a patent cost?](https://www.fu-berlin.de/en/forschung/service/patente-und-lizenzen/faq/kosten.html) * [Mobile, Open Hardware, RISC-V System-on-Chip (SoC) Development Kit](https://www.crowdsupply.com/sutajio-kosagi/precursor) * Hardware * NVIDIA Jetson Xavier NX Developer Kit * WIFI * SparkFun GPS-RTK Dead Reckoning pHAT * Micro Sd card * Mophie Powerstation USB C 20000 * ZED 2 Stereo Camera * 3D-printed box * AWS * AWS S3 * AWS xml.p2.xlarge EC2 instances * AWS Sagemaker * [Hackboard 2 with Ubuntu Linux (99$) Intel CPU](https://www.crowdsupply.com/hackboard/hb2) * [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2) * Post Product to customer by * * * * * * * [easyship](https://www.easyship.com/) * [fulfillmentcrowd](https://www.fulfilmentcrowd.com/) * [ChinaDivision](https://www.chinadivision.com/) * [ORQA FPV](https://orqafpv.com/) * [floship](https://www.floship.com/) Update 26.April.2021 # How to use computer vision with deep learning in IoT devices. Inference machine learning on Edge require some extra steps. I tested several hardware such as Raspberry pi 3, Raspberry pi 4, Intel® Neural Compute Stick 2, OpenCV AI Kit, Google Coral, NVIDIA Jetson Nano, etc. Different OS: real-time operating system (RTOS), Nasa cFS (core Flight System), Real-Time Executive for Multiprocessor Systems (RTEMS), anomaly detection, object detection, object tracking, ... ## Use special frameworks or library for edge devices: * NVIDIA TensorRT * TensorFlow Lite: TensorFlow Lite on Microcontroller Gesture Recognition OpenMV/Tensorflow/ studio.edgeimpulse.com * TensorFlow.js * PyTorch Lightning * PyTorch Mobile * Intel® Distribution of OpenVINO Toolkit * CoreML * ML kit * FRITZ * MediaPipe * Apache TVM * TinyML: enabling ultra-low power machine learning at the edge tiny machine learning with Arduino * Libraries: ffmpeg, GStreamer, celery, * GPU library for python: PyCUDA, NumbaPro, PyOpenCL, CuPy Moreover, think about deep learning model for your specific hardware at first stage. ## In some case you need to enhance model for inference. There are many techniques to use such as, * Pruning * Quantization * Distillation Techniques * Binarized Neural Networks (BNNs) * Apache TVM (incubating) is a compiler stack for deep learning systems * Distributed machine learning and load balancing strategy * Low rank matrix factorization (LRMF) * Compact convolutional filters (Video/CNN) * Knowledge distillation * Neural Networks Compression Framework (NNCF) * Parallel programming ## How Distributed machine learning and load balancing strategy Pruning model pruning: reducing redundant parameters which are not sensitive to the performance. aim: remove all connections with absolute weights below a threshold. 🤔go for bigger size of network with many layers then pruning much better and faster Quantization The best way is using Google library which support most comprehensive methods compresses by reducing the number of bits used to represent the weights quantization effectively constraints the number of different weights we can use inside our kernels per channel quantization for weights, which improves performance by model compression and latency reduction. training a compact neural network with distilled knowledge of a large model distillation (knowledge transfer) from an ensemble of big networks into a much smaller network which learns directly from the cumbersome model's outputs, that is lighter to deploy Distillation Techniques Distill-Net: Application-Specific Distillation of Deep Convolutional Neural Networks for Resource-Constrained IoT Platforms Binarized Neural Networks (BNNs) It is not support by GPU hardware such as Jetson Nano. mostly based on CPU Apache TVM (incubating) is a compiler stack for deep learning systems challenges with large scale models deep neural networks are: expensive computationally expensive memory intensive hindering their deployment in:devices with low memory resources applications with strict latency requirements other issues:data security: tend to memorize everything including PII bias e.g. profanity: trained on large scale public datas elf discovering: instead of manually configuring conversational flows, automatically discover them from your data self training: let your system train itself with new example s self managing: let your system optimize by itself knowledge distillation Distributed machine learning and load balancing strategy run models which use all processing power like CPU,GPU,DSP,AI chip together to enhance inference performance. dynamic pruning of kernels which aims to the parsimonious inference by learning to exploit and dynamically remove the redundant capacity of a CNN architecture. partitioning techniques through convolution layer fusion to dynamically select the optimal partition according to the availability of computational resources and network conditions. Low rank matrix factorization (LRMF) there exists latent structures in the data, by uncovering which we can obtain a compressed representation of the dataLRMF factorizes the original matrix into lower rank matrices while preserving latent structures and addressing the issue of sparseness Compact convolutional filters (Video/CNN) designing special structural convolutional filters to save parameters replace over parametric filters with compact filters to achieve overall speedup while maintaining comparable accuracy Knowledge distillation Neural Networks Compression Framework (NNCF) AI Edge: How to inference deep learning models on edge/IoT Enabling efficient high-performance Accelerators/Optimization on Deep Learning if the object is large and we do not need small anchor in mobileNet we can remove small part of network which related to small objects. in YOLO reduce number of anchor. decrease size of image input but reduce the accuracy Parallel programming and clean code, design pattern, Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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Scaled-YOLOv4:scaling model based on hardware Cost How to use computer vision with deep learning in IoT devices. Inference machine learning on Edge require some extra steps. Use special frameworks or library for edge devices: In some case you need to enhance model for inference. There are many techniques to use such as, How # Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera * Camera * * Camera Specs: Color camera, Stereo pair * [IMX214](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/imx214.html#imx214) (PY014 AF, PY114 FF), [OV7251](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/ov7251.html#ov7251) (PY013) * DFOV / HFOV / VFOV= 81° / 69° / 54° , 86° / 73° / 58° * Resolution: 13MP (4208x3120), 480P (640x480) * Focus: AF: 8cm - ∞ OR FF: 50cm - ∞ , Fixed-Focus 6.5cm - ∞ * Max Framerate: 35 FPS, 120 FPS * Width: 91 mm , Height: 28 mm, Length: 17.5 mm, Baseline: 75 mm, Weight: 61 g * chips: * * Robotics Vision Core 2 (RVC2 in short) Myriad X are integrated into the Robotics Vision Core 2 * Speed ML * * Model name, Size, FPS, Latency [ms], * MobileOne S0 224x224, 165.5, 11.1 * YoloV8n, 416x416, 31.3, 56.9, * YoloV8n, 640x640, 14.3, 123.6 * YoloV8s, 416x416, 15.2, 111.9 * YoloV8m, 416x416, 6.0, 273.8 # Hardware for Deep Learning (machine learning) [https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware) I experiment with many different hardware to train and run deep learning application. The below list shows my suggestion, comparison, expectation of using different hardware. Embedded AI, implementing distributed data parallel, distributed model parallel solutions. [https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware) #hardware #deep_learning #IoT #training_machine_learning_model #pirahansiah Laptop: * NVIDIA Geforce RTX 3080 Ti * Razer Blade 17 - 17.3 inch gaming laptop (NVIDIA Geforce RTX 3080 Ti, Intel i9-12900H, 4K UHD 144Hz display 32GB DDR5 RAM, 1TB SSD, Desktop * eGPU * Razer RC21-01310100-R351 Core X External Graphics Card Case = ~ 300 Euro + GPU * Cooler Master MasterCase EG200 External GPU Enclosure - Thunderbolt 3 Compatible eGPU Enclosure, 1 PWM 92mm Fan, V550 SFX Gold Fully Modular PSU, USB Hub, Vertical Laptop Support - EU Plug = ~ 300 Euro + GPU * GPU * Geforce RTX 3090 24G 384Bit Gddr6x Nvidia Geforce * MSI GeForce RTX 3090 GAMING TRIO 24G Gaming Graphics Card - NVIDIA RTX 3090, GPU 1740MHz, 24GB GDDR6X memory = ~ 2800 Euro IoT: * Raspberry pi 3 (you need accelerator ) * Raspberry pi 4 (you need accelerator ) * Intel® Neural Compute Stick 2 * Intel® Distribution of OpenVINO™ Toolkit * I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models * Google Coral * I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models * Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used * NVIDIA Jetson Nano ( 2GB and 4GB ram) * I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes * NVIDIA JETSON AGX XAVIER * NVIDIA AGX Orin = ~ 1900 Euro * [Compare NVIDIA Jetson AGX Orin with AGX Xavier: 8x AI performance, in-advance Ampere GPU, CPU, Memory & Storage](https://www.seeedstudio.com/blog/2022/03/17/compare-nvidia-jetson-agx-orin-with-agx-xavier-6x-ai-performance-in-advance-ampere-gpu-cpu-memory-storage/) * OpenCV AI Kit * OAK = ~ 100 Euro * OAK—D = ~ 200 Euro * OAK—D + Wifi = ~ 250 Euro * OpenCV AI Kit: OAK—D-PoE = ~ 250 Euro * OAK—D lite = ~ 100 Euro # My experience I tested many different hardware for 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[#GraphQL](https://www.linkedin.com/feed/hashtag/?keywords=graphql&highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6787871627586625536) #imageprocessing #patternrecognition ![](https://lh6.googleusercontent.com/2LsypDJMlRYl1XY38HLkB4EqHyVq3MAMl0CqC9xFAgMvmOLmRkF3rE8Y4i2mu6mB86bYaUQlQfHxSbuWw226YBfXULnaYcPKEm- RR5EIIxqLe1r2k2LNhlWy5xJUcUp1vQ=w1280) # Raspberry Pi 4 How to upgrade Raspberry Pi 4 EEPROM boot recovery; Released 2020-09-14; to install and boot from USB 3 (SSD) 1. update Raspberry Pi 4 EEPROM boot recovery 2. install Ubuntu 20 on SSD 3. change the config.txt and add "program_usb_boot_mode=1" at the end of file 4. remove and micro sd card and boot from ssd ![](https://lh4.googleusercontent.com/z2IpzkJPiuclHKvJ- JZL7E1I45Rx1OUPRngOni40LUX2i8gHt7IBREr0XcOSUcUYa9pi__BZhFiVmikq52ruGu- DONp8cq6bHcRExsm0QnJ4D2DSGqosZUliItD4EyZfnQ=w1280) ![](https://lh3.googleusercontent.com/LmYId4__AqpSmBCXbovkUPT0EopUELf4GMwxk- zrVvhU9UPuYaxxNXyHeblpdqHEqmI5nBsdfPwuGdTE3aSPz03AYgR2RT-0WwSvhOxTYO0WzHtBF- sc32-gdgwY-yWjsw=w1280) ![](https://lh6.googleusercontent.com/wGm7IWs1g2fQHaCOcBXUiLh3qU-4lOkoqiCLF9YpmfgSd9ZHjaTnIk4A2EXDXo6cMLnNA0xTRC3R7r5-HCCgzjkbC1xbSxOJfNQbUv8Pxg97rE5Fhr_OCs2AUVZKZv2QPw=w1280) ![](https://lh3.googleusercontent.com/Hx33mCHy_K3B-j6iXuaCUjuFK22St5r8zIkuSoJ9sBAJxW2-D9ZdTZC0QeNlJkU4vY6yV71uEA0slFCnNZPKkfizwKmUcXtDUOC9FZNyzMH4r5dfCun2phaOQpxvz07baw=w1280) ![](https://lh5.googleusercontent.com/U5FKWGlELP_9GoIwhn4MXh_QPBgvP0I_4rh- Mve1CrOzGeK7bST3U4u8XQzgPxCmZxmo5LYXv37LrnHo35gIiIqQt-eNZN0E9Hmi4g5MdbuNr2fcv- SN0uygv9GV9FwrHw=w1280) ![](https://lh5.googleusercontent.com/Y8uq6FrJOzxjjQ2KpAIaHJf6frFFOFCmgmPRkV-T6dBean13GBUKY84JKlW3_hntVm2mq70DgsLJzM6dPo9xSdq9B326nJHN39St0vsFCQ2LbRH2ErpqzR- MsRumRMeTmg=w1280) ![](https://lh3.googleusercontent.com/4Vwcq2eyzYmYjsm_qs81CZrnYnK553NfseggYjVFHu2cdpFhqkk3-8GId- PrvhR98-xPRsOuI-eDY63wjFJ- Mzg55wRMfaGXejnTmFeWFnqlyrX1vA6uEk_qhAOENuv_Fw=w1280) # Smart AI IoT, Robotic, 3D SLAM, AR, VR * [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2) # [RISC-V](/workshops-and-events/risc-v) # I worked with many different hardware such as * Raspberry pi 3 * Raspberry pi 4 * Intel® Neural Compute Stick 2 * Intel® Distribution of OpenVINO™ Toolkit * I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models * Google Coral * I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models * Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used * NVIDIA Jetson Nano * I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes * NVIDIA JETSON AGX XAVIER * The best hardware * I attended in may conferences and summits in area of Hardware for deep learning such as: * * * AI Hardware Europe Summit (July 2020) * [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit) * Apache TVM And Deep Learning Compilation Conference (December 2020) * RISC-V Summit (December 2020) * OpenCV AI Kit ## Camera I worked with many different cameras such as: * Camera Module V1 * Camera Module V2 * Camera Module V2.1 * multispectral camera * USB webcam * IP camera * high resolution camera > 8K * depth camera * stereo camera ### What is important? * camera calibration is important * Quantum efficiency [%] (spectral response) * Sensor size [inches or mm] and pixel size [micro meter] * Dynamic Range [dB] * Image noise and signal to noise ratio (SNR), PSNR, SSIM, : greater SNR yields better contrast and clarity, as well as improved low light performance * inter face, cable length in m, bandwidth max in MB/s , multi camera, cable costs, real time, plug and play * * firewire, 4.5 , 64, *, *, **, ** * gige, 100, 100, **, **, *, * * usb, 8, 350, *, *, **, ** * link, 10, 850, -, -, **, - * usb-c, 10, 40 GB,,,, * distortions, scaling factors, quality is important, calculate minimum sensor resolution *, determine your sensor size, focal length, * * sensor resolution= image resolution = 2 * ( field of view (FOV) / smallest feature ) * some online tools: baslerweb.com, edmundoptics.com, flir.com * to sum up * use USB-C camera. it will help you in the future upgrades in hardware and easy to use with less issues * find your best trade-off between WD and FOV * sometimes you cannot have everything in life! * your lens aperture (f/#) is your friend, use it! * a larger DOF requires a larger f/# * lens performance curves are the ultimate documentation to read when selecting a lens * understanding them properly requires good knowledge in optics, but it totally worth it. ## Scaled-YOLOv4:scaling model based on hardware # Cost * [How much does a patent cost?](https://www.fu-berlin.de/en/forschung/service/patente-und-lizenzen/faq/kosten.html) * [Mobile, Open Hardware, RISC-V System-on-Chip (SoC) Development Kit](https://www.crowdsupply.com/sutajio-kosagi/precursor) * Hardware * NVIDIA Jetson Xavier NX Developer Kit * WIFI * SparkFun GPS-RTK Dead Reckoning pHAT * Micro Sd card * Mophie Powerstation USB C 20000 * ZED 2 Stereo Camera * 3D-printed box * AWS * AWS S3 * AWS xml.p2.xlarge EC2 instances * AWS Sagemaker * [Hackboard 2 with Ubuntu Linux (99$) Intel CPU](https://www.crowdsupply.com/hackboard/hb2) * [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2) * Post Product to customer by * * * * * * * [easyship](https://www.easyship.com/) * [fulfillmentcrowd](https://www.fulfilmentcrowd.com/) * [ChinaDivision](https://www.chinadivision.com/) * [ORQA FPV](https://orqafpv.com/) * [floship](https://www.floship.com/) Update 26.April.2021 # How to use computer vision with deep learning in IoT devices. Inference machine learning on Edge require some extra steps. I tested several hardware such as Raspberry pi 3, Raspberry pi 4, Intel® Neural Compute Stick 2, OpenCV AI Kit, Google Coral, NVIDIA Jetson Nano, etc. Different OS: real-time operating system (RTOS), Nasa cFS (core Flight System), Real-Time Executive for Multiprocessor Systems (RTEMS), anomaly detection, object detection, object tracking, ... ## Use special frameworks or library for edge devices: * NVIDIA TensorRT * TensorFlow Lite: TensorFlow Lite on Microcontroller Gesture Recognition OpenMV/Tensorflow/ studio.edgeimpulse.com * TensorFlow.js * PyTorch Lightning * PyTorch Mobile * Intel® Distribution of OpenVINO Toolkit * CoreML * ML kit * FRITZ * MediaPipe * Apache TVM * TinyML: enabling ultra-low power machine learning at the edge tiny machine learning with Arduino * Libraries: ffmpeg, GStreamer, celery, * GPU library for python: PyCUDA, NumbaPro, PyOpenCL, CuPy Moreover, think about deep learning model for your specific hardware at first stage. ## In some case you need to enhance model for inference. There are many techniques to use such as, * Pruning * Quantization * Distillation Techniques * Binarized Neural Networks (BNNs) * Apache TVM (incubating) is a compiler stack for deep learning systems * Distributed machine learning and load balancing strategy * Low rank matrix factorization (LRMF) * Compact convolutional filters (Video/CNN) * Knowledge distillation * Neural Networks Compression Framework (NNCF) * Parallel programming ## How Distributed machine learning and load balancing strategy Pruning model pruning: reducing redundant parameters which are not sensitive to the performance. aim: remove all connections with absolute weights below a threshold. 🤔go for bigger size of network with many layers then pruning much better and faster Quantization The best way is using Google library which support most comprehensive methods compresses by reducing the number of bits used to represent the weights quantization effectively constraints the number of different weights we can use inside our kernels per channel quantization for weights, which improves performance by model compression and latency reduction. training a compact neural network with distilled knowledge of a large model distillation (knowledge transfer) from an ensemble of big networks into a much smaller network which learns directly from the cumbersome model's outputs, that is lighter to deploy Distillation Techniques Distill-Net: Application-Specific Distillation of Deep Convolutional Neural Networks for Resource-Constrained IoT Platforms Binarized Neural Networks (BNNs) It is not support by GPU hardware such as Jetson Nano. mostly based on CPU Apache TVM (incubating) is a compiler stack for deep learning systems challenges with large scale models deep neural networks are: expensive computationally expensive memory intensive hindering their deployment in:devices with low memory resources applications with strict latency requirements other issues:data security: tend to memorize everything including PII bias e.g. profanity: trained on large scale public datas elf discovering: instead of manually configuring conversational flows, automatically discover them from your data self training: let your system train itself with new example s self managing: let your system optimize by itself knowledge distillation Distributed machine learning and load balancing strategy run models which use all processing power like CPU,GPU,DSP,AI chip together to enhance inference performance. dynamic pruning of kernels which aims to the parsimonious inference by learning to exploit and dynamically remove the redundant capacity of a CNN architecture. partitioning techniques through convolution layer fusion to dynamically select the optimal partition according to the availability of computational resources and network conditions. Low rank matrix factorization (LRMF) there exists latent structures in the data, by uncovering which we can obtain a compressed representation of the dataLRMF factorizes the original matrix into lower rank matrices while preserving latent structures and addressing the issue of sparseness Compact convolutional filters (Video/CNN) designing special structural convolutional filters to save parameters replace over parametric filters with compact filters to achieve overall speedup while maintaining comparable accuracy Knowledge distillation Neural Networks Compression Framework (NNCF) AI Edge: How to inference deep learning models on edge/IoT Enabling efficient high-performance Accelerators/Optimization on Deep Learning if the object is large and we do not need small anchor in mobileNet we can remove small part of network which related to small objects. in YOLO reduce number of anchor. decrease size of image input but reduce the accuracy Parallel programming and clean code, design pattern, Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/XgaKEt2FLCAUFKSVB7bWm_daXvBUuQ- IMFaLMazoeqc9v81q9tB-xdRfUwaMvMXAPNtdRJ- erMVMYoDarnLyYNw=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/XgaKEt2FLCAUFKSVB7bWm_daXvBUuQ- IMFaLMazoeqc9v81q9tB-xdRfUwaMvMXAPNtdRJ-erMVMYoDarnLyYNw=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Hardware Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera Hardware for Deep Learning (machine learning) My experience Raspberry Pi 4 Smart AI IoT, Robotic, 3D SLAM, AR, VR RISC-V I worked with many different hardware such as Camera What is important? Scaled-YOLOv4:scaling model based on hardware Cost How to use computer vision with deep learning in IoT devices. Inference machine learning on Edge require some extra steps. Use special frameworks or library for edge devices: In some case you need to enhance model for inference. There are many techniques to use such as, How # Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera * Camera * * Camera Specs: Color camera, Stereo pair * [IMX214](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/imx214.html#imx214) (PY014 AF, PY114 FF), [OV7251](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/ov7251.html#ov7251) (PY013) * DFOV / HFOV / VFOV= 81° / 69° / 54° , 86° / 73° / 58° * Resolution: 13MP (4208x3120), 480P (640x480) * Focus: AF: 8cm - ∞ OR FF: 50cm - ∞ , Fixed-Focus 6.5cm - ∞ * Max Framerate: 35 FPS, 120 FPS * Width: 91 mm , Height: 28 mm, Length: 17.5 mm, Baseline: 75 mm, Weight: 61 g * chips: * * Robotics Vision Core 2 (RVC2 in short) Myriad X are integrated into the Robotics Vision Core 2 * Speed ML * * Model name, Size, FPS, Latency [ms], * MobileOne S0 224x224, 165.5, 11.1 * YoloV8n, 416x416, 31.3, 56.9, * YoloV8n, 640x640, 14.3, 123.6 * YoloV8s, 416x416, 15.2, 111.9 * YoloV8m, 416x416, 6.0, 273.8 # Hardware for Deep Learning (machine learning) [https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware) I experiment with many different hardware to train and run deep learning application. The below list shows my suggestion, comparison, expectation of using different hardware. Embedded AI, implementing distributed data parallel, distributed model parallel solutions. [https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware) #hardware #deep_learning #IoT #training_machine_learning_model #pirahansiah Laptop: * NVIDIA Geforce RTX 3080 Ti * Razer Blade 17 - 17.3 inch gaming laptop (NVIDIA Geforce RTX 3080 Ti, Intel i9-12900H, 4K UHD 144Hz display 32GB DDR5 RAM, 1TB SSD, Desktop * eGPU * Razer RC21-01310100-R351 Core X External Graphics Card Case = ~ 300 Euro + GPU * Cooler Master MasterCase EG200 External GPU Enclosure - Thunderbolt 3 Compatible eGPU Enclosure, 1 PWM 92mm Fan, V550 SFX Gold Fully Modular PSU, USB Hub, Vertical Laptop Support - EU Plug = ~ 300 Euro + GPU * GPU * Geforce RTX 3090 24G 384Bit Gddr6x Nvidia Geforce * MSI GeForce RTX 3090 GAMING TRIO 24G Gaming Graphics Card - NVIDIA RTX 3090, GPU 1740MHz, 24GB GDDR6X memory = ~ 2800 Euro IoT: * Raspberry pi 3 (you need accelerator ) * Raspberry pi 4 (you need accelerator ) * Intel® Neural Compute Stick 2 * Intel® Distribution of OpenVINO™ Toolkit * I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models * Google Coral * I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models * Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used * NVIDIA Jetson Nano ( 2GB and 4GB ram) * I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes * NVIDIA JETSON AGX XAVIER * NVIDIA AGX Orin = ~ 1900 Euro * [Compare NVIDIA Jetson AGX Orin with AGX Xavier: 8x AI performance, in-advance Ampere GPU, CPU, Memory & Storage](https://www.seeedstudio.com/blog/2022/03/17/compare-nvidia-jetson-agx-orin-with-agx-xavier-6x-ai-performance-in-advance-ampere-gpu-cpu-memory-storage/) * OpenCV AI Kit * OAK = ~ 100 Euro * OAK—D = ~ 200 Euro * OAK—D + Wifi = ~ 250 Euro * OpenCV AI Kit: OAK—D-PoE = ~ 250 Euro * OAK—D lite = ~ 100 Euro # My experience I tested many different hardware for 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[#GraphQL](https://www.linkedin.com/feed/hashtag/?keywords=graphql&highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6787871627586625536) #imageprocessing #patternrecognition ![](https://lh6.googleusercontent.com/2LsypDJMlRYl1XY38HLkB4EqHyVq3MAMl0CqC9xFAgMvmOLmRkF3rE8Y4i2mu6mB86bYaUQlQfHxSbuWw226YBfXULnaYcPKEm- RR5EIIxqLe1r2k2LNhlWy5xJUcUp1vQ=w1280) # Raspberry Pi 4 How to upgrade Raspberry Pi 4 EEPROM boot recovery; Released 2020-09-14; to install and boot from USB 3 (SSD) 1. update Raspberry Pi 4 EEPROM boot recovery 2. install Ubuntu 20 on SSD 3. change the config.txt and add "program_usb_boot_mode=1" at the end of file 4. remove and micro sd card and boot from ssd ![](https://lh4.googleusercontent.com/z2IpzkJPiuclHKvJ- JZL7E1I45Rx1OUPRngOni40LUX2i8gHt7IBREr0XcOSUcUYa9pi__BZhFiVmikq52ruGu- DONp8cq6bHcRExsm0QnJ4D2DSGqosZUliItD4EyZfnQ=w1280) ![](https://lh3.googleusercontent.com/LmYId4__AqpSmBCXbovkUPT0EopUELf4GMwxk- zrVvhU9UPuYaxxNXyHeblpdqHEqmI5nBsdfPwuGdTE3aSPz03AYgR2RT-0WwSvhOxTYO0WzHtBF- sc32-gdgwY-yWjsw=w1280) ![](https://lh6.googleusercontent.com/wGm7IWs1g2fQHaCOcBXUiLh3qU-4lOkoqiCLF9YpmfgSd9ZHjaTnIk4A2EXDXo6cMLnNA0xTRC3R7r5-HCCgzjkbC1xbSxOJfNQbUv8Pxg97rE5Fhr_OCs2AUVZKZv2QPw=w1280) ![](https://lh3.googleusercontent.com/Hx33mCHy_K3B-j6iXuaCUjuFK22St5r8zIkuSoJ9sBAJxW2-D9ZdTZC0QeNlJkU4vY6yV71uEA0slFCnNZPKkfizwKmUcXtDUOC9FZNyzMH4r5dfCun2phaOQpxvz07baw=w1280) ![](https://lh5.googleusercontent.com/U5FKWGlELP_9GoIwhn4MXh_QPBgvP0I_4rh- Mve1CrOzGeK7bST3U4u8XQzgPxCmZxmo5LYXv37LrnHo35gIiIqQt-eNZN0E9Hmi4g5MdbuNr2fcv- SN0uygv9GV9FwrHw=w1280) ![](https://lh5.googleusercontent.com/Y8uq6FrJOzxjjQ2KpAIaHJf6frFFOFCmgmPRkV-T6dBean13GBUKY84JKlW3_hntVm2mq70DgsLJzM6dPo9xSdq9B326nJHN39St0vsFCQ2LbRH2ErpqzR- MsRumRMeTmg=w1280) ![](https://lh3.googleusercontent.com/4Vwcq2eyzYmYjsm_qs81CZrnYnK553NfseggYjVFHu2cdpFhqkk3-8GId- PrvhR98-xPRsOuI-eDY63wjFJ- Mzg55wRMfaGXejnTmFeWFnqlyrX1vA6uEk_qhAOENuv_Fw=w1280) # Smart AI IoT, Robotic, 3D SLAM, AR, VR * [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2) # [RISC-V](/workshops-and-events/risc-v) # I worked with many different hardware such as * Raspberry pi 3 * Raspberry pi 4 * Intel® Neural Compute Stick 2 * Intel® Distribution of OpenVINO™ Toolkit * I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models * Google Coral * I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models * Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used * NVIDIA Jetson Nano * I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes * NVIDIA JETSON AGX XAVIER * The best hardware * I attended in may conferences and summits in area of Hardware for deep learning such as: * * * AI Hardware Europe Summit (July 2020) * [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit) * Apache TVM And Deep Learning Compilation Conference (December 2020) * RISC-V Summit (December 2020) * OpenCV AI Kit ## Camera I worked with many different cameras such as: * Camera Module V1 * Camera Module V2 * Camera Module V2.1 * multispectral camera * USB webcam * IP camera * high resolution camera > 8K * depth camera * stereo camera ### What is important? * camera calibration is important * Quantum efficiency [%] (spectral response) * Sensor size [inches or mm] and pixel size [micro meter] * Dynamic Range [dB] * Image noise and signal to noise ratio (SNR), PSNR, SSIM, : greater SNR yields better contrast and clarity, as well as improved low light performance * inter face, cable length in m, bandwidth max in MB/s , multi camera, cable costs, real time, plug and play * * firewire, 4.5 , 64, *, *, **, ** * gige, 100, 100, **, **, *, * * usb, 8, 350, *, *, **, ** * link, 10, 850, -, -, **, - * usb-c, 10, 40 GB,,,, * distortions, scaling factors, quality is important, calculate minimum sensor resolution *, determine your sensor size, focal length, * * sensor resolution= image resolution = 2 * ( field of view (FOV) / smallest feature ) * some online tools: baslerweb.com, edmundoptics.com, flir.com * to sum up * use USB-C camera. it will help you in the future upgrades in hardware and easy to use with less issues * find your best trade-off between WD and FOV * sometimes you cannot have everything in life! * your lens aperture (f/#) is your friend, use it! * a larger DOF requires a larger f/# * lens performance curves are the ultimate documentation to read when selecting a lens * understanding them properly requires good knowledge in optics, but it totally worth it. ## Scaled-YOLOv4:scaling model based on hardware # Cost * [How much does a patent cost?](https://www.fu-berlin.de/en/forschung/service/patente-und-lizenzen/faq/kosten.html) * [Mobile, Open Hardware, RISC-V System-on-Chip (SoC) Development Kit](https://www.crowdsupply.com/sutajio-kosagi/precursor) * Hardware * NVIDIA Jetson Xavier NX Developer Kit * WIFI * SparkFun GPS-RTK Dead Reckoning pHAT * Micro Sd card * Mophie Powerstation USB C 20000 * ZED 2 Stereo Camera * 3D-printed box * AWS * AWS S3 * AWS xml.p2.xlarge EC2 instances * AWS Sagemaker * [Hackboard 2 with Ubuntu Linux (99$) Intel CPU](https://www.crowdsupply.com/hackboard/hb2) * [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2) * Post Product to customer by * * * * * * * [easyship](https://www.easyship.com/) * [fulfillmentcrowd](https://www.fulfilmentcrowd.com/) * [ChinaDivision](https://www.chinadivision.com/) * [ORQA FPV](https://orqafpv.com/) * [floship](https://www.floship.com/) Update 26.April.2021 # How to use computer vision with deep learning in IoT devices. Inference machine learning on Edge require some extra steps. I tested several hardware such as Raspberry pi 3, Raspberry pi 4, Intel® Neural Compute Stick 2, OpenCV AI Kit, Google Coral, NVIDIA Jetson Nano, etc. Different OS: real-time operating system (RTOS), Nasa cFS (core Flight System), Real-Time Executive for Multiprocessor Systems (RTEMS), anomaly detection, object detection, object tracking, ... ## Use special frameworks or library for edge devices: * NVIDIA TensorRT * TensorFlow Lite: TensorFlow Lite on Microcontroller Gesture Recognition OpenMV/Tensorflow/ studio.edgeimpulse.com * TensorFlow.js * PyTorch Lightning * PyTorch Mobile * Intel® Distribution of OpenVINO Toolkit * CoreML * ML kit * FRITZ * MediaPipe * Apache TVM * TinyML: enabling ultra-low power machine learning at the edge tiny machine learning with Arduino * Libraries: ffmpeg, GStreamer, celery, * GPU library for python: PyCUDA, NumbaPro, PyOpenCL, CuPy Moreover, think about deep learning model for your specific hardware at first stage. ## In some case you need to enhance model for inference. There are many techniques to use such as, * Pruning * Quantization * Distillation Techniques * Binarized Neural Networks (BNNs) * Apache TVM (incubating) is a compiler stack for deep learning systems * Distributed machine learning and load balancing strategy * Low rank matrix factorization (LRMF) * Compact convolutional filters (Video/CNN) * Knowledge distillation * Neural Networks Compression Framework (NNCF) * Parallel programming ## How Distributed machine learning and load balancing strategy Pruning model pruning: reducing redundant parameters which are not sensitive to the performance. aim: remove all connections with absolute weights below a threshold. 🤔go for bigger size of network with many layers then pruning much better and faster Quantization The best way is using Google library which support most comprehensive methods compresses by reducing the number of bits used to represent the weights quantization effectively constraints the number of different weights we can use inside our kernels per channel quantization for weights, which improves performance by model compression and latency reduction. training a compact neural network with distilled knowledge of a large model distillation (knowledge transfer) from an ensemble of big networks into a much smaller network which learns directly from the cumbersome model's outputs, that is lighter to deploy Distillation Techniques Distill-Net: Application-Specific Distillation of Deep Convolutional Neural Networks for Resource-Constrained IoT Platforms Binarized Neural Networks (BNNs) It is not support by GPU hardware such as Jetson Nano. mostly based on CPU Apache TVM (incubating) is a compiler stack for deep learning systems challenges with large scale models deep neural networks are: expensive computationally expensive memory intensive hindering their deployment in:devices with low memory resources applications with strict latency requirements other issues:data security: tend to memorize everything including PII bias e.g. profanity: trained on large scale public datas elf discovering: instead of manually configuring conversational flows, automatically discover them from your data self training: let your system train itself with new example s self managing: let your system optimize by itself knowledge distillation Distributed machine learning and load balancing strategy run models which use all processing power like CPU,GPU,DSP,AI chip together to enhance inference performance. dynamic pruning of kernels which aims to the parsimonious inference by learning to exploit and dynamically remove the redundant capacity of a CNN architecture. partitioning techniques through convolution layer fusion to dynamically select the optimal partition according to the availability of computational resources and network conditions. Low rank matrix factorization (LRMF) there exists latent structures in the data, by uncovering which we can obtain a compressed representation of the dataLRMF factorizes the original matrix into lower rank matrices while preserving latent structures and addressing the issue of sparseness Compact convolutional filters (Video/CNN) designing special structural convolutional filters to save parameters replace over parametric filters with compact filters to achieve overall speedup while maintaining comparable accuracy Knowledge distillation Neural Networks Compression Framework (NNCF) AI Edge: How to inference deep learning models on edge/IoT Enabling efficient high-performance Accelerators/Optimization on Deep Learning if the object is large and we do not need small anchor in mobileNet we can remove small part of network which related to small objects. in YOLO reduce number of anchor. decrease size of image input but reduce the accuracy Parallel programming and clean code, design pattern, Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/XgaKEt2FLCAUFKSVB7bWm_daXvBUuQ- IMFaLMazoeqc9v81q9tB-xdRfUwaMvMXAPNtdRJ- erMVMYoDarnLyYNw=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/XgaKEt2FLCAUFKSVB7bWm_daXvBUuQ- IMFaLMazoeqc9v81q9tB-xdRfUwaMvMXAPNtdRJ-erMVMYoDarnLyYNw=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Hardware Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera Hardware for Deep Learning (machine learning) My experience Raspberry Pi 4 Smart AI IoT, Robotic, 3D SLAM, AR, VR RISC-V I worked with many different hardware such as Camera What is important? Scaled-YOLOv4:scaling model based on hardware Cost How to use computer vision with deep learning in IoT devices. Inference machine learning on Edge require some extra steps. Use special frameworks or library for edge devices: In some case you need to enhance model for inference. There are many techniques to use such as, How # Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera * Camera * * Camera Specs: Color camera, Stereo pair * [IMX214](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/imx214.html#imx214) (PY014 AF, PY114 FF), [OV7251](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/ov7251.html#ov7251) (PY013) * DFOV / HFOV / VFOV= 81° / 69° / 54° , 86° / 73° / 58° * Resolution: 13MP (4208x3120), 480P (640x480) * Focus: AF: 8cm - ∞ OR FF: 50cm - ∞ , Fixed-Focus 6.5cm - ∞ * Max Framerate: 35 FPS, 120 FPS * Width: 91 mm , Height: 28 mm, Length: 17.5 mm, Baseline: 75 mm, Weight: 61 g * chips: * * Robotics Vision Core 2 (RVC2 in short) Myriad X are integrated into the Robotics Vision Core 2 * Speed ML * * Model name, Size, FPS, Latency [ms], * MobileOne S0 224x224, 165.5, 11.1 * YoloV8n, 416x416, 31.3, 56.9, * YoloV8n, 640x640, 14.3, 123.6 * YoloV8s, 416x416, 15.2, 111.9 * YoloV8m, 416x416, 6.0, 273.8 # Hardware for Deep Learning (machine learning) [https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware) I experiment with many different hardware to train and run deep learning application. The below list shows my suggestion, comparison, expectation of using different hardware. Embedded AI, implementing distributed data parallel, distributed model parallel solutions. [https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware) #hardware #deep_learning #IoT #training_machine_learning_model #pirahansiah Laptop: * NVIDIA Geforce RTX 3080 Ti * Razer Blade 17 - 17.3 inch gaming laptop (NVIDIA Geforce RTX 3080 Ti, Intel i9-12900H, 4K UHD 144Hz display 32GB DDR5 RAM, 1TB SSD, Desktop * eGPU * Razer RC21-01310100-R351 Core X External Graphics Card Case = ~ 300 Euro + GPU * Cooler Master MasterCase EG200 External GPU Enclosure - Thunderbolt 3 Compatible eGPU Enclosure, 1 PWM 92mm Fan, V550 SFX Gold Fully Modular PSU, USB Hub, Vertical Laptop Support - EU Plug = ~ 300 Euro + GPU * GPU * Geforce RTX 3090 24G 384Bit Gddr6x Nvidia Geforce * MSI GeForce RTX 3090 GAMING TRIO 24G Gaming Graphics Card - NVIDIA RTX 3090, GPU 1740MHz, 24GB GDDR6X memory = ~ 2800 Euro IoT: * Raspberry pi 3 (you need accelerator ) * Raspberry pi 4 (you need accelerator ) * Intel® Neural Compute Stick 2 * Intel® Distribution of OpenVINO™ Toolkit * I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models * Google Coral * I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models * Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used * NVIDIA Jetson Nano ( 2GB and 4GB ram) * I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes * NVIDIA JETSON AGX XAVIER * NVIDIA AGX Orin = ~ 1900 Euro * [Compare NVIDIA Jetson AGX Orin with AGX Xavier: 8x AI performance, in-advance Ampere GPU, CPU, Memory & Storage](https://www.seeedstudio.com/blog/2022/03/17/compare-nvidia-jetson-agx-orin-with-agx-xavier-6x-ai-performance-in-advance-ampere-gpu-cpu-memory-storage/) * OpenCV AI Kit * OAK = ~ 100 Euro * OAK—D = ~ 200 Euro * OAK—D + Wifi = ~ 250 Euro * OpenCV AI Kit: OAK—D-PoE = ~ 250 Euro * OAK—D lite = ~ 100 Euro # My experience I tested many different hardware for 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[#GraphQL](https://www.linkedin.com/feed/hashtag/?keywords=graphql&highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6787871627586625536) #imageprocessing #patternrecognition ![](https://lh6.googleusercontent.com/2LsypDJMlRYl1XY38HLkB4EqHyVq3MAMl0CqC9xFAgMvmOLmRkF3rE8Y4i2mu6mB86bYaUQlQfHxSbuWw226YBfXULnaYcPKEm- RR5EIIxqLe1r2k2LNhlWy5xJUcUp1vQ=w1280) # Raspberry Pi 4 How to upgrade Raspberry Pi 4 EEPROM boot recovery; Released 2020-09-14; to install and boot from USB 3 (SSD) 1. update Raspberry Pi 4 EEPROM boot recovery 2. install Ubuntu 20 on SSD 3. change the config.txt and add "program_usb_boot_mode=1" at the end of file 4. remove and micro sd card and boot from ssd ![](https://lh4.googleusercontent.com/z2IpzkJPiuclHKvJ- JZL7E1I45Rx1OUPRngOni40LUX2i8gHt7IBREr0XcOSUcUYa9pi__BZhFiVmikq52ruGu- DONp8cq6bHcRExsm0QnJ4D2DSGqosZUliItD4EyZfnQ=w1280) ![](https://lh3.googleusercontent.com/LmYId4__AqpSmBCXbovkUPT0EopUELf4GMwxk- zrVvhU9UPuYaxxNXyHeblpdqHEqmI5nBsdfPwuGdTE3aSPz03AYgR2RT-0WwSvhOxTYO0WzHtBF- sc32-gdgwY-yWjsw=w1280) ![](https://lh6.googleusercontent.com/wGm7IWs1g2fQHaCOcBXUiLh3qU-4lOkoqiCLF9YpmfgSd9ZHjaTnIk4A2EXDXo6cMLnNA0xTRC3R7r5-HCCgzjkbC1xbSxOJfNQbUv8Pxg97rE5Fhr_OCs2AUVZKZv2QPw=w1280) ![](https://lh3.googleusercontent.com/Hx33mCHy_K3B-j6iXuaCUjuFK22St5r8zIkuSoJ9sBAJxW2-D9ZdTZC0QeNlJkU4vY6yV71uEA0slFCnNZPKkfizwKmUcXtDUOC9FZNyzMH4r5dfCun2phaOQpxvz07baw=w1280) ![](https://lh5.googleusercontent.com/U5FKWGlELP_9GoIwhn4MXh_QPBgvP0I_4rh- Mve1CrOzGeK7bST3U4u8XQzgPxCmZxmo5LYXv37LrnHo35gIiIqQt-eNZN0E9Hmi4g5MdbuNr2fcv- SN0uygv9GV9FwrHw=w1280) ![](https://lh5.googleusercontent.com/Y8uq6FrJOzxjjQ2KpAIaHJf6frFFOFCmgmPRkV-T6dBean13GBUKY84JKlW3_hntVm2mq70DgsLJzM6dPo9xSdq9B326nJHN39St0vsFCQ2LbRH2ErpqzR- MsRumRMeTmg=w1280) ![](https://lh3.googleusercontent.com/4Vwcq2eyzYmYjsm_qs81CZrnYnK553NfseggYjVFHu2cdpFhqkk3-8GId- PrvhR98-xPRsOuI-eDY63wjFJ- Mzg55wRMfaGXejnTmFeWFnqlyrX1vA6uEk_qhAOENuv_Fw=w1280) # Smart AI IoT, Robotic, 3D SLAM, AR, VR * [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2) # [RISC-V](/workshops-and-events/risc-v) # I worked with many different hardware such as * Raspberry pi 3 * Raspberry pi 4 * Intel® Neural Compute Stick 2 * Intel® Distribution of OpenVINO™ Toolkit * I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models * Google Coral * I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models * Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used * NVIDIA Jetson Nano * I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes * NVIDIA JETSON AGX XAVIER * The best hardware * I attended in may conferences and summits in area of Hardware for deep learning such as: * * * AI Hardware Europe Summit (July 2020) * [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit) * Apache TVM And Deep Learning Compilation Conference (December 2020) * RISC-V Summit (December 2020) * OpenCV AI Kit ## Camera I worked with many different cameras such as: * Camera Module V1 * Camera Module V2 * Camera Module V2.1 * multispectral camera * USB webcam * IP camera * high resolution camera > 8K * depth camera * stereo camera ### What is important? * camera calibration is important * Quantum efficiency [%] (spectral response) * Sensor size [inches or mm] and pixel size [micro meter] * Dynamic Range [dB] * Image noise and signal to noise ratio (SNR), PSNR, SSIM, : greater SNR yields better contrast and clarity, as well as improved low light performance * inter face, cable length in m, bandwidth max in MB/s , multi camera, cable costs, real time, plug and play * * firewire, 4.5 , 64, *, *, **, ** * gige, 100, 100, **, **, *, * * usb, 8, 350, *, *, **, ** * link, 10, 850, -, -, **, - * usb-c, 10, 40 GB,,,, * distortions, scaling factors, quality is important, calculate minimum sensor resolution *, determine your sensor size, focal length, * * sensor resolution= image resolution = 2 * ( field of view (FOV) / smallest feature ) * some online tools: baslerweb.com, edmundoptics.com, flir.com * to sum up * use USB-C camera. it will help you in the future upgrades in hardware and easy to use with less issues * find your best trade-off between WD and FOV * sometimes you cannot have everything in life! * your lens aperture (f/#) is your friend, use it! * a larger DOF requires a larger f/# * lens performance curves are the ultimate documentation to read when selecting a lens * understanding them properly requires good knowledge in optics, but it totally worth it. ## Scaled-YOLOv4:scaling model based on hardware # Cost * [How much does a patent cost?](https://www.fu-berlin.de/en/forschung/service/patente-und-lizenzen/faq/kosten.html) * [Mobile, Open Hardware, RISC-V System-on-Chip (SoC) Development Kit](https://www.crowdsupply.com/sutajio-kosagi/precursor) * Hardware * NVIDIA Jetson Xavier NX Developer Kit * WIFI * SparkFun GPS-RTK Dead Reckoning pHAT * Micro Sd card * Mophie Powerstation USB C 20000 * ZED 2 Stereo Camera * 3D-printed box * AWS * AWS S3 * AWS xml.p2.xlarge EC2 instances * AWS Sagemaker * [Hackboard 2 with Ubuntu Linux (99$) Intel CPU](https://www.crowdsupply.com/hackboard/hb2) * [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2) * Post Product to customer by * * * * * * * [easyship](https://www.easyship.com/) * [fulfillmentcrowd](https://www.fulfilmentcrowd.com/) * [ChinaDivision](https://www.chinadivision.com/) * [ORQA FPV](https://orqafpv.com/) * [floship](https://www.floship.com/) Update 26.April.2021 # How to use computer vision with deep learning in IoT devices. Inference machine learning on Edge require some extra steps. I tested several hardware such as Raspberry pi 3, Raspberry pi 4, Intel® Neural Compute Stick 2, OpenCV AI Kit, Google Coral, NVIDIA Jetson Nano, etc. Different OS: real-time operating system (RTOS), Nasa cFS (core Flight System), Real-Time Executive for Multiprocessor Systems (RTEMS), anomaly detection, object detection, object tracking, ... ## Use special frameworks or library for edge devices: * NVIDIA TensorRT * TensorFlow Lite: TensorFlow Lite on Microcontroller Gesture Recognition OpenMV/Tensorflow/ studio.edgeimpulse.com * TensorFlow.js * PyTorch Lightning * PyTorch Mobile * Intel® Distribution of OpenVINO Toolkit * CoreML * ML kit * FRITZ * MediaPipe * Apache TVM * TinyML: enabling ultra-low power machine learning at the edge tiny machine learning with Arduino * Libraries: ffmpeg, GStreamer, celery, * GPU library for python: PyCUDA, NumbaPro, PyOpenCL, CuPy Moreover, think about deep learning model for your specific hardware at first stage. ## In some case you need to enhance model for inference. There are many techniques to use such as, * Pruning * Quantization * Distillation Techniques * Binarized Neural Networks (BNNs) * Apache TVM (incubating) is a compiler stack for deep learning systems * Distributed machine learning and load balancing strategy * Low rank matrix factorization (LRMF) * Compact convolutional filters (Video/CNN) * Knowledge distillation * Neural Networks Compression Framework (NNCF) * Parallel programming ## How Distributed machine learning and load balancing strategy Pruning model pruning: reducing redundant parameters which are not sensitive to the performance. aim: remove all connections with absolute weights below a threshold. 🤔go for bigger size of network with many layers then pruning much better and faster Quantization The best way is using Google library which support most comprehensive methods compresses by reducing the number of bits used to represent the weights quantization effectively constraints the number of different weights we can use inside our kernels per channel quantization for weights, which improves performance by model compression and latency reduction. training a compact neural network with distilled knowledge of a large model distillation (knowledge transfer) from an ensemble of big networks into a much smaller network which learns directly from the cumbersome model's outputs, that is lighter to deploy Distillation Techniques Distill-Net: Application-Specific Distillation of Deep Convolutional Neural Networks for Resource-Constrained IoT Platforms Binarized Neural Networks (BNNs) It is not support by GPU hardware such as Jetson Nano. mostly based on CPU Apache TVM (incubating) is a compiler stack for deep learning systems challenges with large scale models deep neural networks are: expensive computationally expensive memory intensive hindering their deployment in:devices with low memory resources applications with strict latency requirements other issues:data security: tend to memorize everything including PII bias e.g. profanity: trained on large scale public datas elf discovering: instead of manually configuring conversational flows, automatically discover them from your data self training: let your system train itself with new example s self managing: let your system optimize by itself knowledge distillation Distributed machine learning and load balancing strategy run models which use all processing power like CPU,GPU,DSP,AI chip together to enhance inference performance. dynamic pruning of kernels which aims to the parsimonious inference by learning to exploit and dynamically remove the redundant capacity of a CNN architecture. partitioning techniques through convolution layer fusion to dynamically select the optimal partition according to the availability of computational resources and network conditions. Low rank matrix factorization (LRMF) there exists latent structures in the data, by uncovering which we can obtain a compressed representation of the dataLRMF factorizes the original matrix into lower rank matrices while preserving latent structures and addressing the issue of sparseness Compact convolutional filters (Video/CNN) designing special structural convolutional filters to save parameters replace over parametric filters with compact filters to achieve overall speedup while maintaining comparable accuracy Knowledge distillation Neural Networks Compression Framework (NNCF) AI Edge: How to inference deep learning models on edge/IoT Enabling efficient high-performance Accelerators/Optimization on Deep Learning if the object is large and we do not need small anchor in mobileNet we can remove small part of network which related to small objects. in YOLO reduce number of anchor. decrease size of image input but reduce the accuracy Parallel programming and clean code, design pattern, Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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Scaled-YOLOv4:scaling model based on hardware Cost How to use computer vision with deep learning in IoT devices. Inference machine learning on Edge require some extra steps. Use special frameworks or library for edge devices: In some case you need to enhance model for inference. There are many techniques to use such as, How # Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera * Camera * * Camera Specs: Color camera, Stereo pair * [IMX214](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/imx214.html#imx214) (PY014 AF, PY114 FF), [OV7251](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/ov7251.html#ov7251) (PY013) * DFOV / HFOV / VFOV= 81° / 69° / 54° , 86° / 73° / 58° * Resolution: 13MP (4208x3120), 480P (640x480) * Focus: AF: 8cm - ∞ OR FF: 50cm - ∞ , Fixed-Focus 6.5cm - ∞ * Max Framerate: 35 FPS, 120 FPS * Width: 91 mm , Height: 28 mm, Length: 17.5 mm, Baseline: 75 mm, Weight: 61 g * chips: * * Robotics Vision Core 2 (RVC2 in short) Myriad X are integrated into the Robotics Vision Core 2 * Speed ML * * Model name, Size, FPS, Latency [ms], * MobileOne S0 224x224, 165.5, 11.1 * YoloV8n, 416x416, 31.3, 56.9, * YoloV8n, 640x640, 14.3, 123.6 * YoloV8s, 416x416, 15.2, 111.9 * YoloV8m, 416x416, 6.0, 273.8 # Hardware for Deep Learning (machine learning) [https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware) I experiment with many different hardware to train and run deep learning application. The below list shows my suggestion, comparison, expectation of using different hardware. Embedded AI, implementing distributed data parallel, distributed model parallel solutions. [https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware) #hardware #deep_learning #IoT #training_machine_learning_model #pirahansiah Laptop: * NVIDIA Geforce RTX 3080 Ti * Razer Blade 17 - 17.3 inch gaming laptop (NVIDIA Geforce RTX 3080 Ti, Intel i9-12900H, 4K UHD 144Hz display 32GB DDR5 RAM, 1TB SSD, Desktop * eGPU * Razer RC21-01310100-R351 Core X External Graphics Card Case = ~ 300 Euro + GPU * Cooler Master MasterCase EG200 External GPU Enclosure - Thunderbolt 3 Compatible eGPU Enclosure, 1 PWM 92mm Fan, V550 SFX Gold Fully Modular PSU, USB Hub, Vertical Laptop Support - EU Plug = ~ 300 Euro + GPU * GPU * Geforce RTX 3090 24G 384Bit Gddr6x Nvidia Geforce * MSI GeForce RTX 3090 GAMING TRIO 24G Gaming Graphics Card - NVIDIA RTX 3090, GPU 1740MHz, 24GB GDDR6X memory = ~ 2800 Euro IoT: * Raspberry pi 3 (you need accelerator ) * Raspberry pi 4 (you need accelerator ) * Intel® Neural Compute Stick 2 * Intel® Distribution of OpenVINO™ Toolkit * I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models * Google Coral * I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models * Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used * NVIDIA Jetson Nano ( 2GB and 4GB ram) * I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes * NVIDIA JETSON AGX XAVIER * NVIDIA AGX Orin = ~ 1900 Euro * [Compare NVIDIA Jetson AGX Orin with AGX Xavier: 8x AI performance, in-advance Ampere GPU, CPU, Memory & Storage](https://www.seeedstudio.com/blog/2022/03/17/compare-nvidia-jetson-agx-orin-with-agx-xavier-6x-ai-performance-in-advance-ampere-gpu-cpu-memory-storage/) * OpenCV AI Kit * OAK = ~ 100 Euro * OAK—D = ~ 200 Euro * OAK—D + Wifi = ~ 250 Euro * OpenCV AI Kit: OAK—D-PoE = ~ 250 Euro * OAK—D lite = ~ 100 Euro # My experience I tested many different hardware for 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[#GraphQL](https://www.linkedin.com/feed/hashtag/?keywords=graphql&highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6787871627586625536) #imageprocessing #patternrecognition ![](https://lh3.googleusercontent.com/VZMsqBhRJ3f0IALfzNXl_6RzGqAectl1Hxmei9swx6ZJNYFzlXAVxhQ2NXOd7E3RmwEIsWoYSo_zVFj0qLbAfl2VJq4VXYjbD2JYipPaJnmUn8T6FToppIkmAnEV0XNsWA=w1280) # Raspberry Pi 4 How to upgrade Raspberry Pi 4 EEPROM boot recovery; Released 2020-09-14; to install and boot from USB 3 (SSD) 1. update Raspberry Pi 4 EEPROM boot recovery 2. install Ubuntu 20 on SSD 3. change the config.txt and add "program_usb_boot_mode=1" at the end of file 4. remove and micro sd card and boot from ssd ![](https://lh4.googleusercontent.com/hkMt3Fso7Lt0LabRhCrxAFcMs0opFz9S85EeWZLtAIP94iFOF2A7Dv4-Z_z34AGOYSJBEMGkNv69zimrlm5t99XG1Luhrkq_5rMdqXnswsxwDrSGzR0Xv_OTeCJp5aimbQ=w1280) ![](https://lh4.googleusercontent.com/GJ8aBAr0SwcUi0-KPdUIl1PjETkstJdXxfRbNSTSkVIb2p3O3BI95FmBwjibE0te- FN-NoQN3MHm_XqJiLGLvAHZToT_0aK1gTR1_Tbz1OAsreEcFTWH1Sgm5HFyG2u8wQ=w1280) ![](https://lh6.googleusercontent.com/lfYnZv07KUbsrISrffug2JbCfQ2VXRuzxtBL34wXuRQydhUm1TINX5BsTxAMHfJ14LHlYPyJAxgTvjocgMQ7ViCOuqeLuwX9eSSZKdLuZiSaQSspSTLl5aFCyAkp4qbl4A=w1280) ![](https://lh3.googleusercontent.com/JCtH7aainnvRN4eC7WpuJCMlq_I5pSxi1dLYUEfy_5UutuXtOJ-R4JHZsAdAdygOYUW_B1apJT74LtVddIFq6blXAd1T_hV8AY_mCLQZhpgnaySln8mXZsYLgxC2Q6xFWA=w1280) ![](https://lh4.googleusercontent.com/QS8CudtZFOG0blxmnsZ7lP4gyFVRqmY3r6Ws3uju1UF1SPjkETgl74dJlnuD4kGhd0AJ- EmVY2dSjlibqw1oYMSUcxxW4sgvtPS1Syktl4OzcmLA3LUstsCPfeLBu8JQrA=w1280) ![](https://lh4.googleusercontent.com/xw5ZuxPtIVJ67ZnVXtj0CKrBud2Ixtug2sH5B4S__n3YuwQl3AdbwCkZt4nSp1zjHW4lKcNtidtah3Z5a_v4gNnGGD2O5Vn7QbWJkRtHSJRqgFyCW1YUS79kq0NryYYzXQ=w1280) ![](https://lh6.googleusercontent.com/DAJFrD5Lau0EThTdbJqQapkZhiO_eKmxXg1tz_3DWcWC1SJIcrurHzduRFQuLJVJiLf9WdfQu3fPKNiJdXKBtsYdTYFTaKktIgeEtEREznQzeaZ7BQC-1wRz0k3FERpaWg=w1280) # Smart AI IoT, Robotic, 3D SLAM, AR, VR * [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2) # [RISC-V](/workshops-and-events/risc-v) # I worked with many different hardware such as * Raspberry pi 3 * Raspberry pi 4 * Intel® Neural Compute Stick 2 * Intel® Distribution of OpenVINO™ Toolkit * I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models * Google Coral * I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models * Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used * NVIDIA Jetson Nano * I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes * NVIDIA JETSON AGX XAVIER * The best hardware * I attended in may conferences and summits in area of Hardware for deep learning such as: * * * AI Hardware Europe Summit (July 2020) * [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit) * Apache TVM And Deep Learning Compilation Conference (December 2020) * RISC-V Summit (December 2020) * OpenCV AI Kit ## Camera I worked with many different cameras such as: * Camera Module V1 * Camera Module V2 * Camera Module V2.1 * multispectral camera * USB webcam * IP camera * high resolution camera > 8K * depth camera * stereo camera ### What is important? * camera calibration is important * Quantum efficiency [%] (spectral response) * Sensor size [inches or mm] and pixel size [micro meter] * Dynamic Range [dB] * Image noise and signal to noise ratio (SNR), PSNR, SSIM, : greater SNR yields better contrast and clarity, as well as improved low light performance * inter face, cable length in m, bandwidth max in MB/s , multi camera, cable costs, real time, plug and play * * firewire, 4.5 , 64, *, *, **, ** * gige, 100, 100, **, **, *, * * usb, 8, 350, *, *, **, ** * link, 10, 850, -, -, **, - * usb-c, 10, 40 GB,,,, * distortions, scaling factors, quality is important, calculate minimum sensor resolution *, determine your sensor size, focal length, * * sensor resolution= image resolution = 2 * ( field of view (FOV) / smallest feature ) * some online tools: baslerweb.com, edmundoptics.com, flir.com * to sum up * use USB-C camera. it will help you in the future upgrades in hardware and easy to use with less issues * find your best trade-off between WD and FOV * sometimes you cannot have everything in life! * your lens aperture (f/#) is your friend, use it! * a larger DOF requires a larger f/# * lens performance curves are the ultimate documentation to read when selecting a lens * understanding them properly requires good knowledge in optics, but it totally worth it. ## Scaled-YOLOv4:scaling model based on hardware # Cost * [How much does a patent cost?](https://www.fu-berlin.de/en/forschung/service/patente-und-lizenzen/faq/kosten.html) * [Mobile, Open Hardware, RISC-V System-on-Chip (SoC) Development Kit](https://www.crowdsupply.com/sutajio-kosagi/precursor) * Hardware * NVIDIA Jetson Xavier NX Developer Kit * WIFI * SparkFun GPS-RTK Dead Reckoning pHAT * Micro Sd card * Mophie Powerstation USB C 20000 * ZED 2 Stereo Camera * 3D-printed box * AWS * AWS S3 * AWS xml.p2.xlarge EC2 instances * AWS Sagemaker * [Hackboard 2 with Ubuntu Linux (99$) Intel CPU](https://www.crowdsupply.com/hackboard/hb2) * [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2) * Post Product to customer by * * * * * * * [easyship](https://www.easyship.com/) * [fulfillmentcrowd](https://www.fulfilmentcrowd.com/) * [ChinaDivision](https://www.chinadivision.com/) * [ORQA FPV](https://orqafpv.com/) * [floship](https://www.floship.com/) Update 26.April.2021 # How to use computer vision with deep learning in IoT devices. Inference machine learning on Edge require some extra steps. I tested several hardware such as Raspberry pi 3, Raspberry pi 4, Intel® Neural Compute Stick 2, OpenCV AI Kit, Google Coral, NVIDIA Jetson Nano, etc. Different OS: real-time operating system (RTOS), Nasa cFS (core Flight System), Real-Time Executive for Multiprocessor Systems (RTEMS), anomaly detection, object detection, object tracking, ... ## Use special frameworks or library for edge devices: * NVIDIA TensorRT * TensorFlow Lite: TensorFlow Lite on Microcontroller Gesture Recognition OpenMV/Tensorflow/ studio.edgeimpulse.com * TensorFlow.js * PyTorch Lightning * PyTorch Mobile * Intel® Distribution of OpenVINO Toolkit * CoreML * ML kit * FRITZ * MediaPipe * Apache TVM * TinyML: enabling ultra-low power machine learning at the edge tiny machine learning with Arduino * Libraries: ffmpeg, GStreamer, celery, * GPU library for python: PyCUDA, NumbaPro, PyOpenCL, CuPy Moreover, think about deep learning model for your specific hardware at first stage. ## In some case you need to enhance model for inference. There are many techniques to use such as, * Pruning * Quantization * Distillation Techniques * Binarized Neural Networks (BNNs) * Apache TVM (incubating) is a compiler stack for deep learning systems * Distributed machine learning and load balancing strategy * Low rank matrix factorization (LRMF) * Compact convolutional filters (Video/CNN) * Knowledge distillation * Neural Networks Compression Framework (NNCF) * Parallel programming ## How Distributed machine learning and load balancing strategy Pruning model pruning: reducing redundant parameters which are not sensitive to the performance. aim: remove all connections with absolute weights below a threshold. 🤔go for bigger size of network with many layers then pruning much better and faster Quantization The best way is using Google library which support most comprehensive methods compresses by reducing the number of bits used to represent the weights quantization effectively constraints the number of different weights we can use inside our kernels per channel quantization for weights, which improves performance by model compression and latency reduction. training a compact neural network with distilled knowledge of a large model distillation (knowledge transfer) from an ensemble of big networks into a much smaller network which learns directly from the cumbersome model's outputs, that is lighter to deploy Distillation Techniques Distill-Net: Application-Specific Distillation of Deep Convolutional Neural Networks for Resource-Constrained IoT Platforms Binarized Neural Networks (BNNs) It is not support by GPU hardware such as Jetson Nano. mostly based on CPU Apache TVM (incubating) is a compiler stack for deep learning systems challenges with large scale models deep neural networks are: expensive computationally expensive memory intensive hindering their deployment in:devices with low memory resources applications with strict latency requirements other issues:data security: tend to memorize everything including PII bias e.g. profanity: trained on large scale public datas elf discovering: instead of manually configuring conversational flows, automatically discover them from your data self training: let your system train itself with new example s self managing: let your system optimize by itself knowledge distillation Distributed machine learning and load balancing strategy run models which use all processing power like CPU,GPU,DSP,AI chip together to enhance inference performance. dynamic pruning of kernels which aims to the parsimonious inference by learning to exploit and dynamically remove the redundant capacity of a CNN architecture. partitioning techniques through convolution layer fusion to dynamically select the optimal partition according to the availability of computational resources and network conditions. Low rank matrix factorization (LRMF) there exists latent structures in the data, by uncovering which we can obtain a compressed representation of the dataLRMF factorizes the original matrix into lower rank matrices while preserving latent structures and addressing the issue of sparseness Compact convolutional filters (Video/CNN) designing special structural convolutional filters to save parameters replace over parametric filters with compact filters to achieve overall speedup while maintaining comparable accuracy Knowledge distillation Neural Networks Compression Framework (NNCF) AI Edge: How to inference deep learning models on edge/IoT Enabling efficient high-performance Accelerators/Optimization on Deep Learning if the object is large and we do not need small anchor in mobileNet we can remove small part of network which related to small objects. in YOLO reduce number of anchor. decrease size of image input but reduce the accuracy Parallel programming and clean code, design pattern, Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/VBNzuBGkaD3wt3TzoFwX0mTIj70nSDd3lgPVXQioi93Rci- RfkU7Cych8xXEVlNVPXCewW1vMbWtk8T6z_JVwm8=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/VBNzuBGkaD3wt3TzoFwX0mTIj70nSDd3lgPVXQioi93Rci- RfkU7Cych8xXEVlNVPXCewW1vMbWtk8T6z_JVwm8=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Hardware Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera Hardware for Deep Learning (machine learning) My experience Raspberry Pi 4 Smart AI IoT, Robotic, 3D SLAM, AR, VR RISC-V I worked with many different hardware such as Camera What is important? Scaled-YOLOv4:scaling model based on hardware Cost How to use computer vision with deep learning in IoT devices. Inference machine learning on Edge require some extra steps. Use special frameworks or library for edge devices: In some case you need to enhance model for inference. There are many techniques to use such as, How # Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera * Camera * * Camera Specs: Color camera, Stereo pair * [IMX214](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/imx214.html#imx214) (PY014 AF, PY114 FF), [OV7251](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/ov7251.html#ov7251) (PY013) * DFOV / HFOV / VFOV= 81° / 69° / 54° , 86° / 73° / 58° * Resolution: 13MP (4208x3120), 480P (640x480) * Focus: AF: 8cm - ∞ OR FF: 50cm - ∞ , Fixed-Focus 6.5cm - ∞ * Max Framerate: 35 FPS, 120 FPS * Width: 91 mm , Height: 28 mm, Length: 17.5 mm, Baseline: 75 mm, Weight: 61 g * chips: * * Robotics Vision Core 2 (RVC2 in short) Myriad X are integrated into the Robotics Vision Core 2 * Speed ML * * Model name, Size, FPS, Latency [ms], * MobileOne S0 224x224, 165.5, 11.1 * YoloV8n, 416x416, 31.3, 56.9, * YoloV8n, 640x640, 14.3, 123.6 * YoloV8s, 416x416, 15.2, 111.9 * YoloV8m, 416x416, 6.0, 273.8 # Hardware for Deep Learning (machine learning) [https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware) I experiment with many different hardware to train and run deep learning application. The below list shows my suggestion, comparison, expectation of using different hardware. Embedded AI, implementing distributed data parallel, distributed model parallel solutions. [https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware) #hardware #deep_learning #IoT #training_machine_learning_model #pirahansiah Laptop: * NVIDIA Geforce RTX 3080 Ti * Razer Blade 17 - 17.3 inch gaming laptop (NVIDIA Geforce RTX 3080 Ti, Intel i9-12900H, 4K UHD 144Hz display 32GB DDR5 RAM, 1TB SSD, Desktop * eGPU * Razer RC21-01310100-R351 Core X External Graphics Card Case = ~ 300 Euro + GPU * Cooler Master MasterCase EG200 External GPU Enclosure - Thunderbolt 3 Compatible eGPU Enclosure, 1 PWM 92mm Fan, V550 SFX Gold Fully Modular PSU, USB Hub, Vertical Laptop Support - EU Plug = ~ 300 Euro + GPU * GPU * Geforce RTX 3090 24G 384Bit Gddr6x Nvidia Geforce * MSI GeForce RTX 3090 GAMING TRIO 24G Gaming Graphics Card - NVIDIA RTX 3090, GPU 1740MHz, 24GB GDDR6X memory = ~ 2800 Euro IoT: * Raspberry pi 3 (you need accelerator ) * Raspberry pi 4 (you need accelerator ) * Intel® Neural Compute Stick 2 * Intel® Distribution of OpenVINO™ Toolkit * I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models * Google Coral * I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models * Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used * NVIDIA Jetson Nano ( 2GB and 4GB ram) * I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes * NVIDIA JETSON AGX XAVIER * NVIDIA AGX Orin = ~ 1900 Euro * [Compare NVIDIA Jetson AGX Orin with AGX Xavier: 8x AI performance, in-advance Ampere GPU, CPU, Memory & Storage](https://www.seeedstudio.com/blog/2022/03/17/compare-nvidia-jetson-agx-orin-with-agx-xavier-6x-ai-performance-in-advance-ampere-gpu-cpu-memory-storage/) * OpenCV AI Kit * OAK = ~ 100 Euro * OAK—D = ~ 200 Euro * OAK—D + Wifi = ~ 250 Euro * OpenCV AI Kit: OAK—D-PoE = ~ 250 Euro * OAK—D lite = ~ 100 Euro # My experience I tested many different hardware for 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[#GraphQL](https://www.linkedin.com/feed/hashtag/?keywords=graphql&highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6787871627586625536) #imageprocessing #patternrecognition ![](https://lh3.googleusercontent.com/VZMsqBhRJ3f0IALfzNXl_6RzGqAectl1Hxmei9swx6ZJNYFzlXAVxhQ2NXOd7E3RmwEIsWoYSo_zVFj0qLbAfl2VJq4VXYjbD2JYipPaJnmUn8T6FToppIkmAnEV0XNsWA=w1280) # Raspberry Pi 4 How to upgrade Raspberry Pi 4 EEPROM boot recovery; Released 2020-09-14; to install and boot from USB 3 (SSD) 1. update Raspberry Pi 4 EEPROM boot recovery 2. install Ubuntu 20 on SSD 3. change the config.txt and add "program_usb_boot_mode=1" at the end of file 4. remove and micro sd card and boot from ssd ![](https://lh4.googleusercontent.com/hkMt3Fso7Lt0LabRhCrxAFcMs0opFz9S85EeWZLtAIP94iFOF2A7Dv4-Z_z34AGOYSJBEMGkNv69zimrlm5t99XG1Luhrkq_5rMdqXnswsxwDrSGzR0Xv_OTeCJp5aimbQ=w1280) ![](https://lh4.googleusercontent.com/GJ8aBAr0SwcUi0-KPdUIl1PjETkstJdXxfRbNSTSkVIb2p3O3BI95FmBwjibE0te- FN-NoQN3MHm_XqJiLGLvAHZToT_0aK1gTR1_Tbz1OAsreEcFTWH1Sgm5HFyG2u8wQ=w1280) ![](https://lh6.googleusercontent.com/lfYnZv07KUbsrISrffug2JbCfQ2VXRuzxtBL34wXuRQydhUm1TINX5BsTxAMHfJ14LHlYPyJAxgTvjocgMQ7ViCOuqeLuwX9eSSZKdLuZiSaQSspSTLl5aFCyAkp4qbl4A=w1280) ![](https://lh3.googleusercontent.com/JCtH7aainnvRN4eC7WpuJCMlq_I5pSxi1dLYUEfy_5UutuXtOJ-R4JHZsAdAdygOYUW_B1apJT74LtVddIFq6blXAd1T_hV8AY_mCLQZhpgnaySln8mXZsYLgxC2Q6xFWA=w1280) ![](https://lh4.googleusercontent.com/QS8CudtZFOG0blxmnsZ7lP4gyFVRqmY3r6Ws3uju1UF1SPjkETgl74dJlnuD4kGhd0AJ- EmVY2dSjlibqw1oYMSUcxxW4sgvtPS1Syktl4OzcmLA3LUstsCPfeLBu8JQrA=w1280) ![](https://lh4.googleusercontent.com/xw5ZuxPtIVJ67ZnVXtj0CKrBud2Ixtug2sH5B4S__n3YuwQl3AdbwCkZt4nSp1zjHW4lKcNtidtah3Z5a_v4gNnGGD2O5Vn7QbWJkRtHSJRqgFyCW1YUS79kq0NryYYzXQ=w1280) ![](https://lh6.googleusercontent.com/DAJFrD5Lau0EThTdbJqQapkZhiO_eKmxXg1tz_3DWcWC1SJIcrurHzduRFQuLJVJiLf9WdfQu3fPKNiJdXKBtsYdTYFTaKktIgeEtEREznQzeaZ7BQC-1wRz0k3FERpaWg=w1280) # Smart AI IoT, Robotic, 3D SLAM, AR, VR * [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2) # [RISC-V](/workshops-and-events/risc-v) # I worked with many different hardware such as * Raspberry pi 3 * Raspberry pi 4 * Intel® Neural Compute Stick 2 * Intel® Distribution of OpenVINO™ Toolkit * I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models * Google Coral * I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models * Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used * NVIDIA Jetson Nano * I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes * NVIDIA JETSON AGX XAVIER * The best hardware * I attended in may conferences and summits in area of Hardware for deep learning such as: * * * AI Hardware Europe Summit (July 2020) * [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit) * Apache TVM And Deep Learning Compilation Conference (December 2020) * RISC-V Summit (December 2020) * OpenCV AI Kit ## Camera I worked with many different cameras such as: * Camera Module V1 * Camera Module V2 * Camera Module V2.1 * multispectral camera * USB webcam * IP camera * high resolution camera > 8K * depth camera * stereo camera ### What is important? * camera calibration is important * Quantum efficiency [%] (spectral response) * Sensor size [inches or mm] and pixel size [micro meter] * Dynamic Range [dB] * Image noise and signal to noise ratio (SNR), PSNR, SSIM, : greater SNR yields better contrast and clarity, as well as improved low light performance * inter face, cable length in m, bandwidth max in MB/s , multi camera, cable costs, real time, plug and play * * firewire, 4.5 , 64, *, *, **, ** * gige, 100, 100, **, **, *, * * usb, 8, 350, *, *, **, ** * link, 10, 850, -, -, **, - * usb-c, 10, 40 GB,,,, * distortions, scaling factors, quality is important, calculate minimum sensor resolution *, determine your sensor size, focal length, * * sensor resolution= image resolution = 2 * ( field of view (FOV) / smallest feature ) * some online tools: baslerweb.com, edmundoptics.com, flir.com * to sum up * use USB-C camera. it will help you in the future upgrades in hardware and easy to use with less issues * find your best trade-off between WD and FOV * sometimes you cannot have everything in life! * your lens aperture (f/#) is your friend, use it! * a larger DOF requires a larger f/# * lens performance curves are the ultimate documentation to read when selecting a lens * understanding them properly requires good knowledge in optics, but it totally worth it. ## Scaled-YOLOv4:scaling model based on hardware # Cost * [How much does a patent cost?](https://www.fu-berlin.de/en/forschung/service/patente-und-lizenzen/faq/kosten.html) * [Mobile, Open Hardware, RISC-V System-on-Chip (SoC) Development Kit](https://www.crowdsupply.com/sutajio-kosagi/precursor) * Hardware * NVIDIA Jetson Xavier NX Developer Kit * WIFI * SparkFun GPS-RTK Dead Reckoning pHAT * Micro Sd card * Mophie Powerstation USB C 20000 * ZED 2 Stereo Camera * 3D-printed box * AWS * AWS S3 * AWS xml.p2.xlarge EC2 instances * AWS Sagemaker * [Hackboard 2 with Ubuntu Linux (99$) Intel CPU](https://www.crowdsupply.com/hackboard/hb2) * [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2) * Post Product to customer by * * * * * * * [easyship](https://www.easyship.com/) * [fulfillmentcrowd](https://www.fulfilmentcrowd.com/) * [ChinaDivision](https://www.chinadivision.com/) * [ORQA FPV](https://orqafpv.com/) * [floship](https://www.floship.com/) Update 26.April.2021 # How to use computer vision with deep learning in IoT devices. Inference machine learning on Edge require some extra steps. I tested several hardware such as Raspberry pi 3, Raspberry pi 4, Intel® Neural Compute Stick 2, OpenCV AI Kit, Google Coral, NVIDIA Jetson Nano, etc. Different OS: real-time operating system (RTOS), Nasa cFS (core Flight System), Real-Time Executive for Multiprocessor Systems (RTEMS), anomaly detection, object detection, object tracking, ... ## Use special frameworks or library for edge devices: * NVIDIA TensorRT * TensorFlow Lite: TensorFlow Lite on Microcontroller Gesture Recognition OpenMV/Tensorflow/ studio.edgeimpulse.com * TensorFlow.js * PyTorch Lightning * PyTorch Mobile * Intel® Distribution of OpenVINO Toolkit * CoreML * ML kit * FRITZ * MediaPipe * Apache TVM * TinyML: enabling ultra-low power machine learning at the edge tiny machine learning with Arduino * Libraries: ffmpeg, GStreamer, celery, * GPU library for python: PyCUDA, NumbaPro, PyOpenCL, CuPy Moreover, think about deep learning model for your specific hardware at first stage. ## In some case you need to enhance model for inference. There are many techniques to use such as, * Pruning * Quantization * Distillation Techniques * Binarized Neural Networks (BNNs) * Apache TVM (incubating) is a compiler stack for deep learning systems * Distributed machine learning and load balancing strategy * Low rank matrix factorization (LRMF) * Compact convolutional filters (Video/CNN) * Knowledge distillation * Neural Networks Compression Framework (NNCF) * Parallel programming ## How Distributed machine learning and load balancing strategy Pruning model pruning: reducing redundant parameters which are not sensitive to the performance. aim: remove all connections with absolute weights below a threshold. 🤔go for bigger size of network with many layers then pruning much better and faster Quantization The best way is using Google library which support most comprehensive methods compresses by reducing the number of bits used to represent the weights quantization effectively constraints the number of different weights we can use inside our kernels per channel quantization for weights, which improves performance by model compression and latency reduction. training a compact neural network with distilled knowledge of a large model distillation (knowledge transfer) from an ensemble of big networks into a much smaller network which learns directly from the cumbersome model's outputs, that is lighter to deploy Distillation Techniques Distill-Net: Application-Specific Distillation of Deep Convolutional Neural Networks for Resource-Constrained IoT Platforms Binarized Neural Networks (BNNs) It is not support by GPU hardware such as Jetson Nano. mostly based on CPU Apache TVM (incubating) is a compiler stack for deep learning systems challenges with large scale models deep neural networks are: expensive computationally expensive memory intensive hindering their deployment in:devices with low memory resources applications with strict latency requirements other issues:data security: tend to memorize everything including PII bias e.g. profanity: trained on large scale public datas elf discovering: instead of manually configuring conversational flows, automatically discover them from your data self training: let your system train itself with new example s self managing: let your system optimize by itself knowledge distillation Distributed machine learning and load balancing strategy run models which use all processing power like CPU,GPU,DSP,AI chip together to enhance inference performance. dynamic pruning of kernels which aims to the parsimonious inference by learning to exploit and dynamically remove the redundant capacity of a CNN architecture. partitioning techniques through convolution layer fusion to dynamically select the optimal partition according to the availability of computational resources and network conditions. Low rank matrix factorization (LRMF) there exists latent structures in the data, by uncovering which we can obtain a compressed representation of the dataLRMF factorizes the original matrix into lower rank matrices while preserving latent structures and addressing the issue of sparseness Compact convolutional filters (Video/CNN) designing special structural convolutional filters to save parameters replace over parametric filters with compact filters to achieve overall speedup while maintaining comparable accuracy Knowledge distillation Neural Networks Compression Framework (NNCF) AI Edge: How to inference deep learning models on edge/IoT Enabling efficient high-performance Accelerators/Optimization on Deep Learning if the object is large and we do not need small anchor in mobileNet we can remove small part of network which related to small objects. in YOLO reduce number of anchor. decrease size of image input but reduce the accuracy Parallel programming and clean code, design pattern, Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/VBNzuBGkaD3wt3TzoFwX0mTIj70nSDd3lgPVXQioi93Rci- RfkU7Cych8xXEVlNVPXCewW1vMbWtk8T6z_JVwm8=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/VBNzuBGkaD3wt3TzoFwX0mTIj70nSDd3lgPVXQioi93Rci- RfkU7Cych8xXEVlNVPXCewW1vMbWtk8T6z_JVwm8=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Hardware Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera Hardware for Deep Learning (machine learning) My experience Raspberry Pi 4 Smart AI IoT, Robotic, 3D SLAM, AR, VR RISC-V I worked with many different hardware such as Camera What is important? Scaled-YOLOv4:scaling model based on hardware Cost How to use computer vision with deep learning in IoT devices. Inference machine learning on Edge require some extra steps. Use special frameworks or library for edge devices: In some case you need to enhance model for inference. There are many techniques to use such as, How # Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera * Camera * * Camera Specs: Color camera, Stereo pair * [IMX214](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/imx214.html#imx214) (PY014 AF, PY114 FF), [OV7251](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/ov7251.html#ov7251) (PY013) * DFOV / HFOV / VFOV= 81° / 69° / 54° , 86° / 73° / 58° * Resolution: 13MP (4208x3120), 480P (640x480) * Focus: AF: 8cm - ∞ OR FF: 50cm - ∞ , Fixed-Focus 6.5cm - ∞ * Max Framerate: 35 FPS, 120 FPS * Width: 91 mm , Height: 28 mm, Length: 17.5 mm, Baseline: 75 mm, Weight: 61 g * chips: * * Robotics Vision Core 2 (RVC2 in short) Myriad X are integrated into the Robotics Vision Core 2 * Speed ML * * Model name, Size, FPS, Latency [ms], * MobileOne S0 224x224, 165.5, 11.1 * YoloV8n, 416x416, 31.3, 56.9, * YoloV8n, 640x640, 14.3, 123.6 * YoloV8s, 416x416, 15.2, 111.9 * YoloV8m, 416x416, 6.0, 273.8 # Hardware for Deep Learning (machine learning) [https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware) I experiment with many different hardware to train and run deep learning application. The below list shows my suggestion, comparison, expectation of using different hardware. Embedded AI, implementing distributed data parallel, distributed model parallel solutions. [https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware) #hardware #deep_learning #IoT #training_machine_learning_model #pirahansiah Laptop: * NVIDIA Geforce RTX 3080 Ti * Razer Blade 17 - 17.3 inch gaming laptop (NVIDIA Geforce RTX 3080 Ti, Intel i9-12900H, 4K UHD 144Hz display 32GB DDR5 RAM, 1TB SSD, Desktop * eGPU * Razer RC21-01310100-R351 Core X External Graphics Card Case = ~ 300 Euro + GPU * Cooler Master MasterCase EG200 External GPU Enclosure - Thunderbolt 3 Compatible eGPU Enclosure, 1 PWM 92mm Fan, V550 SFX Gold Fully Modular PSU, USB Hub, Vertical Laptop Support - EU Plug = ~ 300 Euro + GPU * GPU * Geforce RTX 3090 24G 384Bit Gddr6x Nvidia Geforce * MSI GeForce RTX 3090 GAMING TRIO 24G Gaming Graphics Card - NVIDIA RTX 3090, GPU 1740MHz, 24GB GDDR6X memory = ~ 2800 Euro IoT: * Raspberry pi 3 (you need accelerator ) * Raspberry pi 4 (you need accelerator ) * Intel® Neural Compute Stick 2 * Intel® Distribution of OpenVINO™ Toolkit * I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models * Google Coral * I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models * Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used * NVIDIA Jetson Nano ( 2GB and 4GB ram) * I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes * NVIDIA JETSON AGX XAVIER * NVIDIA AGX Orin = ~ 1900 Euro * [Compare NVIDIA Jetson AGX Orin with AGX Xavier: 8x AI performance, in-advance Ampere GPU, CPU, Memory & Storage](https://www.seeedstudio.com/blog/2022/03/17/compare-nvidia-jetson-agx-orin-with-agx-xavier-6x-ai-performance-in-advance-ampere-gpu-cpu-memory-storage/) * OpenCV AI Kit * OAK = ~ 100 Euro * OAK—D = ~ 200 Euro * OAK—D + Wifi = ~ 250 Euro * OpenCV AI Kit: OAK—D-PoE = ~ 250 Euro * OAK—D lite = ~ 100 Euro # My experience I tested many different hardware for 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[#GraphQL](https://www.linkedin.com/feed/hashtag/?keywords=graphql&highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6787871627586625536) #imageprocessing #patternrecognition ![](https://lh3.googleusercontent.com/VZMsqBhRJ3f0IALfzNXl_6RzGqAectl1Hxmei9swx6ZJNYFzlXAVxhQ2NXOd7E3RmwEIsWoYSo_zVFj0qLbAfl2VJq4VXYjbD2JYipPaJnmUn8T6FToppIkmAnEV0XNsWA=w1280) # Raspberry Pi 4 How to upgrade Raspberry Pi 4 EEPROM boot recovery; Released 2020-09-14; to install and boot from USB 3 (SSD) 1. update Raspberry Pi 4 EEPROM boot recovery 2. install Ubuntu 20 on SSD 3. change the config.txt and add "program_usb_boot_mode=1" at the end of file 4. remove and micro sd card and boot from ssd ![](https://lh4.googleusercontent.com/hkMt3Fso7Lt0LabRhCrxAFcMs0opFz9S85EeWZLtAIP94iFOF2A7Dv4-Z_z34AGOYSJBEMGkNv69zimrlm5t99XG1Luhrkq_5rMdqXnswsxwDrSGzR0Xv_OTeCJp5aimbQ=w1280) ![](https://lh4.googleusercontent.com/GJ8aBAr0SwcUi0-KPdUIl1PjETkstJdXxfRbNSTSkVIb2p3O3BI95FmBwjibE0te- FN-NoQN3MHm_XqJiLGLvAHZToT_0aK1gTR1_Tbz1OAsreEcFTWH1Sgm5HFyG2u8wQ=w1280) ![](https://lh6.googleusercontent.com/lfYnZv07KUbsrISrffug2JbCfQ2VXRuzxtBL34wXuRQydhUm1TINX5BsTxAMHfJ14LHlYPyJAxgTvjocgMQ7ViCOuqeLuwX9eSSZKdLuZiSaQSspSTLl5aFCyAkp4qbl4A=w1280) ![](https://lh3.googleusercontent.com/JCtH7aainnvRN4eC7WpuJCMlq_I5pSxi1dLYUEfy_5UutuXtOJ-R4JHZsAdAdygOYUW_B1apJT74LtVddIFq6blXAd1T_hV8AY_mCLQZhpgnaySln8mXZsYLgxC2Q6xFWA=w1280) ![](https://lh4.googleusercontent.com/QS8CudtZFOG0blxmnsZ7lP4gyFVRqmY3r6Ws3uju1UF1SPjkETgl74dJlnuD4kGhd0AJ- EmVY2dSjlibqw1oYMSUcxxW4sgvtPS1Syktl4OzcmLA3LUstsCPfeLBu8JQrA=w1280) ![](https://lh4.googleusercontent.com/xw5ZuxPtIVJ67ZnVXtj0CKrBud2Ixtug2sH5B4S__n3YuwQl3AdbwCkZt4nSp1zjHW4lKcNtidtah3Z5a_v4gNnGGD2O5Vn7QbWJkRtHSJRqgFyCW1YUS79kq0NryYYzXQ=w1280) ![](https://lh6.googleusercontent.com/DAJFrD5Lau0EThTdbJqQapkZhiO_eKmxXg1tz_3DWcWC1SJIcrurHzduRFQuLJVJiLf9WdfQu3fPKNiJdXKBtsYdTYFTaKktIgeEtEREznQzeaZ7BQC-1wRz0k3FERpaWg=w1280) # Smart AI IoT, Robotic, 3D SLAM, AR, VR * [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2) # [RISC-V](/workshops-and-events/risc-v) # I worked with many different hardware such as * Raspberry pi 3 * Raspberry pi 4 * Intel® Neural Compute Stick 2 * Intel® Distribution of OpenVINO™ Toolkit * I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models * Google Coral * I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models * Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used * NVIDIA Jetson Nano * I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes * NVIDIA JETSON AGX XAVIER * The best hardware * I attended in may conferences and summits in area of Hardware for deep learning such as: * * * AI Hardware Europe Summit (July 2020) * [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit) * Apache TVM And Deep Learning Compilation Conference (December 2020) * RISC-V Summit (December 2020) * OpenCV AI Kit ## Camera I worked with many different cameras such as: * Camera Module V1 * Camera Module V2 * Camera Module V2.1 * multispectral camera * USB webcam * IP camera * high resolution camera > 8K * depth camera * stereo camera ### What is important? * camera calibration is important * Quantum efficiency [%] (spectral response) * Sensor size [inches or mm] and pixel size [micro meter] * Dynamic Range [dB] * Image noise and signal to noise ratio (SNR), PSNR, SSIM, : greater SNR yields better contrast and clarity, as well as improved low light performance * inter face, cable length in m, bandwidth max in MB/s , multi camera, cable costs, real time, plug and play * * firewire, 4.5 , 64, *, *, **, ** * gige, 100, 100, **, **, *, * * usb, 8, 350, *, *, **, ** * link, 10, 850, -, -, **, - * usb-c, 10, 40 GB,,,, * distortions, scaling factors, quality is important, calculate minimum sensor resolution *, determine your sensor size, focal length, * * sensor resolution= image resolution = 2 * ( field of view (FOV) / smallest feature ) * some online tools: baslerweb.com, edmundoptics.com, flir.com * to sum up * use USB-C camera. it will help you in the future upgrades in hardware and easy to use with less issues * find your best trade-off between WD and FOV * sometimes you cannot have everything in life! * your lens aperture (f/#) is your friend, use it! * a larger DOF requires a larger f/# * lens performance curves are the ultimate documentation to read when selecting a lens * understanding them properly requires good knowledge in optics, but it totally worth it. ## Scaled-YOLOv4:scaling model based on hardware # Cost * [How much does a patent cost?](https://www.fu-berlin.de/en/forschung/service/patente-und-lizenzen/faq/kosten.html) * [Mobile, Open Hardware, RISC-V System-on-Chip (SoC) Development Kit](https://www.crowdsupply.com/sutajio-kosagi/precursor) * Hardware * NVIDIA Jetson Xavier NX Developer Kit * WIFI * SparkFun GPS-RTK Dead Reckoning pHAT * Micro Sd card * Mophie Powerstation USB C 20000 * ZED 2 Stereo Camera * 3D-printed box * AWS * AWS S3 * AWS xml.p2.xlarge EC2 instances * AWS Sagemaker * [Hackboard 2 with Ubuntu Linux (99$) Intel CPU](https://www.crowdsupply.com/hackboard/hb2) * [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2) * Post Product to customer by * * * * * * * [easyship](https://www.easyship.com/) * [fulfillmentcrowd](https://www.fulfilmentcrowd.com/) * [ChinaDivision](https://www.chinadivision.com/) * [ORQA FPV](https://orqafpv.com/) * [floship](https://www.floship.com/) Update 26.April.2021 # How to use computer vision with deep learning in IoT devices. Inference machine learning on Edge require some extra steps. I tested several hardware such as Raspberry pi 3, Raspberry pi 4, Intel® Neural Compute Stick 2, OpenCV AI Kit, Google Coral, NVIDIA Jetson Nano, etc. Different OS: real-time operating system (RTOS), Nasa cFS (core Flight System), Real-Time Executive for Multiprocessor Systems (RTEMS), anomaly detection, object detection, object tracking, ... ## Use special frameworks or library for edge devices: * NVIDIA TensorRT * TensorFlow Lite: TensorFlow Lite on Microcontroller Gesture Recognition OpenMV/Tensorflow/ studio.edgeimpulse.com * TensorFlow.js * PyTorch Lightning * PyTorch Mobile * Intel® Distribution of OpenVINO Toolkit * CoreML * ML kit * FRITZ * MediaPipe * Apache TVM * TinyML: enabling ultra-low power machine learning at the edge tiny machine learning with Arduino * Libraries: ffmpeg, GStreamer, celery, * GPU library for python: PyCUDA, NumbaPro, PyOpenCL, CuPy Moreover, think about deep learning model for your specific hardware at first stage. ## In some case you need to enhance model for inference. There are many techniques to use such as, * Pruning * Quantization * Distillation Techniques * Binarized Neural Networks (BNNs) * Apache TVM (incubating) is a compiler stack for deep learning systems * Distributed machine learning and load balancing strategy * Low rank matrix factorization (LRMF) * Compact convolutional filters (Video/CNN) * Knowledge distillation * Neural Networks Compression Framework (NNCF) * Parallel programming ## How Distributed machine learning and load balancing strategy Pruning model pruning: reducing redundant parameters which are not sensitive to the performance. aim: remove all connections with absolute weights below a threshold. 🤔go for bigger size of network with many layers then pruning much better and faster Quantization The best way is using Google library which support most comprehensive methods compresses by reducing the number of bits used to represent the weights quantization effectively constraints the number of different weights we can use inside our kernels per channel quantization for weights, which improves performance by model compression and latency reduction. training a compact neural network with distilled knowledge of a large model distillation (knowledge transfer) from an ensemble of big networks into a much smaller network which learns directly from the cumbersome model's outputs, that is lighter to deploy Distillation Techniques Distill-Net: Application-Specific Distillation of Deep Convolutional Neural Networks for Resource-Constrained IoT Platforms Binarized Neural Networks (BNNs) It is not support by GPU hardware such as Jetson Nano. mostly based on CPU Apache TVM (incubating) is a compiler stack for deep learning systems challenges with large scale models deep neural networks are: expensive computationally expensive memory intensive hindering their deployment in:devices with low memory resources applications with strict latency requirements other issues:data security: tend to memorize everything including PII bias e.g. profanity: trained on large scale public datas elf discovering: instead of manually configuring conversational flows, automatically discover them from your data self training: let your system train itself with new example s self managing: let your system optimize by itself knowledge distillation Distributed machine learning and load balancing strategy run models which use all processing power like CPU,GPU,DSP,AI chip together to enhance inference performance. dynamic pruning of kernels which aims to the parsimonious inference by learning to exploit and dynamically remove the redundant capacity of a CNN architecture. partitioning techniques through convolution layer fusion to dynamically select the optimal partition according to the availability of computational resources and network conditions. Low rank matrix factorization (LRMF) there exists latent structures in the data, by uncovering which we can obtain a compressed representation of the dataLRMF factorizes the original matrix into lower rank matrices while preserving latent structures and addressing the issue of sparseness Compact convolutional filters (Video/CNN) designing special structural convolutional filters to save parameters replace over parametric filters with compact filters to achieve overall speedup while maintaining comparable accuracy Knowledge distillation Neural Networks Compression Framework (NNCF) AI Edge: How to inference deep learning models on edge/IoT Enabling efficient high-performance Accelerators/Optimization on Deep Learning if the object is large and we do not need small anchor in mobileNet we can remove small part of network which related to small objects. in YOLO reduce number of anchor. decrease size of image input but reduce the accuracy Parallel programming and clean code, design pattern, Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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Scaled-YOLOv4:scaling model based on hardware Cost How to use computer vision with deep learning in IoT devices. Inference machine learning on Edge require some extra steps. Use special frameworks or library for edge devices: In some case you need to enhance model for inference. There are many techniques to use such as, How # Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera * Camera * * Camera Specs: Color camera, Stereo pair * [IMX214](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/imx214.html#imx214) (PY014 AF, PY114 FF), [OV7251](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/ov7251.html#ov7251) (PY013) * DFOV / HFOV / VFOV= 81° / 69° / 54° , 86° / 73° / 58° * Resolution: 13MP (4208x3120), 480P (640x480) * Focus: AF: 8cm - ∞ OR FF: 50cm - ∞ , Fixed-Focus 6.5cm - ∞ * Max Framerate: 35 FPS, 120 FPS * Width: 91 mm , Height: 28 mm, Length: 17.5 mm, Baseline: 75 mm, Weight: 61 g * chips: * * Robotics Vision Core 2 (RVC2 in short) Myriad X are integrated into the Robotics Vision Core 2 * Speed ML * * Model name, Size, FPS, Latency [ms], * MobileOne S0 224x224, 165.5, 11.1 * YoloV8n, 416x416, 31.3, 56.9, * YoloV8n, 640x640, 14.3, 123.6 * YoloV8s, 416x416, 15.2, 111.9 * YoloV8m, 416x416, 6.0, 273.8 # Hardware for Deep Learning (machine learning) [https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware) I experiment with many different hardware to train and run deep learning application. The below list shows my suggestion, comparison, expectation of using different hardware. Embedded AI, implementing distributed data parallel, distributed model parallel solutions. [https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware) #hardware #deep_learning #IoT #training_machine_learning_model #pirahansiah Laptop: * NVIDIA Geforce RTX 3080 Ti * Razer Blade 17 - 17.3 inch gaming laptop (NVIDIA Geforce RTX 3080 Ti, Intel i9-12900H, 4K UHD 144Hz display 32GB DDR5 RAM, 1TB SSD, Desktop * eGPU * Razer RC21-01310100-R351 Core X External Graphics Card Case = ~ 300 Euro + GPU * Cooler Master MasterCase EG200 External GPU Enclosure - Thunderbolt 3 Compatible eGPU Enclosure, 1 PWM 92mm Fan, V550 SFX Gold Fully Modular PSU, USB Hub, Vertical Laptop Support - EU Plug = ~ 300 Euro + GPU * GPU * Geforce RTX 3090 24G 384Bit Gddr6x Nvidia Geforce * MSI GeForce RTX 3090 GAMING TRIO 24G Gaming Graphics Card - NVIDIA RTX 3090, GPU 1740MHz, 24GB GDDR6X memory = ~ 2800 Euro IoT: * Raspberry pi 3 (you need accelerator ) * Raspberry pi 4 (you need accelerator ) * Intel® Neural Compute Stick 2 * Intel® Distribution of OpenVINO™ Toolkit * I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models * Google Coral * I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models * Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used * NVIDIA Jetson Nano ( 2GB and 4GB ram) * I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes * NVIDIA JETSON AGX XAVIER * NVIDIA AGX Orin = ~ 1900 Euro * [Compare NVIDIA Jetson AGX Orin with AGX Xavier: 8x AI performance, in-advance Ampere GPU, CPU, Memory & Storage](https://www.seeedstudio.com/blog/2022/03/17/compare-nvidia-jetson-agx-orin-with-agx-xavier-6x-ai-performance-in-advance-ampere-gpu-cpu-memory-storage/) * OpenCV AI Kit * OAK = ~ 100 Euro * OAK—D = ~ 200 Euro * OAK—D + Wifi = ~ 250 Euro * OpenCV AI Kit: OAK—D-PoE = ~ 250 Euro * OAK—D lite = ~ 100 Euro # My experience I tested many different hardware for 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[#GraphQL](https://www.linkedin.com/feed/hashtag/?keywords=graphql&highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6787871627586625536) #imageprocessing #patternrecognition ![](https://lh3.googleusercontent.com/VZMsqBhRJ3f0IALfzNXl_6RzGqAectl1Hxmei9swx6ZJNYFzlXAVxhQ2NXOd7E3RmwEIsWoYSo_zVFj0qLbAfl2VJq4VXYjbD2JYipPaJnmUn8T6FToppIkmAnEV0XNsWA=w1280) # Raspberry Pi 4 How to upgrade Raspberry Pi 4 EEPROM boot recovery; Released 2020-09-14; to install and boot from USB 3 (SSD) 1. update Raspberry Pi 4 EEPROM boot recovery 2. install Ubuntu 20 on SSD 3. change the config.txt and add "program_usb_boot_mode=1" at the end of file 4. remove and micro sd card and boot from ssd ![](https://lh4.googleusercontent.com/hkMt3Fso7Lt0LabRhCrxAFcMs0opFz9S85EeWZLtAIP94iFOF2A7Dv4-Z_z34AGOYSJBEMGkNv69zimrlm5t99XG1Luhrkq_5rMdqXnswsxwDrSGzR0Xv_OTeCJp5aimbQ=w1280) ![](https://lh4.googleusercontent.com/GJ8aBAr0SwcUi0-KPdUIl1PjETkstJdXxfRbNSTSkVIb2p3O3BI95FmBwjibE0te- FN-NoQN3MHm_XqJiLGLvAHZToT_0aK1gTR1_Tbz1OAsreEcFTWH1Sgm5HFyG2u8wQ=w1280) ![](https://lh6.googleusercontent.com/lfYnZv07KUbsrISrffug2JbCfQ2VXRuzxtBL34wXuRQydhUm1TINX5BsTxAMHfJ14LHlYPyJAxgTvjocgMQ7ViCOuqeLuwX9eSSZKdLuZiSaQSspSTLl5aFCyAkp4qbl4A=w1280) ![](https://lh3.googleusercontent.com/JCtH7aainnvRN4eC7WpuJCMlq_I5pSxi1dLYUEfy_5UutuXtOJ-R4JHZsAdAdygOYUW_B1apJT74LtVddIFq6blXAd1T_hV8AY_mCLQZhpgnaySln8mXZsYLgxC2Q6xFWA=w1280) ![](https://lh4.googleusercontent.com/QS8CudtZFOG0blxmnsZ7lP4gyFVRqmY3r6Ws3uju1UF1SPjkETgl74dJlnuD4kGhd0AJ- EmVY2dSjlibqw1oYMSUcxxW4sgvtPS1Syktl4OzcmLA3LUstsCPfeLBu8JQrA=w1280) ![](https://lh4.googleusercontent.com/xw5ZuxPtIVJ67ZnVXtj0CKrBud2Ixtug2sH5B4S__n3YuwQl3AdbwCkZt4nSp1zjHW4lKcNtidtah3Z5a_v4gNnGGD2O5Vn7QbWJkRtHSJRqgFyCW1YUS79kq0NryYYzXQ=w1280) ![](https://lh6.googleusercontent.com/DAJFrD5Lau0EThTdbJqQapkZhiO_eKmxXg1tz_3DWcWC1SJIcrurHzduRFQuLJVJiLf9WdfQu3fPKNiJdXKBtsYdTYFTaKktIgeEtEREznQzeaZ7BQC-1wRz0k3FERpaWg=w1280) # Smart AI IoT, Robotic, 3D SLAM, AR, VR * [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2) # [RISC-V](/workshops-and-events/risc-v) # I worked with many different hardware such as * Raspberry pi 3 * Raspberry pi 4 * Intel® Neural Compute Stick 2 * Intel® Distribution of OpenVINO™ Toolkit * I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models * Google Coral * I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models * Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used * NVIDIA Jetson Nano * I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes * NVIDIA JETSON AGX XAVIER * The best hardware * I attended in may conferences and summits in area of Hardware for deep learning such as: * * * AI Hardware Europe Summit (July 2020) * [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit) * Apache TVM And Deep Learning Compilation Conference (December 2020) * RISC-V Summit (December 2020) * OpenCV AI Kit ## Camera I worked with many different cameras such as: * Camera Module V1 * Camera Module V2 * Camera Module V2.1 * multispectral camera * USB webcam * IP camera * high resolution camera > 8K * depth camera * stereo camera ### What is important? * camera calibration is important * Quantum efficiency [%] (spectral response) * Sensor size [inches or mm] and pixel size [micro meter] * Dynamic Range [dB] * Image noise and signal to noise ratio (SNR), PSNR, SSIM, : greater SNR yields better contrast and clarity, as well as improved low light performance * inter face, cable length in m, bandwidth max in MB/s , multi camera, cable costs, real time, plug and play * * firewire, 4.5 , 64, *, *, **, ** * gige, 100, 100, **, **, *, * * usb, 8, 350, *, *, **, ** * link, 10, 850, -, -, **, - * usb-c, 10, 40 GB,,,, * distortions, scaling factors, quality is important, calculate minimum sensor resolution *, determine your sensor size, focal length, * * sensor resolution= image resolution = 2 * ( field of view (FOV) / smallest feature ) * some online tools: baslerweb.com, edmundoptics.com, flir.com * to sum up * use USB-C camera. it will help you in the future upgrades in hardware and easy to use with less issues * find your best trade-off between WD and FOV * sometimes you cannot have everything in life! * your lens aperture (f/#) is your friend, use it! * a larger DOF requires a larger f/# * lens performance curves are the ultimate documentation to read when selecting a lens * understanding them properly requires good knowledge in optics, but it totally worth it. ## Scaled-YOLOv4:scaling model based on hardware # Cost * [How much does a patent cost?](https://www.fu-berlin.de/en/forschung/service/patente-und-lizenzen/faq/kosten.html) * [Mobile, Open Hardware, RISC-V System-on-Chip (SoC) Development Kit](https://www.crowdsupply.com/sutajio-kosagi/precursor) * Hardware * NVIDIA Jetson Xavier NX Developer Kit * WIFI * SparkFun GPS-RTK Dead Reckoning pHAT * Micro Sd card * Mophie Powerstation USB C 20000 * ZED 2 Stereo Camera * 3D-printed box * AWS * AWS S3 * AWS xml.p2.xlarge EC2 instances * AWS Sagemaker * [Hackboard 2 with Ubuntu Linux (99$) Intel CPU](https://www.crowdsupply.com/hackboard/hb2) * [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2) * Post Product to customer by * * * * * * * [easyship](https://www.easyship.com/) * [fulfillmentcrowd](https://www.fulfilmentcrowd.com/) * [ChinaDivision](https://www.chinadivision.com/) * [ORQA FPV](https://orqafpv.com/) * [floship](https://www.floship.com/) Update 26.April.2021 # How to use computer vision with deep learning in IoT devices. Inference machine learning on Edge require some extra steps. I tested several hardware such as Raspberry pi 3, Raspberry pi 4, Intel® Neural Compute Stick 2, OpenCV AI Kit, Google Coral, NVIDIA Jetson Nano, etc. Different OS: real-time operating system (RTOS), Nasa cFS (core Flight System), Real-Time Executive for Multiprocessor Systems (RTEMS), anomaly detection, object detection, object tracking, ... ## Use special frameworks or library for edge devices: * NVIDIA TensorRT * TensorFlow Lite: TensorFlow Lite on Microcontroller Gesture Recognition OpenMV/Tensorflow/ studio.edgeimpulse.com * TensorFlow.js * PyTorch Lightning * PyTorch Mobile * Intel® Distribution of OpenVINO Toolkit * CoreML * ML kit * FRITZ * MediaPipe * Apache TVM * TinyML: enabling ultra-low power machine learning at the edge tiny machine learning with Arduino * Libraries: ffmpeg, GStreamer, celery, * GPU library for python: PyCUDA, NumbaPro, PyOpenCL, CuPy Moreover, think about deep learning model for your specific hardware at first stage. ## In some case you need to enhance model for inference. There are many techniques to use such as, * Pruning * Quantization * Distillation Techniques * Binarized Neural Networks (BNNs) * Apache TVM (incubating) is a compiler stack for deep learning systems * Distributed machine learning and load balancing strategy * Low rank matrix factorization (LRMF) * Compact convolutional filters (Video/CNN) * Knowledge distillation * Neural Networks Compression Framework (NNCF) * Parallel programming ## How Distributed machine learning and load balancing strategy Pruning model pruning: reducing redundant parameters which are not sensitive to the performance. aim: remove all connections with absolute weights below a threshold. 🤔go for bigger size of network with many layers then pruning much better and faster Quantization The best way is using Google library which support most comprehensive methods compresses by reducing the number of bits used to represent the weights quantization effectively constraints the number of different weights we can use inside our kernels per channel quantization for weights, which improves performance by model compression and latency reduction. training a compact neural network with distilled knowledge of a large model distillation (knowledge transfer) from an ensemble of big networks into a much smaller network which learns directly from the cumbersome model's outputs, that is lighter to deploy Distillation Techniques Distill-Net: Application-Specific Distillation of Deep Convolutional Neural Networks for Resource-Constrained IoT Platforms Binarized Neural Networks (BNNs) It is not support by GPU hardware such as Jetson Nano. mostly based on CPU Apache TVM (incubating) is a compiler stack for deep learning systems challenges with large scale models deep neural networks are: expensive computationally expensive memory intensive hindering their deployment in:devices with low memory resources applications with strict latency requirements other issues:data security: tend to memorize everything including PII bias e.g. profanity: trained on large scale public datas elf discovering: instead of manually configuring conversational flows, automatically discover them from your data self training: let your system train itself with new example s self managing: let your system optimize by itself knowledge distillation Distributed machine learning and load balancing strategy run models which use all processing power like CPU,GPU,DSP,AI chip together to enhance inference performance. dynamic pruning of kernels which aims to the parsimonious inference by learning to exploit and dynamically remove the redundant capacity of a CNN architecture. partitioning techniques through convolution layer fusion to dynamically select the optimal partition according to the availability of computational resources and network conditions. Low rank matrix factorization (LRMF) there exists latent structures in the data, by uncovering which we can obtain a compressed representation of the dataLRMF factorizes the original matrix into lower rank matrices while preserving latent structures and addressing the issue of sparseness Compact convolutional filters (Video/CNN) designing special structural convolutional filters to save parameters replace over parametric filters with compact filters to achieve overall speedup while maintaining comparable accuracy Knowledge distillation Neural Networks Compression Framework (NNCF) AI Edge: How to inference deep learning models on edge/IoT Enabling efficient high-performance Accelerators/Optimization on Deep Learning if the object is large and we do not need small anchor in mobileNet we can remove small part of network which related to small objects. in YOLO reduce number of anchor. decrease size of image input but reduce the accuracy Parallel programming and clean code, design pattern, Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/VBNzuBGkaD3wt3TzoFwX0mTIj70nSDd3lgPVXQioi93Rci- RfkU7Cych8xXEVlNVPXCewW1vMbWtk8T6z_JVwm8=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/VBNzuBGkaD3wt3TzoFwX0mTIj70nSDd3lgPVXQioi93Rci- RfkU7Cych8xXEVlNVPXCewW1vMbWtk8T6z_JVwm8=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Hardware Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera Hardware for Deep Learning (machine learning) My experience Raspberry Pi 4 Smart AI IoT, Robotic, 3D SLAM, AR, VR RISC-V I worked with many different hardware such as Camera What is important? Scaled-YOLOv4:scaling model based on hardware Cost How to use computer vision with deep learning in IoT devices. Inference machine learning on Edge require some extra steps. Use special frameworks or library for edge devices: In some case you need to enhance model for inference. There are many techniques to use such as, How # Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera * Camera * * Camera Specs: Color camera, Stereo pair * [IMX214](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/imx214.html#imx214) (PY014 AF, PY114 FF), [OV7251](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/ov7251.html#ov7251) (PY013) * DFOV / HFOV / VFOV= 81° / 69° / 54° , 86° / 73° / 58° * Resolution: 13MP (4208x3120), 480P (640x480) * Focus: AF: 8cm - ∞ OR FF: 50cm - ∞ , Fixed-Focus 6.5cm - ∞ * Max Framerate: 35 FPS, 120 FPS * Width: 91 mm , Height: 28 mm, Length: 17.5 mm, Baseline: 75 mm, Weight: 61 g * chips: * * Robotics Vision Core 2 (RVC2 in short) Myriad X are integrated into the Robotics Vision Core 2 * Speed ML * * Model name, Size, FPS, Latency [ms], * MobileOne S0 224x224, 165.5, 11.1 * YoloV8n, 416x416, 31.3, 56.9, * YoloV8n, 640x640, 14.3, 123.6 * YoloV8s, 416x416, 15.2, 111.9 * YoloV8m, 416x416, 6.0, 273.8 # Hardware for Deep Learning (machine learning) [https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware) I experiment with many different hardware to train and run deep learning application. The below list shows my suggestion, comparison, expectation of using different hardware. Embedded AI, implementing distributed data parallel, distributed model parallel solutions. [https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware) #hardware #deep_learning #IoT #training_machine_learning_model #pirahansiah Laptop: * NVIDIA Geforce RTX 3080 Ti * Razer Blade 17 - 17.3 inch gaming laptop (NVIDIA Geforce RTX 3080 Ti, Intel i9-12900H, 4K UHD 144Hz display 32GB DDR5 RAM, 1TB SSD, Desktop * eGPU * Razer RC21-01310100-R351 Core X External Graphics Card Case = ~ 300 Euro + GPU * Cooler Master MasterCase EG200 External GPU Enclosure - Thunderbolt 3 Compatible eGPU Enclosure, 1 PWM 92mm Fan, V550 SFX Gold Fully Modular PSU, USB Hub, Vertical Laptop Support - EU Plug = ~ 300 Euro + GPU * GPU * Geforce RTX 3090 24G 384Bit Gddr6x Nvidia Geforce * MSI GeForce RTX 3090 GAMING TRIO 24G Gaming Graphics Card - NVIDIA RTX 3090, GPU 1740MHz, 24GB GDDR6X memory = ~ 2800 Euro IoT: * Raspberry pi 3 (you need accelerator ) * Raspberry pi 4 (you need accelerator ) * Intel® Neural Compute Stick 2 * Intel® Distribution of OpenVINO™ Toolkit * I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models * Google Coral * I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models * Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used * NVIDIA Jetson Nano ( 2GB and 4GB ram) * I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes * NVIDIA JETSON AGX XAVIER * NVIDIA AGX Orin = ~ 1900 Euro * [Compare NVIDIA Jetson AGX Orin with AGX Xavier: 8x AI performance, in-advance Ampere GPU, CPU, Memory & Storage](https://www.seeedstudio.com/blog/2022/03/17/compare-nvidia-jetson-agx-orin-with-agx-xavier-6x-ai-performance-in-advance-ampere-gpu-cpu-memory-storage/) * OpenCV AI Kit * OAK = ~ 100 Euro * OAK—D = ~ 200 Euro * OAK—D + Wifi = ~ 250 Euro * OpenCV AI Kit: OAK—D-PoE = ~ 250 Euro * OAK—D lite = ~ 100 Euro # My experience I tested many different hardware for 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[#GraphQL](https://www.linkedin.com/feed/hashtag/?keywords=graphql&highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6787871627586625536) #imageprocessing #patternrecognition ![](https://lh3.googleusercontent.com/VZMsqBhRJ3f0IALfzNXl_6RzGqAectl1Hxmei9swx6ZJNYFzlXAVxhQ2NXOd7E3RmwEIsWoYSo_zVFj0qLbAfl2VJq4VXYjbD2JYipPaJnmUn8T6FToppIkmAnEV0XNsWA=w1280) # Raspberry Pi 4 How to upgrade Raspberry Pi 4 EEPROM boot recovery; Released 2020-09-14; to install and boot from USB 3 (SSD) 1. update Raspberry Pi 4 EEPROM boot recovery 2. install Ubuntu 20 on SSD 3. change the config.txt and add "program_usb_boot_mode=1" at the end of file 4. remove and micro sd card and boot from ssd ![](https://lh4.googleusercontent.com/hkMt3Fso7Lt0LabRhCrxAFcMs0opFz9S85EeWZLtAIP94iFOF2A7Dv4-Z_z34AGOYSJBEMGkNv69zimrlm5t99XG1Luhrkq_5rMdqXnswsxwDrSGzR0Xv_OTeCJp5aimbQ=w1280) ![](https://lh4.googleusercontent.com/GJ8aBAr0SwcUi0-KPdUIl1PjETkstJdXxfRbNSTSkVIb2p3O3BI95FmBwjibE0te- FN-NoQN3MHm_XqJiLGLvAHZToT_0aK1gTR1_Tbz1OAsreEcFTWH1Sgm5HFyG2u8wQ=w1280) ![](https://lh6.googleusercontent.com/lfYnZv07KUbsrISrffug2JbCfQ2VXRuzxtBL34wXuRQydhUm1TINX5BsTxAMHfJ14LHlYPyJAxgTvjocgMQ7ViCOuqeLuwX9eSSZKdLuZiSaQSspSTLl5aFCyAkp4qbl4A=w1280) ![](https://lh3.googleusercontent.com/JCtH7aainnvRN4eC7WpuJCMlq_I5pSxi1dLYUEfy_5UutuXtOJ-R4JHZsAdAdygOYUW_B1apJT74LtVddIFq6blXAd1T_hV8AY_mCLQZhpgnaySln8mXZsYLgxC2Q6xFWA=w1280) ![](https://lh4.googleusercontent.com/QS8CudtZFOG0blxmnsZ7lP4gyFVRqmY3r6Ws3uju1UF1SPjkETgl74dJlnuD4kGhd0AJ- EmVY2dSjlibqw1oYMSUcxxW4sgvtPS1Syktl4OzcmLA3LUstsCPfeLBu8JQrA=w1280) ![](https://lh4.googleusercontent.com/xw5ZuxPtIVJ67ZnVXtj0CKrBud2Ixtug2sH5B4S__n3YuwQl3AdbwCkZt4nSp1zjHW4lKcNtidtah3Z5a_v4gNnGGD2O5Vn7QbWJkRtHSJRqgFyCW1YUS79kq0NryYYzXQ=w1280) ![](https://lh6.googleusercontent.com/DAJFrD5Lau0EThTdbJqQapkZhiO_eKmxXg1tz_3DWcWC1SJIcrurHzduRFQuLJVJiLf9WdfQu3fPKNiJdXKBtsYdTYFTaKktIgeEtEREznQzeaZ7BQC-1wRz0k3FERpaWg=w1280) # Smart AI IoT, Robotic, 3D SLAM, AR, VR * [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2) # [RISC-V](/workshops-and-events/risc-v) # I worked with many different hardware such as * Raspberry pi 3 * Raspberry pi 4 * Intel® Neural Compute Stick 2 * Intel® Distribution of OpenVINO™ Toolkit * I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models * Google Coral * I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models * Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used * NVIDIA Jetson Nano * I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes * NVIDIA JETSON AGX XAVIER * The best hardware * I attended in may conferences and summits in area of Hardware for deep learning such as: * * * AI Hardware Europe Summit (July 2020) * [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit) * Apache TVM And Deep Learning Compilation Conference (December 2020) * RISC-V Summit (December 2020) * OpenCV AI Kit ## Camera I worked with many different cameras such as: * Camera Module V1 * Camera Module V2 * Camera Module V2.1 * multispectral camera * USB webcam * IP camera * high resolution camera > 8K * depth camera * stereo camera ### What is important? * camera calibration is important * Quantum efficiency [%] (spectral response) * Sensor size [inches or mm] and pixel size [micro meter] * Dynamic Range [dB] * Image noise and signal to noise ratio (SNR), PSNR, SSIM, : greater SNR yields better contrast and clarity, as well as improved low light performance * inter face, cable length in m, bandwidth max in MB/s , multi camera, cable costs, real time, plug and play * * firewire, 4.5 , 64, *, *, **, ** * gige, 100, 100, **, **, *, * * usb, 8, 350, *, *, **, ** * link, 10, 850, -, -, **, - * usb-c, 10, 40 GB,,,, * distortions, scaling factors, quality is important, calculate minimum sensor resolution *, determine your sensor size, focal length, * * sensor resolution= image resolution = 2 * ( field of view (FOV) / smallest feature ) * some online tools: baslerweb.com, edmundoptics.com, flir.com * to sum up * use USB-C camera. it will help you in the future upgrades in hardware and easy to use with less issues * find your best trade-off between WD and FOV * sometimes you cannot have everything in life! * your lens aperture (f/#) is your friend, use it! * a larger DOF requires a larger f/# * lens performance curves are the ultimate documentation to read when selecting a lens * understanding them properly requires good knowledge in optics, but it totally worth it. ## Scaled-YOLOv4:scaling model based on hardware # Cost * [How much does a patent cost?](https://www.fu-berlin.de/en/forschung/service/patente-und-lizenzen/faq/kosten.html) * [Mobile, Open Hardware, RISC-V System-on-Chip (SoC) Development Kit](https://www.crowdsupply.com/sutajio-kosagi/precursor) * Hardware * NVIDIA Jetson Xavier NX Developer Kit * WIFI * SparkFun GPS-RTK Dead Reckoning pHAT * Micro Sd card * Mophie Powerstation USB C 20000 * ZED 2 Stereo Camera * 3D-printed box * AWS * AWS S3 * AWS xml.p2.xlarge EC2 instances * AWS Sagemaker * [Hackboard 2 with Ubuntu Linux (99$) Intel CPU](https://www.crowdsupply.com/hackboard/hb2) * [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2) * Post Product to customer by * * * * * * * [easyship](https://www.easyship.com/) * [fulfillmentcrowd](https://www.fulfilmentcrowd.com/) * [ChinaDivision](https://www.chinadivision.com/) * [ORQA FPV](https://orqafpv.com/) * [floship](https://www.floship.com/) Update 26.April.2021 # How to use computer vision with deep learning in IoT devices. Inference machine learning on Edge require some extra steps. I tested several hardware such as Raspberry pi 3, Raspberry pi 4, Intel® Neural Compute Stick 2, OpenCV AI Kit, Google Coral, NVIDIA Jetson Nano, etc. Different OS: real-time operating system (RTOS), Nasa cFS (core Flight System), Real-Time Executive for Multiprocessor Systems (RTEMS), anomaly detection, object detection, object tracking, ... ## Use special frameworks or library for edge devices: * NVIDIA TensorRT * TensorFlow Lite: TensorFlow Lite on Microcontroller Gesture Recognition OpenMV/Tensorflow/ studio.edgeimpulse.com * TensorFlow.js * PyTorch Lightning * PyTorch Mobile * Intel® Distribution of OpenVINO Toolkit * CoreML * ML kit * FRITZ * MediaPipe * Apache TVM * TinyML: enabling ultra-low power machine learning at the edge tiny machine learning with Arduino * Libraries: ffmpeg, GStreamer, celery, * GPU library for python: PyCUDA, NumbaPro, PyOpenCL, CuPy Moreover, think about deep learning model for your specific hardware at first stage. ## In some case you need to enhance model for inference. There are many techniques to use such as, * Pruning * Quantization * Distillation Techniques * Binarized Neural Networks (BNNs) * Apache TVM (incubating) is a compiler stack for deep learning systems * Distributed machine learning and load balancing strategy * Low rank matrix factorization (LRMF) * Compact convolutional filters (Video/CNN) * Knowledge distillation * Neural Networks Compression Framework (NNCF) * Parallel programming ## How Distributed machine learning and load balancing strategy Pruning model pruning: reducing redundant parameters which are not sensitive to the performance. aim: remove all connections with absolute weights below a threshold. 🤔go for bigger size of network with many layers then pruning much better and faster Quantization The best way is using Google library which support most comprehensive methods compresses by reducing the number of bits used to represent the weights quantization effectively constraints the number of different weights we can use inside our kernels per channel quantization for weights, which improves performance by model compression and latency reduction. training a compact neural network with distilled knowledge of a large model distillation (knowledge transfer) from an ensemble of big networks into a much smaller network which learns directly from the cumbersome model's outputs, that is lighter to deploy Distillation Techniques Distill-Net: Application-Specific Distillation of Deep Convolutional Neural Networks for Resource-Constrained IoT Platforms Binarized Neural Networks (BNNs) It is not support by GPU hardware such as Jetson Nano. mostly based on CPU Apache TVM (incubating) is a compiler stack for deep learning systems challenges with large scale models deep neural networks are: expensive computationally expensive memory intensive hindering their deployment in:devices with low memory resources applications with strict latency requirements other issues:data security: tend to memorize everything including PII bias e.g. profanity: trained on large scale public datas elf discovering: instead of manually configuring conversational flows, automatically discover them from your data self training: let your system train itself with new example s self managing: let your system optimize by itself knowledge distillation Distributed machine learning and load balancing strategy run models which use all processing power like CPU,GPU,DSP,AI chip together to enhance inference performance. dynamic pruning of kernels which aims to the parsimonious inference by learning to exploit and dynamically remove the redundant capacity of a CNN architecture. partitioning techniques through convolution layer fusion to dynamically select the optimal partition according to the availability of computational resources and network conditions. Low rank matrix factorization (LRMF) there exists latent structures in the data, by uncovering which we can obtain a compressed representation of the dataLRMF factorizes the original matrix into lower rank matrices while preserving latent structures and addressing the issue of sparseness Compact convolutional filters (Video/CNN) designing special structural convolutional filters to save parameters replace over parametric filters with compact filters to achieve overall speedup while maintaining comparable accuracy Knowledge distillation Neural Networks Compression Framework (NNCF) AI Edge: How to inference deep learning models on edge/IoT Enabling efficient high-performance Accelerators/Optimization on Deep Learning if the object is large and we do not need small anchor in mobileNet we can remove small part of network which related to small objects. in YOLO reduce number of anchor. decrease size of image input but reduce the accuracy Parallel programming and clean code, design pattern, Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/VnELnzCZElXe9gLxGYU00_xF7qju2MljSVlgUMwWsc50I88T6vB5ahQjH2kGA --o3hIeJYu2N--BO_uidCis2Ow=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/VnELnzCZElXe9gLxGYU00_xF7qju2MljSVlgUMwWsc50I88T6vB5ahQjH2kGA --o3hIeJYu2N--BO_uidCis2Ow=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Hardware Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera Hardware for Deep Learning (machine learning) My experience Raspberry Pi 4 Smart AI IoT, Robotic, 3D SLAM, AR, VR RISC-V I worked with many different hardware such as Camera What is important? Scaled-YOLOv4:scaling model based on hardware Cost How to use computer vision with deep learning in IoT devices. Inference machine learning on Edge require some extra steps. Use special frameworks or library for edge devices: In some case you need to enhance model for inference. There are many techniques to use such as, How # Jetson Nano + OpenCV AI KIT OAK-D-LITE depth camera * Camera * * Camera Specs: Color camera, Stereo pair * [IMX214](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/imx214.html#imx214) (PY014 AF, PY114 FF), [OV7251](https://docs.luxonis.com/projects/hardware/en/latest/pages/articles/sensors/ov7251.html#ov7251) (PY013) * DFOV / HFOV / VFOV= 81° / 69° / 54° , 86° / 73° / 58° * Resolution: 13MP (4208x3120), 480P (640x480) * Focus: AF: 8cm - ∞ OR FF: 50cm - ∞ , Fixed-Focus 6.5cm - ∞ * Max Framerate: 35 FPS, 120 FPS * Width: 91 mm , Height: 28 mm, Length: 17.5 mm, Baseline: 75 mm, Weight: 61 g * chips: * * Robotics Vision Core 2 (RVC2 in short) Myriad X are integrated into the Robotics Vision Core 2 * Speed ML * * Model name, Size, FPS, Latency [ms], * MobileOne S0 224x224, 165.5, 11.1 * YoloV8n, 416x416, 31.3, 56.9, * YoloV8n, 640x640, 14.3, 123.6 * YoloV8s, 416x416, 15.2, 111.9 * YoloV8m, 416x416, 6.0, 273.8 # Hardware for Deep Learning (machine learning) [https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware) I experiment with many different hardware to train and run deep learning application. The below list shows my suggestion, comparison, expectation of using different hardware. Embedded AI, implementing distributed data parallel, distributed model parallel solutions. [https://www.pirahansiah.com/topics/hardware](https://www.pirahansiah.com/topics/hardware) #hardware #deep_learning #IoT #training_machine_learning_model #pirahansiah Laptop: * NVIDIA Geforce RTX 3080 Ti * Razer Blade 17 - 17.3 inch gaming laptop (NVIDIA Geforce RTX 3080 Ti, Intel i9-12900H, 4K UHD 144Hz display 32GB DDR5 RAM, 1TB SSD, Desktop * eGPU * Razer RC21-01310100-R351 Core X External Graphics Card Case = ~ 300 Euro + GPU * Cooler Master MasterCase EG200 External GPU Enclosure - Thunderbolt 3 Compatible eGPU Enclosure, 1 PWM 92mm Fan, V550 SFX Gold Fully Modular PSU, USB Hub, Vertical Laptop Support - EU Plug = ~ 300 Euro + GPU * GPU * Geforce RTX 3090 24G 384Bit Gddr6x Nvidia Geforce * MSI GeForce RTX 3090 GAMING TRIO 24G Gaming Graphics Card - NVIDIA RTX 3090, GPU 1740MHz, 24GB GDDR6X memory = ~ 2800 Euro IoT: * Raspberry pi 3 (you need accelerator ) * Raspberry pi 4 (you need accelerator ) * Intel® Neural Compute Stick 2 * Intel® Distribution of OpenVINO™ Toolkit * I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models * Google Coral * I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models * Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used * NVIDIA Jetson Nano ( 2GB and 4GB ram) * I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes * NVIDIA JETSON AGX XAVIER * NVIDIA AGX Orin = ~ 1900 Euro * [Compare NVIDIA Jetson AGX Orin with AGX Xavier: 8x AI performance, in-advance Ampere GPU, CPU, Memory & Storage](https://www.seeedstudio.com/blog/2022/03/17/compare-nvidia-jetson-agx-orin-with-agx-xavier-6x-ai-performance-in-advance-ampere-gpu-cpu-memory-storage/) * OpenCV AI Kit * OAK = ~ 100 Euro * OAK—D = ~ 200 Euro * OAK—D + Wifi = ~ 250 Euro * OpenCV AI Kit: OAK—D-PoE = ~ 250 Euro * OAK—D lite = ~ 100 Euro # My experience I tested many different hardware for 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[#GraphQL](https://www.linkedin.com/feed/hashtag/?keywords=graphql&highlightedUpdateUrns=urn%3Ali%3Aactivity%3A6787871627586625536) #imageprocessing #patternrecognition ![](https://lh4.googleusercontent.com/qMKbpc8YDY40u5MTiILnPoaHj9vKqVxPnOaD9fLwnxA7vPBSr_50i6RB8V_SLvdNA1_FKmnKCXlrZPaM6wWVmhgouL3x44_JEqqFIfTsSok-V68D7LnBWkHn47jKDAHG0A=w1280) # Raspberry Pi 4 How to upgrade Raspberry Pi 4 EEPROM boot recovery; Released 2020-09-14; to install and boot from USB 3 (SSD) 1. update Raspberry Pi 4 EEPROM boot recovery 2. install Ubuntu 20 on SSD 3. change the config.txt and add "program_usb_boot_mode=1" at the end of file 4. remove and micro sd card and boot from ssd ![](https://lh6.googleusercontent.com/ujAA9d_- IDq8qY9Ai_CkzcKFF8uSpqWJCM1m4wF2riL0VdBwEJZ9Ue4vbZ88TcEGUa35Xh3aTUnpNeHMDq1vP0gaMoPgnHbpOAeH4GFM_TyT1ok_89sI4g9k81V2V8h3kA=w1280) ![](https://lh4.googleusercontent.com/9G2ZHrGOyP8gP7nbPtfQZgVOPUMcQGV9wrTPOMx-7XkVI9xy7GVlIIwpkzKkygjSa5RsfACFIhJWCkbfZWGPcp01KeDX6QqjuUj0oO4Gd0jDuNDFm54kHehSI_AyoS4lHA=w1280) ![](https://lh4.googleusercontent.com/fMqUHF7c8UrFRa1rhY_dVS9rMVQYxFzWIdmQRUIVsFWTx6-YxyQfpA6nbzT0sLlP9NZeXowijFFPOEQ7_hiB_j1CT5TZp6cjpEgGxnCiWk1j0ikErjQkOXu7fB3juU6fXg=w1280) ![](https://lh6.googleusercontent.com/7uZjP- WilOSboDiZQ2iGUhdjB154E-1AWOUejpM9ye7CNBo2Q_MhrLevpsFnpa-RmAz0yMdJ- LvOPtyJqUK4ZCkFyTXyFDSHCLqSTd_C-tnwVmhvi9OcuPBoZgejF8Jx4A=w1280) ![](https://lh4.googleusercontent.com/GBrj6Hi0qhq6mjHjcVqNaMqkH1eemwE1ozDdjFkqYbq1DxDwkVoIu5fF2UgvQLYEpalElHPeN1EzBRJAvuS3VF81Z4dTZbYX9nr5EoWomWgg7VsgI4H1fbJE5mFTJhMKmw=w1280) ![](https://lh6.googleusercontent.com/SCHwRCZB5yqyiKskSnfxiVzvoJNNK6j4ecKyA9WFW8aX4WTskRYKgTMPxXGnWB8gV_saN_1lSudL5EokukNwYTsOBvtIomUZ3dSaX3QlDTpRRXP3vNdOAU_4IQ68MTW11A=w1280) ![](https://lh5.googleusercontent.com/nEmmvYUSEIJP9OaoHzqUZDiX3Zk1YCW_pnb2nNCkckbMLVwDeCt1Hjx-q8pxyeLvoYvOO8-O6T958f59Y7NdfEIDH-60tGsHz465Bs21WxP0Ue8Vggp2wxKL1s9QLIEV5w=w1280) # Smart AI IoT, Robotic, 3D SLAM, AR, VR * [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2) # [RISC-V](/workshops-and-events/risc-v) # I worked with many different hardware such as * Raspberry pi 3 * Raspberry pi 4 * Intel® Neural Compute Stick 2 * Intel® Distribution of OpenVINO™ Toolkit * I attached to Raspberry pi 4 by USB 3 and work very well for many deep learning models * Google Coral * I attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow models * Why TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to used * NVIDIA Jetson Nano * I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutes * NVIDIA JETSON AGX XAVIER * The best hardware * I attended in may conferences and summits in area of Hardware for deep learning such as: * * * AI Hardware Europe Summit (July 2020) * [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit) * Apache TVM And Deep Learning Compilation Conference (December 2020) * RISC-V Summit (December 2020) * OpenCV AI Kit ## Camera I worked with many different cameras such as: * Camera Module V1 * Camera Module V2 * Camera Module V2.1 * multispectral camera * USB webcam * IP camera * high resolution camera > 8K * depth camera * stereo camera ### What is important? * camera calibration is important * Quantum efficiency [%] (spectral response) * Sensor size [inches or mm] and pixel size [micro meter] * Dynamic Range [dB] * Image noise and signal to noise ratio (SNR), PSNR, SSIM, : greater SNR yields better contrast and clarity, as well as improved low light performance * inter face, cable length in m, bandwidth max in MB/s , multi camera, cable costs, real time, plug and play * * firewire, 4.5 , 64, *, *, **, ** * gige, 100, 100, **, **, *, * * usb, 8, 350, *, *, **, ** * link, 10, 850, -, -, **, - * usb-c, 10, 40 GB,,,, * distortions, scaling factors, quality is important, calculate minimum sensor resolution *, determine your sensor size, focal length, * * sensor resolution= image resolution = 2 * ( field of view (FOV) / smallest feature ) * some online tools: baslerweb.com, edmundoptics.com, flir.com * to sum up * use USB-C camera. it will help you in the future upgrades in hardware and easy to use with less issues * find your best trade-off between WD and FOV * sometimes you cannot have everything in life! * your lens aperture (f/#) is your friend, use it! * a larger DOF requires a larger f/# * lens performance curves are the ultimate documentation to read when selecting a lens * understanding them properly requires good knowledge in optics, but it totally worth it. ## Scaled-YOLOv4:scaling model based on hardware # Cost * [How much does a patent cost?](https://www.fu-berlin.de/en/forschung/service/patente-und-lizenzen/faq/kosten.html) * [Mobile, Open Hardware, RISC-V System-on-Chip (SoC) Development Kit](https://www.crowdsupply.com/sutajio-kosagi/precursor) * Hardware * NVIDIA Jetson Xavier NX Developer Kit * WIFI * SparkFun GPS-RTK Dead Reckoning pHAT * Micro Sd card * Mophie Powerstation USB C 20000 * ZED 2 Stereo Camera * 3D-printed box * AWS * AWS S3 * AWS xml.p2.xlarge EC2 instances * AWS Sagemaker * [Hackboard 2 with Ubuntu Linux (99$) Intel CPU](https://www.crowdsupply.com/hackboard/hb2) * [3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardware](https://github.com/NimbRo/nimbro-op2) * Post Product to customer by * * * * * * * [easyship](https://www.easyship.com/) * [fulfillmentcrowd](https://www.fulfilmentcrowd.com/) * [ChinaDivision](https://www.chinadivision.com/) * [ORQA FPV](https://orqafpv.com/) * [floship](https://www.floship.com/) Update 26.April.2021 # How to use computer vision with deep learning in IoT devices. Inference machine learning on Edge require some extra steps. I tested several hardware such as Raspberry pi 3, Raspberry pi 4, Intel® Neural Compute Stick 2, OpenCV AI Kit, Google Coral, NVIDIA Jetson Nano, etc. Different OS: real-time operating system (RTOS), Nasa cFS (core Flight System), Real-Time Executive for Multiprocessor Systems (RTEMS), anomaly detection, object detection, object tracking, ... ## Use special frameworks or library for edge devices: * NVIDIA TensorRT * TensorFlow Lite: TensorFlow Lite on Microcontroller Gesture Recognition OpenMV/Tensorflow/ studio.edgeimpulse.com * TensorFlow.js * PyTorch Lightning * PyTorch Mobile * Intel® Distribution of OpenVINO Toolkit * CoreML * ML kit * FRITZ * MediaPipe * Apache TVM * TinyML: enabling ultra-low power machine learning at the edge tiny machine learning with Arduino * Libraries: ffmpeg, GStreamer, celery, * GPU library for python: PyCUDA, NumbaPro, PyOpenCL, CuPy Moreover, think about deep learning model for your specific hardware at first stage. ## In some case you need to enhance model for inference. There are many techniques to use such as, * Pruning * Quantization * Distillation Techniques * Binarized Neural Networks (BNNs) * Apache TVM (incubating) is a compiler stack for deep learning systems * Distributed machine learning and load balancing strategy * Low rank matrix factorization (LRMF) * Compact convolutional filters (Video/CNN) * Knowledge distillation * Neural Networks Compression Framework (NNCF) * Parallel programming ## How Distributed machine learning and load balancing strategy Pruning model pruning: reducing redundant parameters which are not sensitive to the performance. aim: remove all connections with absolute weights below a threshold. 🤔go for bigger size of network with many layers then pruning much better and faster Quantization The best way is using Google library which support most comprehensive methods compresses by reducing the number of bits used to represent the weights quantization effectively constraints the number of different weights we can use inside our kernels per channel quantization for weights, which improves performance by model compression and latency reduction. training a compact neural network with distilled knowledge of a large model distillation (knowledge transfer) from an ensemble of big networks into a much smaller network which learns directly from the cumbersome model's outputs, that is lighter to deploy Distillation Techniques Distill-Net: Application-Specific Distillation of Deep Convolutional Neural Networks for Resource-Constrained IoT Platforms Binarized Neural Networks (BNNs) It is not support by GPU hardware such as Jetson Nano. mostly based on CPU Apache TVM (incubating) is a compiler stack for deep learning systems challenges with large scale models deep neural networks are: expensive computationally expensive memory intensive hindering their deployment in:devices with low memory resources applications with strict latency requirements other issues:data security: tend to memorize everything including PII bias e.g. profanity: trained on large scale public datas elf discovering: instead of manually configuring conversational flows, automatically discover them from your data self training: let your system train itself with new example s self managing: let your system optimize by itself knowledge distillation Distributed machine learning and load balancing strategy run models which use all processing power like CPU,GPU,DSP,AI chip together to enhance inference performance. dynamic pruning of kernels which aims to the parsimonious inference by learning to exploit and dynamically remove the redundant capacity of a CNN architecture. partitioning techniques through convolution layer fusion to dynamically select the optimal partition according to the availability of computational resources and network conditions. Low rank matrix factorization (LRMF) there exists latent structures in the data, by uncovering which we can obtain a compressed representation of the dataLRMF factorizes the original matrix into lower rank matrices while preserving latent structures and addressing the issue of sparseness Compact convolutional filters (Video/CNN) designing special structural convolutional filters to save parameters replace over parametric filters with compact filters to achieve overall speedup while maintaining comparable accuracy Knowledge distillation Neural Networks Compression Framework (NNCF) AI Edge: How to inference deep learning models on edge/IoT Enabling efficient high-performance Accelerators/Optimization on Deep Learning if the object is large and we do not need small anchor in mobileNet we can remove small part of network which related to small objects. in YOLO reduce number of anchor. decrease size of image input but reduce the accuracy Parallel programming and clean code, design pattern, Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh4.googleusercontent.com/8sKtLRYlt7H0NnY_mX4T1p1meWrb3BIop8uoE8On8OzYvp2gPqIlrZXSelotoNJtig5cCs9eXhMevV_Clq5XtvM=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh4.googleusercontent.com/8sKtLRYlt7H0NnY_mX4T1p1meWrb3BIop8uoE8On8OzYvp2gPqIlrZXSelotoNJtig5cCs9eXhMevV_Clq5XtvM=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # My paper: A Comprehensive Review on Deep Reinforcement Learning The updates 2021 YouTube Notes and info Links: Reading List (Video, Conference, Workshop, Paper) ### The updates Dear friends, I recently wrote a survey paper on "A Comprehensive Review on Deep Reinforcement Learning: A Survey", with some of the leading AI and DRL researchers (including): In this work, we covered top recent DRL works, grouped into several categories. We were lucky to have you, as the external reviewers of this work. I hope this is useful for the research community. Any feedback will be highly welcomed. You can find its summary here too. Imitation learning, expert (teacher), hierarchical, hybrid imitation, high performance parallelism, # 2021 * [NeurIPS 2020: Key Research Papers in Reinforcement Learning and More](https://www.google.com/url?q=https%3A%2F%2Fwww.topbots.com%2Fneurips-2020-rl-research-papers%2F&sa=D&sntz=1&usg=AOvVaw3fWwOQ2N8Z8mHGNysZedTo) * [Key Papers in Deep RL](https://www.google.com/url?q=https%3A%2F%2Fspinningup.openai.com%2Fen%2Flatest%2Fspinningup%2Fkeypapers.html&sa=D&sntz=1&usg=AOvVaw3sZcgv4fIlIg3pmq6_O_q2) ### YouTube * Simple Deep Q Network w/Pytorch:[ https://youtu.be/UlJzzLYgYoE](https://youtu.be/UlJzzLYgYoE) * Reinforcement Learning Crash Course:[ https://youtu.be/sOiNMW8k4T0](https://youtu.be/sOiNMW8k4T0) * Policy Gradients w/Tensorflow:[ https://youtu.be/UT9pQjVhcaU](https://youtu.be/UT9pQjVhcaU) * Deep Q Learning w/Tensorflow[ https://youtu.be/3Ggq_zoRGP4](https://youtu.be/3Ggq_zoRGP4) * Code Your Own RL Environments[ https://youtu.be/vmrqpHldAQ0](https://youtu.be/vmrqpHldAQ0) * How to Spec a Deep Learning PC:[ https://youtu.be/xsnVlMWQj8o](https://youtu.be/xsnVlMWQj8o) * Deep Q Learning w/ Pytorch:[ https://youtu.be/RfNxXlO6BiA](https://youtu.be/RfNxXlO6BiA) * Machine Learning Freelancing[ https://youtu.be/6M04ZTLE_O4](https://youtu.be/6M04ZTLE_O4) * **Code from video** :[ https://github.com/philtabor/Youtube-Code-Repository](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fphiltabor%2FYoutube-Code-Repository&sa=D&sntz=1&usg=AOvVaw0lZg33Rz9-UZsCI8LPkgPG) ### Notes and info * training on unlabeled data, lifelong learning, and especially letting models explore a simulated environment before transferring what they learn to the real world * Lately, simulation has helped achieve impressive results in reinforcement learning, which is extremely data-intensive. * using reinforcement learning to train robots that reason about how their actions will affect their environment. * How is it that many people learn to drive a car fairly safely in 20 hours of practice, while current imitation learning algorithms take hundreds of thousands of hours, and reinforcement learning algorithms take millions of hours? Clearly we’re missing something big. * In 2021, I expect self-supervised methods to learn features of video and images. Could there be a similar revolution in high-dimensional continuous data like video? * One critical challenge is dealing with uncertainty. Models like BERT can’t tell if a missing word in a sentence is “cat” or “dog,” but they can produce a probability distribution vector. We don’t have a good model of probability distributions for images or video frames. But recent research is coming so close that we’re likely to find it soon. * Suddenly we’ll get really good performance predicting actions in videos with very few training samples, where it wasn’t possible before. That would make the coming year a very exciting time in AI. * DeepMind released the code, model, & dataset behind their groundbreaking "AlphaFold" system. It's predicts protein shapes from genomic data with apps in health, sustainability, & materials design ### Links: * [https://techgrabyte.com/google-framework-reduces-ai-training-costs-seed-rl/?fbclid=IwAR07KznmEO4nCYIMJM9Zv53HDoQEHvHH9FoUHqfPDyRvoucayu3p4nzROfU](https://www.google.com/url?q=https%3A%2F%2Ftechgrabyte.com%2Fgoogle-framework-reduces-ai-training-costs-seed-rl%2F%3Ffbclid%3DIwAR07KznmEO4nCYIMJM9Zv53HDoQEHvHH9FoUHqfPDyRvoucayu3p4nzROfU&sa=D&sntz=1&usg=AOvVaw2YNi0EWFi_liPQK9abco8U) ### Reading List (Video, Conference, Workshop, Paper) * [https://sites.google.com/view/icml19metalearning](https://www.google.com/url?q=https%3A%2F%2Fsites.google.com%2Fview%2Ficml19metalearning&sa=D&sntz=1&usg=AOvVaw0qCxTbeQF-J_3tGBq--FD4) DeepMind Open-Sources Lab2D, A System For The Creation Of 2D Environments For Machine Learning * Github:[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fdeepmind%2Flab2d%3Ffbclid%3DIwAR2axHHCby_-GbXC_VgpytDoJkAq-dxPd2JZ4vOOIEvxUqK_hakvvfo56Zk&sa=D&sntz=1&usg=AOvVaw0uAYAMc2ud2QYDJ68E0Mre)[https://github.com/deepmind/lab2d](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fdeepmind%2Flab2d%3Ffbclid%3DIwAR2axHHCby_-GbXC_VgpytDoJkAq-dxPd2JZ4vOOIEvxUqK_hakvvfo56Zk&sa=D&sntz=1&usg=AOvVaw0uAYAMc2ud2QYDJ68E0Mre) * Paper:[ ](https://www.google.com/url?q=https%3A%2F%2Fl.facebook.com%2Fl.php%3Fu%3Dhttps%253A%252F%252Farxiv.org%252Fpdf%252F2011.07027.pdf%253Ffbclid%253DIwAR2xbhrokYigw3yFTJhg3TI5OOKO-WQKX3n1TMRk1oBsi7Yr0ZSMO-iy1OI%26h%3DAT2hq3duwK0TgamyFGpwGfXdFVwnzjio0hbbMV0kghZsLkgTmoQEC2qTOJq5d9etSs1sufzhMci5ZkgbHXGwnZFNQOGRq10viKy9bcsNcfVmbMcVs-pYbLxcgtY4GwlnwENJ0h0%26__tn__%3D-UK-R%26c%5B0%5D%3DAT0yOKaA4J_MP8vKht-A_dhqDP3hEP_Vygu41a-f4Z5wKa3uQd80YA5NtTbdmq1mVtD4wNLg4V_k1Y6dHg2dcaGQrZ5sN19swM8C0oUWc7IobWSB2kF-wjD7uhtsj2QH1y0d9BdaaELVvniBl7UST1UviF2U5BQquuGFXWIxvIR7-3rq-62c7phKFbMVVoC6ar_N-IlZT8pjhapy0xSq8g&sa=D&sntz=1&usg=AOvVaw2LI1WQU1nUuooUmNb2yNvk)[https://arxiv.org/pdf/2011.07027.pdf](https://www.google.com/url?q=https%3A%2F%2Fl.facebook.com%2Fl.php%3Fu%3Dhttps%253A%252F%252Farxiv.org%252Fpdf%252F2011.07027.pdf%253Ffbclid%253DIwAR2xbhrokYigw3yFTJhg3TI5OOKO-WQKX3n1TMRk1oBsi7Yr0ZSMO-iy1OI%26h%3DAT2hq3duwK0TgamyFGpwGfXdFVwnzjio0hbbMV0kghZsLkgTmoQEC2qTOJq5d9etSs1sufzhMci5ZkgbHXGwnZFNQOGRq10viKy9bcsNcfVmbMcVs-pYbLxcgtY4GwlnwENJ0h0%26__tn__%3D-UK-R%26c%5B0%5D%3DAT0yOKaA4J_MP8vKht-A_dhqDP3hEP_Vygu41a-f4Z5wKa3uQd80YA5NtTbdmq1mVtD4wNLg4V_k1Y6dHg2dcaGQrZ5sN19swM8C0oUWc7IobWSB2kF-wjD7uhtsj2QH1y0d9BdaaELVvniBl7UST1UviF2U5BQquuGFXWIxvIR7-3rq-62c7phKFbMVVoC6ar_N-IlZT8pjhapy0xSq8g&sa=D&sntz=1&usg=AOvVaw2LI1WQU1nUuooUmNb2yNvk) * Summary:[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.marktechpost.com%2F2020%2F11%2F18%2Fdeepmind-open-sources-lab2d-a-system-for-the-creation-of-2d-environments-for-machine-learning%2F%3Ffbclid%3DIwAR1GbPd7fe4Gk2u7UNGi7lJmlVsbB6A1wcryPAtCLe_zQKcEz0-xQZxvnRI&sa=D&sntz=1&usg=AOvVaw3lgQoAseygJ1cfWNGun9UY)[https://www.marktechpost.com/.../deepmind-open-sources.../](https://www.google.com/url?q=https%3A%2F%2Fwww.marktechpost.com%2F2020%2F11%2F18%2Fdeepmind-open-sources-lab2d-a-system-for-the-creation-of-2d-environments-for-machine-learning%2F%3Ffbclid%3DIwAR1GbPd7fe4Gk2u7UNGi7lJmlVsbB6A1wcryPAtCLe_zQKcEz0-xQZxvnRI&sa=D&sntz=1&usg=AOvVaw3lgQoAseygJ1cfWNGun9UY) Google to release DeepMind's StreetLearn for teaching machine-learning agents to navigate cities [https://www.techrepublic.com/article/google-to-release-deepminds-streetlearn- for-teaching-machine-learning-agents-to-navigate- cities/](https://www.google.com/url?q=https%3A%2F%2Fwww.techrepublic.com%2Farticle%2Fgoogle- to-release-deepminds-streetlearn-for-teaching-machine-learning-agents-to- navigate-cities%2F&sa=D&sntz=1&usg=AOvVaw0miSK2RHfMotfGPdU2nvQI) Scalable agent alignment via reward modeling – DeepMind Safety Research – Medium [https://medium.com/@deepmindsafetyresearch/scalable-agent-alignment-via- reward-modeling- bf4ab06dfd84](https://www.google.com/url?q=https%3A%2F%2Fmedium.com%2F%40deepmindsafetyresearch%2Fscalable- agent-alignment-via-reward-modeling- bf4ab06dfd84&sa=D&sntz=1&usg=AOvVaw05WoMuEIbirGGfcT9-mcuR) Google's DeepMind Can Support, Defeat Humans in Quake III Arena - ExtremeTech [https://www.extremetech.com/extreme/292409-googles-deepmind-can-support- defeat-human-players-in-quake-iii- arena](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fextreme%2F292409-googles- deepmind-can-support-defeat-human-players-in-quake-iii- arena&sa=D&sntz=1&usg=AOvVaw3d_Jg-b-40ltqOePZzgPKy) [https://www.extremetech.com/?s=deep+mind](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2F%3Fs%3Ddeep%2Bmind&sa=D&sntz=1&usg=AOvVaw3x5zHAWyaFeEzvDSAtPpV-) | You searched for deep mind - ExtremeTech[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F254017-deepmind- ai-moves-board-games-starcraft-ii&sa=D&sntz=1&usg=AOvVaw1Hqm- oXiqQLVJlKNseT5I0)[https://www.extremetech.com/gaming/254017-deepmind-ai- moves-board-games-starcraft- ii](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F254017-deepmind- ai-moves-board-games-starcraft-ii&sa=D&sntz=1&usg=AOvVaw1Hqm- oXiqQLVJlKNseT5I0) | DeepMind AI Moves on from Board Games to StarCraft II - ExtremeTech[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F284441-deepmind- ai-challenges-pro-starcraft-ii-players-wins-almost-every- match&sa=D&sntz=1&usg=AOvVaw34zt37z9JUzT0vVbdrGars)[https://www.extremetech.com/gaming/284441-deepmind- ai-challenges-pro-starcraft-ii-players-wins-almost-every- match](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F284441-deepmind- ai-challenges-pro-starcraft-ii-players-wins-almost-every- match&sa=D&sntz=1&usg=AOvVaw34zt37z9JUzT0vVbdrGars) | DeepMind AI Challenges Pro StarCraft II Players, Wins Almost Every Match - ExtremeTech[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.engadget.com%2F2016%2F11%2F18%2Fgoogle- deepmind-ai- unreal%2F&sa=D&sntz=1&usg=AOvVaw28gsTYLTDGJdtAWgt6cnzx)[https://www.engadget.com/2016/11/18/google- deepmind-ai- unreal/](https://www.google.com/url?q=https%3A%2F%2Fwww.engadget.com%2F2016%2F11%2F18%2Fgoogle- deepmind-ai-unreal%2F&sa=D&sntz=1&usg=AOvVaw28gsTYLTDGJdtAWgt6cnzx) | Google's DeepMind AI gets a few new tricks to learn faster[ ](https://www.youtube.com/results)? Robot arm **There are 4 Courses in this Specialization** **Course** 1 [ **Fundamentals of Reinforcement Learning**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Ffundamentals- of-reinforcement-learning&sa=D&sntz=1&usg=AOvVaw3PTpSRw_TOX9WayLHdHpIm) 4.8 stars 801 ratings • 205 reviews Reinforcement Learning is a subfield of Machine Learning, but is also a general purpose formalism for automated decision-making and AI. This course introduces you to statistical learning techniques where an agent explicitly takes actions and interacts with the world. Understanding the importance and challenges of learning agents that make decisions is of vital importance today, with more and more companies interested in interactive agents and intelligent decision-making. This course introduces you to the fundamentals of Reinforcement Learning. When you finish this course, you will: - Formalize problems as Markov Decision Processes - Understand basic exploration methods and the exploration/exploitation tradeoff - Understand value functions, as a general- purpose tool for optimal decision-making - Know how to implement dynamic programming as an efficient solution approach to an industrial control problem This course teaches you the key concepts of Reinforcement Learning, underlying classic and modern algorithms in RL. After completing this course, you will be able to start using RL for real problems, where you have or can specify the MDP. This is the first course of the Reinforcement Learning Specialization. [SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement- learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq) **Course** 2 [ **Sample-based Learning Methods**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fsample- based-learning-methods&sa=D&sntz=1&usg=AOvVaw2WxPVveAA-1MWiJhTop9nZ) 4.8 stars 397 ratings • 75 reviews In this course, you will learn about several algorithms that can learn near optimal policies based on trial and error interaction with the environment--- learning from the agent’s own experience. Learning from actual experience is striking because it requires no prior knowledge of the environment’s dynamics, yet can still attain optimal behavior. We will cover intuitively simple but powerful Monte Carlo methods, and temporal difference learning methods including Q-learning. We will wrap up this course investigating how we can get the best of both worlds: algorithms that can combine model-based planning (similar to dynamic programming) and temporal difference updates to radically accelerate learning. By the end of this course you will be able to: - Understand Temporal- Difference learning and Monte Carlo as two strategies for estimating value functions from sampled experience - Understand the importance of exploration, when using sampled experience rather than dynamic programming sweeps within a model - Understand the connections between Monte Carlo and Dynamic Programming and TD. - Implement and apply the TD algorithm, for estimating value functions - Implement and apply Expected Sarsa and Q-learning (two TD methods for control) - Understand the difference between on-policy and off-policy control - Understand planning with simulated experience (as opposed to classic planning strategies) - Implement a model-based approach to RL, called Dyna, which uses simulated experience - Conduct an empirical study to see the improvements in sample efficiency when using Dyna [SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement- learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq) **Course** 3 [ **Prediction and Control with Function Approximation**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fprediction- control-function-approximation&sa=D&sntz=1&usg=AOvVaw0l2Hw5t6C2PY6t-i3FKdsH) 4.8 stars 252 ratings • 40 reviews In this course, you will learn how to solve problems with large, high- dimensional, and potentially infinite state spaces. You will see that estimating value functions can be cast as a supervised learning problem--- function approximation---allowing you to build agents that carefully balance generalization and discrimination in order to maximize reward. We will begin this journey by investigating how our policy evaluation or prediction methods like Monte Carlo and TD can be extended to the function approximation setting. You will learn about feature construction techniques for RL, and representation learning via neural networks and backprop. We conclude this course with a deep-dive into policy gradient methods; a way to learn policies directly without learning a value function. In this course you will solve two continuous-state control tasks and investigate the benefits of policy gradient methods in a continuous-action environment. Prerequisites: This course strongly builds on the fundamentals of Courses 1 and 2, and learners should have completed these before starting this course. Learners should also be comfortable with probabilities & expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), and implementing algorithms from pseudocode. By the end of this course, you will be able to: -Understand how to use supervised learning approaches to approximate value functions -Understand objectives for prediction (value estimation) under function approximation -Implement TD with function approximation (state aggregation), on an environment with an infinite state space (continuous state space) -Understand fixed basis and neural network approaches to feature construction -Implement TD with neural network function approximation in a continuous state environment -Understand new difficulties in exploration when moving to function approximation -Contrast discounted problem formulations for control versus an average reward problem formulation -Implement expected Sarsa and Q-learning with function approximation on a continuous state control task -Understand objectives for directly estimating policies (policy gradient objectives) -Implement a policy gradient method (called Actor-Critic) on a discrete state environment [SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement- learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq) **Course** 4 [ **A Complete Reinforcement Learning System (Capstone)**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fcomplete- reinforcement-learning-system&sa=D&sntz=1&usg=AOvVaw1cPdyaSfUjhZu1SLxl8DIm) 4.6 stars 177 ratings • 33 reviews In this final course, you will put together your knowledge from Courses 1, 2 and 3 to implement a complete RL solution to a problem. This capstone will let you see how each component---problem formulation, algorithm selection, parameter selection and representation design---fits together into a complete solution, and how to make appropriate choices when deploying RL in the real world. This project will require you to implement both the environment to stimulate your problem, and a control agent with Neural Network function approximation. In addition, you will conduct a scientific study of your learning system to develop your ability to assess the robustness of RL agents. To use RL in the real world, it is critical to (a) appropriately formalize the problem as an MDP, (b) select appropriate algorithms, (c ) identify what choices in your implementation will have large impacts on performance and (d) validate the expected behaviour of your algorithms. This capstone is valuable for anyone who is planning on using RL to solve real problems. To be successful in this course, you will need to have completed Courses 1, 2, and 3 of this Specialization or the equivalent. By the end of this course, you will be able to: Complete an RL solution to a problem, starting from problem formulation, appropriate algorithm selection and implementation and empirical study into the effectiveness of the solution. [SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement- learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq) **Using pre trained model to train deeper and lager model** Imitation Learning Safety Gym, a suite of environments and tools for measuring progress towards reinforcement learning agents that respect safety constraints while training. It also provides a standardized method of comparing algorithms and how well they avoid costly mistakes while learning. If deep reinforcement learning is applied to the real world, whether in robotics or internet-based tasks, it will be important to have algorithms that are safe even while learning—like a self-driving car that can learn to avoid accidents without actually having to experience them. Credit: Two Minute Papers, OpenAI Follow me for more AI/ Datascience posts:[ ](https://www.google.com/url?q=https%3A%2F%2Flnkd.in%2FgZu463X&sa=D&sntz=1&usg=AOvVaw04QVerqlEJzF6ri9VsjWi4)[https://lnkd.in/gZu463X](https://www.google.com/url?q=https%3A%2F%2Flnkd.in%2FgZu463X&sa=D&sntz=1&usg=AOvVaw04QVerqlEJzF6ri9VsjWi4) [OpenAI Safety Gym: A Safe Place For AIs To Learn 💪](https://www.youtube.com/watch?v=_s7Bg6yVOdo) **DeepMind proposes novel way to train ‘safe’ reinforcement learning AI** [https://venturebeat.com/2019/12/13/deepmind-proposes-novel-way-to-train-safe- reinforcement-learning-ai/?fbclid=IwAR22JRwC48YaKLICmYQTOjuKP- cEcE4_biuCSAxFuHNqgeJIhhineg1PTIk](https://www.google.com/url?q=https%3A%2F%2Fventurebeat.com%2F2019%2F12%2F13%2Fdeepmind- proposes-novel-way-to-train-safe-reinforcement-learning- ai%2F%3Ffbclid%3DIwAR22JRwC48YaKLICmYQTOjuKP- cEcE4_biuCSAxFuHNqgeJIhhineg1PTIk&sa=D&sntz=1&usg=AOvVaw33y42wwJ7GVtU9yQ- HA8tJ) **The Batch** Issue 35 **Different Skills From Different Demos** Reinforcement learning trains models by trial and error. In batch reinforcement learning (BRL), models learn by observing many demonstrations by a variety of actors. For instance, a robot might learn how to fix ingrown toenails by watching hundreds of surgeons perform the procedure. But what if one doctor is handier with a scalpel while another excels at suturing? A new method lets models absorb the best skills from each. **What’s new:** Ajay Mandlekar and collaborators at Nvidia, Stanford, and the University of Toronto devised a BRL technique that enables models to learn different portions of a task from different examples. This way, the model can gain useful information from inconsistent examples.[ ](https://www.google.com/url?q=https%3A%2F%2Finfo.deeplearning.ai%2Fe2t%2Fc%2F*W93MmRy8ctVj4W8CrRWn19CRs_0%2F*N5ZNrfGWSg4SV- QHFT8ZG-6n0%2F5%2Ff18dQhb0Sjv48XJblHN7fK6lMHyjJqVQsN5n1pgP_0N3hHhdwVMsQMVnQ9Qq8Zy_B4W1Tdc1j55VM8QW5p680S1FW12JMsgqy7twk7pW4bJ02h4b_rKwW7MbC3k1ShBCtVsVwJ95ltllnVJ2dtV2yJF1WVYT2jk6P4lCXW3Wdv8v6Pkt_VW62_rW_5YFJKDW96dt4S4r1QvYVNHCtz8gjY6LW8WBKbV56sy_8W2NhXK38W2TXnVJd1xK2Mn6xFW2d0Xx01RMFRvW9f4wHg7HmNBdW76lKpq2sC8-JW1ysXGq6WYC_zVpjpzw6Vshk0W4hvzF86VX7kWW4cPj6g5-b0qgW5NpS3-7qrJl_W56vZfs2MJXfMW2tBw4W6wd4SGN4lBZ6Fnpt4xW6GTq088Ph58-W594SN81HkWtdW7t3MQ17y2wLbW2F6lCB1L9wgVW4J35Sn2N3p-BW5Wp1wj8_6fCxW754Rys8_bnGcW8LMdgb7jYjT_W39d2Vf6Q3Qs6MrLh9QrHMy0f2Q9pjJ02&sa=D&sntz=1&usg=AOvVaw1n2jZIG6q7VlX9dohRw2XF)[Implicit Reinforcement without Interaction at Scale](https://www.google.com/url?q=https%3A%2F%2Finfo.deeplearning.ai%2Fe2t%2Fc%2F*W93MmRy8ctVj4W8CrRWn19CRs_0%2F*N5ZNrfGWSg4SV- QHFT8ZG-6n0%2F5%2Ff18dQhb0Sjv48XJblHN7fK6lMHyjJqVQsN5n1pgP_0N3hHhdwVMsQMVnQ9Qq8Zy_B4W1Tdc1j55VM8QW5p680S1FW12JMsgqy7twk7pW4bJ02h4b_rKwW7MbC3k1ShBCtVsVwJ95ltllnVJ2dtV2yJF1WVYT2jk6P4lCXW3Wdv8v6Pkt_VW62_rW_5YFJKDW96dt4S4r1QvYVNHCtz8gjY6LW8WBKbV56sy_8W2NhXK38W2TXnVJd1xK2Mn6xFW2d0Xx01RMFRvW9f4wHg7HmNBdW76lKpq2sC8-JW1ysXGq6WYC_zVpjpzw6Vshk0W4hvzF86VX7kWW4cPj6g5-b0qgW5NpS3-7qrJl_W56vZfs2MJXfMW2tBw4W6wd4SGN4lBZ6Fnpt4xW6GTq088Ph58-W594SN81HkWtdW7t3MQ17y2wLbW2F6lCB1L9wgVW4J35Sn2N3p-BW5Wp1wj8_6fCxW754Rys8_bnGcW8LMdgb7jYjT_W39d2Vf6Q3Qs6MrLh9QrHMy0f2Q9pjJ02&sa=D&sntz=1&usg=AOvVaw1n2jZIG6q7VlX9dohRw2XF) (IRIS) achieved state-of-the-art BRL performance in three tasks performed in a virtual environment. **Key insight:** Learning from demonstrations is a double-edged sword. An agent gets to see how to complete a task, but the scope of its action is limited to the most complete demonstration of a given task. IRIS breaks down tasks into sequences of intermediate subgoals. Then it performs the actions required to accomplish each subgoal. In this way, the agent learns from the best parts of each demonstration and combines them to accomplish the task. **How it works:** IRIS includes a subgoal selection model that predicts intermediate points on the way to accomplishing an assigned task. These subgoals are defined automatically by the algorithm, and may not correspond to parts of a task as humans would describe them. A controller network tries to replicate the optimal sequence of actions leading to a given subgoal. * The subgoal selection model is made up of a conditional variational autoencoder that produces a set of possible subgoals and a value function (trained via a BRL version of Q-learning) that predicts which next subgoal will lead to the highest reward. * The controller is a recurrent neural network that decides on the actions required to accomplish the current subgoal. It learns to predict how demonstrations tend to unfold, and to imitate short sequences of actions from specific demonstrations. * Once it’s trained, the subgoal selection model determines the next subgoal. The controller takes the requisite actions. Then the subgoal selection model evaluates the current state and computes a new subgoal, and so on. **Results:** In the Robosuite's lifting and pick-and-place tasks, previous state-of-the-art BRL approaches couldn't pick up objects reliably, nor place them elsewhere at all. IRIS learned to pick up objects with over 80 percent success and placed them with 30 percent success. **Why it matters:** Automatically identifying subgoals has been a holy grail in reinforcement learning, with active research in hierarchical RL and other areas. The method used in this paper applies to relatively simple tasks where things happen in a predictable sequence (such as picking and then placing), but might be a small step in an important direction. **We’re thinking:** Batch reinforcement learning is useful when a model must be interpretable or safe — after all, a robotic surgeon shouldn’t experiment on living patients — but it hasn’t been terribly effective. IRIS could make it a viable option. Dec 11, 2019 Issue 34 **Seeing the World Blindfolded** In reinforcement learning, if researchers want an agent to have an internal representation of its environment, they’ll build and train a world model that it can refer to. New research shows that world models can emerge from standard training, rather than needing to be built separately. **What’s new:** Google Brain researchers C. Daniel Freeman, Luke Metz, and David Ha enabled an agent to build a world model by blindfolding it as it learned to accomplish tasks. They call their approach[ ](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1910.13038&sa=D&sntz=1&usg=AOvVaw19a0ZWmEG6OitJ7uRtgAiJ)[observational dropout](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1910.13038&sa=D&sntz=1&usg=AOvVaw19a0ZWmEG6OitJ7uRtgAiJ). **Key insight:** Blocking an agent's observations of the world at random moments forces it to generate its own internal representation to fill in the gaps. The agent learns this representation without being instructed to predict how the environment will change in response to its actions. **How it works:** At every timestep, the agent acts on either its observation (framed in red in the video above) or its prediction of what it wasn’t able to observe (imagery not framed in red). The agent contains a controller that decides on the most rewarding action. To compute the potential reward of a given action, the agent includes an additional deep net trained using the RL algorithm REINFORCE. * Observational dropout blocks the agent from observing the environment according to a user-defined probability. When this happens, the agent predicts an observation. * If random blindfolding blocks several observations in a row, the agent uses its most recent prediction to generate the next one. * This procedure over many iterations produces a sequence of observations and predictions. The agent learns from this sequence, and its ability to predict blocked observations is tantamount to a world model. **Results:** Observational dropout solved the task known as[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fopenai%2Fgym%2Fwiki%2FCartPole-v0&sa=D&sntz=1&usg=AOvVaw3n6wYAZyMbzRpWSK2gORJW)[Cartpole](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fopenai%2Fgym%2Fwiki%2FCartPole-v0&sa=D&sntz=1&usg=AOvVaw3n6wYAZyMbzRpWSK2gORJW), in which the model must balance a pole upright on a rolling cart, even when its view of the world was blocked 90 percent of the time. In a more complex[ ](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1809.01999&sa=D&sntz=1&usg=AOvVaw2XKaDs9JT_6xvavFR00hoz)[Car Racing](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1809.01999&sa=D&sntz=1&usg=AOvVaw2XKaDs9JT_6xvavFR00hoz) task, in which a model must navigate a car around a track as fast as possible, the model performed almost equally well whether it was allowed to see its surroundings or blindfolded up to 60 percent of the time. **Why it matters:** Modeling reality is often part art and part science. World models generated by observational dropout aren’t perfect representations, but they’re sufficient for some tasks. This work could lead to simple-but-effective world models of complex environments that are impractical to model completely. **We’re thinking:** Technology being imperfect, observational dropout is a fact of life, not just a research technique. A self-driving car or auto- piloted airplane reliant on sensors that drop data points could create a catastrophe. This technique could make high-stakes RL models more robust. Dec 4, 2019 Issue 33 **Is AI Making Mastery Obsolete?** Is there any reason to continue playing games that AI has mastered? Ask the former champions who have been toppled by machines. **What happened:** In 2016, International Go master Lee Sedol famously lost three out of four matches to DeepMind’s AlphaGo model. The 36-year-old announced his retirement from competition on November 27. “Even if I become the number one, there is an entity that cannot be defeated,” he[ ](https://www.google.com/url?q=https%3A%2F%2Fen.yna.co.kr%2Fview%2FAEN20191127004800315&sa=D&sntz=1&usg=AOvVaw0PMZN81SZTeJRnateNxsLl)[told](https://www.google.com/url?q=https%3A%2F%2Fen.yna.co.kr%2Fview%2FAEN20191127004800315&sa=D&sntz=1&usg=AOvVaw0PMZN81SZTeJRnateNxsLl) South Korean's Yonhap News Agency, **Stages of grief:** Prior to the tournament, Lee predicted that he would defeat AlphaGo easily. But the model’s inexplicable — and indefatigable — playing style pushed him into fits of[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.theverge.com%2F2017%2F10%2F11%2F16460118%2Falphago- deepmind-ai-documentary-go-lee-sedol-film- review&sa=D&sntz=1&usg=AOvVaw1BJ4ioQcgpxgNxmnVXr8kN)[shock and disbelief](https://www.google.com/url?q=https%3A%2F%2Fwww.theverge.com%2F2017%2F10%2F11%2F16460118%2Falphago- deepmind-ai-documentary-go-lee-sedol-film- review&sa=D&sntz=1&usg=AOvVaw1BJ4ioQcgpxgNxmnVXr8kN). Afterward, he[ ](https://www.google.com/url?q=https%3A%2F%2Fventurebeat.com%2F2016%2F03%2F12%2Fgo- board-game-champion-lee-sedol-apologizes-for-losing-to-googles- ai%2F&sa=D&sntz=1&usg=AOvVaw18En2cGIvD1uVmya6kwvX-)[apologized](https://www.google.com/url?q=https%3A%2F%2Fventurebeat.com%2F2016%2F03%2F12%2Fgo- board-game-champion-lee-sedol-apologizes-for-losing-to-googles- ai%2F&sa=D&sntz=1&usg=AOvVaw18En2cGIvD1uVmya6kwvX-) for his failure to the South Korean public. **Reaching acceptance:** Garry Kasparov, the former world-champion chess player, went through his own cycle of grief after being defeated by IBM’s DeepBlue in 1997. Although he didn’t retire, Kasparov did accuse IBM’s engineers of cheating. He later retracted the charge, and in 2017 wrote a book[ ](http://www.google.com/url?q=http%3A%2F%2Fwww.kasparov.com%2Fgarry- kasparov-says-ai-can-make-us-more-human-pcmag-interview- march-20th-2019%2F&sa=D&sntz=1&usg=AOvVaw3Lfe53n3SoFR4qDryUTD0P)[arguing](http://www.google.com/url?q=http%3A%2F%2Fwww.kasparov.com%2Fgarry- kasparov-says-ai-can-make-us-more-human-pcmag-interview- march-20th-2019%2F&sa=D&sntz=1&usg=AOvVaw3Lfe53n3SoFR4qDryUTD0P) that, if humans can overcome their feelings of being threatened by AI, they can learn from it. The book advocates an augmented intelligence in which humans and machines work together to solve problems. **The human element:** Although AlphaGo won in the 2016 duel, its human opponent still managed to shine. During the fourth match, Sedol made a[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.wired.com%2F2016%2F03%2Ftwo- moves-alphago-lee-sedol-redefined- future%2F&sa=D&sntz=1&usg=AOvVaw1Pt6XPLhDa8cMRMxOtI951)[move](https://www.google.com/url?q=https%3A%2F%2Fwww.wired.com%2F2016%2F03%2Ftwo- moves-alphago-lee-sedol-redefined- future%2F&sa=D&sntz=1&usg=AOvVaw1Pt6XPLhDa8cMRMxOtI951) so unconventional it defied AlphaGo’s expectation and led to his sole victory. **We’re thinking:** Lee wasn't defeated by a machine alone. He was beaten by a machine built by humans under the direction of AlphaGo research lead David Silver. Human mastery is obsolete only if you ignore people like Silver and his team. Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh4.googleusercontent.com/8sKtLRYlt7H0NnY_mX4T1p1meWrb3BIop8uoE8On8OzYvp2gPqIlrZXSelotoNJtig5cCs9eXhMevV_Clq5XtvM=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh4.googleusercontent.com/8sKtLRYlt7H0NnY_mX4T1p1meWrb3BIop8uoE8On8OzYvp2gPqIlrZXSelotoNJtig5cCs9eXhMevV_Clq5XtvM=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # My paper: A Comprehensive Review on Deep Reinforcement Learning The updates 2021 YouTube Notes and info Links: Reading List (Video, Conference, Workshop, Paper) ### The updates Dear friends, I recently wrote a survey paper on "A Comprehensive Review on Deep Reinforcement Learning: A Survey", with some of the leading AI and DRL researchers (including): In this work, we covered top recent DRL works, grouped into several categories. We were lucky to have you, as the external reviewers of this work. I hope this is useful for the research community. Any feedback will be highly welcomed. You can find its summary here too. Imitation learning, expert (teacher), hierarchical, hybrid imitation, high performance parallelism, # 2021 * [NeurIPS 2020: Key Research Papers in Reinforcement Learning and More](https://www.google.com/url?q=https%3A%2F%2Fwww.topbots.com%2Fneurips-2020-rl-research-papers%2F&sa=D&sntz=1&usg=AOvVaw3fWwOQ2N8Z8mHGNysZedTo) * [Key Papers in Deep RL](https://www.google.com/url?q=https%3A%2F%2Fspinningup.openai.com%2Fen%2Flatest%2Fspinningup%2Fkeypapers.html&sa=D&sntz=1&usg=AOvVaw3sZcgv4fIlIg3pmq6_O_q2) ### YouTube * Simple Deep Q Network w/Pytorch:[ https://youtu.be/UlJzzLYgYoE](https://youtu.be/UlJzzLYgYoE) * Reinforcement Learning Crash Course:[ https://youtu.be/sOiNMW8k4T0](https://youtu.be/sOiNMW8k4T0) * Policy Gradients w/Tensorflow:[ https://youtu.be/UT9pQjVhcaU](https://youtu.be/UT9pQjVhcaU) * Deep Q Learning w/Tensorflow[ https://youtu.be/3Ggq_zoRGP4](https://youtu.be/3Ggq_zoRGP4) * Code Your Own RL Environments[ https://youtu.be/vmrqpHldAQ0](https://youtu.be/vmrqpHldAQ0) * How to Spec a Deep Learning PC:[ https://youtu.be/xsnVlMWQj8o](https://youtu.be/xsnVlMWQj8o) * Deep Q Learning w/ Pytorch:[ https://youtu.be/RfNxXlO6BiA](https://youtu.be/RfNxXlO6BiA) * Machine Learning Freelancing[ https://youtu.be/6M04ZTLE_O4](https://youtu.be/6M04ZTLE_O4) * **Code from video** :[ https://github.com/philtabor/Youtube-Code-Repository](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fphiltabor%2FYoutube-Code-Repository&sa=D&sntz=1&usg=AOvVaw0lZg33Rz9-UZsCI8LPkgPG) ### Notes and info * training on unlabeled data, lifelong learning, and especially letting models explore a simulated environment before transferring what they learn to the real world * Lately, simulation has helped achieve impressive results in reinforcement learning, which is extremely data-intensive. * using reinforcement learning to train robots that reason about how their actions will affect their environment. * How is it that many people learn to drive a car fairly safely in 20 hours of practice, while current imitation learning algorithms take hundreds of thousands of hours, and reinforcement learning algorithms take millions of hours? Clearly we’re missing something big. * In 2021, I expect self-supervised methods to learn features of video and images. Could there be a similar revolution in high-dimensional continuous data like video? * One critical challenge is dealing with uncertainty. Models like BERT can’t tell if a missing word in a sentence is “cat” or “dog,” but they can produce a probability distribution vector. We don’t have a good model of probability distributions for images or video frames. But recent research is coming so close that we’re likely to find it soon. * Suddenly we’ll get really good performance predicting actions in videos with very few training samples, where it wasn’t possible before. That would make the coming year a very exciting time in AI. * DeepMind released the code, model, & dataset behind their groundbreaking "AlphaFold" system. It's predicts protein shapes from genomic data with apps in health, sustainability, & materials design ### Links: * [https://techgrabyte.com/google-framework-reduces-ai-training-costs-seed-rl/?fbclid=IwAR07KznmEO4nCYIMJM9Zv53HDoQEHvHH9FoUHqfPDyRvoucayu3p4nzROfU](https://www.google.com/url?q=https%3A%2F%2Ftechgrabyte.com%2Fgoogle-framework-reduces-ai-training-costs-seed-rl%2F%3Ffbclid%3DIwAR07KznmEO4nCYIMJM9Zv53HDoQEHvHH9FoUHqfPDyRvoucayu3p4nzROfU&sa=D&sntz=1&usg=AOvVaw2YNi0EWFi_liPQK9abco8U) ### Reading List (Video, Conference, Workshop, Paper) * [https://sites.google.com/view/icml19metalearning](https://www.google.com/url?q=https%3A%2F%2Fsites.google.com%2Fview%2Ficml19metalearning&sa=D&sntz=1&usg=AOvVaw0qCxTbeQF-J_3tGBq--FD4) DeepMind Open-Sources Lab2D, A System For The Creation Of 2D Environments For Machine Learning * Github:[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fdeepmind%2Flab2d%3Ffbclid%3DIwAR2axHHCby_-GbXC_VgpytDoJkAq-dxPd2JZ4vOOIEvxUqK_hakvvfo56Zk&sa=D&sntz=1&usg=AOvVaw0uAYAMc2ud2QYDJ68E0Mre)[https://github.com/deepmind/lab2d](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fdeepmind%2Flab2d%3Ffbclid%3DIwAR2axHHCby_-GbXC_VgpytDoJkAq-dxPd2JZ4vOOIEvxUqK_hakvvfo56Zk&sa=D&sntz=1&usg=AOvVaw0uAYAMc2ud2QYDJ68E0Mre) * Paper:[ ](https://www.google.com/url?q=https%3A%2F%2Fl.facebook.com%2Fl.php%3Fu%3Dhttps%253A%252F%252Farxiv.org%252Fpdf%252F2011.07027.pdf%253Ffbclid%253DIwAR2xbhrokYigw3yFTJhg3TI5OOKO-WQKX3n1TMRk1oBsi7Yr0ZSMO-iy1OI%26h%3DAT2hq3duwK0TgamyFGpwGfXdFVwnzjio0hbbMV0kghZsLkgTmoQEC2qTOJq5d9etSs1sufzhMci5ZkgbHXGwnZFNQOGRq10viKy9bcsNcfVmbMcVs-pYbLxcgtY4GwlnwENJ0h0%26__tn__%3D-UK-R%26c%5B0%5D%3DAT0yOKaA4J_MP8vKht-A_dhqDP3hEP_Vygu41a-f4Z5wKa3uQd80YA5NtTbdmq1mVtD4wNLg4V_k1Y6dHg2dcaGQrZ5sN19swM8C0oUWc7IobWSB2kF-wjD7uhtsj2QH1y0d9BdaaELVvniBl7UST1UviF2U5BQquuGFXWIxvIR7-3rq-62c7phKFbMVVoC6ar_N-IlZT8pjhapy0xSq8g&sa=D&sntz=1&usg=AOvVaw2LI1WQU1nUuooUmNb2yNvk)[https://arxiv.org/pdf/2011.07027.pdf](https://www.google.com/url?q=https%3A%2F%2Fl.facebook.com%2Fl.php%3Fu%3Dhttps%253A%252F%252Farxiv.org%252Fpdf%252F2011.07027.pdf%253Ffbclid%253DIwAR2xbhrokYigw3yFTJhg3TI5OOKO-WQKX3n1TMRk1oBsi7Yr0ZSMO-iy1OI%26h%3DAT2hq3duwK0TgamyFGpwGfXdFVwnzjio0hbbMV0kghZsLkgTmoQEC2qTOJq5d9etSs1sufzhMci5ZkgbHXGwnZFNQOGRq10viKy9bcsNcfVmbMcVs-pYbLxcgtY4GwlnwENJ0h0%26__tn__%3D-UK-R%26c%5B0%5D%3DAT0yOKaA4J_MP8vKht-A_dhqDP3hEP_Vygu41a-f4Z5wKa3uQd80YA5NtTbdmq1mVtD4wNLg4V_k1Y6dHg2dcaGQrZ5sN19swM8C0oUWc7IobWSB2kF-wjD7uhtsj2QH1y0d9BdaaELVvniBl7UST1UviF2U5BQquuGFXWIxvIR7-3rq-62c7phKFbMVVoC6ar_N-IlZT8pjhapy0xSq8g&sa=D&sntz=1&usg=AOvVaw2LI1WQU1nUuooUmNb2yNvk) * Summary:[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.marktechpost.com%2F2020%2F11%2F18%2Fdeepmind-open-sources-lab2d-a-system-for-the-creation-of-2d-environments-for-machine-learning%2F%3Ffbclid%3DIwAR1GbPd7fe4Gk2u7UNGi7lJmlVsbB6A1wcryPAtCLe_zQKcEz0-xQZxvnRI&sa=D&sntz=1&usg=AOvVaw3lgQoAseygJ1cfWNGun9UY)[https://www.marktechpost.com/.../deepmind-open-sources.../](https://www.google.com/url?q=https%3A%2F%2Fwww.marktechpost.com%2F2020%2F11%2F18%2Fdeepmind-open-sources-lab2d-a-system-for-the-creation-of-2d-environments-for-machine-learning%2F%3Ffbclid%3DIwAR1GbPd7fe4Gk2u7UNGi7lJmlVsbB6A1wcryPAtCLe_zQKcEz0-xQZxvnRI&sa=D&sntz=1&usg=AOvVaw3lgQoAseygJ1cfWNGun9UY) Google to release DeepMind's StreetLearn for teaching machine-learning agents to navigate cities [https://www.techrepublic.com/article/google-to-release-deepminds-streetlearn- for-teaching-machine-learning-agents-to-navigate- cities/](https://www.google.com/url?q=https%3A%2F%2Fwww.techrepublic.com%2Farticle%2Fgoogle- to-release-deepminds-streetlearn-for-teaching-machine-learning-agents-to- navigate-cities%2F&sa=D&sntz=1&usg=AOvVaw0miSK2RHfMotfGPdU2nvQI) Scalable agent alignment via reward modeling – DeepMind Safety Research – Medium [https://medium.com/@deepmindsafetyresearch/scalable-agent-alignment-via- reward-modeling- bf4ab06dfd84](https://www.google.com/url?q=https%3A%2F%2Fmedium.com%2F%40deepmindsafetyresearch%2Fscalable- agent-alignment-via-reward-modeling- bf4ab06dfd84&sa=D&sntz=1&usg=AOvVaw05WoMuEIbirGGfcT9-mcuR) Google's DeepMind Can Support, Defeat Humans in Quake III Arena - ExtremeTech [https://www.extremetech.com/extreme/292409-googles-deepmind-can-support- defeat-human-players-in-quake-iii- arena](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fextreme%2F292409-googles- deepmind-can-support-defeat-human-players-in-quake-iii- arena&sa=D&sntz=1&usg=AOvVaw3d_Jg-b-40ltqOePZzgPKy) [https://www.extremetech.com/?s=deep+mind](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2F%3Fs%3Ddeep%2Bmind&sa=D&sntz=1&usg=AOvVaw3x5zHAWyaFeEzvDSAtPpV-) | You searched for deep mind - ExtremeTech[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F254017-deepmind- ai-moves-board-games-starcraft-ii&sa=D&sntz=1&usg=AOvVaw1Hqm- oXiqQLVJlKNseT5I0)[https://www.extremetech.com/gaming/254017-deepmind-ai- moves-board-games-starcraft- ii](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F254017-deepmind- ai-moves-board-games-starcraft-ii&sa=D&sntz=1&usg=AOvVaw1Hqm- oXiqQLVJlKNseT5I0) | DeepMind AI Moves on from Board Games to StarCraft II - ExtremeTech[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F284441-deepmind- ai-challenges-pro-starcraft-ii-players-wins-almost-every- match&sa=D&sntz=1&usg=AOvVaw34zt37z9JUzT0vVbdrGars)[https://www.extremetech.com/gaming/284441-deepmind- ai-challenges-pro-starcraft-ii-players-wins-almost-every- match](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F284441-deepmind- ai-challenges-pro-starcraft-ii-players-wins-almost-every- match&sa=D&sntz=1&usg=AOvVaw34zt37z9JUzT0vVbdrGars) | DeepMind AI Challenges Pro StarCraft II Players, Wins Almost Every Match - ExtremeTech[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.engadget.com%2F2016%2F11%2F18%2Fgoogle- deepmind-ai- unreal%2F&sa=D&sntz=1&usg=AOvVaw28gsTYLTDGJdtAWgt6cnzx)[https://www.engadget.com/2016/11/18/google- deepmind-ai- unreal/](https://www.google.com/url?q=https%3A%2F%2Fwww.engadget.com%2F2016%2F11%2F18%2Fgoogle- deepmind-ai-unreal%2F&sa=D&sntz=1&usg=AOvVaw28gsTYLTDGJdtAWgt6cnzx) | Google's DeepMind AI gets a few new tricks to learn faster[ ](https://www.youtube.com/results)? Robot arm **There are 4 Courses in this Specialization** **Course** 1 [ **Fundamentals of Reinforcement Learning**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Ffundamentals- of-reinforcement-learning&sa=D&sntz=1&usg=AOvVaw3PTpSRw_TOX9WayLHdHpIm) 4.8 stars 801 ratings • 205 reviews Reinforcement Learning is a subfield of Machine Learning, but is also a general purpose formalism for automated decision-making and AI. This course introduces you to statistical learning techniques where an agent explicitly takes actions and interacts with the world. Understanding the importance and challenges of learning agents that make decisions is of vital importance today, with more and more companies interested in interactive agents and intelligent decision-making. This course introduces you to the fundamentals of Reinforcement Learning. When you finish this course, you will: - Formalize problems as Markov Decision Processes - Understand basic exploration methods and the exploration/exploitation tradeoff - Understand value functions, as a general- purpose tool for optimal decision-making - Know how to implement dynamic programming as an efficient solution approach to an industrial control problem This course teaches you the key concepts of Reinforcement Learning, underlying classic and modern algorithms in RL. After completing this course, you will be able to start using RL for real problems, where you have or can specify the MDP. This is the first course of the Reinforcement Learning Specialization. [SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement- learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq) **Course** 2 [ **Sample-based Learning Methods**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fsample- based-learning-methods&sa=D&sntz=1&usg=AOvVaw2WxPVveAA-1MWiJhTop9nZ) 4.8 stars 397 ratings • 75 reviews In this course, you will learn about several algorithms that can learn near optimal policies based on trial and error interaction with the environment--- learning from the agent’s own experience. Learning from actual experience is striking because it requires no prior knowledge of the environment’s dynamics, yet can still attain optimal behavior. We will cover intuitively simple but powerful Monte Carlo methods, and temporal difference learning methods including Q-learning. We will wrap up this course investigating how we can get the best of both worlds: algorithms that can combine model-based planning (similar to dynamic programming) and temporal difference updates to radically accelerate learning. By the end of this course you will be able to: - Understand Temporal- Difference learning and Monte Carlo as two strategies for estimating value functions from sampled experience - Understand the importance of exploration, when using sampled experience rather than dynamic programming sweeps within a model - Understand the connections between Monte Carlo and Dynamic Programming and TD. - Implement and apply the TD algorithm, for estimating value functions - Implement and apply Expected Sarsa and Q-learning (two TD methods for control) - Understand the difference between on-policy and off-policy control - Understand planning with simulated experience (as opposed to classic planning strategies) - Implement a model-based approach to RL, called Dyna, which uses simulated experience - Conduct an empirical study to see the improvements in sample efficiency when using Dyna [SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement- learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq) **Course** 3 [ **Prediction and Control with Function Approximation**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fprediction- control-function-approximation&sa=D&sntz=1&usg=AOvVaw0l2Hw5t6C2PY6t-i3FKdsH) 4.8 stars 252 ratings • 40 reviews In this course, you will learn how to solve problems with large, high- dimensional, and potentially infinite state spaces. You will see that estimating value functions can be cast as a supervised learning problem--- function approximation---allowing you to build agents that carefully balance generalization and discrimination in order to maximize reward. We will begin this journey by investigating how our policy evaluation or prediction methods like Monte Carlo and TD can be extended to the function approximation setting. You will learn about feature construction techniques for RL, and representation learning via neural networks and backprop. We conclude this course with a deep-dive into policy gradient methods; a way to learn policies directly without learning a value function. In this course you will solve two continuous-state control tasks and investigate the benefits of policy gradient methods in a continuous-action environment. Prerequisites: This course strongly builds on the fundamentals of Courses 1 and 2, and learners should have completed these before starting this course. Learners should also be comfortable with probabilities & expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), and implementing algorithms from pseudocode. By the end of this course, you will be able to: -Understand how to use supervised learning approaches to approximate value functions -Understand objectives for prediction (value estimation) under function approximation -Implement TD with function approximation (state aggregation), on an environment with an infinite state space (continuous state space) -Understand fixed basis and neural network approaches to feature construction -Implement TD with neural network function approximation in a continuous state environment -Understand new difficulties in exploration when moving to function approximation -Contrast discounted problem formulations for control versus an average reward problem formulation -Implement expected Sarsa and Q-learning with function approximation on a continuous state control task -Understand objectives for directly estimating policies (policy gradient objectives) -Implement a policy gradient method (called Actor-Critic) on a discrete state environment [SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement- learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq) **Course** 4 [ **A Complete Reinforcement Learning System (Capstone)**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fcomplete- reinforcement-learning-system&sa=D&sntz=1&usg=AOvVaw1cPdyaSfUjhZu1SLxl8DIm) 4.6 stars 177 ratings • 33 reviews In this final course, you will put together your knowledge from Courses 1, 2 and 3 to implement a complete RL solution to a problem. This capstone will let you see how each component---problem formulation, algorithm selection, parameter selection and representation design---fits together into a complete solution, and how to make appropriate choices when deploying RL in the real world. This project will require you to implement both the environment to stimulate your problem, and a control agent with Neural Network function approximation. In addition, you will conduct a scientific study of your learning system to develop your ability to assess the robustness of RL agents. To use RL in the real world, it is critical to (a) appropriately formalize the problem as an MDP, (b) select appropriate algorithms, (c ) identify what choices in your implementation will have large impacts on performance and (d) validate the expected behaviour of your algorithms. This capstone is valuable for anyone who is planning on using RL to solve real problems. To be successful in this course, you will need to have completed Courses 1, 2, and 3 of this Specialization or the equivalent. By the end of this course, you will be able to: Complete an RL solution to a problem, starting from problem formulation, appropriate algorithm selection and implementation and empirical study into the effectiveness of the solution. [SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement- learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq) **Using pre trained model to train deeper and lager model** Imitation Learning Safety Gym, a suite of environments and tools for measuring progress towards reinforcement learning agents that respect safety constraints while training. It also provides a standardized method of comparing algorithms and how well they avoid costly mistakes while learning. If deep reinforcement learning is applied to the real world, whether in robotics or internet-based tasks, it will be important to have algorithms that are safe even while learning—like a self-driving car that can learn to avoid accidents without actually having to experience them. Credit: Two Minute Papers, OpenAI Follow me for more AI/ Datascience posts:[ ](https://www.google.com/url?q=https%3A%2F%2Flnkd.in%2FgZu463X&sa=D&sntz=1&usg=AOvVaw04QVerqlEJzF6ri9VsjWi4)[https://lnkd.in/gZu463X](https://www.google.com/url?q=https%3A%2F%2Flnkd.in%2FgZu463X&sa=D&sntz=1&usg=AOvVaw04QVerqlEJzF6ri9VsjWi4) [OpenAI Safety Gym: A Safe Place For AIs To Learn 💪](https://www.youtube.com/watch?v=_s7Bg6yVOdo) **DeepMind proposes novel way to train ‘safe’ reinforcement learning AI** [https://venturebeat.com/2019/12/13/deepmind-proposes-novel-way-to-train-safe- reinforcement-learning-ai/?fbclid=IwAR22JRwC48YaKLICmYQTOjuKP- cEcE4_biuCSAxFuHNqgeJIhhineg1PTIk](https://www.google.com/url?q=https%3A%2F%2Fventurebeat.com%2F2019%2F12%2F13%2Fdeepmind- proposes-novel-way-to-train-safe-reinforcement-learning- ai%2F%3Ffbclid%3DIwAR22JRwC48YaKLICmYQTOjuKP- cEcE4_biuCSAxFuHNqgeJIhhineg1PTIk&sa=D&sntz=1&usg=AOvVaw33y42wwJ7GVtU9yQ- HA8tJ) **The Batch** Issue 35 **Different Skills From Different Demos** Reinforcement learning trains models by trial and error. In batch reinforcement learning (BRL), models learn by observing many demonstrations by a variety of actors. For instance, a robot might learn how to fix ingrown toenails by watching hundreds of surgeons perform the procedure. But what if one doctor is handier with a scalpel while another excels at suturing? A new method lets models absorb the best skills from each. **What’s new:** Ajay Mandlekar and collaborators at Nvidia, Stanford, and the University of Toronto devised a BRL technique that enables models to learn different portions of a task from different examples. This way, the model can gain useful information from inconsistent examples.[ ](https://www.google.com/url?q=https%3A%2F%2Finfo.deeplearning.ai%2Fe2t%2Fc%2F*W93MmRy8ctVj4W8CrRWn19CRs_0%2F*N5ZNrfGWSg4SV- QHFT8ZG-6n0%2F5%2Ff18dQhb0Sjv48XJblHN7fK6lMHyjJqVQsN5n1pgP_0N3hHhdwVMsQMVnQ9Qq8Zy_B4W1Tdc1j55VM8QW5p680S1FW12JMsgqy7twk7pW4bJ02h4b_rKwW7MbC3k1ShBCtVsVwJ95ltllnVJ2dtV2yJF1WVYT2jk6P4lCXW3Wdv8v6Pkt_VW62_rW_5YFJKDW96dt4S4r1QvYVNHCtz8gjY6LW8WBKbV56sy_8W2NhXK38W2TXnVJd1xK2Mn6xFW2d0Xx01RMFRvW9f4wHg7HmNBdW76lKpq2sC8-JW1ysXGq6WYC_zVpjpzw6Vshk0W4hvzF86VX7kWW4cPj6g5-b0qgW5NpS3-7qrJl_W56vZfs2MJXfMW2tBw4W6wd4SGN4lBZ6Fnpt4xW6GTq088Ph58-W594SN81HkWtdW7t3MQ17y2wLbW2F6lCB1L9wgVW4J35Sn2N3p-BW5Wp1wj8_6fCxW754Rys8_bnGcW8LMdgb7jYjT_W39d2Vf6Q3Qs6MrLh9QrHMy0f2Q9pjJ02&sa=D&sntz=1&usg=AOvVaw1n2jZIG6q7VlX9dohRw2XF)[Implicit Reinforcement without Interaction at Scale](https://www.google.com/url?q=https%3A%2F%2Finfo.deeplearning.ai%2Fe2t%2Fc%2F*W93MmRy8ctVj4W8CrRWn19CRs_0%2F*N5ZNrfGWSg4SV- QHFT8ZG-6n0%2F5%2Ff18dQhb0Sjv48XJblHN7fK6lMHyjJqVQsN5n1pgP_0N3hHhdwVMsQMVnQ9Qq8Zy_B4W1Tdc1j55VM8QW5p680S1FW12JMsgqy7twk7pW4bJ02h4b_rKwW7MbC3k1ShBCtVsVwJ95ltllnVJ2dtV2yJF1WVYT2jk6P4lCXW3Wdv8v6Pkt_VW62_rW_5YFJKDW96dt4S4r1QvYVNHCtz8gjY6LW8WBKbV56sy_8W2NhXK38W2TXnVJd1xK2Mn6xFW2d0Xx01RMFRvW9f4wHg7HmNBdW76lKpq2sC8-JW1ysXGq6WYC_zVpjpzw6Vshk0W4hvzF86VX7kWW4cPj6g5-b0qgW5NpS3-7qrJl_W56vZfs2MJXfMW2tBw4W6wd4SGN4lBZ6Fnpt4xW6GTq088Ph58-W594SN81HkWtdW7t3MQ17y2wLbW2F6lCB1L9wgVW4J35Sn2N3p-BW5Wp1wj8_6fCxW754Rys8_bnGcW8LMdgb7jYjT_W39d2Vf6Q3Qs6MrLh9QrHMy0f2Q9pjJ02&sa=D&sntz=1&usg=AOvVaw1n2jZIG6q7VlX9dohRw2XF) (IRIS) achieved state-of-the-art BRL performance in three tasks performed in a virtual environment. **Key insight:** Learning from demonstrations is a double-edged sword. An agent gets to see how to complete a task, but the scope of its action is limited to the most complete demonstration of a given task. IRIS breaks down tasks into sequences of intermediate subgoals. Then it performs the actions required to accomplish each subgoal. In this way, the agent learns from the best parts of each demonstration and combines them to accomplish the task. **How it works:** IRIS includes a subgoal selection model that predicts intermediate points on the way to accomplishing an assigned task. These subgoals are defined automatically by the algorithm, and may not correspond to parts of a task as humans would describe them. A controller network tries to replicate the optimal sequence of actions leading to a given subgoal. * The subgoal selection model is made up of a conditional variational autoencoder that produces a set of possible subgoals and a value function (trained via a BRL version of Q-learning) that predicts which next subgoal will lead to the highest reward. * The controller is a recurrent neural network that decides on the actions required to accomplish the current subgoal. It learns to predict how demonstrations tend to unfold, and to imitate short sequences of actions from specific demonstrations. * Once it’s trained, the subgoal selection model determines the next subgoal. The controller takes the requisite actions. Then the subgoal selection model evaluates the current state and computes a new subgoal, and so on. **Results:** In the Robosuite's lifting and pick-and-place tasks, previous state-of-the-art BRL approaches couldn't pick up objects reliably, nor place them elsewhere at all. IRIS learned to pick up objects with over 80 percent success and placed them with 30 percent success. **Why it matters:** Automatically identifying subgoals has been a holy grail in reinforcement learning, with active research in hierarchical RL and other areas. The method used in this paper applies to relatively simple tasks where things happen in a predictable sequence (such as picking and then placing), but might be a small step in an important direction. **We’re thinking:** Batch reinforcement learning is useful when a model must be interpretable or safe — after all, a robotic surgeon shouldn’t experiment on living patients — but it hasn’t been terribly effective. IRIS could make it a viable option. Dec 11, 2019 Issue 34 **Seeing the World Blindfolded** In reinforcement learning, if researchers want an agent to have an internal representation of its environment, they’ll build and train a world model that it can refer to. New research shows that world models can emerge from standard training, rather than needing to be built separately. **What’s new:** Google Brain researchers C. Daniel Freeman, Luke Metz, and David Ha enabled an agent to build a world model by blindfolding it as it learned to accomplish tasks. They call their approach[ ](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1910.13038&sa=D&sntz=1&usg=AOvVaw19a0ZWmEG6OitJ7uRtgAiJ)[observational dropout](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1910.13038&sa=D&sntz=1&usg=AOvVaw19a0ZWmEG6OitJ7uRtgAiJ). **Key insight:** Blocking an agent's observations of the world at random moments forces it to generate its own internal representation to fill in the gaps. The agent learns this representation without being instructed to predict how the environment will change in response to its actions. **How it works:** At every timestep, the agent acts on either its observation (framed in red in the video above) or its prediction of what it wasn’t able to observe (imagery not framed in red). The agent contains a controller that decides on the most rewarding action. To compute the potential reward of a given action, the agent includes an additional deep net trained using the RL algorithm REINFORCE. * Observational dropout blocks the agent from observing the environment according to a user-defined probability. When this happens, the agent predicts an observation. * If random blindfolding blocks several observations in a row, the agent uses its most recent prediction to generate the next one. * This procedure over many iterations produces a sequence of observations and predictions. The agent learns from this sequence, and its ability to predict blocked observations is tantamount to a world model. **Results:** Observational dropout solved the task known as[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fopenai%2Fgym%2Fwiki%2FCartPole-v0&sa=D&sntz=1&usg=AOvVaw3n6wYAZyMbzRpWSK2gORJW)[Cartpole](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fopenai%2Fgym%2Fwiki%2FCartPole-v0&sa=D&sntz=1&usg=AOvVaw3n6wYAZyMbzRpWSK2gORJW), in which the model must balance a pole upright on a rolling cart, even when its view of the world was blocked 90 percent of the time. In a more complex[ ](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1809.01999&sa=D&sntz=1&usg=AOvVaw2XKaDs9JT_6xvavFR00hoz)[Car Racing](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1809.01999&sa=D&sntz=1&usg=AOvVaw2XKaDs9JT_6xvavFR00hoz) task, in which a model must navigate a car around a track as fast as possible, the model performed almost equally well whether it was allowed to see its surroundings or blindfolded up to 60 percent of the time. **Why it matters:** Modeling reality is often part art and part science. World models generated by observational dropout aren’t perfect representations, but they’re sufficient for some tasks. This work could lead to simple-but-effective world models of complex environments that are impractical to model completely. **We’re thinking:** Technology being imperfect, observational dropout is a fact of life, not just a research technique. A self-driving car or auto- piloted airplane reliant on sensors that drop data points could create a catastrophe. This technique could make high-stakes RL models more robust. Dec 4, 2019 Issue 33 **Is AI Making Mastery Obsolete?** Is there any reason to continue playing games that AI has mastered? Ask the former champions who have been toppled by machines. **What happened:** In 2016, International Go master Lee Sedol famously lost three out of four matches to DeepMind’s AlphaGo model. The 36-year-old announced his retirement from competition on November 27. “Even if I become the number one, there is an entity that cannot be defeated,” he[ ](https://www.google.com/url?q=https%3A%2F%2Fen.yna.co.kr%2Fview%2FAEN20191127004800315&sa=D&sntz=1&usg=AOvVaw0PMZN81SZTeJRnateNxsLl)[told](https://www.google.com/url?q=https%3A%2F%2Fen.yna.co.kr%2Fview%2FAEN20191127004800315&sa=D&sntz=1&usg=AOvVaw0PMZN81SZTeJRnateNxsLl) South Korean's Yonhap News Agency, **Stages of grief:** Prior to the tournament, Lee predicted that he would defeat AlphaGo easily. But the model’s inexplicable — and indefatigable — playing style pushed him into fits of[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.theverge.com%2F2017%2F10%2F11%2F16460118%2Falphago- deepmind-ai-documentary-go-lee-sedol-film- review&sa=D&sntz=1&usg=AOvVaw1BJ4ioQcgpxgNxmnVXr8kN)[shock and disbelief](https://www.google.com/url?q=https%3A%2F%2Fwww.theverge.com%2F2017%2F10%2F11%2F16460118%2Falphago- deepmind-ai-documentary-go-lee-sedol-film- review&sa=D&sntz=1&usg=AOvVaw1BJ4ioQcgpxgNxmnVXr8kN). Afterward, he[ ](https://www.google.com/url?q=https%3A%2F%2Fventurebeat.com%2F2016%2F03%2F12%2Fgo- board-game-champion-lee-sedol-apologizes-for-losing-to-googles- ai%2F&sa=D&sntz=1&usg=AOvVaw18En2cGIvD1uVmya6kwvX-)[apologized](https://www.google.com/url?q=https%3A%2F%2Fventurebeat.com%2F2016%2F03%2F12%2Fgo- board-game-champion-lee-sedol-apologizes-for-losing-to-googles- ai%2F&sa=D&sntz=1&usg=AOvVaw18En2cGIvD1uVmya6kwvX-) for his failure to the South Korean public. **Reaching acceptance:** Garry Kasparov, the former world-champion chess player, went through his own cycle of grief after being defeated by IBM’s DeepBlue in 1997. Although he didn’t retire, Kasparov did accuse IBM’s engineers of cheating. He later retracted the charge, and in 2017 wrote a book[ ](http://www.google.com/url?q=http%3A%2F%2Fwww.kasparov.com%2Fgarry- kasparov-says-ai-can-make-us-more-human-pcmag-interview- march-20th-2019%2F&sa=D&sntz=1&usg=AOvVaw3Lfe53n3SoFR4qDryUTD0P)[arguing](http://www.google.com/url?q=http%3A%2F%2Fwww.kasparov.com%2Fgarry- kasparov-says-ai-can-make-us-more-human-pcmag-interview- march-20th-2019%2F&sa=D&sntz=1&usg=AOvVaw3Lfe53n3SoFR4qDryUTD0P) that, if humans can overcome their feelings of being threatened by AI, they can learn from it. The book advocates an augmented intelligence in which humans and machines work together to solve problems. **The human element:** Although AlphaGo won in the 2016 duel, its human opponent still managed to shine. During the fourth match, Sedol made a[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.wired.com%2F2016%2F03%2Ftwo- moves-alphago-lee-sedol-redefined- future%2F&sa=D&sntz=1&usg=AOvVaw1Pt6XPLhDa8cMRMxOtI951)[move](https://www.google.com/url?q=https%3A%2F%2Fwww.wired.com%2F2016%2F03%2Ftwo- moves-alphago-lee-sedol-redefined- future%2F&sa=D&sntz=1&usg=AOvVaw1Pt6XPLhDa8cMRMxOtI951) so unconventional it defied AlphaGo’s expectation and led to his sole victory. **We’re thinking:** Lee wasn't defeated by a machine alone. He was beaten by a machine built by humans under the direction of AlphaGo research lead David Silver. Human mastery is obsolete only if you ignore people like Silver and his team. Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh4.googleusercontent.com/8sKtLRYlt7H0NnY_mX4T1p1meWrb3BIop8uoE8On8OzYvp2gPqIlrZXSelotoNJtig5cCs9eXhMevV_Clq5XtvM=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh4.googleusercontent.com/8sKtLRYlt7H0NnY_mX4T1p1meWrb3BIop8uoE8On8OzYvp2gPqIlrZXSelotoNJtig5cCs9eXhMevV_Clq5XtvM=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # My paper: A Comprehensive Review on Deep Reinforcement Learning The updates 2021 YouTube Notes and info Links: Reading List (Video, Conference, Workshop, Paper) ### The updates Dear friends, I recently wrote a survey paper on "A Comprehensive Review on Deep Reinforcement Learning: A Survey", with some of the leading AI and DRL researchers (including): In this work, we covered top recent DRL works, grouped into several categories. We were lucky to have you, as the external reviewers of this work. I hope this is useful for the research community. Any feedback will be highly welcomed. You can find its summary here too. Imitation learning, expert (teacher), hierarchical, hybrid imitation, high performance parallelism, # 2021 * [NeurIPS 2020: Key Research Papers in Reinforcement Learning and More](https://www.google.com/url?q=https%3A%2F%2Fwww.topbots.com%2Fneurips-2020-rl-research-papers%2F&sa=D&sntz=1&usg=AOvVaw3fWwOQ2N8Z8mHGNysZedTo) * [Key Papers in Deep RL](https://www.google.com/url?q=https%3A%2F%2Fspinningup.openai.com%2Fen%2Flatest%2Fspinningup%2Fkeypapers.html&sa=D&sntz=1&usg=AOvVaw3sZcgv4fIlIg3pmq6_O_q2) ### YouTube * Simple Deep Q Network w/Pytorch:[ https://youtu.be/UlJzzLYgYoE](https://youtu.be/UlJzzLYgYoE) * Reinforcement Learning Crash Course:[ https://youtu.be/sOiNMW8k4T0](https://youtu.be/sOiNMW8k4T0) * Policy Gradients w/Tensorflow:[ https://youtu.be/UT9pQjVhcaU](https://youtu.be/UT9pQjVhcaU) * Deep Q Learning w/Tensorflow[ https://youtu.be/3Ggq_zoRGP4](https://youtu.be/3Ggq_zoRGP4) * Code Your Own RL Environments[ https://youtu.be/vmrqpHldAQ0](https://youtu.be/vmrqpHldAQ0) * How to Spec a Deep Learning PC:[ https://youtu.be/xsnVlMWQj8o](https://youtu.be/xsnVlMWQj8o) * Deep Q Learning w/ Pytorch:[ https://youtu.be/RfNxXlO6BiA](https://youtu.be/RfNxXlO6BiA) * Machine Learning Freelancing[ https://youtu.be/6M04ZTLE_O4](https://youtu.be/6M04ZTLE_O4) * **Code from video** :[ https://github.com/philtabor/Youtube-Code-Repository](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fphiltabor%2FYoutube-Code-Repository&sa=D&sntz=1&usg=AOvVaw0lZg33Rz9-UZsCI8LPkgPG) ### Notes and info * training on unlabeled data, lifelong learning, and especially letting models explore a simulated environment before transferring what they learn to the real world * Lately, simulation has helped achieve impressive results in reinforcement learning, which is extremely data-intensive. * using reinforcement learning to train robots that reason about how their actions will affect their environment. * How is it that many people learn to drive a car fairly safely in 20 hours of practice, while current imitation learning algorithms take hundreds of thousands of hours, and reinforcement learning algorithms take millions of hours? Clearly we’re missing something big. * In 2021, I expect self-supervised methods to learn features of video and images. Could there be a similar revolution in high-dimensional continuous data like video? * One critical challenge is dealing with uncertainty. Models like BERT can’t tell if a missing word in a sentence is “cat” or “dog,” but they can produce a probability distribution vector. We don’t have a good model of probability distributions for images or video frames. But recent research is coming so close that we’re likely to find it soon. * Suddenly we’ll get really good performance predicting actions in videos with very few training samples, where it wasn’t possible before. That would make the coming year a very exciting time in AI. * DeepMind released the code, model, & dataset behind their groundbreaking "AlphaFold" system. It's predicts protein shapes from genomic data with apps in health, sustainability, & materials design ### Links: * [https://techgrabyte.com/google-framework-reduces-ai-training-costs-seed-rl/?fbclid=IwAR07KznmEO4nCYIMJM9Zv53HDoQEHvHH9FoUHqfPDyRvoucayu3p4nzROfU](https://www.google.com/url?q=https%3A%2F%2Ftechgrabyte.com%2Fgoogle-framework-reduces-ai-training-costs-seed-rl%2F%3Ffbclid%3DIwAR07KznmEO4nCYIMJM9Zv53HDoQEHvHH9FoUHqfPDyRvoucayu3p4nzROfU&sa=D&sntz=1&usg=AOvVaw2YNi0EWFi_liPQK9abco8U) ### Reading List (Video, Conference, Workshop, Paper) * [https://sites.google.com/view/icml19metalearning](https://www.google.com/url?q=https%3A%2F%2Fsites.google.com%2Fview%2Ficml19metalearning&sa=D&sntz=1&usg=AOvVaw0qCxTbeQF-J_3tGBq--FD4) DeepMind Open-Sources Lab2D, A System For The Creation Of 2D Environments For Machine Learning * Github:[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fdeepmind%2Flab2d%3Ffbclid%3DIwAR2axHHCby_-GbXC_VgpytDoJkAq-dxPd2JZ4vOOIEvxUqK_hakvvfo56Zk&sa=D&sntz=1&usg=AOvVaw0uAYAMc2ud2QYDJ68E0Mre)[https://github.com/deepmind/lab2d](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fdeepmind%2Flab2d%3Ffbclid%3DIwAR2axHHCby_-GbXC_VgpytDoJkAq-dxPd2JZ4vOOIEvxUqK_hakvvfo56Zk&sa=D&sntz=1&usg=AOvVaw0uAYAMc2ud2QYDJ68E0Mre) * Paper:[ ](https://www.google.com/url?q=https%3A%2F%2Fl.facebook.com%2Fl.php%3Fu%3Dhttps%253A%252F%252Farxiv.org%252Fpdf%252F2011.07027.pdf%253Ffbclid%253DIwAR2xbhrokYigw3yFTJhg3TI5OOKO-WQKX3n1TMRk1oBsi7Yr0ZSMO-iy1OI%26h%3DAT2hq3duwK0TgamyFGpwGfXdFVwnzjio0hbbMV0kghZsLkgTmoQEC2qTOJq5d9etSs1sufzhMci5ZkgbHXGwnZFNQOGRq10viKy9bcsNcfVmbMcVs-pYbLxcgtY4GwlnwENJ0h0%26__tn__%3D-UK-R%26c%5B0%5D%3DAT0yOKaA4J_MP8vKht-A_dhqDP3hEP_Vygu41a-f4Z5wKa3uQd80YA5NtTbdmq1mVtD4wNLg4V_k1Y6dHg2dcaGQrZ5sN19swM8C0oUWc7IobWSB2kF-wjD7uhtsj2QH1y0d9BdaaELVvniBl7UST1UviF2U5BQquuGFXWIxvIR7-3rq-62c7phKFbMVVoC6ar_N-IlZT8pjhapy0xSq8g&sa=D&sntz=1&usg=AOvVaw2LI1WQU1nUuooUmNb2yNvk)[https://arxiv.org/pdf/2011.07027.pdf](https://www.google.com/url?q=https%3A%2F%2Fl.facebook.com%2Fl.php%3Fu%3Dhttps%253A%252F%252Farxiv.org%252Fpdf%252F2011.07027.pdf%253Ffbclid%253DIwAR2xbhrokYigw3yFTJhg3TI5OOKO-WQKX3n1TMRk1oBsi7Yr0ZSMO-iy1OI%26h%3DAT2hq3duwK0TgamyFGpwGfXdFVwnzjio0hbbMV0kghZsLkgTmoQEC2qTOJq5d9etSs1sufzhMci5ZkgbHXGwnZFNQOGRq10viKy9bcsNcfVmbMcVs-pYbLxcgtY4GwlnwENJ0h0%26__tn__%3D-UK-R%26c%5B0%5D%3DAT0yOKaA4J_MP8vKht-A_dhqDP3hEP_Vygu41a-f4Z5wKa3uQd80YA5NtTbdmq1mVtD4wNLg4V_k1Y6dHg2dcaGQrZ5sN19swM8C0oUWc7IobWSB2kF-wjD7uhtsj2QH1y0d9BdaaELVvniBl7UST1UviF2U5BQquuGFXWIxvIR7-3rq-62c7phKFbMVVoC6ar_N-IlZT8pjhapy0xSq8g&sa=D&sntz=1&usg=AOvVaw2LI1WQU1nUuooUmNb2yNvk) * Summary:[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.marktechpost.com%2F2020%2F11%2F18%2Fdeepmind-open-sources-lab2d-a-system-for-the-creation-of-2d-environments-for-machine-learning%2F%3Ffbclid%3DIwAR1GbPd7fe4Gk2u7UNGi7lJmlVsbB6A1wcryPAtCLe_zQKcEz0-xQZxvnRI&sa=D&sntz=1&usg=AOvVaw3lgQoAseygJ1cfWNGun9UY)[https://www.marktechpost.com/.../deepmind-open-sources.../](https://www.google.com/url?q=https%3A%2F%2Fwww.marktechpost.com%2F2020%2F11%2F18%2Fdeepmind-open-sources-lab2d-a-system-for-the-creation-of-2d-environments-for-machine-learning%2F%3Ffbclid%3DIwAR1GbPd7fe4Gk2u7UNGi7lJmlVsbB6A1wcryPAtCLe_zQKcEz0-xQZxvnRI&sa=D&sntz=1&usg=AOvVaw3lgQoAseygJ1cfWNGun9UY) Google to release DeepMind's StreetLearn for teaching machine-learning agents to navigate cities [https://www.techrepublic.com/article/google-to-release-deepminds-streetlearn- for-teaching-machine-learning-agents-to-navigate- cities/](https://www.google.com/url?q=https%3A%2F%2Fwww.techrepublic.com%2Farticle%2Fgoogle- to-release-deepminds-streetlearn-for-teaching-machine-learning-agents-to- navigate-cities%2F&sa=D&sntz=1&usg=AOvVaw0miSK2RHfMotfGPdU2nvQI) Scalable agent alignment via reward modeling – DeepMind Safety Research – Medium [https://medium.com/@deepmindsafetyresearch/scalable-agent-alignment-via- reward-modeling- bf4ab06dfd84](https://www.google.com/url?q=https%3A%2F%2Fmedium.com%2F%40deepmindsafetyresearch%2Fscalable- agent-alignment-via-reward-modeling- bf4ab06dfd84&sa=D&sntz=1&usg=AOvVaw05WoMuEIbirGGfcT9-mcuR) Google's DeepMind Can Support, Defeat Humans in Quake III Arena - ExtremeTech [https://www.extremetech.com/extreme/292409-googles-deepmind-can-support- defeat-human-players-in-quake-iii- arena](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fextreme%2F292409-googles- deepmind-can-support-defeat-human-players-in-quake-iii- arena&sa=D&sntz=1&usg=AOvVaw3d_Jg-b-40ltqOePZzgPKy) [https://www.extremetech.com/?s=deep+mind](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2F%3Fs%3Ddeep%2Bmind&sa=D&sntz=1&usg=AOvVaw3x5zHAWyaFeEzvDSAtPpV-) | You searched for deep mind - ExtremeTech[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F254017-deepmind- ai-moves-board-games-starcraft-ii&sa=D&sntz=1&usg=AOvVaw1Hqm- oXiqQLVJlKNseT5I0)[https://www.extremetech.com/gaming/254017-deepmind-ai- moves-board-games-starcraft- ii](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F254017-deepmind- ai-moves-board-games-starcraft-ii&sa=D&sntz=1&usg=AOvVaw1Hqm- oXiqQLVJlKNseT5I0) | DeepMind AI Moves on from Board Games to StarCraft II - ExtremeTech[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F284441-deepmind- ai-challenges-pro-starcraft-ii-players-wins-almost-every- match&sa=D&sntz=1&usg=AOvVaw34zt37z9JUzT0vVbdrGars)[https://www.extremetech.com/gaming/284441-deepmind- ai-challenges-pro-starcraft-ii-players-wins-almost-every- match](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F284441-deepmind- ai-challenges-pro-starcraft-ii-players-wins-almost-every- match&sa=D&sntz=1&usg=AOvVaw34zt37z9JUzT0vVbdrGars) | DeepMind AI Challenges Pro StarCraft II Players, Wins Almost Every Match - ExtremeTech[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.engadget.com%2F2016%2F11%2F18%2Fgoogle- deepmind-ai- unreal%2F&sa=D&sntz=1&usg=AOvVaw28gsTYLTDGJdtAWgt6cnzx)[https://www.engadget.com/2016/11/18/google- deepmind-ai- unreal/](https://www.google.com/url?q=https%3A%2F%2Fwww.engadget.com%2F2016%2F11%2F18%2Fgoogle- deepmind-ai-unreal%2F&sa=D&sntz=1&usg=AOvVaw28gsTYLTDGJdtAWgt6cnzx) | Google's DeepMind AI gets a few new tricks to learn faster[ ](https://www.youtube.com/results)? Robot arm **There are 4 Courses in this Specialization** **Course** 1 [ **Fundamentals of Reinforcement Learning**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Ffundamentals- of-reinforcement-learning&sa=D&sntz=1&usg=AOvVaw3PTpSRw_TOX9WayLHdHpIm) 4.8 stars 801 ratings • 205 reviews Reinforcement Learning is a subfield of Machine Learning, but is also a general purpose formalism for automated decision-making and AI. This course introduces you to statistical learning techniques where an agent explicitly takes actions and interacts with the world. Understanding the importance and challenges of learning agents that make decisions is of vital importance today, with more and more companies interested in interactive agents and intelligent decision-making. This course introduces you to the fundamentals of Reinforcement Learning. When you finish this course, you will: - Formalize problems as Markov Decision Processes - Understand basic exploration methods and the exploration/exploitation tradeoff - Understand value functions, as a general- purpose tool for optimal decision-making - Know how to implement dynamic programming as an efficient solution approach to an industrial control problem This course teaches you the key concepts of Reinforcement Learning, underlying classic and modern algorithms in RL. After completing this course, you will be able to start using RL for real problems, where you have or can specify the MDP. This is the first course of the Reinforcement Learning Specialization. [SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement- learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq) **Course** 2 [ **Sample-based Learning Methods**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fsample- based-learning-methods&sa=D&sntz=1&usg=AOvVaw2WxPVveAA-1MWiJhTop9nZ) 4.8 stars 397 ratings • 75 reviews In this course, you will learn about several algorithms that can learn near optimal policies based on trial and error interaction with the environment--- learning from the agent’s own experience. Learning from actual experience is striking because it requires no prior knowledge of the environment’s dynamics, yet can still attain optimal behavior. We will cover intuitively simple but powerful Monte Carlo methods, and temporal difference learning methods including Q-learning. We will wrap up this course investigating how we can get the best of both worlds: algorithms that can combine model-based planning (similar to dynamic programming) and temporal difference updates to radically accelerate learning. By the end of this course you will be able to: - Understand Temporal- Difference learning and Monte Carlo as two strategies for estimating value functions from sampled experience - Understand the importance of exploration, when using sampled experience rather than dynamic programming sweeps within a model - Understand the connections between Monte Carlo and Dynamic Programming and TD. - Implement and apply the TD algorithm, for estimating value functions - Implement and apply Expected Sarsa and Q-learning (two TD methods for control) - Understand the difference between on-policy and off-policy control - Understand planning with simulated experience (as opposed to classic planning strategies) - Implement a model-based approach to RL, called Dyna, which uses simulated experience - Conduct an empirical study to see the improvements in sample efficiency when using Dyna [SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement- learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq) **Course** 3 [ **Prediction and Control with Function Approximation**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fprediction- control-function-approximation&sa=D&sntz=1&usg=AOvVaw0l2Hw5t6C2PY6t-i3FKdsH) 4.8 stars 252 ratings • 40 reviews In this course, you will learn how to solve problems with large, high- dimensional, and potentially infinite state spaces. You will see that estimating value functions can be cast as a supervised learning problem--- function approximation---allowing you to build agents that carefully balance generalization and discrimination in order to maximize reward. We will begin this journey by investigating how our policy evaluation or prediction methods like Monte Carlo and TD can be extended to the function approximation setting. You will learn about feature construction techniques for RL, and representation learning via neural networks and backprop. We conclude this course with a deep-dive into policy gradient methods; a way to learn policies directly without learning a value function. In this course you will solve two continuous-state control tasks and investigate the benefits of policy gradient methods in a continuous-action environment. Prerequisites: This course strongly builds on the fundamentals of Courses 1 and 2, and learners should have completed these before starting this course. Learners should also be comfortable with probabilities & expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), and implementing algorithms from pseudocode. By the end of this course, you will be able to: -Understand how to use supervised learning approaches to approximate value functions -Understand objectives for prediction (value estimation) under function approximation -Implement TD with function approximation (state aggregation), on an environment with an infinite state space (continuous state space) -Understand fixed basis and neural network approaches to feature construction -Implement TD with neural network function approximation in a continuous state environment -Understand new difficulties in exploration when moving to function approximation -Contrast discounted problem formulations for control versus an average reward problem formulation -Implement expected Sarsa and Q-learning with function approximation on a continuous state control task -Understand objectives for directly estimating policies (policy gradient objectives) -Implement a policy gradient method (called Actor-Critic) on a discrete state environment [SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement- learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq) **Course** 4 [ **A Complete Reinforcement Learning System (Capstone)**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fcomplete- reinforcement-learning-system&sa=D&sntz=1&usg=AOvVaw1cPdyaSfUjhZu1SLxl8DIm) 4.6 stars 177 ratings • 33 reviews In this final course, you will put together your knowledge from Courses 1, 2 and 3 to implement a complete RL solution to a problem. This capstone will let you see how each component---problem formulation, algorithm selection, parameter selection and representation design---fits together into a complete solution, and how to make appropriate choices when deploying RL in the real world. This project will require you to implement both the environment to stimulate your problem, and a control agent with Neural Network function approximation. In addition, you will conduct a scientific study of your learning system to develop your ability to assess the robustness of RL agents. To use RL in the real world, it is critical to (a) appropriately formalize the problem as an MDP, (b) select appropriate algorithms, (c ) identify what choices in your implementation will have large impacts on performance and (d) validate the expected behaviour of your algorithms. This capstone is valuable for anyone who is planning on using RL to solve real problems. To be successful in this course, you will need to have completed Courses 1, 2, and 3 of this Specialization or the equivalent. By the end of this course, you will be able to: Complete an RL solution to a problem, starting from problem formulation, appropriate algorithm selection and implementation and empirical study into the effectiveness of the solution. [SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement- learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq) **Using pre trained model to train deeper and lager model** Imitation Learning Safety Gym, a suite of environments and tools for measuring progress towards reinforcement learning agents that respect safety constraints while training. It also provides a standardized method of comparing algorithms and how well they avoid costly mistakes while learning. If deep reinforcement learning is applied to the real world, whether in robotics or internet-based tasks, it will be important to have algorithms that are safe even while learning—like a self-driving car that can learn to avoid accidents without actually having to experience them. Credit: Two Minute Papers, OpenAI Follow me for more AI/ Datascience posts:[ ](https://www.google.com/url?q=https%3A%2F%2Flnkd.in%2FgZu463X&sa=D&sntz=1&usg=AOvVaw04QVerqlEJzF6ri9VsjWi4)[https://lnkd.in/gZu463X](https://www.google.com/url?q=https%3A%2F%2Flnkd.in%2FgZu463X&sa=D&sntz=1&usg=AOvVaw04QVerqlEJzF6ri9VsjWi4) [OpenAI Safety Gym: A Safe Place For AIs To Learn 💪](https://www.youtube.com/watch?v=_s7Bg6yVOdo) **DeepMind proposes novel way to train ‘safe’ reinforcement learning AI** [https://venturebeat.com/2019/12/13/deepmind-proposes-novel-way-to-train-safe- reinforcement-learning-ai/?fbclid=IwAR22JRwC48YaKLICmYQTOjuKP- cEcE4_biuCSAxFuHNqgeJIhhineg1PTIk](https://www.google.com/url?q=https%3A%2F%2Fventurebeat.com%2F2019%2F12%2F13%2Fdeepmind- proposes-novel-way-to-train-safe-reinforcement-learning- ai%2F%3Ffbclid%3DIwAR22JRwC48YaKLICmYQTOjuKP- cEcE4_biuCSAxFuHNqgeJIhhineg1PTIk&sa=D&sntz=1&usg=AOvVaw33y42wwJ7GVtU9yQ- HA8tJ) **The Batch** Issue 35 **Different Skills From Different Demos** Reinforcement learning trains models by trial and error. In batch reinforcement learning (BRL), models learn by observing many demonstrations by a variety of actors. For instance, a robot might learn how to fix ingrown toenails by watching hundreds of surgeons perform the procedure. But what if one doctor is handier with a scalpel while another excels at suturing? A new method lets models absorb the best skills from each. **What’s new:** Ajay Mandlekar and collaborators at Nvidia, Stanford, and the University of Toronto devised a BRL technique that enables models to learn different portions of a task from different examples. This way, the model can gain useful information from inconsistent examples.[ ](https://www.google.com/url?q=https%3A%2F%2Finfo.deeplearning.ai%2Fe2t%2Fc%2F*W93MmRy8ctVj4W8CrRWn19CRs_0%2F*N5ZNrfGWSg4SV- QHFT8ZG-6n0%2F5%2Ff18dQhb0Sjv48XJblHN7fK6lMHyjJqVQsN5n1pgP_0N3hHhdwVMsQMVnQ9Qq8Zy_B4W1Tdc1j55VM8QW5p680S1FW12JMsgqy7twk7pW4bJ02h4b_rKwW7MbC3k1ShBCtVsVwJ95ltllnVJ2dtV2yJF1WVYT2jk6P4lCXW3Wdv8v6Pkt_VW62_rW_5YFJKDW96dt4S4r1QvYVNHCtz8gjY6LW8WBKbV56sy_8W2NhXK38W2TXnVJd1xK2Mn6xFW2d0Xx01RMFRvW9f4wHg7HmNBdW76lKpq2sC8-JW1ysXGq6WYC_zVpjpzw6Vshk0W4hvzF86VX7kWW4cPj6g5-b0qgW5NpS3-7qrJl_W56vZfs2MJXfMW2tBw4W6wd4SGN4lBZ6Fnpt4xW6GTq088Ph58-W594SN81HkWtdW7t3MQ17y2wLbW2F6lCB1L9wgVW4J35Sn2N3p-BW5Wp1wj8_6fCxW754Rys8_bnGcW8LMdgb7jYjT_W39d2Vf6Q3Qs6MrLh9QrHMy0f2Q9pjJ02&sa=D&sntz=1&usg=AOvVaw1n2jZIG6q7VlX9dohRw2XF)[Implicit Reinforcement without Interaction at Scale](https://www.google.com/url?q=https%3A%2F%2Finfo.deeplearning.ai%2Fe2t%2Fc%2F*W93MmRy8ctVj4W8CrRWn19CRs_0%2F*N5ZNrfGWSg4SV- QHFT8ZG-6n0%2F5%2Ff18dQhb0Sjv48XJblHN7fK6lMHyjJqVQsN5n1pgP_0N3hHhdwVMsQMVnQ9Qq8Zy_B4W1Tdc1j55VM8QW5p680S1FW12JMsgqy7twk7pW4bJ02h4b_rKwW7MbC3k1ShBCtVsVwJ95ltllnVJ2dtV2yJF1WVYT2jk6P4lCXW3Wdv8v6Pkt_VW62_rW_5YFJKDW96dt4S4r1QvYVNHCtz8gjY6LW8WBKbV56sy_8W2NhXK38W2TXnVJd1xK2Mn6xFW2d0Xx01RMFRvW9f4wHg7HmNBdW76lKpq2sC8-JW1ysXGq6WYC_zVpjpzw6Vshk0W4hvzF86VX7kWW4cPj6g5-b0qgW5NpS3-7qrJl_W56vZfs2MJXfMW2tBw4W6wd4SGN4lBZ6Fnpt4xW6GTq088Ph58-W594SN81HkWtdW7t3MQ17y2wLbW2F6lCB1L9wgVW4J35Sn2N3p-BW5Wp1wj8_6fCxW754Rys8_bnGcW8LMdgb7jYjT_W39d2Vf6Q3Qs6MrLh9QrHMy0f2Q9pjJ02&sa=D&sntz=1&usg=AOvVaw1n2jZIG6q7VlX9dohRw2XF) (IRIS) achieved state-of-the-art BRL performance in three tasks performed in a virtual environment. **Key insight:** Learning from demonstrations is a double-edged sword. An agent gets to see how to complete a task, but the scope of its action is limited to the most complete demonstration of a given task. IRIS breaks down tasks into sequences of intermediate subgoals. Then it performs the actions required to accomplish each subgoal. In this way, the agent learns from the best parts of each demonstration and combines them to accomplish the task. **How it works:** IRIS includes a subgoal selection model that predicts intermediate points on the way to accomplishing an assigned task. These subgoals are defined automatically by the algorithm, and may not correspond to parts of a task as humans would describe them. A controller network tries to replicate the optimal sequence of actions leading to a given subgoal. * The subgoal selection model is made up of a conditional variational autoencoder that produces a set of possible subgoals and a value function (trained via a BRL version of Q-learning) that predicts which next subgoal will lead to the highest reward. * The controller is a recurrent neural network that decides on the actions required to accomplish the current subgoal. It learns to predict how demonstrations tend to unfold, and to imitate short sequences of actions from specific demonstrations. * Once it’s trained, the subgoal selection model determines the next subgoal. The controller takes the requisite actions. Then the subgoal selection model evaluates the current state and computes a new subgoal, and so on. **Results:** In the Robosuite's lifting and pick-and-place tasks, previous state-of-the-art BRL approaches couldn't pick up objects reliably, nor place them elsewhere at all. IRIS learned to pick up objects with over 80 percent success and placed them with 30 percent success. **Why it matters:** Automatically identifying subgoals has been a holy grail in reinforcement learning, with active research in hierarchical RL and other areas. The method used in this paper applies to relatively simple tasks where things happen in a predictable sequence (such as picking and then placing), but might be a small step in an important direction. **We’re thinking:** Batch reinforcement learning is useful when a model must be interpretable or safe — after all, a robotic surgeon shouldn’t experiment on living patients — but it hasn’t been terribly effective. IRIS could make it a viable option. Dec 11, 2019 Issue 34 **Seeing the World Blindfolded** In reinforcement learning, if researchers want an agent to have an internal representation of its environment, they’ll build and train a world model that it can refer to. New research shows that world models can emerge from standard training, rather than needing to be built separately. **What’s new:** Google Brain researchers C. Daniel Freeman, Luke Metz, and David Ha enabled an agent to build a world model by blindfolding it as it learned to accomplish tasks. They call their approach[ ](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1910.13038&sa=D&sntz=1&usg=AOvVaw19a0ZWmEG6OitJ7uRtgAiJ)[observational dropout](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1910.13038&sa=D&sntz=1&usg=AOvVaw19a0ZWmEG6OitJ7uRtgAiJ). **Key insight:** Blocking an agent's observations of the world at random moments forces it to generate its own internal representation to fill in the gaps. The agent learns this representation without being instructed to predict how the environment will change in response to its actions. **How it works:** At every timestep, the agent acts on either its observation (framed in red in the video above) or its prediction of what it wasn’t able to observe (imagery not framed in red). The agent contains a controller that decides on the most rewarding action. To compute the potential reward of a given action, the agent includes an additional deep net trained using the RL algorithm REINFORCE. * Observational dropout blocks the agent from observing the environment according to a user-defined probability. When this happens, the agent predicts an observation. * If random blindfolding blocks several observations in a row, the agent uses its most recent prediction to generate the next one. * This procedure over many iterations produces a sequence of observations and predictions. The agent learns from this sequence, and its ability to predict blocked observations is tantamount to a world model. **Results:** Observational dropout solved the task known as[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fopenai%2Fgym%2Fwiki%2FCartPole-v0&sa=D&sntz=1&usg=AOvVaw3n6wYAZyMbzRpWSK2gORJW)[Cartpole](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fopenai%2Fgym%2Fwiki%2FCartPole-v0&sa=D&sntz=1&usg=AOvVaw3n6wYAZyMbzRpWSK2gORJW), in which the model must balance a pole upright on a rolling cart, even when its view of the world was blocked 90 percent of the time. In a more complex[ ](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1809.01999&sa=D&sntz=1&usg=AOvVaw2XKaDs9JT_6xvavFR00hoz)[Car Racing](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1809.01999&sa=D&sntz=1&usg=AOvVaw2XKaDs9JT_6xvavFR00hoz) task, in which a model must navigate a car around a track as fast as possible, the model performed almost equally well whether it was allowed to see its surroundings or blindfolded up to 60 percent of the time. **Why it matters:** Modeling reality is often part art and part science. World models generated by observational dropout aren’t perfect representations, but they’re sufficient for some tasks. This work could lead to simple-but-effective world models of complex environments that are impractical to model completely. **We’re thinking:** Technology being imperfect, observational dropout is a fact of life, not just a research technique. A self-driving car or auto- piloted airplane reliant on sensors that drop data points could create a catastrophe. This technique could make high-stakes RL models more robust. Dec 4, 2019 Issue 33 **Is AI Making Mastery Obsolete?** Is there any reason to continue playing games that AI has mastered? Ask the former champions who have been toppled by machines. **What happened:** In 2016, International Go master Lee Sedol famously lost three out of four matches to DeepMind’s AlphaGo model. The 36-year-old announced his retirement from competition on November 27. “Even if I become the number one, there is an entity that cannot be defeated,” he[ ](https://www.google.com/url?q=https%3A%2F%2Fen.yna.co.kr%2Fview%2FAEN20191127004800315&sa=D&sntz=1&usg=AOvVaw0PMZN81SZTeJRnateNxsLl)[told](https://www.google.com/url?q=https%3A%2F%2Fen.yna.co.kr%2Fview%2FAEN20191127004800315&sa=D&sntz=1&usg=AOvVaw0PMZN81SZTeJRnateNxsLl) South Korean's Yonhap News Agency, **Stages of grief:** Prior to the tournament, Lee predicted that he would defeat AlphaGo easily. But the model’s inexplicable — and indefatigable — playing style pushed him into fits of[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.theverge.com%2F2017%2F10%2F11%2F16460118%2Falphago- deepmind-ai-documentary-go-lee-sedol-film- review&sa=D&sntz=1&usg=AOvVaw1BJ4ioQcgpxgNxmnVXr8kN)[shock and disbelief](https://www.google.com/url?q=https%3A%2F%2Fwww.theverge.com%2F2017%2F10%2F11%2F16460118%2Falphago- deepmind-ai-documentary-go-lee-sedol-film- review&sa=D&sntz=1&usg=AOvVaw1BJ4ioQcgpxgNxmnVXr8kN). Afterward, he[ ](https://www.google.com/url?q=https%3A%2F%2Fventurebeat.com%2F2016%2F03%2F12%2Fgo- board-game-champion-lee-sedol-apologizes-for-losing-to-googles- ai%2F&sa=D&sntz=1&usg=AOvVaw18En2cGIvD1uVmya6kwvX-)[apologized](https://www.google.com/url?q=https%3A%2F%2Fventurebeat.com%2F2016%2F03%2F12%2Fgo- board-game-champion-lee-sedol-apologizes-for-losing-to-googles- ai%2F&sa=D&sntz=1&usg=AOvVaw18En2cGIvD1uVmya6kwvX-) for his failure to the South Korean public. **Reaching acceptance:** Garry Kasparov, the former world-champion chess player, went through his own cycle of grief after being defeated by IBM’s DeepBlue in 1997. Although he didn’t retire, Kasparov did accuse IBM’s engineers of cheating. He later retracted the charge, and in 2017 wrote a book[ ](http://www.google.com/url?q=http%3A%2F%2Fwww.kasparov.com%2Fgarry- kasparov-says-ai-can-make-us-more-human-pcmag-interview- march-20th-2019%2F&sa=D&sntz=1&usg=AOvVaw3Lfe53n3SoFR4qDryUTD0P)[arguing](http://www.google.com/url?q=http%3A%2F%2Fwww.kasparov.com%2Fgarry- kasparov-says-ai-can-make-us-more-human-pcmag-interview- march-20th-2019%2F&sa=D&sntz=1&usg=AOvVaw3Lfe53n3SoFR4qDryUTD0P) that, if humans can overcome their feelings of being threatened by AI, they can learn from it. The book advocates an augmented intelligence in which humans and machines work together to solve problems. **The human element:** Although AlphaGo won in the 2016 duel, its human opponent still managed to shine. During the fourth match, Sedol made a[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.wired.com%2F2016%2F03%2Ftwo- moves-alphago-lee-sedol-redefined- future%2F&sa=D&sntz=1&usg=AOvVaw1Pt6XPLhDa8cMRMxOtI951)[move](https://www.google.com/url?q=https%3A%2F%2Fwww.wired.com%2F2016%2F03%2Ftwo- moves-alphago-lee-sedol-redefined- future%2F&sa=D&sntz=1&usg=AOvVaw1Pt6XPLhDa8cMRMxOtI951) so unconventional it defied AlphaGo’s expectation and led to his sole victory. **We’re thinking:** Lee wasn't defeated by a machine alone. He was beaten by a machine built by humans under the direction of AlphaGo research lead David Silver. Human mastery is obsolete only if you ignore people like Silver and his team. Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh4.googleusercontent.com/8sKtLRYlt7H0NnY_mX4T1p1meWrb3BIop8uoE8On8OzYvp2gPqIlrZXSelotoNJtig5cCs9eXhMevV_Clq5XtvM=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh4.googleusercontent.com/8sKtLRYlt7H0NnY_mX4T1p1meWrb3BIop8uoE8On8OzYvp2gPqIlrZXSelotoNJtig5cCs9eXhMevV_Clq5XtvM=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # My paper: A Comprehensive Review on Deep Reinforcement Learning The updates 2021 YouTube Notes and info Links: Reading List (Video, Conference, Workshop, Paper) ### The updates Dear friends, I recently wrote a survey paper on "A Comprehensive Review on Deep Reinforcement Learning: A Survey", with some of the leading AI and DRL researchers (including): In this work, we covered top recent DRL works, grouped into several categories. We were lucky to have you, as the external reviewers of this work. I hope this is useful for the research community. Any feedback will be highly welcomed. You can find its summary here too. Imitation learning, expert (teacher), hierarchical, hybrid imitation, high performance parallelism, # 2021 * [NeurIPS 2020: Key Research Papers in Reinforcement Learning and More](https://www.google.com/url?q=https%3A%2F%2Fwww.topbots.com%2Fneurips-2020-rl-research-papers%2F&sa=D&sntz=1&usg=AOvVaw3fWwOQ2N8Z8mHGNysZedTo) * [Key Papers in Deep RL](https://www.google.com/url?q=https%3A%2F%2Fspinningup.openai.com%2Fen%2Flatest%2Fspinningup%2Fkeypapers.html&sa=D&sntz=1&usg=AOvVaw3sZcgv4fIlIg3pmq6_O_q2) ### YouTube * Simple Deep Q Network w/Pytorch:[ https://youtu.be/UlJzzLYgYoE](https://youtu.be/UlJzzLYgYoE) * Reinforcement Learning Crash Course:[ https://youtu.be/sOiNMW8k4T0](https://youtu.be/sOiNMW8k4T0) * Policy Gradients w/Tensorflow:[ https://youtu.be/UT9pQjVhcaU](https://youtu.be/UT9pQjVhcaU) * Deep Q Learning w/Tensorflow[ https://youtu.be/3Ggq_zoRGP4](https://youtu.be/3Ggq_zoRGP4) * Code Your Own RL Environments[ https://youtu.be/vmrqpHldAQ0](https://youtu.be/vmrqpHldAQ0) * How to Spec a Deep Learning PC:[ https://youtu.be/xsnVlMWQj8o](https://youtu.be/xsnVlMWQj8o) * Deep Q Learning w/ Pytorch:[ https://youtu.be/RfNxXlO6BiA](https://youtu.be/RfNxXlO6BiA) * Machine Learning Freelancing[ https://youtu.be/6M04ZTLE_O4](https://youtu.be/6M04ZTLE_O4) * **Code from video** :[ https://github.com/philtabor/Youtube-Code-Repository](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fphiltabor%2FYoutube-Code-Repository&sa=D&sntz=1&usg=AOvVaw0lZg33Rz9-UZsCI8LPkgPG) ### Notes and info * training on unlabeled data, lifelong learning, and especially letting models explore a simulated environment before transferring what they learn to the real world * Lately, simulation has helped achieve impressive results in reinforcement learning, which is extremely data-intensive. * using reinforcement learning to train robots that reason about how their actions will affect their environment. * How is it that many people learn to drive a car fairly safely in 20 hours of practice, while current imitation learning algorithms take hundreds of thousands of hours, and reinforcement learning algorithms take millions of hours? Clearly we’re missing something big. * In 2021, I expect self-supervised methods to learn features of video and images. Could there be a similar revolution in high-dimensional continuous data like video? * One critical challenge is dealing with uncertainty. Models like BERT can’t tell if a missing word in a sentence is “cat” or “dog,” but they can produce a probability distribution vector. We don’t have a good model of probability distributions for images or video frames. But recent research is coming so close that we’re likely to find it soon. * Suddenly we’ll get really good performance predicting actions in videos with very few training samples, where it wasn’t possible before. That would make the coming year a very exciting time in AI. * DeepMind released the code, model, & dataset behind their groundbreaking "AlphaFold" system. It's predicts protein shapes from genomic data with apps in health, sustainability, & materials design ### Links: * [https://techgrabyte.com/google-framework-reduces-ai-training-costs-seed-rl/?fbclid=IwAR07KznmEO4nCYIMJM9Zv53HDoQEHvHH9FoUHqfPDyRvoucayu3p4nzROfU](https://www.google.com/url?q=https%3A%2F%2Ftechgrabyte.com%2Fgoogle-framework-reduces-ai-training-costs-seed-rl%2F%3Ffbclid%3DIwAR07KznmEO4nCYIMJM9Zv53HDoQEHvHH9FoUHqfPDyRvoucayu3p4nzROfU&sa=D&sntz=1&usg=AOvVaw2YNi0EWFi_liPQK9abco8U) ### Reading List (Video, Conference, Workshop, Paper) * [https://sites.google.com/view/icml19metalearning](https://www.google.com/url?q=https%3A%2F%2Fsites.google.com%2Fview%2Ficml19metalearning&sa=D&sntz=1&usg=AOvVaw0qCxTbeQF-J_3tGBq--FD4) DeepMind Open-Sources Lab2D, A System For The Creation Of 2D Environments For Machine Learning * Github:[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fdeepmind%2Flab2d%3Ffbclid%3DIwAR2axHHCby_-GbXC_VgpytDoJkAq-dxPd2JZ4vOOIEvxUqK_hakvvfo56Zk&sa=D&sntz=1&usg=AOvVaw0uAYAMc2ud2QYDJ68E0Mre)[https://github.com/deepmind/lab2d](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fdeepmind%2Flab2d%3Ffbclid%3DIwAR2axHHCby_-GbXC_VgpytDoJkAq-dxPd2JZ4vOOIEvxUqK_hakvvfo56Zk&sa=D&sntz=1&usg=AOvVaw0uAYAMc2ud2QYDJ68E0Mre) * Paper:[ ](https://www.google.com/url?q=https%3A%2F%2Fl.facebook.com%2Fl.php%3Fu%3Dhttps%253A%252F%252Farxiv.org%252Fpdf%252F2011.07027.pdf%253Ffbclid%253DIwAR2xbhrokYigw3yFTJhg3TI5OOKO-WQKX3n1TMRk1oBsi7Yr0ZSMO-iy1OI%26h%3DAT2hq3duwK0TgamyFGpwGfXdFVwnzjio0hbbMV0kghZsLkgTmoQEC2qTOJq5d9etSs1sufzhMci5ZkgbHXGwnZFNQOGRq10viKy9bcsNcfVmbMcVs-pYbLxcgtY4GwlnwENJ0h0%26__tn__%3D-UK-R%26c%5B0%5D%3DAT0yOKaA4J_MP8vKht-A_dhqDP3hEP_Vygu41a-f4Z5wKa3uQd80YA5NtTbdmq1mVtD4wNLg4V_k1Y6dHg2dcaGQrZ5sN19swM8C0oUWc7IobWSB2kF-wjD7uhtsj2QH1y0d9BdaaELVvniBl7UST1UviF2U5BQquuGFXWIxvIR7-3rq-62c7phKFbMVVoC6ar_N-IlZT8pjhapy0xSq8g&sa=D&sntz=1&usg=AOvVaw2LI1WQU1nUuooUmNb2yNvk)[https://arxiv.org/pdf/2011.07027.pdf](https://www.google.com/url?q=https%3A%2F%2Fl.facebook.com%2Fl.php%3Fu%3Dhttps%253A%252F%252Farxiv.org%252Fpdf%252F2011.07027.pdf%253Ffbclid%253DIwAR2xbhrokYigw3yFTJhg3TI5OOKO-WQKX3n1TMRk1oBsi7Yr0ZSMO-iy1OI%26h%3DAT2hq3duwK0TgamyFGpwGfXdFVwnzjio0hbbMV0kghZsLkgTmoQEC2qTOJq5d9etSs1sufzhMci5ZkgbHXGwnZFNQOGRq10viKy9bcsNcfVmbMcVs-pYbLxcgtY4GwlnwENJ0h0%26__tn__%3D-UK-R%26c%5B0%5D%3DAT0yOKaA4J_MP8vKht-A_dhqDP3hEP_Vygu41a-f4Z5wKa3uQd80YA5NtTbdmq1mVtD4wNLg4V_k1Y6dHg2dcaGQrZ5sN19swM8C0oUWc7IobWSB2kF-wjD7uhtsj2QH1y0d9BdaaELVvniBl7UST1UviF2U5BQquuGFXWIxvIR7-3rq-62c7phKFbMVVoC6ar_N-IlZT8pjhapy0xSq8g&sa=D&sntz=1&usg=AOvVaw2LI1WQU1nUuooUmNb2yNvk) * Summary:[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.marktechpost.com%2F2020%2F11%2F18%2Fdeepmind-open-sources-lab2d-a-system-for-the-creation-of-2d-environments-for-machine-learning%2F%3Ffbclid%3DIwAR1GbPd7fe4Gk2u7UNGi7lJmlVsbB6A1wcryPAtCLe_zQKcEz0-xQZxvnRI&sa=D&sntz=1&usg=AOvVaw3lgQoAseygJ1cfWNGun9UY)[https://www.marktechpost.com/.../deepmind-open-sources.../](https://www.google.com/url?q=https%3A%2F%2Fwww.marktechpost.com%2F2020%2F11%2F18%2Fdeepmind-open-sources-lab2d-a-system-for-the-creation-of-2d-environments-for-machine-learning%2F%3Ffbclid%3DIwAR1GbPd7fe4Gk2u7UNGi7lJmlVsbB6A1wcryPAtCLe_zQKcEz0-xQZxvnRI&sa=D&sntz=1&usg=AOvVaw3lgQoAseygJ1cfWNGun9UY) Google to release DeepMind's StreetLearn for teaching machine-learning agents to navigate cities [https://www.techrepublic.com/article/google-to-release-deepminds-streetlearn- for-teaching-machine-learning-agents-to-navigate- cities/](https://www.google.com/url?q=https%3A%2F%2Fwww.techrepublic.com%2Farticle%2Fgoogle- to-release-deepminds-streetlearn-for-teaching-machine-learning-agents-to- navigate-cities%2F&sa=D&sntz=1&usg=AOvVaw0miSK2RHfMotfGPdU2nvQI) Scalable agent alignment via reward modeling – DeepMind Safety Research – Medium [https://medium.com/@deepmindsafetyresearch/scalable-agent-alignment-via- reward-modeling- bf4ab06dfd84](https://www.google.com/url?q=https%3A%2F%2Fmedium.com%2F%40deepmindsafetyresearch%2Fscalable- agent-alignment-via-reward-modeling- bf4ab06dfd84&sa=D&sntz=1&usg=AOvVaw05WoMuEIbirGGfcT9-mcuR) Google's DeepMind Can Support, Defeat Humans in Quake III Arena - ExtremeTech [https://www.extremetech.com/extreme/292409-googles-deepmind-can-support- defeat-human-players-in-quake-iii- arena](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fextreme%2F292409-googles- deepmind-can-support-defeat-human-players-in-quake-iii- arena&sa=D&sntz=1&usg=AOvVaw3d_Jg-b-40ltqOePZzgPKy) [https://www.extremetech.com/?s=deep+mind](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2F%3Fs%3Ddeep%2Bmind&sa=D&sntz=1&usg=AOvVaw3x5zHAWyaFeEzvDSAtPpV-) | You searched for deep mind - ExtremeTech[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F254017-deepmind- ai-moves-board-games-starcraft-ii&sa=D&sntz=1&usg=AOvVaw1Hqm- oXiqQLVJlKNseT5I0)[https://www.extremetech.com/gaming/254017-deepmind-ai- moves-board-games-starcraft- ii](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F254017-deepmind- ai-moves-board-games-starcraft-ii&sa=D&sntz=1&usg=AOvVaw1Hqm- oXiqQLVJlKNseT5I0) | DeepMind AI Moves on from Board Games to StarCraft II - ExtremeTech[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F284441-deepmind- ai-challenges-pro-starcraft-ii-players-wins-almost-every- match&sa=D&sntz=1&usg=AOvVaw34zt37z9JUzT0vVbdrGars)[https://www.extremetech.com/gaming/284441-deepmind- ai-challenges-pro-starcraft-ii-players-wins-almost-every- match](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F284441-deepmind- ai-challenges-pro-starcraft-ii-players-wins-almost-every- match&sa=D&sntz=1&usg=AOvVaw34zt37z9JUzT0vVbdrGars) | DeepMind AI Challenges Pro StarCraft II Players, Wins Almost Every Match - ExtremeTech[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.engadget.com%2F2016%2F11%2F18%2Fgoogle- deepmind-ai- unreal%2F&sa=D&sntz=1&usg=AOvVaw28gsTYLTDGJdtAWgt6cnzx)[https://www.engadget.com/2016/11/18/google- deepmind-ai- unreal/](https://www.google.com/url?q=https%3A%2F%2Fwww.engadget.com%2F2016%2F11%2F18%2Fgoogle- deepmind-ai-unreal%2F&sa=D&sntz=1&usg=AOvVaw28gsTYLTDGJdtAWgt6cnzx) | Google's DeepMind AI gets a few new tricks to learn faster[ ](https://www.youtube.com/results)? Robot arm **There are 4 Courses in this Specialization** **Course** 1 [ **Fundamentals of Reinforcement Learning**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Ffundamentals- of-reinforcement-learning&sa=D&sntz=1&usg=AOvVaw3PTpSRw_TOX9WayLHdHpIm) 4.8 stars 801 ratings • 205 reviews Reinforcement Learning is a subfield of Machine Learning, but is also a general purpose formalism for automated decision-making and AI. This course introduces you to statistical learning techniques where an agent explicitly takes actions and interacts with the world. Understanding the importance and challenges of learning agents that make decisions is of vital importance today, with more and more companies interested in interactive agents and intelligent decision-making. This course introduces you to the fundamentals of Reinforcement Learning. When you finish this course, you will: - Formalize problems as Markov Decision Processes - Understand basic exploration methods and the exploration/exploitation tradeoff - Understand value functions, as a general- purpose tool for optimal decision-making - Know how to implement dynamic programming as an efficient solution approach to an industrial control problem This course teaches you the key concepts of Reinforcement Learning, underlying classic and modern algorithms in RL. After completing this course, you will be able to start using RL for real problems, where you have or can specify the MDP. This is the first course of the Reinforcement Learning Specialization. [SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement- learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq) **Course** 2 [ **Sample-based Learning Methods**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fsample- based-learning-methods&sa=D&sntz=1&usg=AOvVaw2WxPVveAA-1MWiJhTop9nZ) 4.8 stars 397 ratings • 75 reviews In this course, you will learn about several algorithms that can learn near optimal policies based on trial and error interaction with the environment--- learning from the agent’s own experience. Learning from actual experience is striking because it requires no prior knowledge of the environment’s dynamics, yet can still attain optimal behavior. We will cover intuitively simple but powerful Monte Carlo methods, and temporal difference learning methods including Q-learning. We will wrap up this course investigating how we can get the best of both worlds: algorithms that can combine model-based planning (similar to dynamic programming) and temporal difference updates to radically accelerate learning. By the end of this course you will be able to: - Understand Temporal- Difference learning and Monte Carlo as two strategies for estimating value functions from sampled experience - Understand the importance of exploration, when using sampled experience rather than dynamic programming sweeps within a model - Understand the connections between Monte Carlo and Dynamic Programming and TD. - Implement and apply the TD algorithm, for estimating value functions - Implement and apply Expected Sarsa and Q-learning (two TD methods for control) - Understand the difference between on-policy and off-policy control - Understand planning with simulated experience (as opposed to classic planning strategies) - Implement a model-based approach to RL, called Dyna, which uses simulated experience - Conduct an empirical study to see the improvements in sample efficiency when using Dyna [SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement- learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq) **Course** 3 [ **Prediction and Control with Function Approximation**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fprediction- control-function-approximation&sa=D&sntz=1&usg=AOvVaw0l2Hw5t6C2PY6t-i3FKdsH) 4.8 stars 252 ratings • 40 reviews In this course, you will learn how to solve problems with large, high- dimensional, and potentially infinite state spaces. You will see that estimating value functions can be cast as a supervised learning problem--- function approximation---allowing you to build agents that carefully balance generalization and discrimination in order to maximize reward. We will begin this journey by investigating how our policy evaluation or prediction methods like Monte Carlo and TD can be extended to the function approximation setting. You will learn about feature construction techniques for RL, and representation learning via neural networks and backprop. We conclude this course with a deep-dive into policy gradient methods; a way to learn policies directly without learning a value function. In this course you will solve two continuous-state control tasks and investigate the benefits of policy gradient methods in a continuous-action environment. Prerequisites: This course strongly builds on the fundamentals of Courses 1 and 2, and learners should have completed these before starting this course. Learners should also be comfortable with probabilities & expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), and implementing algorithms from pseudocode. By the end of this course, you will be able to: -Understand how to use supervised learning approaches to approximate value functions -Understand objectives for prediction (value estimation) under function approximation -Implement TD with function approximation (state aggregation), on an environment with an infinite state space (continuous state space) -Understand fixed basis and neural network approaches to feature construction -Implement TD with neural network function approximation in a continuous state environment -Understand new difficulties in exploration when moving to function approximation -Contrast discounted problem formulations for control versus an average reward problem formulation -Implement expected Sarsa and Q-learning with function approximation on a continuous state control task -Understand objectives for directly estimating policies (policy gradient objectives) -Implement a policy gradient method (called Actor-Critic) on a discrete state environment [SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement- learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq) **Course** 4 [ **A Complete Reinforcement Learning System (Capstone)**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fcomplete- reinforcement-learning-system&sa=D&sntz=1&usg=AOvVaw1cPdyaSfUjhZu1SLxl8DIm) 4.6 stars 177 ratings • 33 reviews In this final course, you will put together your knowledge from Courses 1, 2 and 3 to implement a complete RL solution to a problem. This capstone will let you see how each component---problem formulation, algorithm selection, parameter selection and representation design---fits together into a complete solution, and how to make appropriate choices when deploying RL in the real world. This project will require you to implement both the environment to stimulate your problem, and a control agent with Neural Network function approximation. In addition, you will conduct a scientific study of your learning system to develop your ability to assess the robustness of RL agents. To use RL in the real world, it is critical to (a) appropriately formalize the problem as an MDP, (b) select appropriate algorithms, (c ) identify what choices in your implementation will have large impacts on performance and (d) validate the expected behaviour of your algorithms. This capstone is valuable for anyone who is planning on using RL to solve real problems. To be successful in this course, you will need to have completed Courses 1, 2, and 3 of this Specialization or the equivalent. By the end of this course, you will be able to: Complete an RL solution to a problem, starting from problem formulation, appropriate algorithm selection and implementation and empirical study into the effectiveness of the solution. [SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement- learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq) **Using pre trained model to train deeper and lager model** Imitation Learning Safety Gym, a suite of environments and tools for measuring progress towards reinforcement learning agents that respect safety constraints while training. It also provides a standardized method of comparing algorithms and how well they avoid costly mistakes while learning. If deep reinforcement learning is applied to the real world, whether in robotics or internet-based tasks, it will be important to have algorithms that are safe even while learning—like a self-driving car that can learn to avoid accidents without actually having to experience them. Credit: Two Minute Papers, OpenAI Follow me for more AI/ Datascience posts:[ ](https://www.google.com/url?q=https%3A%2F%2Flnkd.in%2FgZu463X&sa=D&sntz=1&usg=AOvVaw04QVerqlEJzF6ri9VsjWi4)[https://lnkd.in/gZu463X](https://www.google.com/url?q=https%3A%2F%2Flnkd.in%2FgZu463X&sa=D&sntz=1&usg=AOvVaw04QVerqlEJzF6ri9VsjWi4) [OpenAI Safety Gym: A Safe Place For AIs To Learn 💪](https://www.youtube.com/watch?v=_s7Bg6yVOdo) **DeepMind proposes novel way to train ‘safe’ reinforcement learning AI** [https://venturebeat.com/2019/12/13/deepmind-proposes-novel-way-to-train-safe- reinforcement-learning-ai/?fbclid=IwAR22JRwC48YaKLICmYQTOjuKP- cEcE4_biuCSAxFuHNqgeJIhhineg1PTIk](https://www.google.com/url?q=https%3A%2F%2Fventurebeat.com%2F2019%2F12%2F13%2Fdeepmind- proposes-novel-way-to-train-safe-reinforcement-learning- ai%2F%3Ffbclid%3DIwAR22JRwC48YaKLICmYQTOjuKP- cEcE4_biuCSAxFuHNqgeJIhhineg1PTIk&sa=D&sntz=1&usg=AOvVaw33y42wwJ7GVtU9yQ- HA8tJ) **The Batch** Issue 35 **Different Skills From Different Demos** Reinforcement learning trains models by trial and error. In batch reinforcement learning (BRL), models learn by observing many demonstrations by a variety of actors. For instance, a robot might learn how to fix ingrown toenails by watching hundreds of surgeons perform the procedure. But what if one doctor is handier with a scalpel while another excels at suturing? A new method lets models absorb the best skills from each. **What’s new:** Ajay Mandlekar and collaborators at Nvidia, Stanford, and the University of Toronto devised a BRL technique that enables models to learn different portions of a task from different examples. This way, the model can gain useful information from inconsistent examples.[ ](https://www.google.com/url?q=https%3A%2F%2Finfo.deeplearning.ai%2Fe2t%2Fc%2F*W93MmRy8ctVj4W8CrRWn19CRs_0%2F*N5ZNrfGWSg4SV- QHFT8ZG-6n0%2F5%2Ff18dQhb0Sjv48XJblHN7fK6lMHyjJqVQsN5n1pgP_0N3hHhdwVMsQMVnQ9Qq8Zy_B4W1Tdc1j55VM8QW5p680S1FW12JMsgqy7twk7pW4bJ02h4b_rKwW7MbC3k1ShBCtVsVwJ95ltllnVJ2dtV2yJF1WVYT2jk6P4lCXW3Wdv8v6Pkt_VW62_rW_5YFJKDW96dt4S4r1QvYVNHCtz8gjY6LW8WBKbV56sy_8W2NhXK38W2TXnVJd1xK2Mn6xFW2d0Xx01RMFRvW9f4wHg7HmNBdW76lKpq2sC8-JW1ysXGq6WYC_zVpjpzw6Vshk0W4hvzF86VX7kWW4cPj6g5-b0qgW5NpS3-7qrJl_W56vZfs2MJXfMW2tBw4W6wd4SGN4lBZ6Fnpt4xW6GTq088Ph58-W594SN81HkWtdW7t3MQ17y2wLbW2F6lCB1L9wgVW4J35Sn2N3p-BW5Wp1wj8_6fCxW754Rys8_bnGcW8LMdgb7jYjT_W39d2Vf6Q3Qs6MrLh9QrHMy0f2Q9pjJ02&sa=D&sntz=1&usg=AOvVaw1n2jZIG6q7VlX9dohRw2XF)[Implicit Reinforcement without Interaction at Scale](https://www.google.com/url?q=https%3A%2F%2Finfo.deeplearning.ai%2Fe2t%2Fc%2F*W93MmRy8ctVj4W8CrRWn19CRs_0%2F*N5ZNrfGWSg4SV- QHFT8ZG-6n0%2F5%2Ff18dQhb0Sjv48XJblHN7fK6lMHyjJqVQsN5n1pgP_0N3hHhdwVMsQMVnQ9Qq8Zy_B4W1Tdc1j55VM8QW5p680S1FW12JMsgqy7twk7pW4bJ02h4b_rKwW7MbC3k1ShBCtVsVwJ95ltllnVJ2dtV2yJF1WVYT2jk6P4lCXW3Wdv8v6Pkt_VW62_rW_5YFJKDW96dt4S4r1QvYVNHCtz8gjY6LW8WBKbV56sy_8W2NhXK38W2TXnVJd1xK2Mn6xFW2d0Xx01RMFRvW9f4wHg7HmNBdW76lKpq2sC8-JW1ysXGq6WYC_zVpjpzw6Vshk0W4hvzF86VX7kWW4cPj6g5-b0qgW5NpS3-7qrJl_W56vZfs2MJXfMW2tBw4W6wd4SGN4lBZ6Fnpt4xW6GTq088Ph58-W594SN81HkWtdW7t3MQ17y2wLbW2F6lCB1L9wgVW4J35Sn2N3p-BW5Wp1wj8_6fCxW754Rys8_bnGcW8LMdgb7jYjT_W39d2Vf6Q3Qs6MrLh9QrHMy0f2Q9pjJ02&sa=D&sntz=1&usg=AOvVaw1n2jZIG6q7VlX9dohRw2XF) (IRIS) achieved state-of-the-art BRL performance in three tasks performed in a virtual environment. **Key insight:** Learning from demonstrations is a double-edged sword. An agent gets to see how to complete a task, but the scope of its action is limited to the most complete demonstration of a given task. IRIS breaks down tasks into sequences of intermediate subgoals. Then it performs the actions required to accomplish each subgoal. In this way, the agent learns from the best parts of each demonstration and combines them to accomplish the task. **How it works:** IRIS includes a subgoal selection model that predicts intermediate points on the way to accomplishing an assigned task. These subgoals are defined automatically by the algorithm, and may not correspond to parts of a task as humans would describe them. A controller network tries to replicate the optimal sequence of actions leading to a given subgoal. * The subgoal selection model is made up of a conditional variational autoencoder that produces a set of possible subgoals and a value function (trained via a BRL version of Q-learning) that predicts which next subgoal will lead to the highest reward. * The controller is a recurrent neural network that decides on the actions required to accomplish the current subgoal. It learns to predict how demonstrations tend to unfold, and to imitate short sequences of actions from specific demonstrations. * Once it’s trained, the subgoal selection model determines the next subgoal. The controller takes the requisite actions. Then the subgoal selection model evaluates the current state and computes a new subgoal, and so on. **Results:** In the Robosuite's lifting and pick-and-place tasks, previous state-of-the-art BRL approaches couldn't pick up objects reliably, nor place them elsewhere at all. IRIS learned to pick up objects with over 80 percent success and placed them with 30 percent success. **Why it matters:** Automatically identifying subgoals has been a holy grail in reinforcement learning, with active research in hierarchical RL and other areas. The method used in this paper applies to relatively simple tasks where things happen in a predictable sequence (such as picking and then placing), but might be a small step in an important direction. **We’re thinking:** Batch reinforcement learning is useful when a model must be interpretable or safe — after all, a robotic surgeon shouldn’t experiment on living patients — but it hasn’t been terribly effective. IRIS could make it a viable option. Dec 11, 2019 Issue 34 **Seeing the World Blindfolded** In reinforcement learning, if researchers want an agent to have an internal representation of its environment, they’ll build and train a world model that it can refer to. New research shows that world models can emerge from standard training, rather than needing to be built separately. **What’s new:** Google Brain researchers C. Daniel Freeman, Luke Metz, and David Ha enabled an agent to build a world model by blindfolding it as it learned to accomplish tasks. They call their approach[ ](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1910.13038&sa=D&sntz=1&usg=AOvVaw19a0ZWmEG6OitJ7uRtgAiJ)[observational dropout](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1910.13038&sa=D&sntz=1&usg=AOvVaw19a0ZWmEG6OitJ7uRtgAiJ). **Key insight:** Blocking an agent's observations of the world at random moments forces it to generate its own internal representation to fill in the gaps. The agent learns this representation without being instructed to predict how the environment will change in response to its actions. **How it works:** At every timestep, the agent acts on either its observation (framed in red in the video above) or its prediction of what it wasn’t able to observe (imagery not framed in red). The agent contains a controller that decides on the most rewarding action. To compute the potential reward of a given action, the agent includes an additional deep net trained using the RL algorithm REINFORCE. * Observational dropout blocks the agent from observing the environment according to a user-defined probability. When this happens, the agent predicts an observation. * If random blindfolding blocks several observations in a row, the agent uses its most recent prediction to generate the next one. * This procedure over many iterations produces a sequence of observations and predictions. The agent learns from this sequence, and its ability to predict blocked observations is tantamount to a world model. **Results:** Observational dropout solved the task known as[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fopenai%2Fgym%2Fwiki%2FCartPole-v0&sa=D&sntz=1&usg=AOvVaw3n6wYAZyMbzRpWSK2gORJW)[Cartpole](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fopenai%2Fgym%2Fwiki%2FCartPole-v0&sa=D&sntz=1&usg=AOvVaw3n6wYAZyMbzRpWSK2gORJW), in which the model must balance a pole upright on a rolling cart, even when its view of the world was blocked 90 percent of the time. In a more complex[ ](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1809.01999&sa=D&sntz=1&usg=AOvVaw2XKaDs9JT_6xvavFR00hoz)[Car Racing](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1809.01999&sa=D&sntz=1&usg=AOvVaw2XKaDs9JT_6xvavFR00hoz) task, in which a model must navigate a car around a track as fast as possible, the model performed almost equally well whether it was allowed to see its surroundings or blindfolded up to 60 percent of the time. **Why it matters:** Modeling reality is often part art and part science. World models generated by observational dropout aren’t perfect representations, but they’re sufficient for some tasks. This work could lead to simple-but-effective world models of complex environments that are impractical to model completely. **We’re thinking:** Technology being imperfect, observational dropout is a fact of life, not just a research technique. A self-driving car or auto- piloted airplane reliant on sensors that drop data points could create a catastrophe. This technique could make high-stakes RL models more robust. Dec 4, 2019 Issue 33 **Is AI Making Mastery Obsolete?** Is there any reason to continue playing games that AI has mastered? Ask the former champions who have been toppled by machines. **What happened:** In 2016, International Go master Lee Sedol famously lost three out of four matches to DeepMind’s AlphaGo model. The 36-year-old announced his retirement from competition on November 27. “Even if I become the number one, there is an entity that cannot be defeated,” he[ ](https://www.google.com/url?q=https%3A%2F%2Fen.yna.co.kr%2Fview%2FAEN20191127004800315&sa=D&sntz=1&usg=AOvVaw0PMZN81SZTeJRnateNxsLl)[told](https://www.google.com/url?q=https%3A%2F%2Fen.yna.co.kr%2Fview%2FAEN20191127004800315&sa=D&sntz=1&usg=AOvVaw0PMZN81SZTeJRnateNxsLl) South Korean's Yonhap News Agency, **Stages of grief:** Prior to the tournament, Lee predicted that he would defeat AlphaGo easily. But the model’s inexplicable — and indefatigable — playing style pushed him into fits of[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.theverge.com%2F2017%2F10%2F11%2F16460118%2Falphago- deepmind-ai-documentary-go-lee-sedol-film- review&sa=D&sntz=1&usg=AOvVaw1BJ4ioQcgpxgNxmnVXr8kN)[shock and disbelief](https://www.google.com/url?q=https%3A%2F%2Fwww.theverge.com%2F2017%2F10%2F11%2F16460118%2Falphago- deepmind-ai-documentary-go-lee-sedol-film- review&sa=D&sntz=1&usg=AOvVaw1BJ4ioQcgpxgNxmnVXr8kN). Afterward, he[ ](https://www.google.com/url?q=https%3A%2F%2Fventurebeat.com%2F2016%2F03%2F12%2Fgo- board-game-champion-lee-sedol-apologizes-for-losing-to-googles- ai%2F&sa=D&sntz=1&usg=AOvVaw18En2cGIvD1uVmya6kwvX-)[apologized](https://www.google.com/url?q=https%3A%2F%2Fventurebeat.com%2F2016%2F03%2F12%2Fgo- board-game-champion-lee-sedol-apologizes-for-losing-to-googles- ai%2F&sa=D&sntz=1&usg=AOvVaw18En2cGIvD1uVmya6kwvX-) for his failure to the South Korean public. **Reaching acceptance:** Garry Kasparov, the former world-champion chess player, went through his own cycle of grief after being defeated by IBM’s DeepBlue in 1997. Although he didn’t retire, Kasparov did accuse IBM’s engineers of cheating. He later retracted the charge, and in 2017 wrote a book[ ](http://www.google.com/url?q=http%3A%2F%2Fwww.kasparov.com%2Fgarry- kasparov-says-ai-can-make-us-more-human-pcmag-interview- march-20th-2019%2F&sa=D&sntz=1&usg=AOvVaw3Lfe53n3SoFR4qDryUTD0P)[arguing](http://www.google.com/url?q=http%3A%2F%2Fwww.kasparov.com%2Fgarry- kasparov-says-ai-can-make-us-more-human-pcmag-interview- march-20th-2019%2F&sa=D&sntz=1&usg=AOvVaw3Lfe53n3SoFR4qDryUTD0P) that, if humans can overcome their feelings of being threatened by AI, they can learn from it. The book advocates an augmented intelligence in which humans and machines work together to solve problems. **The human element:** Although AlphaGo won in the 2016 duel, its human opponent still managed to shine. During the fourth match, Sedol made a[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.wired.com%2F2016%2F03%2Ftwo- moves-alphago-lee-sedol-redefined- future%2F&sa=D&sntz=1&usg=AOvVaw1Pt6XPLhDa8cMRMxOtI951)[move](https://www.google.com/url?q=https%3A%2F%2Fwww.wired.com%2F2016%2F03%2Ftwo- moves-alphago-lee-sedol-redefined- future%2F&sa=D&sntz=1&usg=AOvVaw1Pt6XPLhDa8cMRMxOtI951) so unconventional it defied AlphaGo’s expectation and led to his sole victory. **We’re thinking:** Lee wasn't defeated by a machine alone. He was beaten by a machine built by humans under the direction of AlphaGo research lead David Silver. Human mastery is obsolete only if you ignore people like Silver and his team. Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/XgaKEt2FLCAUFKSVB7bWm_daXvBUuQ- IMFaLMazoeqc9v81q9tB-xdRfUwaMvMXAPNtdRJ- erMVMYoDarnLyYNw=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/XgaKEt2FLCAUFKSVB7bWm_daXvBUuQ- IMFaLMazoeqc9v81q9tB-xdRfUwaMvMXAPNtdRJ-erMVMYoDarnLyYNw=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # My paper: A Comprehensive Review on Deep Reinforcement Learning The updates 2021 YouTube Notes and info Links: Reading List (Video, Conference, Workshop, Paper) ### The updates Dear friends, I recently wrote a survey paper on "A Comprehensive Review on Deep Reinforcement Learning: A Survey", with some of the leading AI and DRL researchers (including): In this work, we covered top recent DRL works, grouped into several categories. We were lucky to have you, as the external reviewers of this work. I hope this is useful for the research community. Any feedback will be highly welcomed. You can find its summary here too. Imitation learning, expert (teacher), hierarchical, hybrid imitation, high performance parallelism, # 2021 * [NeurIPS 2020: Key Research Papers in Reinforcement Learning and More](https://www.google.com/url?q=https%3A%2F%2Fwww.topbots.com%2Fneurips-2020-rl-research-papers%2F&sa=D&sntz=1&usg=AOvVaw3fWwOQ2N8Z8mHGNysZedTo) * [Key Papers in Deep RL](https://www.google.com/url?q=https%3A%2F%2Fspinningup.openai.com%2Fen%2Flatest%2Fspinningup%2Fkeypapers.html&sa=D&sntz=1&usg=AOvVaw3sZcgv4fIlIg3pmq6_O_q2) ### YouTube * Simple Deep Q Network w/Pytorch:[ https://youtu.be/UlJzzLYgYoE](https://youtu.be/UlJzzLYgYoE) * Reinforcement Learning Crash Course:[ https://youtu.be/sOiNMW8k4T0](https://youtu.be/sOiNMW8k4T0) * Policy Gradients w/Tensorflow:[ https://youtu.be/UT9pQjVhcaU](https://youtu.be/UT9pQjVhcaU) * Deep Q Learning w/Tensorflow[ https://youtu.be/3Ggq_zoRGP4](https://youtu.be/3Ggq_zoRGP4) * Code Your Own RL Environments[ https://youtu.be/vmrqpHldAQ0](https://youtu.be/vmrqpHldAQ0) * How to Spec a Deep Learning PC:[ https://youtu.be/xsnVlMWQj8o](https://youtu.be/xsnVlMWQj8o) * Deep Q Learning w/ Pytorch:[ https://youtu.be/RfNxXlO6BiA](https://youtu.be/RfNxXlO6BiA) * Machine Learning Freelancing[ https://youtu.be/6M04ZTLE_O4](https://youtu.be/6M04ZTLE_O4) * **Code from video** :[ https://github.com/philtabor/Youtube-Code-Repository](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fphiltabor%2FYoutube-Code-Repository&sa=D&sntz=1&usg=AOvVaw0lZg33Rz9-UZsCI8LPkgPG) ### Notes and info * training on unlabeled data, lifelong learning, and especially letting models explore a simulated environment before transferring what they learn to the real world * Lately, simulation has helped achieve impressive results in reinforcement learning, which is extremely data-intensive. * using reinforcement learning to train robots that reason about how their actions will affect their environment. * How is it that many people learn to drive a car fairly safely in 20 hours of practice, while current imitation learning algorithms take hundreds of thousands of hours, and reinforcement learning algorithms take millions of hours? Clearly we’re missing something big. * In 2021, I expect self-supervised methods to learn features of video and images. Could there be a similar revolution in high-dimensional continuous data like video? * One critical challenge is dealing with uncertainty. Models like BERT can’t tell if a missing word in a sentence is “cat” or “dog,” but they can produce a probability distribution vector. We don’t have a good model of probability distributions for images or video frames. But recent research is coming so close that we’re likely to find it soon. * Suddenly we’ll get really good performance predicting actions in videos with very few training samples, where it wasn’t possible before. That would make the coming year a very exciting time in AI. * DeepMind released the code, model, & dataset behind their groundbreaking "AlphaFold" system. It's predicts protein shapes from genomic data with apps in health, sustainability, & materials design ### Links: * [https://techgrabyte.com/google-framework-reduces-ai-training-costs-seed-rl/?fbclid=IwAR07KznmEO4nCYIMJM9Zv53HDoQEHvHH9FoUHqfPDyRvoucayu3p4nzROfU](https://www.google.com/url?q=https%3A%2F%2Ftechgrabyte.com%2Fgoogle-framework-reduces-ai-training-costs-seed-rl%2F%3Ffbclid%3DIwAR07KznmEO4nCYIMJM9Zv53HDoQEHvHH9FoUHqfPDyRvoucayu3p4nzROfU&sa=D&sntz=1&usg=AOvVaw2YNi0EWFi_liPQK9abco8U) ### Reading List (Video, Conference, Workshop, Paper) * [https://sites.google.com/view/icml19metalearning](https://www.google.com/url?q=https%3A%2F%2Fsites.google.com%2Fview%2Ficml19metalearning&sa=D&sntz=1&usg=AOvVaw0qCxTbeQF-J_3tGBq--FD4) DeepMind Open-Sources Lab2D, A System For The Creation Of 2D Environments For Machine Learning * Github:[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fdeepmind%2Flab2d%3Ffbclid%3DIwAR2axHHCby_-GbXC_VgpytDoJkAq-dxPd2JZ4vOOIEvxUqK_hakvvfo56Zk&sa=D&sntz=1&usg=AOvVaw0uAYAMc2ud2QYDJ68E0Mre)[https://github.com/deepmind/lab2d](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fdeepmind%2Flab2d%3Ffbclid%3DIwAR2axHHCby_-GbXC_VgpytDoJkAq-dxPd2JZ4vOOIEvxUqK_hakvvfo56Zk&sa=D&sntz=1&usg=AOvVaw0uAYAMc2ud2QYDJ68E0Mre) * Paper:[ ](https://www.google.com/url?q=https%3A%2F%2Fl.facebook.com%2Fl.php%3Fu%3Dhttps%253A%252F%252Farxiv.org%252Fpdf%252F2011.07027.pdf%253Ffbclid%253DIwAR2xbhrokYigw3yFTJhg3TI5OOKO-WQKX3n1TMRk1oBsi7Yr0ZSMO-iy1OI%26h%3DAT2hq3duwK0TgamyFGpwGfXdFVwnzjio0hbbMV0kghZsLkgTmoQEC2qTOJq5d9etSs1sufzhMci5ZkgbHXGwnZFNQOGRq10viKy9bcsNcfVmbMcVs-pYbLxcgtY4GwlnwENJ0h0%26__tn__%3D-UK-R%26c%5B0%5D%3DAT0yOKaA4J_MP8vKht-A_dhqDP3hEP_Vygu41a-f4Z5wKa3uQd80YA5NtTbdmq1mVtD4wNLg4V_k1Y6dHg2dcaGQrZ5sN19swM8C0oUWc7IobWSB2kF-wjD7uhtsj2QH1y0d9BdaaELVvniBl7UST1UviF2U5BQquuGFXWIxvIR7-3rq-62c7phKFbMVVoC6ar_N-IlZT8pjhapy0xSq8g&sa=D&sntz=1&usg=AOvVaw2LI1WQU1nUuooUmNb2yNvk)[https://arxiv.org/pdf/2011.07027.pdf](https://www.google.com/url?q=https%3A%2F%2Fl.facebook.com%2Fl.php%3Fu%3Dhttps%253A%252F%252Farxiv.org%252Fpdf%252F2011.07027.pdf%253Ffbclid%253DIwAR2xbhrokYigw3yFTJhg3TI5OOKO-WQKX3n1TMRk1oBsi7Yr0ZSMO-iy1OI%26h%3DAT2hq3duwK0TgamyFGpwGfXdFVwnzjio0hbbMV0kghZsLkgTmoQEC2qTOJq5d9etSs1sufzhMci5ZkgbHXGwnZFNQOGRq10viKy9bcsNcfVmbMcVs-pYbLxcgtY4GwlnwENJ0h0%26__tn__%3D-UK-R%26c%5B0%5D%3DAT0yOKaA4J_MP8vKht-A_dhqDP3hEP_Vygu41a-f4Z5wKa3uQd80YA5NtTbdmq1mVtD4wNLg4V_k1Y6dHg2dcaGQrZ5sN19swM8C0oUWc7IobWSB2kF-wjD7uhtsj2QH1y0d9BdaaELVvniBl7UST1UviF2U5BQquuGFXWIxvIR7-3rq-62c7phKFbMVVoC6ar_N-IlZT8pjhapy0xSq8g&sa=D&sntz=1&usg=AOvVaw2LI1WQU1nUuooUmNb2yNvk) * Summary:[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.marktechpost.com%2F2020%2F11%2F18%2Fdeepmind-open-sources-lab2d-a-system-for-the-creation-of-2d-environments-for-machine-learning%2F%3Ffbclid%3DIwAR1GbPd7fe4Gk2u7UNGi7lJmlVsbB6A1wcryPAtCLe_zQKcEz0-xQZxvnRI&sa=D&sntz=1&usg=AOvVaw3lgQoAseygJ1cfWNGun9UY)[https://www.marktechpost.com/.../deepmind-open-sources.../](https://www.google.com/url?q=https%3A%2F%2Fwww.marktechpost.com%2F2020%2F11%2F18%2Fdeepmind-open-sources-lab2d-a-system-for-the-creation-of-2d-environments-for-machine-learning%2F%3Ffbclid%3DIwAR1GbPd7fe4Gk2u7UNGi7lJmlVsbB6A1wcryPAtCLe_zQKcEz0-xQZxvnRI&sa=D&sntz=1&usg=AOvVaw3lgQoAseygJ1cfWNGun9UY) Google to release DeepMind's StreetLearn for teaching machine-learning agents to navigate cities [https://www.techrepublic.com/article/google-to-release-deepminds-streetlearn- for-teaching-machine-learning-agents-to-navigate- cities/](https://www.google.com/url?q=https%3A%2F%2Fwww.techrepublic.com%2Farticle%2Fgoogle- to-release-deepminds-streetlearn-for-teaching-machine-learning-agents-to- navigate-cities%2F&sa=D&sntz=1&usg=AOvVaw0miSK2RHfMotfGPdU2nvQI) Scalable agent alignment via reward modeling – DeepMind Safety Research – Medium [https://medium.com/@deepmindsafetyresearch/scalable-agent-alignment-via- reward-modeling- bf4ab06dfd84](https://www.google.com/url?q=https%3A%2F%2Fmedium.com%2F%40deepmindsafetyresearch%2Fscalable- agent-alignment-via-reward-modeling- bf4ab06dfd84&sa=D&sntz=1&usg=AOvVaw05WoMuEIbirGGfcT9-mcuR) Google's DeepMind Can Support, Defeat Humans in Quake III Arena - ExtremeTech [https://www.extremetech.com/extreme/292409-googles-deepmind-can-support- defeat-human-players-in-quake-iii- arena](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fextreme%2F292409-googles- deepmind-can-support-defeat-human-players-in-quake-iii- arena&sa=D&sntz=1&usg=AOvVaw3d_Jg-b-40ltqOePZzgPKy) [https://www.extremetech.com/?s=deep+mind](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2F%3Fs%3Ddeep%2Bmind&sa=D&sntz=1&usg=AOvVaw3x5zHAWyaFeEzvDSAtPpV-) | You searched for deep mind - ExtremeTech[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F254017-deepmind- ai-moves-board-games-starcraft-ii&sa=D&sntz=1&usg=AOvVaw1Hqm- oXiqQLVJlKNseT5I0)[https://www.extremetech.com/gaming/254017-deepmind-ai- moves-board-games-starcraft- ii](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F254017-deepmind- ai-moves-board-games-starcraft-ii&sa=D&sntz=1&usg=AOvVaw1Hqm- oXiqQLVJlKNseT5I0) | DeepMind AI Moves on from Board Games to StarCraft II - ExtremeTech[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F284441-deepmind- ai-challenges-pro-starcraft-ii-players-wins-almost-every- match&sa=D&sntz=1&usg=AOvVaw34zt37z9JUzT0vVbdrGars)[https://www.extremetech.com/gaming/284441-deepmind- ai-challenges-pro-starcraft-ii-players-wins-almost-every- match](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F284441-deepmind- ai-challenges-pro-starcraft-ii-players-wins-almost-every- match&sa=D&sntz=1&usg=AOvVaw34zt37z9JUzT0vVbdrGars) | DeepMind AI Challenges Pro StarCraft II Players, Wins Almost Every Match - ExtremeTech[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.engadget.com%2F2016%2F11%2F18%2Fgoogle- deepmind-ai- unreal%2F&sa=D&sntz=1&usg=AOvVaw28gsTYLTDGJdtAWgt6cnzx)[https://www.engadget.com/2016/11/18/google- deepmind-ai- unreal/](https://www.google.com/url?q=https%3A%2F%2Fwww.engadget.com%2F2016%2F11%2F18%2Fgoogle- deepmind-ai-unreal%2F&sa=D&sntz=1&usg=AOvVaw28gsTYLTDGJdtAWgt6cnzx) | Google's DeepMind AI gets a few new tricks to learn faster[ ](https://www.youtube.com/results)? Robot arm **There are 4 Courses in this Specialization** **Course** 1 [ **Fundamentals of Reinforcement Learning**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Ffundamentals- of-reinforcement-learning&sa=D&sntz=1&usg=AOvVaw3PTpSRw_TOX9WayLHdHpIm) 4.8 stars 801 ratings • 205 reviews Reinforcement Learning is a subfield of Machine Learning, but is also a general purpose formalism for automated decision-making and AI. This course introduces you to statistical learning techniques where an agent explicitly takes actions and interacts with the world. Understanding the importance and challenges of learning agents that make decisions is of vital importance today, with more and more companies interested in interactive agents and intelligent decision-making. This course introduces you to the fundamentals of Reinforcement Learning. When you finish this course, you will: - Formalize problems as Markov Decision Processes - Understand basic exploration methods and the exploration/exploitation tradeoff - Understand value functions, as a general- purpose tool for optimal decision-making - Know how to implement dynamic programming as an efficient solution approach to an industrial control problem This course teaches you the key concepts of Reinforcement Learning, underlying classic and modern algorithms in RL. After completing this course, you will be able to start using RL for real problems, where you have or can specify the MDP. This is the first course of the Reinforcement Learning Specialization. [SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement- learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq) **Course** 2 [ **Sample-based Learning Methods**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fsample- based-learning-methods&sa=D&sntz=1&usg=AOvVaw2WxPVveAA-1MWiJhTop9nZ) 4.8 stars 397 ratings • 75 reviews In this course, you will learn about several algorithms that can learn near optimal policies based on trial and error interaction with the environment--- learning from the agent’s own experience. Learning from actual experience is striking because it requires no prior knowledge of the environment’s dynamics, yet can still attain optimal behavior. We will cover intuitively simple but powerful Monte Carlo methods, and temporal difference learning methods including Q-learning. We will wrap up this course investigating how we can get the best of both worlds: algorithms that can combine model-based planning (similar to dynamic programming) and temporal difference updates to radically accelerate learning. By the end of this course you will be able to: - Understand Temporal- Difference learning and Monte Carlo as two strategies for estimating value functions from sampled experience - Understand the importance of exploration, when using sampled experience rather than dynamic programming sweeps within a model - Understand the connections between Monte Carlo and Dynamic Programming and TD. - Implement and apply the TD algorithm, for estimating value functions - Implement and apply Expected Sarsa and Q-learning (two TD methods for control) - Understand the difference between on-policy and off-policy control - Understand planning with simulated experience (as opposed to classic planning strategies) - Implement a model-based approach to RL, called Dyna, which uses simulated experience - Conduct an empirical study to see the improvements in sample efficiency when using Dyna [SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement- learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq) **Course** 3 [ **Prediction and Control with Function Approximation**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fprediction- control-function-approximation&sa=D&sntz=1&usg=AOvVaw0l2Hw5t6C2PY6t-i3FKdsH) 4.8 stars 252 ratings • 40 reviews In this course, you will learn how to solve problems with large, high- dimensional, and potentially infinite state spaces. You will see that estimating value functions can be cast as a supervised learning problem--- function approximation---allowing you to build agents that carefully balance generalization and discrimination in order to maximize reward. We will begin this journey by investigating how our policy evaluation or prediction methods like Monte Carlo and TD can be extended to the function approximation setting. You will learn about feature construction techniques for RL, and representation learning via neural networks and backprop. We conclude this course with a deep-dive into policy gradient methods; a way to learn policies directly without learning a value function. In this course you will solve two continuous-state control tasks and investigate the benefits of policy gradient methods in a continuous-action environment. Prerequisites: This course strongly builds on the fundamentals of Courses 1 and 2, and learners should have completed these before starting this course. Learners should also be comfortable with probabilities & expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), and implementing algorithms from pseudocode. By the end of this course, you will be able to: -Understand how to use supervised learning approaches to approximate value functions -Understand objectives for prediction (value estimation) under function approximation -Implement TD with function approximation (state aggregation), on an environment with an infinite state space (continuous state space) -Understand fixed basis and neural network approaches to feature construction -Implement TD with neural network function approximation in a continuous state environment -Understand new difficulties in exploration when moving to function approximation -Contrast discounted problem formulations for control versus an average reward problem formulation -Implement expected Sarsa and Q-learning with function approximation on a continuous state control task -Understand objectives for directly estimating policies (policy gradient objectives) -Implement a policy gradient method (called Actor-Critic) on a discrete state environment [SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement- learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq) **Course** 4 [ **A Complete Reinforcement Learning System (Capstone)**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fcomplete- reinforcement-learning-system&sa=D&sntz=1&usg=AOvVaw1cPdyaSfUjhZu1SLxl8DIm) 4.6 stars 177 ratings • 33 reviews In this final course, you will put together your knowledge from Courses 1, 2 and 3 to implement a complete RL solution to a problem. This capstone will let you see how each component---problem formulation, algorithm selection, parameter selection and representation design---fits together into a complete solution, and how to make appropriate choices when deploying RL in the real world. This project will require you to implement both the environment to stimulate your problem, and a control agent with Neural Network function approximation. In addition, you will conduct a scientific study of your learning system to develop your ability to assess the robustness of RL agents. To use RL in the real world, it is critical to (a) appropriately formalize the problem as an MDP, (b) select appropriate algorithms, (c ) identify what choices in your implementation will have large impacts on performance and (d) validate the expected behaviour of your algorithms. This capstone is valuable for anyone who is planning on using RL to solve real problems. To be successful in this course, you will need to have completed Courses 1, 2, and 3 of this Specialization or the equivalent. By the end of this course, you will be able to: Complete an RL solution to a problem, starting from problem formulation, appropriate algorithm selection and implementation and empirical study into the effectiveness of the solution. [SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement- learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq) **Using pre trained model to train deeper and lager model** Imitation Learning Safety Gym, a suite of environments and tools for measuring progress towards reinforcement learning agents that respect safety constraints while training. It also provides a standardized method of comparing algorithms and how well they avoid costly mistakes while learning. If deep reinforcement learning is applied to the real world, whether in robotics or internet-based tasks, it will be important to have algorithms that are safe even while learning—like a self-driving car that can learn to avoid accidents without actually having to experience them. Credit: Two Minute Papers, OpenAI Follow me for more AI/ Datascience posts:[ ](https://www.google.com/url?q=https%3A%2F%2Flnkd.in%2FgZu463X&sa=D&sntz=1&usg=AOvVaw04QVerqlEJzF6ri9VsjWi4)[https://lnkd.in/gZu463X](https://www.google.com/url?q=https%3A%2F%2Flnkd.in%2FgZu463X&sa=D&sntz=1&usg=AOvVaw04QVerqlEJzF6ri9VsjWi4) [OpenAI Safety Gym: A Safe Place For AIs To Learn 💪](https://www.youtube.com/watch?v=_s7Bg6yVOdo) **DeepMind proposes novel way to train ‘safe’ reinforcement learning AI** [https://venturebeat.com/2019/12/13/deepmind-proposes-novel-way-to-train-safe- reinforcement-learning-ai/?fbclid=IwAR22JRwC48YaKLICmYQTOjuKP- cEcE4_biuCSAxFuHNqgeJIhhineg1PTIk](https://www.google.com/url?q=https%3A%2F%2Fventurebeat.com%2F2019%2F12%2F13%2Fdeepmind- proposes-novel-way-to-train-safe-reinforcement-learning- ai%2F%3Ffbclid%3DIwAR22JRwC48YaKLICmYQTOjuKP- cEcE4_biuCSAxFuHNqgeJIhhineg1PTIk&sa=D&sntz=1&usg=AOvVaw33y42wwJ7GVtU9yQ- HA8tJ) **The Batch** Issue 35 **Different Skills From Different Demos** Reinforcement learning trains models by trial and error. In batch reinforcement learning (BRL), models learn by observing many demonstrations by a variety of actors. For instance, a robot might learn how to fix ingrown toenails by watching hundreds of surgeons perform the procedure. But what if one doctor is handier with a scalpel while another excels at suturing? A new method lets models absorb the best skills from each. **What’s new:** Ajay Mandlekar and collaborators at Nvidia, Stanford, and the University of Toronto devised a BRL technique that enables models to learn different portions of a task from different examples. This way, the model can gain useful information from inconsistent examples.[ ](https://www.google.com/url?q=https%3A%2F%2Finfo.deeplearning.ai%2Fe2t%2Fc%2F*W93MmRy8ctVj4W8CrRWn19CRs_0%2F*N5ZNrfGWSg4SV- QHFT8ZG-6n0%2F5%2Ff18dQhb0Sjv48XJblHN7fK6lMHyjJqVQsN5n1pgP_0N3hHhdwVMsQMVnQ9Qq8Zy_B4W1Tdc1j55VM8QW5p680S1FW12JMsgqy7twk7pW4bJ02h4b_rKwW7MbC3k1ShBCtVsVwJ95ltllnVJ2dtV2yJF1WVYT2jk6P4lCXW3Wdv8v6Pkt_VW62_rW_5YFJKDW96dt4S4r1QvYVNHCtz8gjY6LW8WBKbV56sy_8W2NhXK38W2TXnVJd1xK2Mn6xFW2d0Xx01RMFRvW9f4wHg7HmNBdW76lKpq2sC8-JW1ysXGq6WYC_zVpjpzw6Vshk0W4hvzF86VX7kWW4cPj6g5-b0qgW5NpS3-7qrJl_W56vZfs2MJXfMW2tBw4W6wd4SGN4lBZ6Fnpt4xW6GTq088Ph58-W594SN81HkWtdW7t3MQ17y2wLbW2F6lCB1L9wgVW4J35Sn2N3p-BW5Wp1wj8_6fCxW754Rys8_bnGcW8LMdgb7jYjT_W39d2Vf6Q3Qs6MrLh9QrHMy0f2Q9pjJ02&sa=D&sntz=1&usg=AOvVaw1n2jZIG6q7VlX9dohRw2XF)[Implicit Reinforcement without Interaction at Scale](https://www.google.com/url?q=https%3A%2F%2Finfo.deeplearning.ai%2Fe2t%2Fc%2F*W93MmRy8ctVj4W8CrRWn19CRs_0%2F*N5ZNrfGWSg4SV- QHFT8ZG-6n0%2F5%2Ff18dQhb0Sjv48XJblHN7fK6lMHyjJqVQsN5n1pgP_0N3hHhdwVMsQMVnQ9Qq8Zy_B4W1Tdc1j55VM8QW5p680S1FW12JMsgqy7twk7pW4bJ02h4b_rKwW7MbC3k1ShBCtVsVwJ95ltllnVJ2dtV2yJF1WVYT2jk6P4lCXW3Wdv8v6Pkt_VW62_rW_5YFJKDW96dt4S4r1QvYVNHCtz8gjY6LW8WBKbV56sy_8W2NhXK38W2TXnVJd1xK2Mn6xFW2d0Xx01RMFRvW9f4wHg7HmNBdW76lKpq2sC8-JW1ysXGq6WYC_zVpjpzw6Vshk0W4hvzF86VX7kWW4cPj6g5-b0qgW5NpS3-7qrJl_W56vZfs2MJXfMW2tBw4W6wd4SGN4lBZ6Fnpt4xW6GTq088Ph58-W594SN81HkWtdW7t3MQ17y2wLbW2F6lCB1L9wgVW4J35Sn2N3p-BW5Wp1wj8_6fCxW754Rys8_bnGcW8LMdgb7jYjT_W39d2Vf6Q3Qs6MrLh9QrHMy0f2Q9pjJ02&sa=D&sntz=1&usg=AOvVaw1n2jZIG6q7VlX9dohRw2XF) (IRIS) achieved state-of-the-art BRL performance in three tasks performed in a virtual environment. **Key insight:** Learning from demonstrations is a double-edged sword. An agent gets to see how to complete a task, but the scope of its action is limited to the most complete demonstration of a given task. IRIS breaks down tasks into sequences of intermediate subgoals. Then it performs the actions required to accomplish each subgoal. In this way, the agent learns from the best parts of each demonstration and combines them to accomplish the task. **How it works:** IRIS includes a subgoal selection model that predicts intermediate points on the way to accomplishing an assigned task. These subgoals are defined automatically by the algorithm, and may not correspond to parts of a task as humans would describe them. A controller network tries to replicate the optimal sequence of actions leading to a given subgoal. * The subgoal selection model is made up of a conditional variational autoencoder that produces a set of possible subgoals and a value function (trained via a BRL version of Q-learning) that predicts which next subgoal will lead to the highest reward. * The controller is a recurrent neural network that decides on the actions required to accomplish the current subgoal. It learns to predict how demonstrations tend to unfold, and to imitate short sequences of actions from specific demonstrations. * Once it’s trained, the subgoal selection model determines the next subgoal. The controller takes the requisite actions. Then the subgoal selection model evaluates the current state and computes a new subgoal, and so on. **Results:** In the Robosuite's lifting and pick-and-place tasks, previous state-of-the-art BRL approaches couldn't pick up objects reliably, nor place them elsewhere at all. IRIS learned to pick up objects with over 80 percent success and placed them with 30 percent success. **Why it matters:** Automatically identifying subgoals has been a holy grail in reinforcement learning, with active research in hierarchical RL and other areas. The method used in this paper applies to relatively simple tasks where things happen in a predictable sequence (such as picking and then placing), but might be a small step in an important direction. **We’re thinking:** Batch reinforcement learning is useful when a model must be interpretable or safe — after all, a robotic surgeon shouldn’t experiment on living patients — but it hasn’t been terribly effective. IRIS could make it a viable option. Dec 11, 2019 Issue 34 **Seeing the World Blindfolded** In reinforcement learning, if researchers want an agent to have an internal representation of its environment, they’ll build and train a world model that it can refer to. New research shows that world models can emerge from standard training, rather than needing to be built separately. **What’s new:** Google Brain researchers C. Daniel Freeman, Luke Metz, and David Ha enabled an agent to build a world model by blindfolding it as it learned to accomplish tasks. They call their approach[ ](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1910.13038&sa=D&sntz=1&usg=AOvVaw19a0ZWmEG6OitJ7uRtgAiJ)[observational dropout](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1910.13038&sa=D&sntz=1&usg=AOvVaw19a0ZWmEG6OitJ7uRtgAiJ). **Key insight:** Blocking an agent's observations of the world at random moments forces it to generate its own internal representation to fill in the gaps. The agent learns this representation without being instructed to predict how the environment will change in response to its actions. **How it works:** At every timestep, the agent acts on either its observation (framed in red in the video above) or its prediction of what it wasn’t able to observe (imagery not framed in red). The agent contains a controller that decides on the most rewarding action. To compute the potential reward of a given action, the agent includes an additional deep net trained using the RL algorithm REINFORCE. * Observational dropout blocks the agent from observing the environment according to a user-defined probability. When this happens, the agent predicts an observation. * If random blindfolding blocks several observations in a row, the agent uses its most recent prediction to generate the next one. * This procedure over many iterations produces a sequence of observations and predictions. The agent learns from this sequence, and its ability to predict blocked observations is tantamount to a world model. **Results:** Observational dropout solved the task known as[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fopenai%2Fgym%2Fwiki%2FCartPole-v0&sa=D&sntz=1&usg=AOvVaw3n6wYAZyMbzRpWSK2gORJW)[Cartpole](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fopenai%2Fgym%2Fwiki%2FCartPole-v0&sa=D&sntz=1&usg=AOvVaw3n6wYAZyMbzRpWSK2gORJW), in which the model must balance a pole upright on a rolling cart, even when its view of the world was blocked 90 percent of the time. In a more complex[ ](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1809.01999&sa=D&sntz=1&usg=AOvVaw2XKaDs9JT_6xvavFR00hoz)[Car Racing](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1809.01999&sa=D&sntz=1&usg=AOvVaw2XKaDs9JT_6xvavFR00hoz) task, in which a model must navigate a car around a track as fast as possible, the model performed almost equally well whether it was allowed to see its surroundings or blindfolded up to 60 percent of the time. **Why it matters:** Modeling reality is often part art and part science. World models generated by observational dropout aren’t perfect representations, but they’re sufficient for some tasks. This work could lead to simple-but-effective world models of complex environments that are impractical to model completely. **We’re thinking:** Technology being imperfect, observational dropout is a fact of life, not just a research technique. A self-driving car or auto- piloted airplane reliant on sensors that drop data points could create a catastrophe. This technique could make high-stakes RL models more robust. Dec 4, 2019 Issue 33 **Is AI Making Mastery Obsolete?** Is there any reason to continue playing games that AI has mastered? Ask the former champions who have been toppled by machines. **What happened:** In 2016, International Go master Lee Sedol famously lost three out of four matches to DeepMind’s AlphaGo model. The 36-year-old announced his retirement from competition on November 27. “Even if I become the number one, there is an entity that cannot be defeated,” he[ ](https://www.google.com/url?q=https%3A%2F%2Fen.yna.co.kr%2Fview%2FAEN20191127004800315&sa=D&sntz=1&usg=AOvVaw0PMZN81SZTeJRnateNxsLl)[told](https://www.google.com/url?q=https%3A%2F%2Fen.yna.co.kr%2Fview%2FAEN20191127004800315&sa=D&sntz=1&usg=AOvVaw0PMZN81SZTeJRnateNxsLl) South Korean's Yonhap News Agency, **Stages of grief:** Prior to the tournament, Lee predicted that he would defeat AlphaGo easily. But the model’s inexplicable — and indefatigable — playing style pushed him into fits of[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.theverge.com%2F2017%2F10%2F11%2F16460118%2Falphago- deepmind-ai-documentary-go-lee-sedol-film- review&sa=D&sntz=1&usg=AOvVaw1BJ4ioQcgpxgNxmnVXr8kN)[shock and disbelief](https://www.google.com/url?q=https%3A%2F%2Fwww.theverge.com%2F2017%2F10%2F11%2F16460118%2Falphago- deepmind-ai-documentary-go-lee-sedol-film- review&sa=D&sntz=1&usg=AOvVaw1BJ4ioQcgpxgNxmnVXr8kN). Afterward, he[ ](https://www.google.com/url?q=https%3A%2F%2Fventurebeat.com%2F2016%2F03%2F12%2Fgo- board-game-champion-lee-sedol-apologizes-for-losing-to-googles- ai%2F&sa=D&sntz=1&usg=AOvVaw18En2cGIvD1uVmya6kwvX-)[apologized](https://www.google.com/url?q=https%3A%2F%2Fventurebeat.com%2F2016%2F03%2F12%2Fgo- board-game-champion-lee-sedol-apologizes-for-losing-to-googles- ai%2F&sa=D&sntz=1&usg=AOvVaw18En2cGIvD1uVmya6kwvX-) for his failure to the South Korean public. **Reaching acceptance:** Garry Kasparov, the former world-champion chess player, went through his own cycle of grief after being defeated by IBM’s DeepBlue in 1997. Although he didn’t retire, Kasparov did accuse IBM’s engineers of cheating. He later retracted the charge, and in 2017 wrote a book[ ](http://www.google.com/url?q=http%3A%2F%2Fwww.kasparov.com%2Fgarry- kasparov-says-ai-can-make-us-more-human-pcmag-interview- march-20th-2019%2F&sa=D&sntz=1&usg=AOvVaw3Lfe53n3SoFR4qDryUTD0P)[arguing](http://www.google.com/url?q=http%3A%2F%2Fwww.kasparov.com%2Fgarry- kasparov-says-ai-can-make-us-more-human-pcmag-interview- march-20th-2019%2F&sa=D&sntz=1&usg=AOvVaw3Lfe53n3SoFR4qDryUTD0P) that, if humans can overcome their feelings of being threatened by AI, they can learn from it. The book advocates an augmented intelligence in which humans and machines work together to solve problems. **The human element:** Although AlphaGo won in the 2016 duel, its human opponent still managed to shine. During the fourth match, Sedol made a[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.wired.com%2F2016%2F03%2Ftwo- moves-alphago-lee-sedol-redefined- future%2F&sa=D&sntz=1&usg=AOvVaw1Pt6XPLhDa8cMRMxOtI951)[move](https://www.google.com/url?q=https%3A%2F%2Fwww.wired.com%2F2016%2F03%2Ftwo- moves-alphago-lee-sedol-redefined- future%2F&sa=D&sntz=1&usg=AOvVaw1Pt6XPLhDa8cMRMxOtI951) so unconventional it defied AlphaGo’s expectation and led to his sole victory. **We’re thinking:** Lee wasn't defeated by a machine alone. He was beaten by a machine built by humans under the direction of AlphaGo research lead David Silver. Human mastery is obsolete only if you ignore people like Silver and his team. Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/XgaKEt2FLCAUFKSVB7bWm_daXvBUuQ- IMFaLMazoeqc9v81q9tB-xdRfUwaMvMXAPNtdRJ- erMVMYoDarnLyYNw=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/XgaKEt2FLCAUFKSVB7bWm_daXvBUuQ- IMFaLMazoeqc9v81q9tB-xdRfUwaMvMXAPNtdRJ-erMVMYoDarnLyYNw=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # My paper: A Comprehensive Review on Deep Reinforcement Learning The updates 2021 YouTube Notes and info Links: Reading List (Video, Conference, Workshop, Paper) ### The updates Dear friends, I recently wrote a survey paper on "A Comprehensive Review on Deep Reinforcement Learning: A Survey", with some of the leading AI and DRL researchers (including): In this work, we covered top recent DRL works, grouped into several categories. We were lucky to have you, as the external reviewers of this work. I hope this is useful for the research community. Any feedback will be highly welcomed. You can find its summary here too. Imitation learning, expert (teacher), hierarchical, hybrid imitation, high performance parallelism, # 2021 * [NeurIPS 2020: Key Research Papers in Reinforcement Learning and More](https://www.google.com/url?q=https%3A%2F%2Fwww.topbots.com%2Fneurips-2020-rl-research-papers%2F&sa=D&sntz=1&usg=AOvVaw3fWwOQ2N8Z8mHGNysZedTo) * [Key Papers in Deep RL](https://www.google.com/url?q=https%3A%2F%2Fspinningup.openai.com%2Fen%2Flatest%2Fspinningup%2Fkeypapers.html&sa=D&sntz=1&usg=AOvVaw3sZcgv4fIlIg3pmq6_O_q2) ### YouTube * Simple Deep Q Network w/Pytorch:[ https://youtu.be/UlJzzLYgYoE](https://youtu.be/UlJzzLYgYoE) * Reinforcement Learning Crash Course:[ https://youtu.be/sOiNMW8k4T0](https://youtu.be/sOiNMW8k4T0) * Policy Gradients w/Tensorflow:[ https://youtu.be/UT9pQjVhcaU](https://youtu.be/UT9pQjVhcaU) * Deep Q Learning w/Tensorflow[ https://youtu.be/3Ggq_zoRGP4](https://youtu.be/3Ggq_zoRGP4) * Code Your Own RL Environments[ https://youtu.be/vmrqpHldAQ0](https://youtu.be/vmrqpHldAQ0) * How to Spec a Deep Learning PC:[ https://youtu.be/xsnVlMWQj8o](https://youtu.be/xsnVlMWQj8o) * Deep Q Learning w/ Pytorch:[ https://youtu.be/RfNxXlO6BiA](https://youtu.be/RfNxXlO6BiA) * Machine Learning Freelancing[ https://youtu.be/6M04ZTLE_O4](https://youtu.be/6M04ZTLE_O4) * **Code from video** :[ https://github.com/philtabor/Youtube-Code-Repository](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fphiltabor%2FYoutube-Code-Repository&sa=D&sntz=1&usg=AOvVaw0lZg33Rz9-UZsCI8LPkgPG) ### Notes and info * training on unlabeled data, lifelong learning, and especially letting models explore a simulated environment before transferring what they learn to the real world * Lately, simulation has helped achieve impressive results in reinforcement learning, which is extremely data-intensive. * using reinforcement learning to train robots that reason about how their actions will affect their environment. * How is it that many people learn to drive a car fairly safely in 20 hours of practice, while current imitation learning algorithms take hundreds of thousands of hours, and reinforcement learning algorithms take millions of hours? Clearly we’re missing something big. * In 2021, I expect self-supervised methods to learn features of video and images. Could there be a similar revolution in high-dimensional continuous data like video? * One critical challenge is dealing with uncertainty. Models like BERT can’t tell if a missing word in a sentence is “cat” or “dog,” but they can produce a probability distribution vector. We don’t have a good model of probability distributions for images or video frames. But recent research is coming so close that we’re likely to find it soon. * Suddenly we’ll get really good performance predicting actions in videos with very few training samples, where it wasn’t possible before. That would make the coming year a very exciting time in AI. * DeepMind released the code, model, & dataset behind their groundbreaking "AlphaFold" system. It's predicts protein shapes from genomic data with apps in health, sustainability, & materials design ### Links: * [https://techgrabyte.com/google-framework-reduces-ai-training-costs-seed-rl/?fbclid=IwAR07KznmEO4nCYIMJM9Zv53HDoQEHvHH9FoUHqfPDyRvoucayu3p4nzROfU](https://www.google.com/url?q=https%3A%2F%2Ftechgrabyte.com%2Fgoogle-framework-reduces-ai-training-costs-seed-rl%2F%3Ffbclid%3DIwAR07KznmEO4nCYIMJM9Zv53HDoQEHvHH9FoUHqfPDyRvoucayu3p4nzROfU&sa=D&sntz=1&usg=AOvVaw2YNi0EWFi_liPQK9abco8U) ### Reading List (Video, Conference, Workshop, Paper) * [https://sites.google.com/view/icml19metalearning](https://www.google.com/url?q=https%3A%2F%2Fsites.google.com%2Fview%2Ficml19metalearning&sa=D&sntz=1&usg=AOvVaw0qCxTbeQF-J_3tGBq--FD4) DeepMind Open-Sources Lab2D, A System For The Creation Of 2D Environments For Machine Learning * Github:[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fdeepmind%2Flab2d%3Ffbclid%3DIwAR2axHHCby_-GbXC_VgpytDoJkAq-dxPd2JZ4vOOIEvxUqK_hakvvfo56Zk&sa=D&sntz=1&usg=AOvVaw0uAYAMc2ud2QYDJ68E0Mre)[https://github.com/deepmind/lab2d](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fdeepmind%2Flab2d%3Ffbclid%3DIwAR2axHHCby_-GbXC_VgpytDoJkAq-dxPd2JZ4vOOIEvxUqK_hakvvfo56Zk&sa=D&sntz=1&usg=AOvVaw0uAYAMc2ud2QYDJ68E0Mre) * Paper:[ ](https://www.google.com/url?q=https%3A%2F%2Fl.facebook.com%2Fl.php%3Fu%3Dhttps%253A%252F%252Farxiv.org%252Fpdf%252F2011.07027.pdf%253Ffbclid%253DIwAR2xbhrokYigw3yFTJhg3TI5OOKO-WQKX3n1TMRk1oBsi7Yr0ZSMO-iy1OI%26h%3DAT2hq3duwK0TgamyFGpwGfXdFVwnzjio0hbbMV0kghZsLkgTmoQEC2qTOJq5d9etSs1sufzhMci5ZkgbHXGwnZFNQOGRq10viKy9bcsNcfVmbMcVs-pYbLxcgtY4GwlnwENJ0h0%26__tn__%3D-UK-R%26c%5B0%5D%3DAT0yOKaA4J_MP8vKht-A_dhqDP3hEP_Vygu41a-f4Z5wKa3uQd80YA5NtTbdmq1mVtD4wNLg4V_k1Y6dHg2dcaGQrZ5sN19swM8C0oUWc7IobWSB2kF-wjD7uhtsj2QH1y0d9BdaaELVvniBl7UST1UviF2U5BQquuGFXWIxvIR7-3rq-62c7phKFbMVVoC6ar_N-IlZT8pjhapy0xSq8g&sa=D&sntz=1&usg=AOvVaw2LI1WQU1nUuooUmNb2yNvk)[https://arxiv.org/pdf/2011.07027.pdf](https://www.google.com/url?q=https%3A%2F%2Fl.facebook.com%2Fl.php%3Fu%3Dhttps%253A%252F%252Farxiv.org%252Fpdf%252F2011.07027.pdf%253Ffbclid%253DIwAR2xbhrokYigw3yFTJhg3TI5OOKO-WQKX3n1TMRk1oBsi7Yr0ZSMO-iy1OI%26h%3DAT2hq3duwK0TgamyFGpwGfXdFVwnzjio0hbbMV0kghZsLkgTmoQEC2qTOJq5d9etSs1sufzhMci5ZkgbHXGwnZFNQOGRq10viKy9bcsNcfVmbMcVs-pYbLxcgtY4GwlnwENJ0h0%26__tn__%3D-UK-R%26c%5B0%5D%3DAT0yOKaA4J_MP8vKht-A_dhqDP3hEP_Vygu41a-f4Z5wKa3uQd80YA5NtTbdmq1mVtD4wNLg4V_k1Y6dHg2dcaGQrZ5sN19swM8C0oUWc7IobWSB2kF-wjD7uhtsj2QH1y0d9BdaaELVvniBl7UST1UviF2U5BQquuGFXWIxvIR7-3rq-62c7phKFbMVVoC6ar_N-IlZT8pjhapy0xSq8g&sa=D&sntz=1&usg=AOvVaw2LI1WQU1nUuooUmNb2yNvk) * Summary:[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.marktechpost.com%2F2020%2F11%2F18%2Fdeepmind-open-sources-lab2d-a-system-for-the-creation-of-2d-environments-for-machine-learning%2F%3Ffbclid%3DIwAR1GbPd7fe4Gk2u7UNGi7lJmlVsbB6A1wcryPAtCLe_zQKcEz0-xQZxvnRI&sa=D&sntz=1&usg=AOvVaw3lgQoAseygJ1cfWNGun9UY)[https://www.marktechpost.com/.../deepmind-open-sources.../](https://www.google.com/url?q=https%3A%2F%2Fwww.marktechpost.com%2F2020%2F11%2F18%2Fdeepmind-open-sources-lab2d-a-system-for-the-creation-of-2d-environments-for-machine-learning%2F%3Ffbclid%3DIwAR1GbPd7fe4Gk2u7UNGi7lJmlVsbB6A1wcryPAtCLe_zQKcEz0-xQZxvnRI&sa=D&sntz=1&usg=AOvVaw3lgQoAseygJ1cfWNGun9UY) Google to release DeepMind's StreetLearn for teaching machine-learning agents to navigate cities [https://www.techrepublic.com/article/google-to-release-deepminds-streetlearn- for-teaching-machine-learning-agents-to-navigate- cities/](https://www.google.com/url?q=https%3A%2F%2Fwww.techrepublic.com%2Farticle%2Fgoogle- to-release-deepminds-streetlearn-for-teaching-machine-learning-agents-to- navigate-cities%2F&sa=D&sntz=1&usg=AOvVaw0miSK2RHfMotfGPdU2nvQI) Scalable agent alignment via reward modeling – DeepMind Safety Research – Medium [https://medium.com/@deepmindsafetyresearch/scalable-agent-alignment-via- reward-modeling- bf4ab06dfd84](https://www.google.com/url?q=https%3A%2F%2Fmedium.com%2F%40deepmindsafetyresearch%2Fscalable- agent-alignment-via-reward-modeling- bf4ab06dfd84&sa=D&sntz=1&usg=AOvVaw05WoMuEIbirGGfcT9-mcuR) Google's DeepMind Can Support, Defeat Humans in Quake III Arena - ExtremeTech [https://www.extremetech.com/extreme/292409-googles-deepmind-can-support- defeat-human-players-in-quake-iii- arena](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fextreme%2F292409-googles- deepmind-can-support-defeat-human-players-in-quake-iii- arena&sa=D&sntz=1&usg=AOvVaw3d_Jg-b-40ltqOePZzgPKy) [https://www.extremetech.com/?s=deep+mind](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2F%3Fs%3Ddeep%2Bmind&sa=D&sntz=1&usg=AOvVaw3x5zHAWyaFeEzvDSAtPpV-) | You searched for deep mind - ExtremeTech[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F254017-deepmind- ai-moves-board-games-starcraft-ii&sa=D&sntz=1&usg=AOvVaw1Hqm- oXiqQLVJlKNseT5I0)[https://www.extremetech.com/gaming/254017-deepmind-ai- moves-board-games-starcraft- ii](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F254017-deepmind- ai-moves-board-games-starcraft-ii&sa=D&sntz=1&usg=AOvVaw1Hqm- oXiqQLVJlKNseT5I0) | DeepMind AI Moves on from Board Games to StarCraft II - ExtremeTech[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F284441-deepmind- ai-challenges-pro-starcraft-ii-players-wins-almost-every- match&sa=D&sntz=1&usg=AOvVaw34zt37z9JUzT0vVbdrGars)[https://www.extremetech.com/gaming/284441-deepmind- ai-challenges-pro-starcraft-ii-players-wins-almost-every- match](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F284441-deepmind- ai-challenges-pro-starcraft-ii-players-wins-almost-every- match&sa=D&sntz=1&usg=AOvVaw34zt37z9JUzT0vVbdrGars) | DeepMind AI Challenges Pro StarCraft II Players, Wins Almost Every Match - ExtremeTech[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.engadget.com%2F2016%2F11%2F18%2Fgoogle- deepmind-ai- unreal%2F&sa=D&sntz=1&usg=AOvVaw28gsTYLTDGJdtAWgt6cnzx)[https://www.engadget.com/2016/11/18/google- deepmind-ai- unreal/](https://www.google.com/url?q=https%3A%2F%2Fwww.engadget.com%2F2016%2F11%2F18%2Fgoogle- deepmind-ai-unreal%2F&sa=D&sntz=1&usg=AOvVaw28gsTYLTDGJdtAWgt6cnzx) | Google's DeepMind AI gets a few new tricks to learn faster[ ](https://www.youtube.com/results)? Robot arm **There are 4 Courses in this Specialization** **Course** 1 [ **Fundamentals of Reinforcement Learning**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Ffundamentals- of-reinforcement-learning&sa=D&sntz=1&usg=AOvVaw3PTpSRw_TOX9WayLHdHpIm) 4.8 stars 801 ratings • 205 reviews Reinforcement Learning is a subfield of Machine Learning, but is also a general purpose formalism for automated decision-making and AI. This course introduces you to statistical learning techniques where an agent explicitly takes actions and interacts with the world. Understanding the importance and challenges of learning agents that make decisions is of vital importance today, with more and more companies interested in interactive agents and intelligent decision-making. This course introduces you to the fundamentals of Reinforcement Learning. When you finish this course, you will: - Formalize problems as Markov Decision Processes - Understand basic exploration methods and the exploration/exploitation tradeoff - Understand value functions, as a general- purpose tool for optimal decision-making - Know how to implement dynamic programming as an efficient solution approach to an industrial control problem This course teaches you the key concepts of Reinforcement Learning, underlying classic and modern algorithms in RL. After completing this course, you will be able to start using RL for real problems, where you have or can specify the MDP. This is the first course of the Reinforcement Learning Specialization. [SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement- learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq) **Course** 2 [ **Sample-based Learning Methods**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fsample- based-learning-methods&sa=D&sntz=1&usg=AOvVaw2WxPVveAA-1MWiJhTop9nZ) 4.8 stars 397 ratings • 75 reviews In this course, you will learn about several algorithms that can learn near optimal policies based on trial and error interaction with the environment--- learning from the agent’s own experience. Learning from actual experience is striking because it requires no prior knowledge of the environment’s dynamics, yet can still attain optimal behavior. We will cover intuitively simple but powerful Monte Carlo methods, and temporal difference learning methods including Q-learning. We will wrap up this course investigating how we can get the best of both worlds: algorithms that can combine model-based planning (similar to dynamic programming) and temporal difference updates to radically accelerate learning. By the end of this course you will be able to: - Understand Temporal- Difference learning and Monte Carlo as two strategies for estimating value functions from sampled experience - Understand the importance of exploration, when using sampled experience rather than dynamic programming sweeps within a model - Understand the connections between Monte Carlo and Dynamic Programming and TD. - Implement and apply the TD algorithm, for estimating value functions - Implement and apply Expected Sarsa and Q-learning (two TD methods for control) - Understand the difference between on-policy and off-policy control - Understand planning with simulated experience (as opposed to classic planning strategies) - Implement a model-based approach to RL, called Dyna, which uses simulated experience - Conduct an empirical study to see the improvements in sample efficiency when using Dyna [SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement- learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq) **Course** 3 [ **Prediction and Control with Function Approximation**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fprediction- control-function-approximation&sa=D&sntz=1&usg=AOvVaw0l2Hw5t6C2PY6t-i3FKdsH) 4.8 stars 252 ratings • 40 reviews In this course, you will learn how to solve problems with large, high- dimensional, and potentially infinite state spaces. You will see that estimating value functions can be cast as a supervised learning problem--- function approximation---allowing you to build agents that carefully balance generalization and discrimination in order to maximize reward. We will begin this journey by investigating how our policy evaluation or prediction methods like Monte Carlo and TD can be extended to the function approximation setting. You will learn about feature construction techniques for RL, and representation learning via neural networks and backprop. We conclude this course with a deep-dive into policy gradient methods; a way to learn policies directly without learning a value function. In this course you will solve two continuous-state control tasks and investigate the benefits of policy gradient methods in a continuous-action environment. Prerequisites: This course strongly builds on the fundamentals of Courses 1 and 2, and learners should have completed these before starting this course. Learners should also be comfortable with probabilities & expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), and implementing algorithms from pseudocode. By the end of this course, you will be able to: -Understand how to use supervised learning approaches to approximate value functions -Understand objectives for prediction (value estimation) under function approximation -Implement TD with function approximation (state aggregation), on an environment with an infinite state space (continuous state space) -Understand fixed basis and neural network approaches to feature construction -Implement TD with neural network function approximation in a continuous state environment -Understand new difficulties in exploration when moving to function approximation -Contrast discounted problem formulations for control versus an average reward problem formulation -Implement expected Sarsa and Q-learning with function approximation on a continuous state control task -Understand objectives for directly estimating policies (policy gradient objectives) -Implement a policy gradient method (called Actor-Critic) on a discrete state environment [SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement- learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq) **Course** 4 [ **A Complete Reinforcement Learning System (Capstone)**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fcomplete- reinforcement-learning-system&sa=D&sntz=1&usg=AOvVaw1cPdyaSfUjhZu1SLxl8DIm) 4.6 stars 177 ratings • 33 reviews In this final course, you will put together your knowledge from Courses 1, 2 and 3 to implement a complete RL solution to a problem. This capstone will let you see how each component---problem formulation, algorithm selection, parameter selection and representation design---fits together into a complete solution, and how to make appropriate choices when deploying RL in the real world. This project will require you to implement both the environment to stimulate your problem, and a control agent with Neural Network function approximation. In addition, you will conduct a scientific study of your learning system to develop your ability to assess the robustness of RL agents. To use RL in the real world, it is critical to (a) appropriately formalize the problem as an MDP, (b) select appropriate algorithms, (c ) identify what choices in your implementation will have large impacts on performance and (d) validate the expected behaviour of your algorithms. This capstone is valuable for anyone who is planning on using RL to solve real problems. To be successful in this course, you will need to have completed Courses 1, 2, and 3 of this Specialization or the equivalent. By the end of this course, you will be able to: Complete an RL solution to a problem, starting from problem formulation, appropriate algorithm selection and implementation and empirical study into the effectiveness of the solution. [SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement- learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq) **Using pre trained model to train deeper and lager model** Imitation Learning Safety Gym, a suite of environments and tools for measuring progress towards reinforcement learning agents that respect safety constraints while training. It also provides a standardized method of comparing algorithms and how well they avoid costly mistakes while learning. If deep reinforcement learning is applied to the real world, whether in robotics or internet-based tasks, it will be important to have algorithms that are safe even while learning—like a self-driving car that can learn to avoid accidents without actually having to experience them. Credit: Two Minute Papers, OpenAI Follow me for more AI/ Datascience posts:[ ](https://www.google.com/url?q=https%3A%2F%2Flnkd.in%2FgZu463X&sa=D&sntz=1&usg=AOvVaw04QVerqlEJzF6ri9VsjWi4)[https://lnkd.in/gZu463X](https://www.google.com/url?q=https%3A%2F%2Flnkd.in%2FgZu463X&sa=D&sntz=1&usg=AOvVaw04QVerqlEJzF6ri9VsjWi4) [OpenAI Safety Gym: A Safe Place For AIs To Learn 💪](https://www.youtube.com/watch?v=_s7Bg6yVOdo) **DeepMind proposes novel way to train ‘safe’ reinforcement learning AI** [https://venturebeat.com/2019/12/13/deepmind-proposes-novel-way-to-train-safe- reinforcement-learning-ai/?fbclid=IwAR22JRwC48YaKLICmYQTOjuKP- cEcE4_biuCSAxFuHNqgeJIhhineg1PTIk](https://www.google.com/url?q=https%3A%2F%2Fventurebeat.com%2F2019%2F12%2F13%2Fdeepmind- proposes-novel-way-to-train-safe-reinforcement-learning- ai%2F%3Ffbclid%3DIwAR22JRwC48YaKLICmYQTOjuKP- cEcE4_biuCSAxFuHNqgeJIhhineg1PTIk&sa=D&sntz=1&usg=AOvVaw33y42wwJ7GVtU9yQ- HA8tJ) **The Batch** Issue 35 **Different Skills From Different Demos** Reinforcement learning trains models by trial and error. In batch reinforcement learning (BRL), models learn by observing many demonstrations by a variety of actors. For instance, a robot might learn how to fix ingrown toenails by watching hundreds of surgeons perform the procedure. But what if one doctor is handier with a scalpel while another excels at suturing? A new method lets models absorb the best skills from each. **What’s new:** Ajay Mandlekar and collaborators at Nvidia, Stanford, and the University of Toronto devised a BRL technique that enables models to learn different portions of a task from different examples. This way, the model can gain useful information from inconsistent examples.[ ](https://www.google.com/url?q=https%3A%2F%2Finfo.deeplearning.ai%2Fe2t%2Fc%2F*W93MmRy8ctVj4W8CrRWn19CRs_0%2F*N5ZNrfGWSg4SV- QHFT8ZG-6n0%2F5%2Ff18dQhb0Sjv48XJblHN7fK6lMHyjJqVQsN5n1pgP_0N3hHhdwVMsQMVnQ9Qq8Zy_B4W1Tdc1j55VM8QW5p680S1FW12JMsgqy7twk7pW4bJ02h4b_rKwW7MbC3k1ShBCtVsVwJ95ltllnVJ2dtV2yJF1WVYT2jk6P4lCXW3Wdv8v6Pkt_VW62_rW_5YFJKDW96dt4S4r1QvYVNHCtz8gjY6LW8WBKbV56sy_8W2NhXK38W2TXnVJd1xK2Mn6xFW2d0Xx01RMFRvW9f4wHg7HmNBdW76lKpq2sC8-JW1ysXGq6WYC_zVpjpzw6Vshk0W4hvzF86VX7kWW4cPj6g5-b0qgW5NpS3-7qrJl_W56vZfs2MJXfMW2tBw4W6wd4SGN4lBZ6Fnpt4xW6GTq088Ph58-W594SN81HkWtdW7t3MQ17y2wLbW2F6lCB1L9wgVW4J35Sn2N3p-BW5Wp1wj8_6fCxW754Rys8_bnGcW8LMdgb7jYjT_W39d2Vf6Q3Qs6MrLh9QrHMy0f2Q9pjJ02&sa=D&sntz=1&usg=AOvVaw1n2jZIG6q7VlX9dohRw2XF)[Implicit Reinforcement without Interaction at Scale](https://www.google.com/url?q=https%3A%2F%2Finfo.deeplearning.ai%2Fe2t%2Fc%2F*W93MmRy8ctVj4W8CrRWn19CRs_0%2F*N5ZNrfGWSg4SV- QHFT8ZG-6n0%2F5%2Ff18dQhb0Sjv48XJblHN7fK6lMHyjJqVQsN5n1pgP_0N3hHhdwVMsQMVnQ9Qq8Zy_B4W1Tdc1j55VM8QW5p680S1FW12JMsgqy7twk7pW4bJ02h4b_rKwW7MbC3k1ShBCtVsVwJ95ltllnVJ2dtV2yJF1WVYT2jk6P4lCXW3Wdv8v6Pkt_VW62_rW_5YFJKDW96dt4S4r1QvYVNHCtz8gjY6LW8WBKbV56sy_8W2NhXK38W2TXnVJd1xK2Mn6xFW2d0Xx01RMFRvW9f4wHg7HmNBdW76lKpq2sC8-JW1ysXGq6WYC_zVpjpzw6Vshk0W4hvzF86VX7kWW4cPj6g5-b0qgW5NpS3-7qrJl_W56vZfs2MJXfMW2tBw4W6wd4SGN4lBZ6Fnpt4xW6GTq088Ph58-W594SN81HkWtdW7t3MQ17y2wLbW2F6lCB1L9wgVW4J35Sn2N3p-BW5Wp1wj8_6fCxW754Rys8_bnGcW8LMdgb7jYjT_W39d2Vf6Q3Qs6MrLh9QrHMy0f2Q9pjJ02&sa=D&sntz=1&usg=AOvVaw1n2jZIG6q7VlX9dohRw2XF) (IRIS) achieved state-of-the-art BRL performance in three tasks performed in a virtual environment. **Key insight:** Learning from demonstrations is a double-edged sword. An agent gets to see how to complete a task, but the scope of its action is limited to the most complete demonstration of a given task. IRIS breaks down tasks into sequences of intermediate subgoals. Then it performs the actions required to accomplish each subgoal. In this way, the agent learns from the best parts of each demonstration and combines them to accomplish the task. **How it works:** IRIS includes a subgoal selection model that predicts intermediate points on the way to accomplishing an assigned task. These subgoals are defined automatically by the algorithm, and may not correspond to parts of a task as humans would describe them. A controller network tries to replicate the optimal sequence of actions leading to a given subgoal. * The subgoal selection model is made up of a conditional variational autoencoder that produces a set of possible subgoals and a value function (trained via a BRL version of Q-learning) that predicts which next subgoal will lead to the highest reward. * The controller is a recurrent neural network that decides on the actions required to accomplish the current subgoal. It learns to predict how demonstrations tend to unfold, and to imitate short sequences of actions from specific demonstrations. * Once it’s trained, the subgoal selection model determines the next subgoal. The controller takes the requisite actions. Then the subgoal selection model evaluates the current state and computes a new subgoal, and so on. **Results:** In the Robosuite's lifting and pick-and-place tasks, previous state-of-the-art BRL approaches couldn't pick up objects reliably, nor place them elsewhere at all. IRIS learned to pick up objects with over 80 percent success and placed them with 30 percent success. **Why it matters:** Automatically identifying subgoals has been a holy grail in reinforcement learning, with active research in hierarchical RL and other areas. The method used in this paper applies to relatively simple tasks where things happen in a predictable sequence (such as picking and then placing), but might be a small step in an important direction. **We’re thinking:** Batch reinforcement learning is useful when a model must be interpretable or safe — after all, a robotic surgeon shouldn’t experiment on living patients — but it hasn’t been terribly effective. IRIS could make it a viable option. Dec 11, 2019 Issue 34 **Seeing the World Blindfolded** In reinforcement learning, if researchers want an agent to have an internal representation of its environment, they’ll build and train a world model that it can refer to. New research shows that world models can emerge from standard training, rather than needing to be built separately. **What’s new:** Google Brain researchers C. Daniel Freeman, Luke Metz, and David Ha enabled an agent to build a world model by blindfolding it as it learned to accomplish tasks. They call their approach[ ](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1910.13038&sa=D&sntz=1&usg=AOvVaw19a0ZWmEG6OitJ7uRtgAiJ)[observational dropout](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1910.13038&sa=D&sntz=1&usg=AOvVaw19a0ZWmEG6OitJ7uRtgAiJ). **Key insight:** Blocking an agent's observations of the world at random moments forces it to generate its own internal representation to fill in the gaps. The agent learns this representation without being instructed to predict how the environment will change in response to its actions. **How it works:** At every timestep, the agent acts on either its observation (framed in red in the video above) or its prediction of what it wasn’t able to observe (imagery not framed in red). The agent contains a controller that decides on the most rewarding action. To compute the potential reward of a given action, the agent includes an additional deep net trained using the RL algorithm REINFORCE. * Observational dropout blocks the agent from observing the environment according to a user-defined probability. When this happens, the agent predicts an observation. * If random blindfolding blocks several observations in a row, the agent uses its most recent prediction to generate the next one. * This procedure over many iterations produces a sequence of observations and predictions. The agent learns from this sequence, and its ability to predict blocked observations is tantamount to a world model. **Results:** Observational dropout solved the task known as[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fopenai%2Fgym%2Fwiki%2FCartPole-v0&sa=D&sntz=1&usg=AOvVaw3n6wYAZyMbzRpWSK2gORJW)[Cartpole](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fopenai%2Fgym%2Fwiki%2FCartPole-v0&sa=D&sntz=1&usg=AOvVaw3n6wYAZyMbzRpWSK2gORJW), in which the model must balance a pole upright on a rolling cart, even when its view of the world was blocked 90 percent of the time. In a more complex[ ](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1809.01999&sa=D&sntz=1&usg=AOvVaw2XKaDs9JT_6xvavFR00hoz)[Car Racing](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1809.01999&sa=D&sntz=1&usg=AOvVaw2XKaDs9JT_6xvavFR00hoz) task, in which a model must navigate a car around a track as fast as possible, the model performed almost equally well whether it was allowed to see its surroundings or blindfolded up to 60 percent of the time. **Why it matters:** Modeling reality is often part art and part science. World models generated by observational dropout aren’t perfect representations, but they’re sufficient for some tasks. This work could lead to simple-but-effective world models of complex environments that are impractical to model completely. **We’re thinking:** Technology being imperfect, observational dropout is a fact of life, not just a research technique. A self-driving car or auto- piloted airplane reliant on sensors that drop data points could create a catastrophe. This technique could make high-stakes RL models more robust. Dec 4, 2019 Issue 33 **Is AI Making Mastery Obsolete?** Is there any reason to continue playing games that AI has mastered? Ask the former champions who have been toppled by machines. **What happened:** In 2016, International Go master Lee Sedol famously lost three out of four matches to DeepMind’s AlphaGo model. The 36-year-old announced his retirement from competition on November 27. “Even if I become the number one, there is an entity that cannot be defeated,” he[ ](https://www.google.com/url?q=https%3A%2F%2Fen.yna.co.kr%2Fview%2FAEN20191127004800315&sa=D&sntz=1&usg=AOvVaw0PMZN81SZTeJRnateNxsLl)[told](https://www.google.com/url?q=https%3A%2F%2Fen.yna.co.kr%2Fview%2FAEN20191127004800315&sa=D&sntz=1&usg=AOvVaw0PMZN81SZTeJRnateNxsLl) South Korean's Yonhap News Agency, **Stages of grief:** Prior to the tournament, Lee predicted that he would defeat AlphaGo easily. But the model’s inexplicable — and indefatigable — playing style pushed him into fits of[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.theverge.com%2F2017%2F10%2F11%2F16460118%2Falphago- deepmind-ai-documentary-go-lee-sedol-film- review&sa=D&sntz=1&usg=AOvVaw1BJ4ioQcgpxgNxmnVXr8kN)[shock and disbelief](https://www.google.com/url?q=https%3A%2F%2Fwww.theverge.com%2F2017%2F10%2F11%2F16460118%2Falphago- deepmind-ai-documentary-go-lee-sedol-film- review&sa=D&sntz=1&usg=AOvVaw1BJ4ioQcgpxgNxmnVXr8kN). Afterward, he[ ](https://www.google.com/url?q=https%3A%2F%2Fventurebeat.com%2F2016%2F03%2F12%2Fgo- board-game-champion-lee-sedol-apologizes-for-losing-to-googles- ai%2F&sa=D&sntz=1&usg=AOvVaw18En2cGIvD1uVmya6kwvX-)[apologized](https://www.google.com/url?q=https%3A%2F%2Fventurebeat.com%2F2016%2F03%2F12%2Fgo- board-game-champion-lee-sedol-apologizes-for-losing-to-googles- ai%2F&sa=D&sntz=1&usg=AOvVaw18En2cGIvD1uVmya6kwvX-) for his failure to the South Korean public. **Reaching acceptance:** Garry Kasparov, the former world-champion chess player, went through his own cycle of grief after being defeated by IBM’s DeepBlue in 1997. Although he didn’t retire, Kasparov did accuse IBM’s engineers of cheating. He later retracted the charge, and in 2017 wrote a book[ ](http://www.google.com/url?q=http%3A%2F%2Fwww.kasparov.com%2Fgarry- kasparov-says-ai-can-make-us-more-human-pcmag-interview- march-20th-2019%2F&sa=D&sntz=1&usg=AOvVaw3Lfe53n3SoFR4qDryUTD0P)[arguing](http://www.google.com/url?q=http%3A%2F%2Fwww.kasparov.com%2Fgarry- kasparov-says-ai-can-make-us-more-human-pcmag-interview- march-20th-2019%2F&sa=D&sntz=1&usg=AOvVaw3Lfe53n3SoFR4qDryUTD0P) that, if humans can overcome their feelings of being threatened by AI, they can learn from it. The book advocates an augmented intelligence in which humans and machines work together to solve problems. **The human element:** Although AlphaGo won in the 2016 duel, its human opponent still managed to shine. During the fourth match, Sedol made a[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.wired.com%2F2016%2F03%2Ftwo- moves-alphago-lee-sedol-redefined- future%2F&sa=D&sntz=1&usg=AOvVaw1Pt6XPLhDa8cMRMxOtI951)[move](https://www.google.com/url?q=https%3A%2F%2Fwww.wired.com%2F2016%2F03%2Ftwo- moves-alphago-lee-sedol-redefined- future%2F&sa=D&sntz=1&usg=AOvVaw1Pt6XPLhDa8cMRMxOtI951) so unconventional it defied AlphaGo’s expectation and led to his sole victory. **We’re thinking:** Lee wasn't defeated by a machine alone. He was beaten by a machine built by humans under the direction of AlphaGo research lead David Silver. Human mastery is obsolete only if you ignore people like Silver and his team. Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/VnELnzCZElXe9gLxGYU00_xF7qju2MljSVlgUMwWsc50I88T6vB5ahQjH2kGA --o3hIeJYu2N--BO_uidCis2Ow=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/VnELnzCZElXe9gLxGYU00_xF7qju2MljSVlgUMwWsc50I88T6vB5ahQjH2kGA --o3hIeJYu2N--BO_uidCis2Ow=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # My paper: A Comprehensive Review on Deep Reinforcement Learning The updates 2021 YouTube Notes and info Links: Reading List (Video, Conference, Workshop, Paper) ### The updates Dear friends, I recently wrote a survey paper on "A Comprehensive Review on Deep Reinforcement Learning: A Survey", with some of the leading AI and DRL researchers (including): In this work, we covered top recent DRL works, grouped into several categories. We were lucky to have you, as the external reviewers of this work. I hope this is useful for the research community. Any feedback will be highly welcomed. You can find its summary here too. Imitation learning, expert (teacher), hierarchical, hybrid imitation, high performance parallelism, # 2021 * [NeurIPS 2020: Key Research Papers in Reinforcement Learning and More](https://www.google.com/url?q=https%3A%2F%2Fwww.topbots.com%2Fneurips-2020-rl-research-papers%2F&sa=D&sntz=1&usg=AOvVaw3fWwOQ2N8Z8mHGNysZedTo) * [Key Papers in Deep RL](https://www.google.com/url?q=https%3A%2F%2Fspinningup.openai.com%2Fen%2Flatest%2Fspinningup%2Fkeypapers.html&sa=D&sntz=1&usg=AOvVaw3sZcgv4fIlIg3pmq6_O_q2) ### YouTube * Simple Deep Q Network w/Pytorch:[ https://youtu.be/UlJzzLYgYoE](https://youtu.be/UlJzzLYgYoE) * Reinforcement Learning Crash Course:[ https://youtu.be/sOiNMW8k4T0](https://youtu.be/sOiNMW8k4T0) * Policy Gradients w/Tensorflow:[ https://youtu.be/UT9pQjVhcaU](https://youtu.be/UT9pQjVhcaU) * Deep Q Learning w/Tensorflow[ https://youtu.be/3Ggq_zoRGP4](https://youtu.be/3Ggq_zoRGP4) * Code Your Own RL Environments[ https://youtu.be/vmrqpHldAQ0](https://youtu.be/vmrqpHldAQ0) * How to Spec a Deep Learning PC:[ https://youtu.be/xsnVlMWQj8o](https://youtu.be/xsnVlMWQj8o) * Deep Q Learning w/ Pytorch:[ https://youtu.be/RfNxXlO6BiA](https://youtu.be/RfNxXlO6BiA) * Machine Learning Freelancing[ https://youtu.be/6M04ZTLE_O4](https://youtu.be/6M04ZTLE_O4) * **Code from video** :[ https://github.com/philtabor/Youtube-Code-Repository](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fphiltabor%2FYoutube-Code-Repository&sa=D&sntz=1&usg=AOvVaw0lZg33Rz9-UZsCI8LPkgPG) ### Notes and info * training on unlabeled data, lifelong learning, and especially letting models explore a simulated environment before transferring what they learn to the real world * Lately, simulation has helped achieve impressive results in reinforcement learning, which is extremely data-intensive. * using reinforcement learning to train robots that reason about how their actions will affect their environment. * How is it that many people learn to drive a car fairly safely in 20 hours of practice, while current imitation learning algorithms take hundreds of thousands of hours, and reinforcement learning algorithms take millions of hours? Clearly we’re missing something big. * In 2021, I expect self-supervised methods to learn features of video and images. Could there be a similar revolution in high-dimensional continuous data like video? * One critical challenge is dealing with uncertainty. Models like BERT can’t tell if a missing word in a sentence is “cat” or “dog,” but they can produce a probability distribution vector. We don’t have a good model of probability distributions for images or video frames. But recent research is coming so close that we’re likely to find it soon. * Suddenly we’ll get really good performance predicting actions in videos with very few training samples, where it wasn’t possible before. That would make the coming year a very exciting time in AI. * DeepMind released the code, model, & dataset behind their groundbreaking "AlphaFold" system. It's predicts protein shapes from genomic data with apps in health, sustainability, & materials design ### Links: * [https://techgrabyte.com/google-framework-reduces-ai-training-costs-seed-rl/?fbclid=IwAR07KznmEO4nCYIMJM9Zv53HDoQEHvHH9FoUHqfPDyRvoucayu3p4nzROfU](https://www.google.com/url?q=https%3A%2F%2Ftechgrabyte.com%2Fgoogle-framework-reduces-ai-training-costs-seed-rl%2F%3Ffbclid%3DIwAR07KznmEO4nCYIMJM9Zv53HDoQEHvHH9FoUHqfPDyRvoucayu3p4nzROfU&sa=D&sntz=1&usg=AOvVaw2YNi0EWFi_liPQK9abco8U) ### Reading List (Video, Conference, Workshop, Paper) * [https://sites.google.com/view/icml19metalearning](https://www.google.com/url?q=https%3A%2F%2Fsites.google.com%2Fview%2Ficml19metalearning&sa=D&sntz=1&usg=AOvVaw0qCxTbeQF-J_3tGBq--FD4) DeepMind Open-Sources Lab2D, A System For The Creation Of 2D Environments For Machine Learning * Github:[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fdeepmind%2Flab2d%3Ffbclid%3DIwAR2axHHCby_-GbXC_VgpytDoJkAq-dxPd2JZ4vOOIEvxUqK_hakvvfo56Zk&sa=D&sntz=1&usg=AOvVaw0uAYAMc2ud2QYDJ68E0Mre)[https://github.com/deepmind/lab2d](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fdeepmind%2Flab2d%3Ffbclid%3DIwAR2axHHCby_-GbXC_VgpytDoJkAq-dxPd2JZ4vOOIEvxUqK_hakvvfo56Zk&sa=D&sntz=1&usg=AOvVaw0uAYAMc2ud2QYDJ68E0Mre) * Paper:[ ](https://www.google.com/url?q=https%3A%2F%2Fl.facebook.com%2Fl.php%3Fu%3Dhttps%253A%252F%252Farxiv.org%252Fpdf%252F2011.07027.pdf%253Ffbclid%253DIwAR2xbhrokYigw3yFTJhg3TI5OOKO-WQKX3n1TMRk1oBsi7Yr0ZSMO-iy1OI%26h%3DAT2hq3duwK0TgamyFGpwGfXdFVwnzjio0hbbMV0kghZsLkgTmoQEC2qTOJq5d9etSs1sufzhMci5ZkgbHXGwnZFNQOGRq10viKy9bcsNcfVmbMcVs-pYbLxcgtY4GwlnwENJ0h0%26__tn__%3D-UK-R%26c%5B0%5D%3DAT0yOKaA4J_MP8vKht-A_dhqDP3hEP_Vygu41a-f4Z5wKa3uQd80YA5NtTbdmq1mVtD4wNLg4V_k1Y6dHg2dcaGQrZ5sN19swM8C0oUWc7IobWSB2kF-wjD7uhtsj2QH1y0d9BdaaELVvniBl7UST1UviF2U5BQquuGFXWIxvIR7-3rq-62c7phKFbMVVoC6ar_N-IlZT8pjhapy0xSq8g&sa=D&sntz=1&usg=AOvVaw2LI1WQU1nUuooUmNb2yNvk)[https://arxiv.org/pdf/2011.07027.pdf](https://www.google.com/url?q=https%3A%2F%2Fl.facebook.com%2Fl.php%3Fu%3Dhttps%253A%252F%252Farxiv.org%252Fpdf%252F2011.07027.pdf%253Ffbclid%253DIwAR2xbhrokYigw3yFTJhg3TI5OOKO-WQKX3n1TMRk1oBsi7Yr0ZSMO-iy1OI%26h%3DAT2hq3duwK0TgamyFGpwGfXdFVwnzjio0hbbMV0kghZsLkgTmoQEC2qTOJq5d9etSs1sufzhMci5ZkgbHXGwnZFNQOGRq10viKy9bcsNcfVmbMcVs-pYbLxcgtY4GwlnwENJ0h0%26__tn__%3D-UK-R%26c%5B0%5D%3DAT0yOKaA4J_MP8vKht-A_dhqDP3hEP_Vygu41a-f4Z5wKa3uQd80YA5NtTbdmq1mVtD4wNLg4V_k1Y6dHg2dcaGQrZ5sN19swM8C0oUWc7IobWSB2kF-wjD7uhtsj2QH1y0d9BdaaELVvniBl7UST1UviF2U5BQquuGFXWIxvIR7-3rq-62c7phKFbMVVoC6ar_N-IlZT8pjhapy0xSq8g&sa=D&sntz=1&usg=AOvVaw2LI1WQU1nUuooUmNb2yNvk) * Summary:[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.marktechpost.com%2F2020%2F11%2F18%2Fdeepmind-open-sources-lab2d-a-system-for-the-creation-of-2d-environments-for-machine-learning%2F%3Ffbclid%3DIwAR1GbPd7fe4Gk2u7UNGi7lJmlVsbB6A1wcryPAtCLe_zQKcEz0-xQZxvnRI&sa=D&sntz=1&usg=AOvVaw3lgQoAseygJ1cfWNGun9UY)[https://www.marktechpost.com/.../deepmind-open-sources.../](https://www.google.com/url?q=https%3A%2F%2Fwww.marktechpost.com%2F2020%2F11%2F18%2Fdeepmind-open-sources-lab2d-a-system-for-the-creation-of-2d-environments-for-machine-learning%2F%3Ffbclid%3DIwAR1GbPd7fe4Gk2u7UNGi7lJmlVsbB6A1wcryPAtCLe_zQKcEz0-xQZxvnRI&sa=D&sntz=1&usg=AOvVaw3lgQoAseygJ1cfWNGun9UY) Google to release DeepMind's StreetLearn for teaching machine-learning agents to navigate cities [https://www.techrepublic.com/article/google-to-release-deepminds-streetlearn- for-teaching-machine-learning-agents-to-navigate- cities/](https://www.google.com/url?q=https%3A%2F%2Fwww.techrepublic.com%2Farticle%2Fgoogle- to-release-deepminds-streetlearn-for-teaching-machine-learning-agents-to- navigate-cities%2F&sa=D&sntz=1&usg=AOvVaw0miSK2RHfMotfGPdU2nvQI) Scalable agent alignment via reward modeling – DeepMind Safety Research – Medium [https://medium.com/@deepmindsafetyresearch/scalable-agent-alignment-via- reward-modeling- bf4ab06dfd84](https://www.google.com/url?q=https%3A%2F%2Fmedium.com%2F%40deepmindsafetyresearch%2Fscalable- agent-alignment-via-reward-modeling- bf4ab06dfd84&sa=D&sntz=1&usg=AOvVaw05WoMuEIbirGGfcT9-mcuR) Google's DeepMind Can Support, Defeat Humans in Quake III Arena - ExtremeTech [https://www.extremetech.com/extreme/292409-googles-deepmind-can-support- defeat-human-players-in-quake-iii- arena](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fextreme%2F292409-googles- deepmind-can-support-defeat-human-players-in-quake-iii- arena&sa=D&sntz=1&usg=AOvVaw3d_Jg-b-40ltqOePZzgPKy) [https://www.extremetech.com/?s=deep+mind](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2F%3Fs%3Ddeep%2Bmind&sa=D&sntz=1&usg=AOvVaw3x5zHAWyaFeEzvDSAtPpV-) | You searched for deep mind - ExtremeTech[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F254017-deepmind- ai-moves-board-games-starcraft-ii&sa=D&sntz=1&usg=AOvVaw1Hqm- oXiqQLVJlKNseT5I0)[https://www.extremetech.com/gaming/254017-deepmind-ai- moves-board-games-starcraft- ii](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F254017-deepmind- ai-moves-board-games-starcraft-ii&sa=D&sntz=1&usg=AOvVaw1Hqm- oXiqQLVJlKNseT5I0) | DeepMind AI Moves on from Board Games to StarCraft II - ExtremeTech[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F284441-deepmind- ai-challenges-pro-starcraft-ii-players-wins-almost-every- match&sa=D&sntz=1&usg=AOvVaw34zt37z9JUzT0vVbdrGars)[https://www.extremetech.com/gaming/284441-deepmind- ai-challenges-pro-starcraft-ii-players-wins-almost-every- match](https://www.google.com/url?q=https%3A%2F%2Fwww.extremetech.com%2Fgaming%2F284441-deepmind- ai-challenges-pro-starcraft-ii-players-wins-almost-every- match&sa=D&sntz=1&usg=AOvVaw34zt37z9JUzT0vVbdrGars) | DeepMind AI Challenges Pro StarCraft II Players, Wins Almost Every Match - ExtremeTech[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.engadget.com%2F2016%2F11%2F18%2Fgoogle- deepmind-ai- unreal%2F&sa=D&sntz=1&usg=AOvVaw28gsTYLTDGJdtAWgt6cnzx)[https://www.engadget.com/2016/11/18/google- deepmind-ai- unreal/](https://www.google.com/url?q=https%3A%2F%2Fwww.engadget.com%2F2016%2F11%2F18%2Fgoogle- deepmind-ai-unreal%2F&sa=D&sntz=1&usg=AOvVaw28gsTYLTDGJdtAWgt6cnzx) | Google's DeepMind AI gets a few new tricks to learn faster[ ](https://www.youtube.com/results)? Robot arm **There are 4 Courses in this Specialization** **Course** 1 [ **Fundamentals of Reinforcement Learning**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Ffundamentals- of-reinforcement-learning&sa=D&sntz=1&usg=AOvVaw3PTpSRw_TOX9WayLHdHpIm) 4.8 stars 801 ratings • 205 reviews Reinforcement Learning is a subfield of Machine Learning, but is also a general purpose formalism for automated decision-making and AI. This course introduces you to statistical learning techniques where an agent explicitly takes actions and interacts with the world. Understanding the importance and challenges of learning agents that make decisions is of vital importance today, with more and more companies interested in interactive agents and intelligent decision-making. This course introduces you to the fundamentals of Reinforcement Learning. When you finish this course, you will: - Formalize problems as Markov Decision Processes - Understand basic exploration methods and the exploration/exploitation tradeoff - Understand value functions, as a general- purpose tool for optimal decision-making - Know how to implement dynamic programming as an efficient solution approach to an industrial control problem This course teaches you the key concepts of Reinforcement Learning, underlying classic and modern algorithms in RL. After completing this course, you will be able to start using RL for real problems, where you have or can specify the MDP. This is the first course of the Reinforcement Learning Specialization. [SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement- learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq) **Course** 2 [ **Sample-based Learning Methods**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fsample- based-learning-methods&sa=D&sntz=1&usg=AOvVaw2WxPVveAA-1MWiJhTop9nZ) 4.8 stars 397 ratings • 75 reviews In this course, you will learn about several algorithms that can learn near optimal policies based on trial and error interaction with the environment--- learning from the agent’s own experience. Learning from actual experience is striking because it requires no prior knowledge of the environment’s dynamics, yet can still attain optimal behavior. We will cover intuitively simple but powerful Monte Carlo methods, and temporal difference learning methods including Q-learning. We will wrap up this course investigating how we can get the best of both worlds: algorithms that can combine model-based planning (similar to dynamic programming) and temporal difference updates to radically accelerate learning. By the end of this course you will be able to: - Understand Temporal- Difference learning and Monte Carlo as two strategies for estimating value functions from sampled experience - Understand the importance of exploration, when using sampled experience rather than dynamic programming sweeps within a model - Understand the connections between Monte Carlo and Dynamic Programming and TD. - Implement and apply the TD algorithm, for estimating value functions - Implement and apply Expected Sarsa and Q-learning (two TD methods for control) - Understand the difference between on-policy and off-policy control - Understand planning with simulated experience (as opposed to classic planning strategies) - Implement a model-based approach to RL, called Dyna, which uses simulated experience - Conduct an empirical study to see the improvements in sample efficiency when using Dyna [SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement- learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq) **Course** 3 [ **Prediction and Control with Function Approximation**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fprediction- control-function-approximation&sa=D&sntz=1&usg=AOvVaw0l2Hw5t6C2PY6t-i3FKdsH) 4.8 stars 252 ratings • 40 reviews In this course, you will learn how to solve problems with large, high- dimensional, and potentially infinite state spaces. You will see that estimating value functions can be cast as a supervised learning problem--- function approximation---allowing you to build agents that carefully balance generalization and discrimination in order to maximize reward. We will begin this journey by investigating how our policy evaluation or prediction methods like Monte Carlo and TD can be extended to the function approximation setting. You will learn about feature construction techniques for RL, and representation learning via neural networks and backprop. We conclude this course with a deep-dive into policy gradient methods; a way to learn policies directly without learning a value function. In this course you will solve two continuous-state control tasks and investigate the benefits of policy gradient methods in a continuous-action environment. Prerequisites: This course strongly builds on the fundamentals of Courses 1 and 2, and learners should have completed these before starting this course. Learners should also be comfortable with probabilities & expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), and implementing algorithms from pseudocode. By the end of this course, you will be able to: -Understand how to use supervised learning approaches to approximate value functions -Understand objectives for prediction (value estimation) under function approximation -Implement TD with function approximation (state aggregation), on an environment with an infinite state space (continuous state space) -Understand fixed basis and neural network approaches to feature construction -Implement TD with neural network function approximation in a continuous state environment -Understand new difficulties in exploration when moving to function approximation -Contrast discounted problem formulations for control versus an average reward problem formulation -Implement expected Sarsa and Q-learning with function approximation on a continuous state control task -Understand objectives for directly estimating policies (policy gradient objectives) -Implement a policy gradient method (called Actor-Critic) on a discrete state environment [SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement- learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq) **Course** 4 [ **A Complete Reinforcement Learning System (Capstone)**](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fcomplete- reinforcement-learning-system&sa=D&sntz=1&usg=AOvVaw1cPdyaSfUjhZu1SLxl8DIm) 4.6 stars 177 ratings • 33 reviews In this final course, you will put together your knowledge from Courses 1, 2 and 3 to implement a complete RL solution to a problem. This capstone will let you see how each component---problem formulation, algorithm selection, parameter selection and representation design---fits together into a complete solution, and how to make appropriate choices when deploying RL in the real world. This project will require you to implement both the environment to stimulate your problem, and a control agent with Neural Network function approximation. In addition, you will conduct a scientific study of your learning system to develop your ability to assess the robustness of RL agents. To use RL in the real world, it is critical to (a) appropriately formalize the problem as an MDP, (b) select appropriate algorithms, (c ) identify what choices in your implementation will have large impacts on performance and (d) validate the expected behaviour of your algorithms. This capstone is valuable for anyone who is planning on using RL to solve real problems. To be successful in this course, you will need to have completed Courses 1, 2, and 3 of this Specialization or the equivalent. By the end of this course, you will be able to: Complete an RL solution to a problem, starting from problem formulation, appropriate algorithm selection and implementation and empirical study into the effectiveness of the solution. [SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW ALL](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Freinforcement- learning%23&sa=D&sntz=1&usg=AOvVaw27ev_Po4bBjyWe9QRNv2Kq) **Using pre trained model to train deeper and lager model** Imitation Learning Safety Gym, a suite of environments and tools for measuring progress towards reinforcement learning agents that respect safety constraints while training. It also provides a standardized method of comparing algorithms and how well they avoid costly mistakes while learning. If deep reinforcement learning is applied to the real world, whether in robotics or internet-based tasks, it will be important to have algorithms that are safe even while learning—like a self-driving car that can learn to avoid accidents without actually having to experience them. Credit: Two Minute Papers, OpenAI Follow me for more AI/ Datascience posts:[ ](https://www.google.com/url?q=https%3A%2F%2Flnkd.in%2FgZu463X&sa=D&sntz=1&usg=AOvVaw04QVerqlEJzF6ri9VsjWi4)[https://lnkd.in/gZu463X](https://www.google.com/url?q=https%3A%2F%2Flnkd.in%2FgZu463X&sa=D&sntz=1&usg=AOvVaw04QVerqlEJzF6ri9VsjWi4) [OpenAI Safety Gym: A Safe Place For AIs To Learn 💪](https://www.youtube.com/watch?v=_s7Bg6yVOdo) **DeepMind proposes novel way to train ‘safe’ reinforcement learning AI** [https://venturebeat.com/2019/12/13/deepmind-proposes-novel-way-to-train-safe- reinforcement-learning-ai/?fbclid=IwAR22JRwC48YaKLICmYQTOjuKP- cEcE4_biuCSAxFuHNqgeJIhhineg1PTIk](https://www.google.com/url?q=https%3A%2F%2Fventurebeat.com%2F2019%2F12%2F13%2Fdeepmind- proposes-novel-way-to-train-safe-reinforcement-learning- ai%2F%3Ffbclid%3DIwAR22JRwC48YaKLICmYQTOjuKP- cEcE4_biuCSAxFuHNqgeJIhhineg1PTIk&sa=D&sntz=1&usg=AOvVaw33y42wwJ7GVtU9yQ- HA8tJ) **The Batch** Issue 35 **Different Skills From Different Demos** Reinforcement learning trains models by trial and error. In batch reinforcement learning (BRL), models learn by observing many demonstrations by a variety of actors. For instance, a robot might learn how to fix ingrown toenails by watching hundreds of surgeons perform the procedure. But what if one doctor is handier with a scalpel while another excels at suturing? A new method lets models absorb the best skills from each. **What’s new:** Ajay Mandlekar and collaborators at Nvidia, Stanford, and the University of Toronto devised a BRL technique that enables models to learn different portions of a task from different examples. This way, the model can gain useful information from inconsistent examples.[ ](https://www.google.com/url?q=https%3A%2F%2Finfo.deeplearning.ai%2Fe2t%2Fc%2F*W93MmRy8ctVj4W8CrRWn19CRs_0%2F*N5ZNrfGWSg4SV- QHFT8ZG-6n0%2F5%2Ff18dQhb0Sjv48XJblHN7fK6lMHyjJqVQsN5n1pgP_0N3hHhdwVMsQMVnQ9Qq8Zy_B4W1Tdc1j55VM8QW5p680S1FW12JMsgqy7twk7pW4bJ02h4b_rKwW7MbC3k1ShBCtVsVwJ95ltllnVJ2dtV2yJF1WVYT2jk6P4lCXW3Wdv8v6Pkt_VW62_rW_5YFJKDW96dt4S4r1QvYVNHCtz8gjY6LW8WBKbV56sy_8W2NhXK38W2TXnVJd1xK2Mn6xFW2d0Xx01RMFRvW9f4wHg7HmNBdW76lKpq2sC8-JW1ysXGq6WYC_zVpjpzw6Vshk0W4hvzF86VX7kWW4cPj6g5-b0qgW5NpS3-7qrJl_W56vZfs2MJXfMW2tBw4W6wd4SGN4lBZ6Fnpt4xW6GTq088Ph58-W594SN81HkWtdW7t3MQ17y2wLbW2F6lCB1L9wgVW4J35Sn2N3p-BW5Wp1wj8_6fCxW754Rys8_bnGcW8LMdgb7jYjT_W39d2Vf6Q3Qs6MrLh9QrHMy0f2Q9pjJ02&sa=D&sntz=1&usg=AOvVaw1n2jZIG6q7VlX9dohRw2XF)[Implicit Reinforcement without Interaction at Scale](https://www.google.com/url?q=https%3A%2F%2Finfo.deeplearning.ai%2Fe2t%2Fc%2F*W93MmRy8ctVj4W8CrRWn19CRs_0%2F*N5ZNrfGWSg4SV- QHFT8ZG-6n0%2F5%2Ff18dQhb0Sjv48XJblHN7fK6lMHyjJqVQsN5n1pgP_0N3hHhdwVMsQMVnQ9Qq8Zy_B4W1Tdc1j55VM8QW5p680S1FW12JMsgqy7twk7pW4bJ02h4b_rKwW7MbC3k1ShBCtVsVwJ95ltllnVJ2dtV2yJF1WVYT2jk6P4lCXW3Wdv8v6Pkt_VW62_rW_5YFJKDW96dt4S4r1QvYVNHCtz8gjY6LW8WBKbV56sy_8W2NhXK38W2TXnVJd1xK2Mn6xFW2d0Xx01RMFRvW9f4wHg7HmNBdW76lKpq2sC8-JW1ysXGq6WYC_zVpjpzw6Vshk0W4hvzF86VX7kWW4cPj6g5-b0qgW5NpS3-7qrJl_W56vZfs2MJXfMW2tBw4W6wd4SGN4lBZ6Fnpt4xW6GTq088Ph58-W594SN81HkWtdW7t3MQ17y2wLbW2F6lCB1L9wgVW4J35Sn2N3p-BW5Wp1wj8_6fCxW754Rys8_bnGcW8LMdgb7jYjT_W39d2Vf6Q3Qs6MrLh9QrHMy0f2Q9pjJ02&sa=D&sntz=1&usg=AOvVaw1n2jZIG6q7VlX9dohRw2XF) (IRIS) achieved state-of-the-art BRL performance in three tasks performed in a virtual environment. **Key insight:** Learning from demonstrations is a double-edged sword. An agent gets to see how to complete a task, but the scope of its action is limited to the most complete demonstration of a given task. IRIS breaks down tasks into sequences of intermediate subgoals. Then it performs the actions required to accomplish each subgoal. In this way, the agent learns from the best parts of each demonstration and combines them to accomplish the task. **How it works:** IRIS includes a subgoal selection model that predicts intermediate points on the way to accomplishing an assigned task. These subgoals are defined automatically by the algorithm, and may not correspond to parts of a task as humans would describe them. A controller network tries to replicate the optimal sequence of actions leading to a given subgoal. * The subgoal selection model is made up of a conditional variational autoencoder that produces a set of possible subgoals and a value function (trained via a BRL version of Q-learning) that predicts which next subgoal will lead to the highest reward. * The controller is a recurrent neural network that decides on the actions required to accomplish the current subgoal. It learns to predict how demonstrations tend to unfold, and to imitate short sequences of actions from specific demonstrations. * Once it’s trained, the subgoal selection model determines the next subgoal. The controller takes the requisite actions. Then the subgoal selection model evaluates the current state and computes a new subgoal, and so on. **Results:** In the Robosuite's lifting and pick-and-place tasks, previous state-of-the-art BRL approaches couldn't pick up objects reliably, nor place them elsewhere at all. IRIS learned to pick up objects with over 80 percent success and placed them with 30 percent success. **Why it matters:** Automatically identifying subgoals has been a holy grail in reinforcement learning, with active research in hierarchical RL and other areas. The method used in this paper applies to relatively simple tasks where things happen in a predictable sequence (such as picking and then placing), but might be a small step in an important direction. **We’re thinking:** Batch reinforcement learning is useful when a model must be interpretable or safe — after all, a robotic surgeon shouldn’t experiment on living patients — but it hasn’t been terribly effective. IRIS could make it a viable option. Dec 11, 2019 Issue 34 **Seeing the World Blindfolded** In reinforcement learning, if researchers want an agent to have an internal representation of its environment, they’ll build and train a world model that it can refer to. New research shows that world models can emerge from standard training, rather than needing to be built separately. **What’s new:** Google Brain researchers C. Daniel Freeman, Luke Metz, and David Ha enabled an agent to build a world model by blindfolding it as it learned to accomplish tasks. They call their approach[ ](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1910.13038&sa=D&sntz=1&usg=AOvVaw19a0ZWmEG6OitJ7uRtgAiJ)[observational dropout](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1910.13038&sa=D&sntz=1&usg=AOvVaw19a0ZWmEG6OitJ7uRtgAiJ). **Key insight:** Blocking an agent's observations of the world at random moments forces it to generate its own internal representation to fill in the gaps. The agent learns this representation without being instructed to predict how the environment will change in response to its actions. **How it works:** At every timestep, the agent acts on either its observation (framed in red in the video above) or its prediction of what it wasn’t able to observe (imagery not framed in red). The agent contains a controller that decides on the most rewarding action. To compute the potential reward of a given action, the agent includes an additional deep net trained using the RL algorithm REINFORCE. * Observational dropout blocks the agent from observing the environment according to a user-defined probability. When this happens, the agent predicts an observation. * If random blindfolding blocks several observations in a row, the agent uses its most recent prediction to generate the next one. * This procedure over many iterations produces a sequence of observations and predictions. The agent learns from this sequence, and its ability to predict blocked observations is tantamount to a world model. **Results:** Observational dropout solved the task known as[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fopenai%2Fgym%2Fwiki%2FCartPole-v0&sa=D&sntz=1&usg=AOvVaw3n6wYAZyMbzRpWSK2gORJW)[Cartpole](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fopenai%2Fgym%2Fwiki%2FCartPole-v0&sa=D&sntz=1&usg=AOvVaw3n6wYAZyMbzRpWSK2gORJW), in which the model must balance a pole upright on a rolling cart, even when its view of the world was blocked 90 percent of the time. In a more complex[ ](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1809.01999&sa=D&sntz=1&usg=AOvVaw2XKaDs9JT_6xvavFR00hoz)[Car Racing](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1809.01999&sa=D&sntz=1&usg=AOvVaw2XKaDs9JT_6xvavFR00hoz) task, in which a model must navigate a car around a track as fast as possible, the model performed almost equally well whether it was allowed to see its surroundings or blindfolded up to 60 percent of the time. **Why it matters:** Modeling reality is often part art and part science. World models generated by observational dropout aren’t perfect representations, but they’re sufficient for some tasks. This work could lead to simple-but-effective world models of complex environments that are impractical to model completely. **We’re thinking:** Technology being imperfect, observational dropout is a fact of life, not just a research technique. A self-driving car or auto- piloted airplane reliant on sensors that drop data points could create a catastrophe. This technique could make high-stakes RL models more robust. Dec 4, 2019 Issue 33 **Is AI Making Mastery Obsolete?** Is there any reason to continue playing games that AI has mastered? Ask the former champions who have been toppled by machines. **What happened:** In 2016, International Go master Lee Sedol famously lost three out of four matches to DeepMind’s AlphaGo model. The 36-year-old announced his retirement from competition on November 27. “Even if I become the number one, there is an entity that cannot be defeated,” he[ ](https://www.google.com/url?q=https%3A%2F%2Fen.yna.co.kr%2Fview%2FAEN20191127004800315&sa=D&sntz=1&usg=AOvVaw0PMZN81SZTeJRnateNxsLl)[told](https://www.google.com/url?q=https%3A%2F%2Fen.yna.co.kr%2Fview%2FAEN20191127004800315&sa=D&sntz=1&usg=AOvVaw0PMZN81SZTeJRnateNxsLl) South Korean's Yonhap News Agency, **Stages of grief:** Prior to the tournament, Lee predicted that he would defeat AlphaGo easily. But the model’s inexplicable — and indefatigable — playing style pushed him into fits of[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.theverge.com%2F2017%2F10%2F11%2F16460118%2Falphago- deepmind-ai-documentary-go-lee-sedol-film- review&sa=D&sntz=1&usg=AOvVaw1BJ4ioQcgpxgNxmnVXr8kN)[shock and disbelief](https://www.google.com/url?q=https%3A%2F%2Fwww.theverge.com%2F2017%2F10%2F11%2F16460118%2Falphago- deepmind-ai-documentary-go-lee-sedol-film- review&sa=D&sntz=1&usg=AOvVaw1BJ4ioQcgpxgNxmnVXr8kN). Afterward, he[ ](https://www.google.com/url?q=https%3A%2F%2Fventurebeat.com%2F2016%2F03%2F12%2Fgo- board-game-champion-lee-sedol-apologizes-for-losing-to-googles- ai%2F&sa=D&sntz=1&usg=AOvVaw18En2cGIvD1uVmya6kwvX-)[apologized](https://www.google.com/url?q=https%3A%2F%2Fventurebeat.com%2F2016%2F03%2F12%2Fgo- board-game-champion-lee-sedol-apologizes-for-losing-to-googles- ai%2F&sa=D&sntz=1&usg=AOvVaw18En2cGIvD1uVmya6kwvX-) for his failure to the South Korean public. **Reaching acceptance:** Garry Kasparov, the former world-champion chess player, went through his own cycle of grief after being defeated by IBM’s DeepBlue in 1997. Although he didn’t retire, Kasparov did accuse IBM’s engineers of cheating. He later retracted the charge, and in 2017 wrote a book[ ](http://www.google.com/url?q=http%3A%2F%2Fwww.kasparov.com%2Fgarry- kasparov-says-ai-can-make-us-more-human-pcmag-interview- march-20th-2019%2F&sa=D&sntz=1&usg=AOvVaw3Lfe53n3SoFR4qDryUTD0P)[arguing](http://www.google.com/url?q=http%3A%2F%2Fwww.kasparov.com%2Fgarry- kasparov-says-ai-can-make-us-more-human-pcmag-interview- march-20th-2019%2F&sa=D&sntz=1&usg=AOvVaw3Lfe53n3SoFR4qDryUTD0P) that, if humans can overcome their feelings of being threatened by AI, they can learn from it. The book advocates an augmented intelligence in which humans and machines work together to solve problems. **The human element:** Although AlphaGo won in the 2016 duel, its human opponent still managed to shine. During the fourth match, Sedol made a[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.wired.com%2F2016%2F03%2Ftwo- moves-alphago-lee-sedol-redefined- future%2F&sa=D&sntz=1&usg=AOvVaw1Pt6XPLhDa8cMRMxOtI951)[move](https://www.google.com/url?q=https%3A%2F%2Fwww.wired.com%2F2016%2F03%2Ftwo- moves-alphago-lee-sedol-redefined- future%2F&sa=D&sntz=1&usg=AOvVaw1Pt6XPLhDa8cMRMxOtI951) so unconventional it defied AlphaGo’s expectation and led to his sole victory. **We’re thinking:** Lee wasn't defeated by a machine alone. He was beaten by a machine built by humans under the direction of AlphaGo research lead David Silver. Human mastery is obsolete only if you ignore people like Silver and his team. Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh4.googleusercontent.com/8sKtLRYlt7H0NnY_mX4T1p1meWrb3BIop8uoE8On8OzYvp2gPqIlrZXSelotoNJtig5cCs9eXhMevV_Clq5XtvM=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh4.googleusercontent.com/8sKtLRYlt7H0NnY_mX4T1p1meWrb3BIop8uoE8On8OzYvp2gPqIlrZXSelotoNJtig5cCs9eXhMevV_Clq5XtvM=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Camera_Calibration Multi Camera (Stereo Vision) Calibration for AR/VR headset (extended reality/mixed reality) 3D Image Processing with Deep Learning introduction Source code Reference # Multi Camera (Stereo Vision) Calibration for AR/VR headset (extended reality/mixed reality) 3D Image Processing with Deep Learning ## introduction Geometric camera calibration, also referred to as camera re-sectioning, estimates the parameters of a lens and image sensor of an image or video camera. These parameters can be used to correct for lens distortion, measure the size of an object in world units, or determine the location of the camera in a scene. These tasks are used in applications such as machine vision to detect and measure objects. They are also used in robotics, navigation systems, and 3-D scene reconstruction. Without any knowledge of the calibration of the cameras, it is impossible to do better than projective reconstruction (MathWorks). Non-intrusive scene measurement tasks, such as 3D reconstruction, object inspection, target or self-localization or scene mapping require a calibrated camera model (Orghidan et al. 2011). Camera calibration is the process of approximating the parameters of a pinhole camera model (Tsai 1987; Stein 1995; Heikkila & Silven 1997) of a given photograph or video. There are four main categories of camera calibration methods whereby a number of algorithms have been proposed for each categories/methods, namely knowing object based camera calibration, semi auto calibration, camera self- calibration method, and camera calibration method based on active vision. In computer vision methods, image information from cameras can yield geometric information pertaining to three-dimensional objects. Non-intrusive scene measurement tasks, such as 3D reconstruction, object inspection, target or self-localization, or scene mapping require a calibrated camera model (Orghidan et al. 2011). The correlation between the geographical point and camera image pixel is necessary for camera calibration. Hence, the camera’s parameter, which constitutes the geometric model of camera imaging, are utilized to establish the correlation between the three-dimensional geometric location of one point and a corresponding point in an image (Wang et al. 2010). Typically, experiments are conducted to attain the aforementioned parameters and relevant calculation, which is a process called camera calibration (Hyunjoon et al. 2014; Jianyang et al. 2014; Mohedano et al. 2014; Navarro et al. 2014). Image information from cameras can be used to elucidate the geometric information of a 3D object. The process of estimating the parameters of a pinhole camera model is called camera calibration. The more accurate the estimated parameters, the better the compensation that can be performed for the next stage of the application. In the data collection stage, a camera will take photos of a camera calibration pattern(Tsai 1987; Stein 1995; Heikkila & Silven 1997; Zhengyou 2000). Another angle of the issue is to create a set of pair images from both cameras via high quality images and increased range of slope of calibration pattern. The current methods simply create images upon the detection of calibration pattern. Nonetheless, the consensus in literature is that accurate camera calibration necessitates pure rotation (Zhang et al. 2008) and require sharp images. Recent breakthrough methods, such as Zhang’s (Zhengyou 2000), use fixed threshold to elucidate pixel difference between the frames and pre-setting variables, where slope information for image frame selection in camera calibration phase has been neglected (Audet & Okutomi 2009). Conversely, these approaches become less reliable when image frames are blurred. These problems necessitates that the camera calibration algorithm be enhanced (Wang et al. 2010). OpenCV Deep Learning ![](https://lh4.googleusercontent.com/TASkNRsNgHHlIwnUFnfwqlBeAwXxWpFwXL417Njjn55h4SnyGgeez8P0nYqx- cTjdGAYhnsWdIglXXmb6I4F7pAb_4GdMkKx0oIf7XeSDeXZwyzwBLD1-NGL59QZr1wRHg=w1280) [ **https://lh6.googleusercontent.com/nBRe4lAJJrxdt9XSz9OaUl0GRAnUU2cb2YKJYlxkhZmxPliOZHx- QPAvNYOyJcLrB2hqTbIGpOZ5UvPqx6IJj__RjYiCPc2q8CNmxuEy4emdYsg- zy1CXgeEwRHbfcCHeA=w1280**](https://lh6.googleusercontent.com/nBRe4lAJJrxdt9XSz9OaUl0GRAnUU2cb2YKJYlxkhZmxPliOZHx- QPAvNYOyJcLrB2hqTbIGpOZ5UvPqx6IJj__RjYiCPc2q8CNmxuEy4emdYsg- zy1CXgeEwRHbfcCHeA=w1280) **** **Engineering of Camera Calibration** Occasionally the out-of-the-box solution does not work, and you need some modified version of the algorithms. The first step of camera calibration is using known pattern images, such as chessboard. However, sometimes the image quality and pattern are not match with standard approach of calibration process. I use some other technique to enhance the result. In the first step, we need to improve the corner detection, and it may be done by fallowing steps. * The chessboard is used as a pattern of alternating black and white squares, \- which ensures that there is no bias toward one side or the other in measurement. * The image must be an grayscale (single-channel) image. \- img - Input image. It should be grayscale and float32 type. * gradianet x and y direction together (for better detection) \- cv.morphologyEx( src, op, kernel[, dst[, anchor[, iterations[, borderType[, borderValue]]]]] ) -> dst # different kernel is required * using Harris corner detection, which is a matrix of the second-order derivatives of the image intensities. \- cv.cornerHarris( src, blockSize, ksize, k[, dst[, borderType]] ) -> dst # the parameters a and b and c should be modified > img - Input image. It should be grayscale and float32 type. > blockSize - It is the size of neighborhood considered for corner detection > ksize - Aperture parameter of the Sobel derivative used. > k - Harris detector free parameter in the equation. * contours to remove some noise: - cv.connectedComponentsWithStats( image[, labels[, stats[, centroids[, connectivity[, ltype]]]]] ) -> retval, labels, stats, centroids * subpixel corners: corner detection come with integer coordinates but sometimes require real-valued coordinates cv.cornerSubPix( image, corners, winSize, zeroZone, criteria ) -> corners \- image Input single-channel, 8-bit or float image. \- corners Initial coordinates of the input corners and refined coordinates provided for output. \- winSize Half of the side length of the search window. (5*5 will be 11) \- zeroZone It is used sometimes to avoid possible singularities of the auto correlation matrix. \- criteria Criteria for termination of the iterative process of corner refinement. * remove duplicate corners: for example corners are in less than 5 pixels should be remove Reference: [https://theailearner.com/tag/cv2-cornersubpix/](https://www.google.com/url?q=https%3A%2F%2Ftheailearner.com%2Ftag%2Fcv2-cornersubpix%2F&sa=D&sntz=1&usg=AOvVaw1LDrIDpdKUACBUnVjQPB5i) [https://docs.opencv.org/3.4/dc/d0d/tutorial_py_features_harris.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fdc%2Fd0d%2Ftutorial_py_features_harris.html&sa=D&sntz=1&usg=AOvVaw28cWci42D6B_nRD0F_RXjJ) #Camera_Calibration #Camera-resectioning See more:[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmachine- learning-specialization%2Fmachine-learning-foundations-a-case-study- approach&sa=D&sntz=1&usg=AOvVaw2Qxr7mG3gMjiA5NEeF- stB)[**https://www.pirahansiah.com/topics/camera_calibration**](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcamera_calibration&sa=D&sntz=1&usg=AOvVaw0qw-5DwttHds1L2nVs4rQh) **** If you found the content informative, you may Follow me by [LinkedIn](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Fin%2Fpirahansiah%2F&sa=D&sntz=1&usg=AOvVaw0ETpuSejDWH6Dz0IId5L5j), [twitter](https://www.google.com/url?q=https%3A%2F%2Ftwitter.com%2Fpirahansiah&sa=D&sntz=1&usg=AOvVaw3hfltLQuPqA2GdOTjpZSOC), for more! **#FarshidPirahanSiah #pirahansiah** ## Source code Basic camear calibration source code by using OpenCV library in Jupyter notebook [https://github.com/pirahansiah/pirahansiah/blob/d0053451e760151c45a1208bb909772c2fedb644/CV_metaverse/3D_multi_camera_calibration/corner_detection/cornerDetection.ipynb](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fpirahansiah%2Fblob%2Fd0053451e760151c45a1208bb909772c2fedb644%2FCV_metaverse%2F3D_multi_camera_calibration%2Fcorner_detection%2FcornerDetection.ipynb&sa=D&sntz=1&usg=AOvVaw1bCpfvb8Zzhut0Me9z6PfQ) ## Reference Semi-Auto Calibration for multi-camera system (Pirahansiah's method 2022) + prognostic analysis [ using QR code in center of calibration pattern with four different colors in each courners of the QR code for show the direction which use for sincronize the points for all cameras) Book Chapter (Springer): Camera Calibration and Video Stabilization Framework for Robot Localization [https://link.springer.com/chapter/10.1007/978-3-030-74540-0_12](https://www.google.com/url?q=https%3A%2F%2Flink.springer.com%2Fchapter%2F10.1007%2F978-3-030-74540-0_12&sa=D&sntz=1&usg=AOvVaw2F-HQeuD0NJee8C7oGOCbN) IEEE paper: Pattern image significance for camera calibration [https://ieeexplore.ieee.org/abstract/document/8305440](https://www.google.com/url?q=https%3A%2F%2Fieeexplore.ieee.org%2Fabstract%2Fdocument%2F8305440&sa=D&sntz=1&usg=AOvVaw1BVeY_8PWNRXlfb4hlzjyi) Camera calibration for multi-modal robot vision based on image quality assessment [https://www.researchgate.net/profile/Farshid- Pirahansiah/publication/288174690_Camera_calibration_for_multi- modal_robot_vision_based_on_image_quality_assessment/links/5735bc2908aea45ee83c999e/Camera- calibration-for-multi-modal-robot-vision-based-on-image-quality- assessment.pdf](https://www.google.com/url?q=https%3A%2F%2Fwww.researchgate.net%2Fprofile%2FFarshid- Pirahansiah%2Fpublication%2F288174690_Camera_calibration_for_multi- modal_robot_vision_based_on_image_quality_assessment%2Flinks%2F5735bc2908aea45ee83c999e%2FCamera- calibration-for-multi-modal-robot-vision-based-on-image-quality- assessment.pdf&sa=D&sntz=1&usg=AOvVaw3OH6mE5ODgRSkTmNTsNpvh) ![](https://lh4.googleusercontent.com/S64xTdqIKz6ZgVj919NRnjEi07lvw1H-PuBrYeQTk7JP- IoPJDYNCYtA_RuLcWUzL3cqexSriTW2xkw0kCI- lc45KuZ9iXRZoZxASvnGd6IhB0QsyGDq7ty91gQbqa-e1w=w1280) Part 3. Basic of camera calibration + source code (Python+OpenCV) [https://www.pirahansiah.com/topics/camera_calibration](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcamera_calibration&sa=D&sntz=1&usg=AOvVaw0qw-5DwttHds1L2nVs4rQh) Geometric camera calibration, also referred to as camera re-sectioning, estimates the parameters of a lens and image sensor of an image or video camera. These parameters can be used to correct for lens distortion, measure the size of an object in world units, or determine the location of the camera in a scene. These tasks are used in applications such as machine vision to detect and measure objects. They are also used in robotics, navigation systems, and 3-D scene reconstruction. Without any knowledge of the calibration of the cameras, it is impossible to do better than projective reconstruction (MathWorks). Non-intrusive scene measurement tasks, such as 3D reconstruction, object inspection, target or self-localization or scene mapping require a calibrated camera model (Orghidan et al. 2011). Camera calibration is the process of approximating the parameters of a pinhole camera model (Tsai 1987; Stein 1995; Heikkila & Silven 1997) of a given photograph or video. There are four main categories of camera calibration methods whereby a number of algorithms have been proposed for each categories/methods, namely knowing object based camera calibration, semi auto calibration, camera self- calibration method, and camera calibration method based on active vision. [https://github.com/pirahansiah/pirahansiah/blob/d0053451e760151c45a1208bb909772c2fedb644/CV_metaverse/3D_multi_camera_calibration/corner_detection/cornerDetection.ipynb](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fpirahansiah%2Fblob%2Fd0053451e760151c45a1208bb909772c2fedb644%2FCV_metaverse%2F3D_multi_camera_calibration%2Fcorner_detection%2FcornerDetection.ipynb&sa=D&sntz=1&usg=AOvVaw1bCpfvb8Zzhut0Me9z6PfQ) #camera_calibration #3D #multi_camera_calibration #extended_reality #mixed_reality **REFERENCES** Abdullah, S. N. H. S., F. PirahanSiah, M. Khalid & K. Omar 2010. An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis. _2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010)_. Malaysia, 28-30 July, 2010. Abdullah, S. N. H. S., F. PirahanSiah, N. H. Zainal Abidin & S. Sahran 2010. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh4.googleusercontent.com/8sKtLRYlt7H0NnY_mX4T1p1meWrb3BIop8uoE8On8OzYvp2gPqIlrZXSelotoNJtig5cCs9eXhMevV_Clq5XtvM=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh4.googleusercontent.com/8sKtLRYlt7H0NnY_mX4T1p1meWrb3BIop8uoE8On8OzYvp2gPqIlrZXSelotoNJtig5cCs9eXhMevV_Clq5XtvM=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Camera_Calibration Multi Camera (Stereo Vision) Calibration for AR/VR headset (extended reality/mixed reality) 3D Image Processing with Deep Learning introduction Source code Reference # Multi Camera (Stereo Vision) Calibration for AR/VR headset (extended reality/mixed reality) 3D Image Processing with Deep Learning ## introduction Geometric camera calibration, also referred to as camera re-sectioning, estimates the parameters of a lens and image sensor of an image or video camera. These parameters can be used to correct for lens distortion, measure the size of an object in world units, or determine the location of the camera in a scene. These tasks are used in applications such as machine vision to detect and measure objects. They are also used in robotics, navigation systems, and 3-D scene reconstruction. Without any knowledge of the calibration of the cameras, it is impossible to do better than projective reconstruction (MathWorks). Non-intrusive scene measurement tasks, such as 3D reconstruction, object inspection, target or self-localization or scene mapping require a calibrated camera model (Orghidan et al. 2011). Camera calibration is the process of approximating the parameters of a pinhole camera model (Tsai 1987; Stein 1995; Heikkila & Silven 1997) of a given photograph or video. There are four main categories of camera calibration methods whereby a number of algorithms have been proposed for each categories/methods, namely knowing object based camera calibration, semi auto calibration, camera self- calibration method, and camera calibration method based on active vision. In computer vision methods, image information from cameras can yield geometric information pertaining to three-dimensional objects. Non-intrusive scene measurement tasks, such as 3D reconstruction, object inspection, target or self-localization, or scene mapping require a calibrated camera model (Orghidan et al. 2011). The correlation between the geographical point and camera image pixel is necessary for camera calibration. Hence, the camera’s parameter, which constitutes the geometric model of camera imaging, are utilized to establish the correlation between the three-dimensional geometric location of one point and a corresponding point in an image (Wang et al. 2010). Typically, experiments are conducted to attain the aforementioned parameters and relevant calculation, which is a process called camera calibration (Hyunjoon et al. 2014; Jianyang et al. 2014; Mohedano et al. 2014; Navarro et al. 2014). Image information from cameras can be used to elucidate the geometric information of a 3D object. The process of estimating the parameters of a pinhole camera model is called camera calibration. The more accurate the estimated parameters, the better the compensation that can be performed for the next stage of the application. In the data collection stage, a camera will take photos of a camera calibration pattern(Tsai 1987; Stein 1995; Heikkila & Silven 1997; Zhengyou 2000). Another angle of the issue is to create a set of pair images from both cameras via high quality images and increased range of slope of calibration pattern. The current methods simply create images upon the detection of calibration pattern. Nonetheless, the consensus in literature is that accurate camera calibration necessitates pure rotation (Zhang et al. 2008) and require sharp images. Recent breakthrough methods, such as Zhang’s (Zhengyou 2000), use fixed threshold to elucidate pixel difference between the frames and pre-setting variables, where slope information for image frame selection in camera calibration phase has been neglected (Audet & Okutomi 2009). Conversely, these approaches become less reliable when image frames are blurred. These problems necessitates that the camera calibration algorithm be enhanced (Wang et al. 2010). OpenCV Deep Learning ![](https://lh4.googleusercontent.com/TASkNRsNgHHlIwnUFnfwqlBeAwXxWpFwXL417Njjn55h4SnyGgeez8P0nYqx- cTjdGAYhnsWdIglXXmb6I4F7pAb_4GdMkKx0oIf7XeSDeXZwyzwBLD1-NGL59QZr1wRHg=w1280) [ **https://lh6.googleusercontent.com/nBRe4lAJJrxdt9XSz9OaUl0GRAnUU2cb2YKJYlxkhZmxPliOZHx- QPAvNYOyJcLrB2hqTbIGpOZ5UvPqx6IJj__RjYiCPc2q8CNmxuEy4emdYsg- zy1CXgeEwRHbfcCHeA=w1280**](https://lh6.googleusercontent.com/nBRe4lAJJrxdt9XSz9OaUl0GRAnUU2cb2YKJYlxkhZmxPliOZHx- QPAvNYOyJcLrB2hqTbIGpOZ5UvPqx6IJj__RjYiCPc2q8CNmxuEy4emdYsg- zy1CXgeEwRHbfcCHeA=w1280) **** **Engineering of Camera Calibration** Occasionally the out-of-the-box solution does not work, and you need some modified version of the algorithms. The first step of camera calibration is using known pattern images, such as chessboard. However, sometimes the image quality and pattern are not match with standard approach of calibration process. I use some other technique to enhance the result. In the first step, we need to improve the corner detection, and it may be done by fallowing steps. * The chessboard is used as a pattern of alternating black and white squares, \- which ensures that there is no bias toward one side or the other in measurement. * The image must be an grayscale (single-channel) image. \- img - Input image. It should be grayscale and float32 type. * gradianet x and y direction together (for better detection) \- cv.morphologyEx( src, op, kernel[, dst[, anchor[, iterations[, borderType[, borderValue]]]]] ) -> dst # different kernel is required * using Harris corner detection, which is a matrix of the second-order derivatives of the image intensities. \- cv.cornerHarris( src, blockSize, ksize, k[, dst[, borderType]] ) -> dst # the parameters a and b and c should be modified > img - Input image. It should be grayscale and float32 type. > blockSize - It is the size of neighborhood considered for corner detection > ksize - Aperture parameter of the Sobel derivative used. > k - Harris detector free parameter in the equation. * contours to remove some noise: - cv.connectedComponentsWithStats( image[, labels[, stats[, centroids[, connectivity[, ltype]]]]] ) -> retval, labels, stats, centroids * subpixel corners: corner detection come with integer coordinates but sometimes require real-valued coordinates cv.cornerSubPix( image, corners, winSize, zeroZone, criteria ) -> corners \- image Input single-channel, 8-bit or float image. \- corners Initial coordinates of the input corners and refined coordinates provided for output. \- winSize Half of the side length of the search window. (5*5 will be 11) \- zeroZone It is used sometimes to avoid possible singularities of the auto correlation matrix. \- criteria Criteria for termination of the iterative process of corner refinement. * remove duplicate corners: for example corners are in less than 5 pixels should be remove Reference: [https://theailearner.com/tag/cv2-cornersubpix/](https://www.google.com/url?q=https%3A%2F%2Ftheailearner.com%2Ftag%2Fcv2-cornersubpix%2F&sa=D&sntz=1&usg=AOvVaw1LDrIDpdKUACBUnVjQPB5i) [https://docs.opencv.org/3.4/dc/d0d/tutorial_py_features_harris.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fdc%2Fd0d%2Ftutorial_py_features_harris.html&sa=D&sntz=1&usg=AOvVaw28cWci42D6B_nRD0F_RXjJ) #Camera_Calibration #Camera-resectioning See more:[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmachine- learning-specialization%2Fmachine-learning-foundations-a-case-study- approach&sa=D&sntz=1&usg=AOvVaw2Qxr7mG3gMjiA5NEeF- stB)[**https://www.pirahansiah.com/topics/camera_calibration**](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcamera_calibration&sa=D&sntz=1&usg=AOvVaw0qw-5DwttHds1L2nVs4rQh) **** If you found the content informative, you may Follow me by [LinkedIn](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Fin%2Fpirahansiah%2F&sa=D&sntz=1&usg=AOvVaw0ETpuSejDWH6Dz0IId5L5j), [twitter](https://www.google.com/url?q=https%3A%2F%2Ftwitter.com%2Fpirahansiah&sa=D&sntz=1&usg=AOvVaw3hfltLQuPqA2GdOTjpZSOC), for more! **#FarshidPirahanSiah #pirahansiah** ## Source code Basic camear calibration source code by using OpenCV library in Jupyter notebook [https://github.com/pirahansiah/pirahansiah/blob/d0053451e760151c45a1208bb909772c2fedb644/CV_metaverse/3D_multi_camera_calibration/corner_detection/cornerDetection.ipynb](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fpirahansiah%2Fblob%2Fd0053451e760151c45a1208bb909772c2fedb644%2FCV_metaverse%2F3D_multi_camera_calibration%2Fcorner_detection%2FcornerDetection.ipynb&sa=D&sntz=1&usg=AOvVaw1bCpfvb8Zzhut0Me9z6PfQ) ## Reference Semi-Auto Calibration for multi-camera system (Pirahansiah's method 2022) + prognostic analysis [ using QR code in center of calibration pattern with four different colors in each courners of the QR code for show the direction which use for sincronize the points for all cameras) Book Chapter (Springer): Camera Calibration and Video Stabilization Framework for Robot Localization [https://link.springer.com/chapter/10.1007/978-3-030-74540-0_12](https://www.google.com/url?q=https%3A%2F%2Flink.springer.com%2Fchapter%2F10.1007%2F978-3-030-74540-0_12&sa=D&sntz=1&usg=AOvVaw2F-HQeuD0NJee8C7oGOCbN) IEEE paper: Pattern image significance for camera calibration [https://ieeexplore.ieee.org/abstract/document/8305440](https://www.google.com/url?q=https%3A%2F%2Fieeexplore.ieee.org%2Fabstract%2Fdocument%2F8305440&sa=D&sntz=1&usg=AOvVaw1BVeY_8PWNRXlfb4hlzjyi) Camera calibration for multi-modal robot vision based on image quality assessment [https://www.researchgate.net/profile/Farshid- Pirahansiah/publication/288174690_Camera_calibration_for_multi- modal_robot_vision_based_on_image_quality_assessment/links/5735bc2908aea45ee83c999e/Camera- calibration-for-multi-modal-robot-vision-based-on-image-quality- assessment.pdf](https://www.google.com/url?q=https%3A%2F%2Fwww.researchgate.net%2Fprofile%2FFarshid- Pirahansiah%2Fpublication%2F288174690_Camera_calibration_for_multi- modal_robot_vision_based_on_image_quality_assessment%2Flinks%2F5735bc2908aea45ee83c999e%2FCamera- calibration-for-multi-modal-robot-vision-based-on-image-quality- assessment.pdf&sa=D&sntz=1&usg=AOvVaw3OH6mE5ODgRSkTmNTsNpvh) ![](https://lh4.googleusercontent.com/S64xTdqIKz6ZgVj919NRnjEi07lvw1H-PuBrYeQTk7JP- IoPJDYNCYtA_RuLcWUzL3cqexSriTW2xkw0kCI- lc45KuZ9iXRZoZxASvnGd6IhB0QsyGDq7ty91gQbqa-e1w=w1280) Part 3. Basic of camera calibration + source code (Python+OpenCV) [https://www.pirahansiah.com/topics/camera_calibration](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcamera_calibration&sa=D&sntz=1&usg=AOvVaw0qw-5DwttHds1L2nVs4rQh) Geometric camera calibration, also referred to as camera re-sectioning, estimates the parameters of a lens and image sensor of an image or video camera. These parameters can be used to correct for lens distortion, measure the size of an object in world units, or determine the location of the camera in a scene. These tasks are used in applications such as machine vision to detect and measure objects. They are also used in robotics, navigation systems, and 3-D scene reconstruction. Without any knowledge of the calibration of the cameras, it is impossible to do better than projective reconstruction (MathWorks). Non-intrusive scene measurement tasks, such as 3D reconstruction, object inspection, target or self-localization or scene mapping require a calibrated camera model (Orghidan et al. 2011). Camera calibration is the process of approximating the parameters of a pinhole camera model (Tsai 1987; Stein 1995; Heikkila & Silven 1997) of a given photograph or video. There are four main categories of camera calibration methods whereby a number of algorithms have been proposed for each categories/methods, namely knowing object based camera calibration, semi auto calibration, camera self- calibration method, and camera calibration method based on active vision. [https://github.com/pirahansiah/pirahansiah/blob/d0053451e760151c45a1208bb909772c2fedb644/CV_metaverse/3D_multi_camera_calibration/corner_detection/cornerDetection.ipynb](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fpirahansiah%2Fblob%2Fd0053451e760151c45a1208bb909772c2fedb644%2FCV_metaverse%2F3D_multi_camera_calibration%2Fcorner_detection%2FcornerDetection.ipynb&sa=D&sntz=1&usg=AOvVaw1bCpfvb8Zzhut0Me9z6PfQ) #camera_calibration #3D #multi_camera_calibration #extended_reality #mixed_reality **REFERENCES** Abdullah, S. N. H. S., F. PirahanSiah, M. Khalid & K. Omar 2010. An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis. _2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010)_. Malaysia, 28-30 July, 2010. Abdullah, S. N. H. S., F. PirahanSiah, N. H. Zainal Abidin & S. Sahran 2010. Multi-threshold approach for license plate recognition system. _International Conference on Signal and Image Processing WASET Singapore August 25-27, 2010 ICSIP_. pp. 1046-1050. Abidin, N. H. Z., S. N. H. S. Abdullah, S. Sahran & F. PirahanSiah 2011. License plate recognition with multi-threshold based on entropy. _Electrical Engineering and Informatics (ICEEI), 2011 International Conference on_. pp. 1-6. Agapito, L., E. Hayman & I. Reid 2001. Self-calibration of rotating and zooming cameras. _International Journal of Computer Vision_ **45** (2): 107-127. Alcala-Fdez, J. & J. M. Alonso 2015. A Survey of Fuzzy Systems Software: Taxonomy, Current Research Trends and Prospects. _Fuzzy Systems, IEEE Transactions on_ **PP** (99): 40-56. Alcantarilla, P., O. Stasse, S. Druon, L. Bergasa & F. Dellaert 2013. How to localize humanoids with a single camera? _Autonomous Robots_ **34** (1-2): 47-71. Alejandro Héctor Toselli, E. Vidal & F. Casacuberta. 2011. 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These parameters can be used to correct for lens distortion, measure the size of an object in world units, or determine the location of the camera in a scene. These tasks are used in applications such as machine vision to detect and measure objects. They are also used in robotics, navigation systems, and 3-D scene reconstruction. Without any knowledge of the calibration of the cameras, it is impossible to do better than projective reconstruction (MathWorks). Non-intrusive scene measurement tasks, such as 3D reconstruction, object inspection, target or self-localization or scene mapping require a calibrated camera model (Orghidan et al. 2011). Camera calibration is the process of approximating the parameters of a pinhole camera model (Tsai 1987; Stein 1995; Heikkila & Silven 1997) of a given photograph or video. There are four main categories of camera calibration methods whereby a number of algorithms have been proposed for each categories/methods, namely knowing object based camera calibration, semi auto calibration, camera self- calibration method, and camera calibration method based on active vision. In computer vision methods, image information from cameras can yield geometric information pertaining to three-dimensional objects. Non-intrusive scene measurement tasks, such as 3D reconstruction, object inspection, target or self-localization, or scene mapping require a calibrated camera model (Orghidan et al. 2011). The correlation between the geographical point and camera image pixel is necessary for camera calibration. Hence, the camera’s parameter, which constitutes the geometric model of camera imaging, are utilized to establish the correlation between the three-dimensional geometric location of one point and a corresponding point in an image (Wang et al. 2010). Typically, experiments are conducted to attain the aforementioned parameters and relevant calculation, which is a process called camera calibration (Hyunjoon et al. 2014; Jianyang et al. 2014; Mohedano et al. 2014; Navarro et al. 2014). Image information from cameras can be used to elucidate the geometric information of a 3D object. The process of estimating the parameters of a pinhole camera model is called camera calibration. The more accurate the estimated parameters, the better the compensation that can be performed for the next stage of the application. In the data collection stage, a camera will take photos of a camera calibration pattern(Tsai 1987; Stein 1995; Heikkila & Silven 1997; Zhengyou 2000). Another angle of the issue is to create a set of pair images from both cameras via high quality images and increased range of slope of calibration pattern. The current methods simply create images upon the detection of calibration pattern. Nonetheless, the consensus in literature is that accurate camera calibration necessitates pure rotation (Zhang et al. 2008) and require sharp images. Recent breakthrough methods, such as Zhang’s (Zhengyou 2000), use fixed threshold to elucidate pixel difference between the frames and pre-setting variables, where slope information for image frame selection in camera calibration phase has been neglected (Audet & Okutomi 2009). Conversely, these approaches become less reliable when image frames are blurred. These problems necessitates that the camera calibration algorithm be enhanced (Wang et al. 2010). OpenCV Deep Learning ![](https://lh4.googleusercontent.com/TASkNRsNgHHlIwnUFnfwqlBeAwXxWpFwXL417Njjn55h4SnyGgeez8P0nYqx- cTjdGAYhnsWdIglXXmb6I4F7pAb_4GdMkKx0oIf7XeSDeXZwyzwBLD1-NGL59QZr1wRHg=w1280) [ **https://lh6.googleusercontent.com/nBRe4lAJJrxdt9XSz9OaUl0GRAnUU2cb2YKJYlxkhZmxPliOZHx- QPAvNYOyJcLrB2hqTbIGpOZ5UvPqx6IJj__RjYiCPc2q8CNmxuEy4emdYsg- zy1CXgeEwRHbfcCHeA=w1280**](https://lh6.googleusercontent.com/nBRe4lAJJrxdt9XSz9OaUl0GRAnUU2cb2YKJYlxkhZmxPliOZHx- QPAvNYOyJcLrB2hqTbIGpOZ5UvPqx6IJj__RjYiCPc2q8CNmxuEy4emdYsg- zy1CXgeEwRHbfcCHeA=w1280) **** **Engineering of Camera Calibration** Occasionally the out-of-the-box solution does not work, and you need some modified version of the algorithms. The first step of camera calibration is using known pattern images, such as chessboard. However, sometimes the image quality and pattern are not match with standard approach of calibration process. I use some other technique to enhance the result. In the first step, we need to improve the corner detection, and it may be done by fallowing steps. * The chessboard is used as a pattern of alternating black and white squares, \- which ensures that there is no bias toward one side or the other in measurement. * The image must be an grayscale (single-channel) image. \- img - Input image. It should be grayscale and float32 type. * gradianet x and y direction together (for better detection) \- cv.morphologyEx( src, op, kernel[, dst[, anchor[, iterations[, borderType[, borderValue]]]]] ) -> dst # different kernel is required * using Harris corner detection, which is a matrix of the second-order derivatives of the image intensities. \- cv.cornerHarris( src, blockSize, ksize, k[, dst[, borderType]] ) -> dst # the parameters a and b and c should be modified > img - Input image. It should be grayscale and float32 type. > blockSize - It is the size of neighborhood considered for corner detection > ksize - Aperture parameter of the Sobel derivative used. > k - Harris detector free parameter in the equation. * contours to remove some noise: - cv.connectedComponentsWithStats( image[, labels[, stats[, centroids[, connectivity[, ltype]]]]] ) -> retval, labels, stats, centroids * subpixel corners: corner detection come with integer coordinates but sometimes require real-valued coordinates cv.cornerSubPix( image, corners, winSize, zeroZone, criteria ) -> corners \- image Input single-channel, 8-bit or float image. \- corners Initial coordinates of the input corners and refined coordinates provided for output. \- winSize Half of the side length of the search window. (5*5 will be 11) \- zeroZone It is used sometimes to avoid possible singularities of the auto correlation matrix. \- criteria Criteria for termination of the iterative process of corner refinement. * remove duplicate corners: for example corners are in less than 5 pixels should be remove Reference: [https://theailearner.com/tag/cv2-cornersubpix/](https://www.google.com/url?q=https%3A%2F%2Ftheailearner.com%2Ftag%2Fcv2-cornersubpix%2F&sa=D&sntz=1&usg=AOvVaw1LDrIDpdKUACBUnVjQPB5i) [https://docs.opencv.org/3.4/dc/d0d/tutorial_py_features_harris.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fdc%2Fd0d%2Ftutorial_py_features_harris.html&sa=D&sntz=1&usg=AOvVaw28cWci42D6B_nRD0F_RXjJ) #Camera_Calibration #Camera-resectioning See more:[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmachine- learning-specialization%2Fmachine-learning-foundations-a-case-study- approach&sa=D&sntz=1&usg=AOvVaw2Qxr7mG3gMjiA5NEeF- stB)[**https://www.pirahansiah.com/topics/camera_calibration**](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcamera_calibration&sa=D&sntz=1&usg=AOvVaw0qw-5DwttHds1L2nVs4rQh) **** If you found the content informative, you may Follow me by [LinkedIn](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Fin%2Fpirahansiah%2F&sa=D&sntz=1&usg=AOvVaw0ETpuSejDWH6Dz0IId5L5j), [twitter](https://www.google.com/url?q=https%3A%2F%2Ftwitter.com%2Fpirahansiah&sa=D&sntz=1&usg=AOvVaw3hfltLQuPqA2GdOTjpZSOC), for more! **#FarshidPirahanSiah #pirahansiah** ## Source code Basic camear calibration source code by using OpenCV library in Jupyter notebook [https://github.com/pirahansiah/pirahansiah/blob/d0053451e760151c45a1208bb909772c2fedb644/CV_metaverse/3D_multi_camera_calibration/corner_detection/cornerDetection.ipynb](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fpirahansiah%2Fblob%2Fd0053451e760151c45a1208bb909772c2fedb644%2FCV_metaverse%2F3D_multi_camera_calibration%2Fcorner_detection%2FcornerDetection.ipynb&sa=D&sntz=1&usg=AOvVaw1bCpfvb8Zzhut0Me9z6PfQ) ## Reference Semi-Auto Calibration for multi-camera system (Pirahansiah's method 2022) + prognostic analysis [ using QR code in center of calibration pattern with four different colors in each courners of the QR code for show the direction which use for sincronize the points for all cameras) Book Chapter (Springer): Camera Calibration and Video Stabilization Framework for Robot Localization [https://link.springer.com/chapter/10.1007/978-3-030-74540-0_12](https://www.google.com/url?q=https%3A%2F%2Flink.springer.com%2Fchapter%2F10.1007%2F978-3-030-74540-0_12&sa=D&sntz=1&usg=AOvVaw2F-HQeuD0NJee8C7oGOCbN) IEEE paper: Pattern image significance for camera calibration [https://ieeexplore.ieee.org/abstract/document/8305440](https://www.google.com/url?q=https%3A%2F%2Fieeexplore.ieee.org%2Fabstract%2Fdocument%2F8305440&sa=D&sntz=1&usg=AOvVaw1BVeY_8PWNRXlfb4hlzjyi) Camera calibration for multi-modal robot vision based on image quality assessment [https://www.researchgate.net/profile/Farshid- Pirahansiah/publication/288174690_Camera_calibration_for_multi- modal_robot_vision_based_on_image_quality_assessment/links/5735bc2908aea45ee83c999e/Camera- calibration-for-multi-modal-robot-vision-based-on-image-quality- assessment.pdf](https://www.google.com/url?q=https%3A%2F%2Fwww.researchgate.net%2Fprofile%2FFarshid- Pirahansiah%2Fpublication%2F288174690_Camera_calibration_for_multi- modal_robot_vision_based_on_image_quality_assessment%2Flinks%2F5735bc2908aea45ee83c999e%2FCamera- calibration-for-multi-modal-robot-vision-based-on-image-quality- assessment.pdf&sa=D&sntz=1&usg=AOvVaw3OH6mE5ODgRSkTmNTsNpvh) ![](https://lh4.googleusercontent.com/S64xTdqIKz6ZgVj919NRnjEi07lvw1H-PuBrYeQTk7JP- IoPJDYNCYtA_RuLcWUzL3cqexSriTW2xkw0kCI- lc45KuZ9iXRZoZxASvnGd6IhB0QsyGDq7ty91gQbqa-e1w=w1280) Part 3. Basic of camera calibration + source code (Python+OpenCV) [https://www.pirahansiah.com/topics/camera_calibration](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcamera_calibration&sa=D&sntz=1&usg=AOvVaw0qw-5DwttHds1L2nVs4rQh) Geometric camera calibration, also referred to as camera re-sectioning, estimates the parameters of a lens and image sensor of an image or video camera. These parameters can be used to correct for lens distortion, measure the size of an object in world units, or determine the location of the camera in a scene. These tasks are used in applications such as machine vision to detect and measure objects. They are also used in robotics, navigation systems, and 3-D scene reconstruction. Without any knowledge of the calibration of the cameras, it is impossible to do better than projective reconstruction (MathWorks). Non-intrusive scene measurement tasks, such as 3D reconstruction, object inspection, target or self-localization or scene mapping require a calibrated camera model (Orghidan et al. 2011). Camera calibration is the process of approximating the parameters of a pinhole camera model (Tsai 1987; Stein 1995; Heikkila & Silven 1997) of a given photograph or video. There are four main categories of camera calibration methods whereby a number of algorithms have been proposed for each categories/methods, namely knowing object based camera calibration, semi auto calibration, camera self- calibration method, and camera calibration method based on active vision. [https://github.com/pirahansiah/pirahansiah/blob/d0053451e760151c45a1208bb909772c2fedb644/CV_metaverse/3D_multi_camera_calibration/corner_detection/cornerDetection.ipynb](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fpirahansiah%2Fblob%2Fd0053451e760151c45a1208bb909772c2fedb644%2FCV_metaverse%2F3D_multi_camera_calibration%2Fcorner_detection%2FcornerDetection.ipynb&sa=D&sntz=1&usg=AOvVaw1bCpfvb8Zzhut0Me9z6PfQ) #camera_calibration #3D #multi_camera_calibration #extended_reality #mixed_reality **REFERENCES** Abdullah, S. N. H. S., F. PirahanSiah, M. Khalid & K. Omar 2010. An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis. _2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010)_. Malaysia, 28-30 July, 2010. Abdullah, S. N. H. S., F. PirahanSiah, N. H. Zainal Abidin & S. Sahran 2010. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh4.googleusercontent.com/8sKtLRYlt7H0NnY_mX4T1p1meWrb3BIop8uoE8On8OzYvp2gPqIlrZXSelotoNJtig5cCs9eXhMevV_Clq5XtvM=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh4.googleusercontent.com/8sKtLRYlt7H0NnY_mX4T1p1meWrb3BIop8uoE8On8OzYvp2gPqIlrZXSelotoNJtig5cCs9eXhMevV_Clq5XtvM=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Camera_Calibration Multi Camera (Stereo Vision) Calibration for AR/VR headset (extended reality/mixed reality) 3D Image Processing with Deep Learning introduction Source code Reference # Multi Camera (Stereo Vision) Calibration for AR/VR headset (extended reality/mixed reality) 3D Image Processing with Deep Learning ## introduction Geometric camera calibration, also referred to as camera re-sectioning, estimates the parameters of a lens and image sensor of an image or video camera. These parameters can be used to correct for lens distortion, measure the size of an object in world units, or determine the location of the camera in a scene. These tasks are used in applications such as machine vision to detect and measure objects. They are also used in robotics, navigation systems, and 3-D scene reconstruction. Without any knowledge of the calibration of the cameras, it is impossible to do better than projective reconstruction (MathWorks). Non-intrusive scene measurement tasks, such as 3D reconstruction, object inspection, target or self-localization or scene mapping require a calibrated camera model (Orghidan et al. 2011). Camera calibration is the process of approximating the parameters of a pinhole camera model (Tsai 1987; Stein 1995; Heikkila & Silven 1997) of a given photograph or video. There are four main categories of camera calibration methods whereby a number of algorithms have been proposed for each categories/methods, namely knowing object based camera calibration, semi auto calibration, camera self- calibration method, and camera calibration method based on active vision. In computer vision methods, image information from cameras can yield geometric information pertaining to three-dimensional objects. Non-intrusive scene measurement tasks, such as 3D reconstruction, object inspection, target or self-localization, or scene mapping require a calibrated camera model (Orghidan et al. 2011). The correlation between the geographical point and camera image pixel is necessary for camera calibration. Hence, the camera’s parameter, which constitutes the geometric model of camera imaging, are utilized to establish the correlation between the three-dimensional geometric location of one point and a corresponding point in an image (Wang et al. 2010). Typically, experiments are conducted to attain the aforementioned parameters and relevant calculation, which is a process called camera calibration (Hyunjoon et al. 2014; Jianyang et al. 2014; Mohedano et al. 2014; Navarro et al. 2014). Image information from cameras can be used to elucidate the geometric information of a 3D object. The process of estimating the parameters of a pinhole camera model is called camera calibration. The more accurate the estimated parameters, the better the compensation that can be performed for the next stage of the application. In the data collection stage, a camera will take photos of a camera calibration pattern(Tsai 1987; Stein 1995; Heikkila & Silven 1997; Zhengyou 2000). Another angle of the issue is to create a set of pair images from both cameras via high quality images and increased range of slope of calibration pattern. The current methods simply create images upon the detection of calibration pattern. Nonetheless, the consensus in literature is that accurate camera calibration necessitates pure rotation (Zhang et al. 2008) and require sharp images. Recent breakthrough methods, such as Zhang’s (Zhengyou 2000), use fixed threshold to elucidate pixel difference between the frames and pre-setting variables, where slope information for image frame selection in camera calibration phase has been neglected (Audet & Okutomi 2009). Conversely, these approaches become less reliable when image frames are blurred. These problems necessitates that the camera calibration algorithm be enhanced (Wang et al. 2010). OpenCV Deep Learning ![](https://lh4.googleusercontent.com/TASkNRsNgHHlIwnUFnfwqlBeAwXxWpFwXL417Njjn55h4SnyGgeez8P0nYqx- cTjdGAYhnsWdIglXXmb6I4F7pAb_4GdMkKx0oIf7XeSDeXZwyzwBLD1-NGL59QZr1wRHg=w1280) [ **https://lh6.googleusercontent.com/nBRe4lAJJrxdt9XSz9OaUl0GRAnUU2cb2YKJYlxkhZmxPliOZHx- QPAvNYOyJcLrB2hqTbIGpOZ5UvPqx6IJj__RjYiCPc2q8CNmxuEy4emdYsg- zy1CXgeEwRHbfcCHeA=w1280**](https://lh6.googleusercontent.com/nBRe4lAJJrxdt9XSz9OaUl0GRAnUU2cb2YKJYlxkhZmxPliOZHx- QPAvNYOyJcLrB2hqTbIGpOZ5UvPqx6IJj__RjYiCPc2q8CNmxuEy4emdYsg- zy1CXgeEwRHbfcCHeA=w1280) **** **Engineering of Camera Calibration** Occasionally the out-of-the-box solution does not work, and you need some modified version of the algorithms. The first step of camera calibration is using known pattern images, such as chessboard. However, sometimes the image quality and pattern are not match with standard approach of calibration process. I use some other technique to enhance the result. In the first step, we need to improve the corner detection, and it may be done by fallowing steps. * The chessboard is used as a pattern of alternating black and white squares, \- which ensures that there is no bias toward one side or the other in measurement. * The image must be an grayscale (single-channel) image. \- img - Input image. It should be grayscale and float32 type. * gradianet x and y direction together (for better detection) \- cv.morphologyEx( src, op, kernel[, dst[, anchor[, iterations[, borderType[, borderValue]]]]] ) -> dst # different kernel is required * using Harris corner detection, which is a matrix of the second-order derivatives of the image intensities. \- cv.cornerHarris( src, blockSize, ksize, k[, dst[, borderType]] ) -> dst # the parameters a and b and c should be modified > img - Input image. It should be grayscale and float32 type. > blockSize - It is the size of neighborhood considered for corner detection > ksize - Aperture parameter of the Sobel derivative used. > k - Harris detector free parameter in the equation. * contours to remove some noise: - cv.connectedComponentsWithStats( image[, labels[, stats[, centroids[, connectivity[, ltype]]]]] ) -> retval, labels, stats, centroids * subpixel corners: corner detection come with integer coordinates but sometimes require real-valued coordinates cv.cornerSubPix( image, corners, winSize, zeroZone, criteria ) -> corners \- image Input single-channel, 8-bit or float image. \- corners Initial coordinates of the input corners and refined coordinates provided for output. \- winSize Half of the side length of the search window. (5*5 will be 11) \- zeroZone It is used sometimes to avoid possible singularities of the auto correlation matrix. \- criteria Criteria for termination of the iterative process of corner refinement. * remove duplicate corners: for example corners are in less than 5 pixels should be remove Reference: [https://theailearner.com/tag/cv2-cornersubpix/](https://www.google.com/url?q=https%3A%2F%2Ftheailearner.com%2Ftag%2Fcv2-cornersubpix%2F&sa=D&sntz=1&usg=AOvVaw1LDrIDpdKUACBUnVjQPB5i) [https://docs.opencv.org/3.4/dc/d0d/tutorial_py_features_harris.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fdc%2Fd0d%2Ftutorial_py_features_harris.html&sa=D&sntz=1&usg=AOvVaw28cWci42D6B_nRD0F_RXjJ) #Camera_Calibration #Camera-resectioning See more:[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmachine- learning-specialization%2Fmachine-learning-foundations-a-case-study- approach&sa=D&sntz=1&usg=AOvVaw2Qxr7mG3gMjiA5NEeF- stB)[**https://www.pirahansiah.com/topics/camera_calibration**](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcamera_calibration&sa=D&sntz=1&usg=AOvVaw0qw-5DwttHds1L2nVs4rQh) **** If you found the content informative, you may Follow me by [LinkedIn](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Fin%2Fpirahansiah%2F&sa=D&sntz=1&usg=AOvVaw0ETpuSejDWH6Dz0IId5L5j), [twitter](https://www.google.com/url?q=https%3A%2F%2Ftwitter.com%2Fpirahansiah&sa=D&sntz=1&usg=AOvVaw3hfltLQuPqA2GdOTjpZSOC), for more! **#FarshidPirahanSiah #pirahansiah** ## Source code Basic camear calibration source code by using OpenCV library in Jupyter notebook [https://github.com/pirahansiah/pirahansiah/blob/d0053451e760151c45a1208bb909772c2fedb644/CV_metaverse/3D_multi_camera_calibration/corner_detection/cornerDetection.ipynb](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fpirahansiah%2Fblob%2Fd0053451e760151c45a1208bb909772c2fedb644%2FCV_metaverse%2F3D_multi_camera_calibration%2Fcorner_detection%2FcornerDetection.ipynb&sa=D&sntz=1&usg=AOvVaw1bCpfvb8Zzhut0Me9z6PfQ) ## Reference Semi-Auto Calibration for multi-camera system (Pirahansiah's method 2022) + prognostic analysis [ using QR code in center of calibration pattern with four different colors in each courners of the QR code for show the direction which use for sincronize the points for all cameras) Book Chapter (Springer): Camera Calibration and Video Stabilization Framework for Robot Localization [https://link.springer.com/chapter/10.1007/978-3-030-74540-0_12](https://www.google.com/url?q=https%3A%2F%2Flink.springer.com%2Fchapter%2F10.1007%2F978-3-030-74540-0_12&sa=D&sntz=1&usg=AOvVaw2F-HQeuD0NJee8C7oGOCbN) IEEE paper: Pattern image significance for camera calibration [https://ieeexplore.ieee.org/abstract/document/8305440](https://www.google.com/url?q=https%3A%2F%2Fieeexplore.ieee.org%2Fabstract%2Fdocument%2F8305440&sa=D&sntz=1&usg=AOvVaw1BVeY_8PWNRXlfb4hlzjyi) Camera calibration for multi-modal robot vision based on image quality assessment [https://www.researchgate.net/profile/Farshid- Pirahansiah/publication/288174690_Camera_calibration_for_multi- modal_robot_vision_based_on_image_quality_assessment/links/5735bc2908aea45ee83c999e/Camera- calibration-for-multi-modal-robot-vision-based-on-image-quality- assessment.pdf](https://www.google.com/url?q=https%3A%2F%2Fwww.researchgate.net%2Fprofile%2FFarshid- Pirahansiah%2Fpublication%2F288174690_Camera_calibration_for_multi- modal_robot_vision_based_on_image_quality_assessment%2Flinks%2F5735bc2908aea45ee83c999e%2FCamera- calibration-for-multi-modal-robot-vision-based-on-image-quality- assessment.pdf&sa=D&sntz=1&usg=AOvVaw3OH6mE5ODgRSkTmNTsNpvh) ![](https://lh4.googleusercontent.com/S64xTdqIKz6ZgVj919NRnjEi07lvw1H-PuBrYeQTk7JP- IoPJDYNCYtA_RuLcWUzL3cqexSriTW2xkw0kCI- lc45KuZ9iXRZoZxASvnGd6IhB0QsyGDq7ty91gQbqa-e1w=w1280) Part 3. Basic of camera calibration + source code (Python+OpenCV) [https://www.pirahansiah.com/topics/camera_calibration](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcamera_calibration&sa=D&sntz=1&usg=AOvVaw0qw-5DwttHds1L2nVs4rQh) Geometric camera calibration, also referred to as camera re-sectioning, estimates the parameters of a lens and image sensor of an image or video camera. These parameters can be used to correct for lens distortion, measure the size of an object in world units, or determine the location of the camera in a scene. These tasks are used in applications such as machine vision to detect and measure objects. They are also used in robotics, navigation systems, and 3-D scene reconstruction. Without any knowledge of the calibration of the cameras, it is impossible to do better than projective reconstruction (MathWorks). Non-intrusive scene measurement tasks, such as 3D reconstruction, object inspection, target or self-localization or scene mapping require a calibrated camera model (Orghidan et al. 2011). Camera calibration is the process of approximating the parameters of a pinhole camera model (Tsai 1987; Stein 1995; Heikkila & Silven 1997) of a given photograph or video. There are four main categories of camera calibration methods whereby a number of algorithms have been proposed for each categories/methods, namely knowing object based camera calibration, semi auto calibration, camera self- calibration method, and camera calibration method based on active vision. [https://github.com/pirahansiah/pirahansiah/blob/d0053451e760151c45a1208bb909772c2fedb644/CV_metaverse/3D_multi_camera_calibration/corner_detection/cornerDetection.ipynb](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fpirahansiah%2Fblob%2Fd0053451e760151c45a1208bb909772c2fedb644%2FCV_metaverse%2F3D_multi_camera_calibration%2Fcorner_detection%2FcornerDetection.ipynb&sa=D&sntz=1&usg=AOvVaw1bCpfvb8Zzhut0Me9z6PfQ) #camera_calibration #3D #multi_camera_calibration #extended_reality #mixed_reality **REFERENCES** Abdullah, S. N. H. S., F. PirahanSiah, M. Khalid & K. Omar 2010. An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis. _2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010)_. Malaysia, 28-30 July, 2010. Abdullah, S. N. H. S., F. PirahanSiah, N. H. Zainal Abidin & S. Sahran 2010. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/VnELnzCZElXe9gLxGYU00_xF7qju2MljSVlgUMwWsc50I88T6vB5ahQjH2kGA --o3hIeJYu2N--BO_uidCis2Ow=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/VnELnzCZElXe9gLxGYU00_xF7qju2MljSVlgUMwWsc50I88T6vB5ahQjH2kGA --o3hIeJYu2N--BO_uidCis2Ow=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Camera_Calibration Multi Camera (Stereo Vision) Calibration for AR/VR headset (extended reality/mixed reality) 3D Image Processing with Deep Learning introduction Source code Reference # Multi Camera (Stereo Vision) Calibration for AR/VR headset (extended reality/mixed reality) 3D Image Processing with Deep Learning ## introduction Geometric camera calibration, also referred to as camera re-sectioning, estimates the parameters of a lens and image sensor of an image or video camera. These parameters can be used to correct for lens distortion, measure the size of an object in world units, or determine the location of the camera in a scene. These tasks are used in applications such as machine vision to detect and measure objects. They are also used in robotics, navigation systems, and 3-D scene reconstruction. Without any knowledge of the calibration of the cameras, it is impossible to do better than projective reconstruction (MathWorks). Non-intrusive scene measurement tasks, such as 3D reconstruction, object inspection, target or self-localization or scene mapping require a calibrated camera model (Orghidan et al. 2011). Camera calibration is the process of approximating the parameters of a pinhole camera model (Tsai 1987; Stein 1995; Heikkila & Silven 1997) of a given photograph or video. There are four main categories of camera calibration methods whereby a number of algorithms have been proposed for each categories/methods, namely knowing object based camera calibration, semi auto calibration, camera self- calibration method, and camera calibration method based on active vision. In computer vision methods, image information from cameras can yield geometric information pertaining to three-dimensional objects. Non-intrusive scene measurement tasks, such as 3D reconstruction, object inspection, target or self-localization, or scene mapping require a calibrated camera model (Orghidan et al. 2011). The correlation between the geographical point and camera image pixel is necessary for camera calibration. Hence, the camera’s parameter, which constitutes the geometric model of camera imaging, are utilized to establish the correlation between the three-dimensional geometric location of one point and a corresponding point in an image (Wang et al. 2010). Typically, experiments are conducted to attain the aforementioned parameters and relevant calculation, which is a process called camera calibration (Hyunjoon et al. 2014; Jianyang et al. 2014; Mohedano et al. 2014; Navarro et al. 2014). Image information from cameras can be used to elucidate the geometric information of a 3D object. The process of estimating the parameters of a pinhole camera model is called camera calibration. The more accurate the estimated parameters, the better the compensation that can be performed for the next stage of the application. In the data collection stage, a camera will take photos of a camera calibration pattern(Tsai 1987; Stein 1995; Heikkila & Silven 1997; Zhengyou 2000). Another angle of the issue is to create a set of pair images from both cameras via high quality images and increased range of slope of calibration pattern. The current methods simply create images upon the detection of calibration pattern. Nonetheless, the consensus in literature is that accurate camera calibration necessitates pure rotation (Zhang et al. 2008) and require sharp images. Recent breakthrough methods, such as Zhang’s (Zhengyou 2000), use fixed threshold to elucidate pixel difference between the frames and pre-setting variables, where slope information for image frame selection in camera calibration phase has been neglected (Audet & Okutomi 2009). Conversely, these approaches become less reliable when image frames are blurred. These problems necessitates that the camera calibration algorithm be enhanced (Wang et al. 2010). OpenCV Deep Learning ![](https://lh5.googleusercontent.com/Y6MKt2LVbV97Es- fnI-z-8uaWvnRUwL4OZu2VNvVaWp0aOaXWkZln1ZCHTu2a0m91EDnnqMkffkiwcHHkt8ZOhYxulFrIehCAyK6PVYBZ_s2aEXa11q8USOhKoAVkL6fyQ=w1280) [ **https://lh6.googleusercontent.com/nBRe4lAJJrxdt9XSz9OaUl0GRAnUU2cb2YKJYlxkhZmxPliOZHx- QPAvNYOyJcLrB2hqTbIGpOZ5UvPqx6IJj__RjYiCPc2q8CNmxuEy4emdYsg- zy1CXgeEwRHbfcCHeA=w1280**](https://lh6.googleusercontent.com/nBRe4lAJJrxdt9XSz9OaUl0GRAnUU2cb2YKJYlxkhZmxPliOZHx- QPAvNYOyJcLrB2hqTbIGpOZ5UvPqx6IJj__RjYiCPc2q8CNmxuEy4emdYsg- zy1CXgeEwRHbfcCHeA=w1280) **** **Engineering of Camera Calibration** Occasionally the out-of-the-box solution does not work, and you need some modified version of the algorithms. The first step of camera calibration is using known pattern images, such as chessboard. However, sometimes the image quality and pattern are not match with standard approach of calibration process. I use some other technique to enhance the result. In the first step, we need to improve the corner detection, and it may be done by fallowing steps. * The chessboard is used as a pattern of alternating black and white squares, \- which ensures that there is no bias toward one side or the other in measurement. * The image must be an grayscale (single-channel) image. \- img - Input image. It should be grayscale and float32 type. * gradianet x and y direction together (for better detection) \- cv.morphologyEx( src, op, kernel[, dst[, anchor[, iterations[, borderType[, borderValue]]]]] ) -> dst # different kernel is required * using Harris corner detection, which is a matrix of the second-order derivatives of the image intensities. \- cv.cornerHarris( src, blockSize, ksize, k[, dst[, borderType]] ) -> dst # the parameters a and b and c should be modified > img - Input image. It should be grayscale and float32 type. > blockSize - It is the size of neighborhood considered for corner detection > ksize - Aperture parameter of the Sobel derivative used. > k - Harris detector free parameter in the equation. * contours to remove some noise: - cv.connectedComponentsWithStats( image[, labels[, stats[, centroids[, connectivity[, ltype]]]]] ) -> retval, labels, stats, centroids * subpixel corners: corner detection come with integer coordinates but sometimes require real-valued coordinates cv.cornerSubPix( image, corners, winSize, zeroZone, criteria ) -> corners \- image Input single-channel, 8-bit or float image. \- corners Initial coordinates of the input corners and refined coordinates provided for output. \- winSize Half of the side length of the search window. (5*5 will be 11) \- zeroZone It is used sometimes to avoid possible singularities of the auto correlation matrix. \- criteria Criteria for termination of the iterative process of corner refinement. * remove duplicate corners: for example corners are in less than 5 pixels should be remove Reference: [https://theailearner.com/tag/cv2-cornersubpix/](https://www.google.com/url?q=https%3A%2F%2Ftheailearner.com%2Ftag%2Fcv2-cornersubpix%2F&sa=D&sntz=1&usg=AOvVaw1LDrIDpdKUACBUnVjQPB5i) [https://docs.opencv.org/3.4/dc/d0d/tutorial_py_features_harris.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fdc%2Fd0d%2Ftutorial_py_features_harris.html&sa=D&sntz=1&usg=AOvVaw28cWci42D6B_nRD0F_RXjJ) #Camera_Calibration #Camera-resectioning See more:[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmachine- learning-specialization%2Fmachine-learning-foundations-a-case-study- approach&sa=D&sntz=1&usg=AOvVaw2Qxr7mG3gMjiA5NEeF- stB)[**https://www.pirahansiah.com/topics/camera_calibration**](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcamera_calibration&sa=D&sntz=1&usg=AOvVaw0qw-5DwttHds1L2nVs4rQh) **** If you found the content informative, you may Follow me by [LinkedIn](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Fin%2Fpirahansiah%2F&sa=D&sntz=1&usg=AOvVaw0ETpuSejDWH6Dz0IId5L5j), [twitter](https://www.google.com/url?q=https%3A%2F%2Ftwitter.com%2Fpirahansiah&sa=D&sntz=1&usg=AOvVaw3hfltLQuPqA2GdOTjpZSOC), for more! **#FarshidPirahanSiah #pirahansiah** ## Source code Basic camear calibration source code by using OpenCV library in Jupyter notebook [https://github.com/pirahansiah/pirahansiah/blob/d0053451e760151c45a1208bb909772c2fedb644/CV_metaverse/3D_multi_camera_calibration/corner_detection/cornerDetection.ipynb](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fpirahansiah%2Fblob%2Fd0053451e760151c45a1208bb909772c2fedb644%2FCV_metaverse%2F3D_multi_camera_calibration%2Fcorner_detection%2FcornerDetection.ipynb&sa=D&sntz=1&usg=AOvVaw1bCpfvb8Zzhut0Me9z6PfQ) ## Reference Semi-Auto Calibration for multi-camera system (Pirahansiah's method 2022) + prognostic analysis [ using QR code in center of calibration pattern with four different colors in each courners of the QR code for show the direction which use for sincronize the points for all cameras) Book Chapter (Springer): Camera Calibration and Video Stabilization Framework for Robot Localization [https://link.springer.com/chapter/10.1007/978-3-030-74540-0_12](https://www.google.com/url?q=https%3A%2F%2Flink.springer.com%2Fchapter%2F10.1007%2F978-3-030-74540-0_12&sa=D&sntz=1&usg=AOvVaw2F-HQeuD0NJee8C7oGOCbN) IEEE paper: Pattern image significance for camera calibration [https://ieeexplore.ieee.org/abstract/document/8305440](https://www.google.com/url?q=https%3A%2F%2Fieeexplore.ieee.org%2Fabstract%2Fdocument%2F8305440&sa=D&sntz=1&usg=AOvVaw1BVeY_8PWNRXlfb4hlzjyi) Camera calibration for multi-modal robot vision based on image quality assessment [https://www.researchgate.net/profile/Farshid- Pirahansiah/publication/288174690_Camera_calibration_for_multi- modal_robot_vision_based_on_image_quality_assessment/links/5735bc2908aea45ee83c999e/Camera- calibration-for-multi-modal-robot-vision-based-on-image-quality- assessment.pdf](https://www.google.com/url?q=https%3A%2F%2Fwww.researchgate.net%2Fprofile%2FFarshid- Pirahansiah%2Fpublication%2F288174690_Camera_calibration_for_multi- modal_robot_vision_based_on_image_quality_assessment%2Flinks%2F5735bc2908aea45ee83c999e%2FCamera- calibration-for-multi-modal-robot-vision-based-on-image-quality- assessment.pdf&sa=D&sntz=1&usg=AOvVaw3OH6mE5ODgRSkTmNTsNpvh) ![](https://lh3.googleusercontent.com/OBlP3C7uTdKmZCrtHGLpNLlfimYwN4vxzKKL_5xBp9paqsllfCIndGivzKqX- oPBNNVm6OtyburBB0aGqK5YMpF3y6XSfQu-8gtbj4fOK4YzQFWD6fPOMFvlk1Nj87zOrQ=w1280) Part 3. Basic of camera calibration + source code (Python+OpenCV) [https://www.pirahansiah.com/topics/camera_calibration](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcamera_calibration&sa=D&sntz=1&usg=AOvVaw0qw-5DwttHds1L2nVs4rQh) Geometric camera calibration, also referred to as camera re-sectioning, estimates the parameters of a lens and image sensor of an image or video camera. These parameters can be used to correct for lens distortion, measure the size of an object in world units, or determine the location of the camera in a scene. These tasks are used in applications such as machine vision to detect and measure objects. They are also used in robotics, navigation systems, and 3-D scene reconstruction. Without any knowledge of the calibration of the cameras, it is impossible to do better than projective reconstruction (MathWorks). Non-intrusive scene measurement tasks, such as 3D reconstruction, object inspection, target or self-localization or scene mapping require a calibrated camera model (Orghidan et al. 2011). Camera calibration is the process of approximating the parameters of a pinhole camera model (Tsai 1987; Stein 1995; Heikkila & Silven 1997) of a given photograph or video. There are four main categories of camera calibration methods whereby a number of algorithms have been proposed for each categories/methods, namely knowing object based camera calibration, semi auto calibration, camera self- calibration method, and camera calibration method based on active vision. [https://github.com/pirahansiah/pirahansiah/blob/d0053451e760151c45a1208bb909772c2fedb644/CV_metaverse/3D_multi_camera_calibration/corner_detection/cornerDetection.ipynb](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fpirahansiah%2Fblob%2Fd0053451e760151c45a1208bb909772c2fedb644%2FCV_metaverse%2F3D_multi_camera_calibration%2Fcorner_detection%2FcornerDetection.ipynb&sa=D&sntz=1&usg=AOvVaw1bCpfvb8Zzhut0Me9z6PfQ) #camera_calibration #3D #multi_camera_calibration #extended_reality #mixed_reality **REFERENCES** Abdullah, S. N. H. S., F. PirahanSiah, M. Khalid & K. Omar 2010. An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis. _2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010)_. Malaysia, 28-30 July, 2010. Abdullah, S. N. H. S., F. PirahanSiah, N. H. Zainal Abidin & S. Sahran 2010. 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The versioning of different deep learning frameworks are crucial. For example the latest version of OS for Jetson Nano [Jetpack](https://www.google.com/url?q=https%3A%2F%2Fdeveloper.nvidia.com%2Fembedded%2Fjetpack&sa=D&sntz=1&usg=AOvVaw12B66A0ktALSyHLcxN- Xx0) which use latest CUDA but the Pytorch only support up to 10.1 now. So we need to install lower Jetpack version on Jetson Nano or compile the Pytorch. I compile Pytorch and it takes few hours with a lot of issue to solve. For the Ubuntu 20 there is not support for CUDA 10 you need install CUDA 11 and compile the Pytorch with a lot of library. install CUDA 10.x on Ubuntu 20 is possible but takes time to solve conflicts also supporting eGPU is another issue for lower ubuntu. On MacOS installing everything is easy because of not supporting GPU but many library and frameworks of the source codes of tracking require GPU version. Even install CPU version of all library does not grantee to run tracking methods. Another aspect is speed. Running tracking even on GPU is very slow based on my experience using Yolo version 3 which is one of the fastest object detection on GTX 2070 can process up to 15 FPS with Full HD videos. Methods on tracking is very different. First generation, it is completely based on computer vision. The second generation combining Kalman filter and advanced computer vision (SIFT), the third generation using deep learning and some of the methods of previous generation like Kalman filter. The fourth generation using combination of two deep learning methods. And the latest generation using complete end to end models like RNN. Object tracking works with all combination of environments such as, moving objects, moving objects and camera in dynamic environments. As long as object appear in the frame until disappeared it the tracking can track and identification as one objects. No mater how many FPS. ![](https://lh6.googleusercontent.com/GrlgOPkT- YjCOfCPVqdMTFh7YATdG8i43aySblQ32f5lYbF4L5uKYWK8e8j9lE- xzlS5H_z3BmPDB0jdpRqg8VAl-2Obz5iscyVN2BbOW1W2OG10Pl1SFTqnTvpHYfqrPg=w1280) ![](https://lh5.googleusercontent.com/3OpYYQA0JUTtvpYBE2uba5FtI3o1e45ieUeM02heJZKobWLMg5LfsbaHCjHnbAVC1WKVjKvZTUJWSjb5286-gAavbKQPohSRNAAHEwZXkC31bHCtWhGalnOepFCb_HPczw=w1280) ![](https://lh6.googleusercontent.com/MPWSDbFeFhPaIpTPufkx- Lfub5XMg3CpemQwDcs4p1RWgPFuNDkhpESbVYPHmAt5VOxITo5IvFlNNZZyxt3QiJpG54qABsGfqombmsOnb1GGt3t8cFriQ7AQRClIddZQgQ=w1280) ![](https://lh5.googleusercontent.com/MkiPU_u3aULSjsA6-ZM7thrVjM- ZGAoJbwbnLRXpUqp- lXGZavIuijln0eZBIvR0nWshaThulIm48lY8KYm9RLajGyYp5ge5teMUBgbZ2Hy7sMC-TnpVSzrqW- dKQl8f=w1280) ![](https://lh5.googleusercontent.com/9PuWB3svPA63SBkzP9-TU9TiV_Pn8ppe8Cjmx76PQFTidX4E-8V7VPcXzBpcPW1pOqsoR0 --oOc_H88gSea0iyXyxzOO2MWuAAhWZNt6eU6mb2aemB2c-UooXzjQ0EGatA=w1280) ![](https://lh5.googleusercontent.com/s333ScrmCHiimrwEMBB0zcJQu- Pd8VjpDFN0mrmXoZIJ0ppxmNRLcFPJrX5qdA0f2h3a6bUclrk4KUZF0CLgK8ahaGw6cbVDUHpFB5Aj_BQy9SqtTvCpUv3a7lmea-F0Dw=w1280) # Tracking * Classic object tracking * * * classic feature detection (SIFT and SURF), combined with a machine learning algorithm like KNN or SVM for classification, or with a description matcher like FLANN for object detection. * Kalman filtering, sparse and dense optical flow, * Example: Simple Online and Realtime Tracking (SORT), which uses a combination of the Hungarian algorithm and Kalman filter * SOT is a hot topic in the last decade. Early visual tracking methods rely on extracting hand-crafted features of candidate target regions, and use matching algorithms or hand-crafted discriminative classifiers to generate tracking results. * The MOT track aims to recover the trajectories of objects in video sequences, which is an important problem in computer vision with many applications, such as surveillance, activity analysis, and sport video analysis. * Video object detection datasets. The video object detection task aims to detect objects of different categories in video sequences. * Multi-object tracking datasets * large-scale benchmark Multi-Class Multi-object tracking datasets * VisDrone datasets is captured in various unconstrained scenes, focusing on four core problems in computer vision fields, i.e., image object detection, video object detection, single object tracking, and multi- object tracking. * the accuracy of detection methods suffers from degenerated object appearances in videos such as motion blur, pose variations, and video de-focus. Exploiting temporal coherence and aggregating features in consecutive frames might to be two effective ways to handle such issue. * Temporal coherence. A feasible way to exploit temporal coherence is using object trackers * Feature aggregation. Aggregating features in consecutive frames is also a useful way to improve the performance. * ### List of Datasets * **MOT20** * KITTI Tracking * MOTChallenge 2015 * UA-DETRAC Tracking * DukeMTMC * Campus * MOT17 * UAVDT-MOT * VisDrone ### Source code * ROLO * TensorFlow: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FGuanghan%2FROLO&sa=D&sntz=1&usg=AOvVaw2amdiMCe2vunOIdlpvqghX) * SiamMask * PyTorch 0.4.1: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Ffoolwood%2FSiamMask&sa=D&sntz=1&usg=AOvVaw1oXduEQoxzhx-VIMIMQ9Rn) * Deep SORT * PyTorch ≥ 0.4.0: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FZQPei%2Fdeep_sort_pytorch&sa=D&sntz=1&usg=AOvVaw2ZTw3IYJO4rvuF2hUPjCpH) * TensorFlow ≥ 1.0: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fnwojke%2Fdeep_sort&sa=D&sntz=1&usg=AOvVaw2FXwdcfCz1RZbndLyQSlBe) * TrackR-CNN * TensorFlow 1.13.1: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FVisualComputingInstitute%2FTrackR-CNN&sa=D&sntz=1&usg=AOvVaw3i3kALogli3ZyH93E3zhy5) * Tracktor++ * PyTorch 1.3.1: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fphil-bergmann%2Ftracking_wo_bnw&sa=D&sntz=1&usg=AOvVaw2xvx7s_sfJOiwLiwhmhqR8) * JDE * PyTorch ≥ 1.2.0: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FZhongdao%2FTowards-Realtime-MOT&sa=D&sntz=1&usg=AOvVaw0PcQNbW8igbtHaJ1x8IOVO) * [MCMOT: One-shot multi-class multi-object tracking](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FCaptainEven%2FMCMOT&sa=D&sntz=1&usg=AOvVaw2nrFmqa-Eh4-FoWPWBXrJn) # Self collected datasets ## [Video labeling](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fheartexlabs%2Fawesome- data-labeling&sa=D&sntz=1&usg=AOvVaw1Eb2rubldhdODxp4c4_Xnc) * [VATIC](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fcvondrick%2Fvatic&sa=D&sntz=1&usg=AOvVaw3dbkWnFtLPwO3znnZZz3Jy) * [UltimateLabeling](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Falexandre01%2FUltimateLabeling&sa=D&sntz=1&usg=AOvVaw2CpIp2ojyJ4dgWdKVlYC_r) * [https://github.com/pirahansiah/UltimateLabeling](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2FUltimateLabeling&sa=D&sntz=1&usg=AOvVaw2mQJcLjtHwG4L8YcUkET64) # Reference 1. Vision Meets Drones: Past, Present and Future 2. [https://blog.netcetera.com/object-detection-and-tracking-in-2020-f10fb6ff9af3](https://www.google.com/url?q=https%3A%2F%2Fblog.netcetera.com%2Fobject-detection-and-tracking-in-2020-f10fb6ff9af3&sa=D&sntz=1&usg=AOvVaw1eVxYhtOaKsaR5fPlbFyv6) 3. 4. [https://pythonawesome.com/yolo-rcnn-object-detection-and-multi-object-tracking/](https://www.google.com/url?q=https%3A%2F%2Fpythonawesome.com%2Fyolo-rcnn-object-detection-and-multi-object-tracking%2F&sa=D&sntz=1&usg=AOvVaw0mC4uPk0uEFfs41gzkjf2R) 5. [https://cv-tricks.com/object-tracking/quick-guide-mdnet-goturn-rolo/](https://www.google.com/url?q=https%3A%2F%2Fcv-tricks.com%2Fobject-tracking%2Fquick-guide-mdnet-goturn-rolo%2F&sa=D&sntz=1&usg=AOvVaw08eNSdqkFWRxKgChSpTmGv) 6. Deep Learning in Video Multi-Object Tracking: A Survey [https://arxiv.org/abs/1907.12740](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1907.12740&sa=D&sntz=1&usg=AOvVaw3UJVcBbBuZ6mdflbt5vdp8) 7. **HOTA: A Higher Order Metric for Evaluating Multi-object Tracking** [https://link.springer.com/article/10.1007/s11263-020-01375-2](https://www.google.com/url?q=https%3A%2F%2Flink.springer.com%2Farticle%2F10.1007%2Fs11263-020-01375-2&sa=D&sntz=1&usg=AOvVaw3Nnnf1zeZD4vmMHhiGaMVC) 8. some examples Endeavor to summarize MOT: The best methods running on GPU. The versioning of different deep learning frameworks are crucial. For example the latest version of OS for Jetson Nano "Jetpack" use CUDA 11 but the Pytorch only support up to 10.1 now. So we need to install lower Jetpack version on Jetson Nano or compile the Pytorch. I compile Pytorch and it takes few hours with a lot of issue to solve. For the Ubuntu 20 there is not support for CUDA 10 you need install CUDA 11 and compile the Pytorch with a lot of library. install CUDA 10.x on Ubuntu 20 is possible but takes time to solve conflicts also supporting eGPU is another issue for lower ubuntu. On MacOS installing everything is easy because of not supporting GPU but many library and frameworks of the source codes of tracking require GPU version. Even install CPU version of all library does not grantee to run tracking methods. Another aspect is speed. Running tracking even on GPU is very slow based on my experience using Yolo version 3 which is one of the fastest object detection on GTX 2070 may run in real time. Methods on tracking is very different. First generation, it is completely based on computer vision. The second generation combining machine learning, Kalman filter and advanced computer vision (SIFT), the third generation using deep learning and some of the methods of previous generation like Kalman filter. The fourth generation using combination of two deep learning methods. And the latest generation using complete end to end models with RNN. Object tracking works with all combination of environments such as, moving objects, moving objects and camera in dynamic environments. As long as object appear in the frame until disappeared it the tracking can track and identification as one objects. No mater how many FPS. In around 130 videos of the course of Multiple Object Tracking on EDEX means this topic is huge and require more attention for the more research and development. Running MOT on Jetson nano is tricky and hacky in many way. First, the cup is arm based and not many package are build for it. Datasets for Tracking: MOTChallenge MOT15 MOT16/17 MOT19 KITTI UA-DETRAC tracking benchmark _metrics_ * _Mostly Tracked_ (MT) trajectories: number of ground-truth trajectories that are correctly tracked in at least 80% of the frames. * _Fragments_ : trajectory hypotheses which cover at most 80% of a ground truth trajectory. Observe that a true trajectory can be covered by more than one fragment. * _Mostly Lost_ (ML) trajectories: number of ground-truth trajectories that are correctly tracked in less than 20% of the frames. _False trajectories_ : predicted trajectories which do not correspond to a real object (i.e. to a ground truth trajectory). * _ID switches_ : number of times when the object is correctly tracked, but the associated ID for the object is mistakenly changed. Test: [https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD) Only Ubuntu, Not mac, can based on GPU, webcam not working [https://github.com/tianweiy/CenterPoint](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Ftianweiy%2FCenterPoint&sa=D&sntz=1&usg=AOvVaw1VC56JGagYnHa4SCXM2hFp) Only GPU YouTube: OpenCV [Tracking Objects | OpenCV Python Tutorials for Beginners 2020](https://www.youtube.com/watch?v=1FJWXOO1SRI&ab_channel=Murtaza%27sWorkshop- RoboticsandAI) Multiple Object Tracking [Python: Real-time Multiple Object Tracking (MOT) with Yolov3, Tensorflow and Deep SORT [FULL COURSE]](https://www.youtube.com/watch?v=zi-62z-3c4U&ab_channel=eMasterClassAcademy) There are at least 7 types of tracker algorithms that can be[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.pyimagesearch.com%2F2019%2F12%2F02%2Fopencv- vehicle-detection-tracking-and-speed- estimation%2F&sa=D&sntz=1&usg=AOvVaw1bSowsM3cTzPpHyDtqvvTH)[used](https://www.google.com/url?q=https%3A%2F%2Fwww.pyimagesearch.com%2F2019%2F12%2F02%2Fopencv- vehicle-detection-tracking-and-speed- estimation%2F&sa=D&sntz=1&usg=AOvVaw1bSowsM3cTzPpHyDtqvvTH) in[ ](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd2%2Fd0a%2Ftutorial_introduction_to_tracker.html&sa=D&sntz=1&usg=AOvVaw2-nw055tVNmJbtGsqDgyDC)[OpenCV](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd2%2Fd0a%2Ftutorial_introduction_to_tracker.html&sa=D&sntz=1&usg=AOvVaw2-nw055tVNmJbtGsqDgyDC): not[ ](https://www.google.com/url?q=https%3A%2F%2Fblog.netcetera.com%2Fobject- detection-and-tracking- in-2020-f10fb6ff9af3&sa=D&sntz=1&usg=AOvVaw1eVxYhtOaKsaR5fPlbFyv6)[DL](https://www.google.com/url?q=https%3A%2F%2Fblog.netcetera.com%2Fobject- detection-and-tracking- in-2020-f10fb6ff9af3&sa=D&sntz=1&usg=AOvVaw1eVxYhtOaKsaR5fPlbFyv6) * MIL * BOOSTING * MEDIANFLOW * TLD * KCF * GOTURN * MOSSE Kalman filtering, sparse and dense optical flow are[ ](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1602.00763&sa=D&sntz=1&usg=AOvVaw3wgxS3anU0zhzcI30qN- xK)[Simple Online and Realtime Tracking (SORT)](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1602.00763&sa=D&sntz=1&usg=AOvVaw3wgxS3anU0zhzcI30qN- xK), which uses a combination of the Hungarian algorithm and Kalman filter to achieve decent object tracking. R-CNN around 2000 region proposals [selective search](http://www.google.com/url?q=http%3A%2F%2Fhuppelen.nl%2Fpublications%2FselectiveSearchDraft.pdf&sa=D&sntz=1&usg=AOvVaw0SePhTa1eR5orNEduks3o7) share colors and textures, lightning conditions slow to train and test Fast R-CNN computes a convolutional feature map for the entire input image in a single forward pass of the network architecture is trained end-to-end with a multi-task loss [https://github.com/ZQPei/deep_sort_pytorch](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FZQPei%2Fdeep_sort_pytorch&sa=D&sntz=1&usg=AOvVaw2ZTw3IYJO4rvuF2hUPjCpH) Simple Online and Realtime Tracking with a Deep Association Metric. 2017 [https://arxiv.org/pdf/1703.07402](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fpdf%2F1703.07402&sa=D&sntz=1&usg=AOvVaw2FicrkTufsUcJdkQyC7MEp) [https://mcv-m6-video.github.io/deepvideo-2019/](https://www.google.com/url?q=https%3A%2F%2Fmcv-m6-video.github.io%2Fdeepvideo-2019%2F&sa=D&sntz=1&usg=AOvVaw0NRE06tCEyly2eOp8bmxhg) # [ **The online course about multiple object tracking in Edx:**](https://www.google.com/url?q=https%3A%2F%2Fwww.edx.org%2Fcourse%2Fmulti- object-tracking-for-automotive- systems%3Futm_source%3Dsailthru%26utm_medium%3Demail%26utm_campaign%3Dtriggered_shareit%2520&sa=D&sntz=1&usg=AOvVaw2h6dAyUrPoPcNJBKhz_eql) Course Section 0: Welcome and Introduction ' Part 1: Introduction to Multiple Object Tracking (MOT): good ; many definition and definitions: 15 videos [Introductory examples](https://www.youtube.com/watch?v=ay_QLAHcZLY&list=PLadnyz93xCLhSlm2tMYJSKaik39EZV_Uk) Is about the accurate perception of the driving environment Avoid collisions at the airport Crowd surveillance Crowd behavior Planning of emergency procedures Pedestrian tracking using LIDAR Tracking based on detections Group behavior Part 2: Single Object Tracking in clutter (SOT): Many math; basic methods, 23 videos [Introduction to SOT in Clutter](https://www.youtube.com/watch?v=UpXpUjgqhTw&list=PLadnyz93xCLiHWjLcLFdzc- SidNL1kRF7) Pruning and merging Pruning : remove hypotheses with small weights (and renormalize) Merging: approximate a mixture of densities by a single density (often Gaussian) Gating: technique to disregard unreasonable detections [pruning] SOT * Gaussian densities * Nearest neighbour (NN) filter [pruning] * Probabilistic data association (PDA) filter [merging] * Gaussian mixture densites * Gaussian sum filter (GSF) [pruning/merging] Part 3: Tracking a known number of objects in clutter 30 3.3.6 Predicting the n object density **3.4.1 Introduction to data association** Part 4: Random Finite Sets 24 Part 5: Multiple Object Tracking using conjugate priors 25 [only in YouTube] Part 6: Outlook - what is next? 18 [only in YouTube] Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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The versioning of different deep learning frameworks are crucial. For example the latest version of OS for Jetson Nano [Jetpack](https://www.google.com/url?q=https%3A%2F%2Fdeveloper.nvidia.com%2Fembedded%2Fjetpack&sa=D&sntz=1&usg=AOvVaw12B66A0ktALSyHLcxN- Xx0) which use latest CUDA but the Pytorch only support up to 10.1 now. So we need to install lower Jetpack version on Jetson Nano or compile the Pytorch. I compile Pytorch and it takes few hours with a lot of issue to solve. For the Ubuntu 20 there is not support for CUDA 10 you need install CUDA 11 and compile the Pytorch with a lot of library. install CUDA 10.x on Ubuntu 20 is possible but takes time to solve conflicts also supporting eGPU is another issue for lower ubuntu. On MacOS installing everything is easy because of not supporting GPU but many library and frameworks of the source codes of tracking require GPU version. Even install CPU version of all library does not grantee to run tracking methods. Another aspect is speed. Running tracking even on GPU is very slow based on my experience using Yolo version 3 which is one of the fastest object detection on GTX 2070 can process up to 15 FPS with Full HD videos. Methods on tracking is very different. First generation, it is completely based on computer vision. The second generation combining Kalman filter and advanced computer vision (SIFT), the third generation using deep learning and some of the methods of previous generation like Kalman filter. The fourth generation using combination of two deep learning methods. And the latest generation using complete end to end models like RNN. Object tracking works with all combination of environments such as, moving objects, moving objects and camera in dynamic environments. As long as object appear in the frame until disappeared it the tracking can track and identification as one objects. No mater how many FPS. ![](https://lh6.googleusercontent.com/GrlgOPkT- YjCOfCPVqdMTFh7YATdG8i43aySblQ32f5lYbF4L5uKYWK8e8j9lE- xzlS5H_z3BmPDB0jdpRqg8VAl-2Obz5iscyVN2BbOW1W2OG10Pl1SFTqnTvpHYfqrPg=w1280) ![](https://lh5.googleusercontent.com/3OpYYQA0JUTtvpYBE2uba5FtI3o1e45ieUeM02heJZKobWLMg5LfsbaHCjHnbAVC1WKVjKvZTUJWSjb5286-gAavbKQPohSRNAAHEwZXkC31bHCtWhGalnOepFCb_HPczw=w1280) ![](https://lh6.googleusercontent.com/MPWSDbFeFhPaIpTPufkx- Lfub5XMg3CpemQwDcs4p1RWgPFuNDkhpESbVYPHmAt5VOxITo5IvFlNNZZyxt3QiJpG54qABsGfqombmsOnb1GGt3t8cFriQ7AQRClIddZQgQ=w1280) ![](https://lh5.googleusercontent.com/MkiPU_u3aULSjsA6-ZM7thrVjM- ZGAoJbwbnLRXpUqp- lXGZavIuijln0eZBIvR0nWshaThulIm48lY8KYm9RLajGyYp5ge5teMUBgbZ2Hy7sMC-TnpVSzrqW- dKQl8f=w1280) ![](https://lh5.googleusercontent.com/9PuWB3svPA63SBkzP9-TU9TiV_Pn8ppe8Cjmx76PQFTidX4E-8V7VPcXzBpcPW1pOqsoR0 --oOc_H88gSea0iyXyxzOO2MWuAAhWZNt6eU6mb2aemB2c-UooXzjQ0EGatA=w1280) ![](https://lh5.googleusercontent.com/s333ScrmCHiimrwEMBB0zcJQu- Pd8VjpDFN0mrmXoZIJ0ppxmNRLcFPJrX5qdA0f2h3a6bUclrk4KUZF0CLgK8ahaGw6cbVDUHpFB5Aj_BQy9SqtTvCpUv3a7lmea-F0Dw=w1280) # Tracking * Classic object tracking * * * classic feature detection (SIFT and SURF), combined with a machine learning algorithm like KNN or SVM for classification, or with a description matcher like FLANN for object detection. * Kalman filtering, sparse and dense optical flow, * Example: Simple Online and Realtime Tracking (SORT), which uses a combination of the Hungarian algorithm and Kalman filter * SOT is a hot topic in the last decade. Early visual tracking methods rely on extracting hand-crafted features of candidate target regions, and use matching algorithms or hand-crafted discriminative classifiers to generate tracking results. * The MOT track aims to recover the trajectories of objects in video sequences, which is an important problem in computer vision with many applications, such as surveillance, activity analysis, and sport video analysis. * Video object detection datasets. The video object detection task aims to detect objects of different categories in video sequences. * Multi-object tracking datasets * large-scale benchmark Multi-Class Multi-object tracking datasets * VisDrone datasets is captured in various unconstrained scenes, focusing on four core problems in computer vision fields, i.e., image object detection, video object detection, single object tracking, and multi- object tracking. * the accuracy of detection methods suffers from degenerated object appearances in videos such as motion blur, pose variations, and video de-focus. Exploiting temporal coherence and aggregating features in consecutive frames might to be two effective ways to handle such issue. * Temporal coherence. A feasible way to exploit temporal coherence is using object trackers * Feature aggregation. Aggregating features in consecutive frames is also a useful way to improve the performance. * ### List of Datasets * **MOT20** * KITTI Tracking * MOTChallenge 2015 * UA-DETRAC Tracking * DukeMTMC * Campus * MOT17 * UAVDT-MOT * VisDrone ### Source code * ROLO * TensorFlow: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FGuanghan%2FROLO&sa=D&sntz=1&usg=AOvVaw2amdiMCe2vunOIdlpvqghX) * SiamMask * PyTorch 0.4.1: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Ffoolwood%2FSiamMask&sa=D&sntz=1&usg=AOvVaw1oXduEQoxzhx-VIMIMQ9Rn) * Deep SORT * PyTorch ≥ 0.4.0: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FZQPei%2Fdeep_sort_pytorch&sa=D&sntz=1&usg=AOvVaw2ZTw3IYJO4rvuF2hUPjCpH) * TensorFlow ≥ 1.0: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fnwojke%2Fdeep_sort&sa=D&sntz=1&usg=AOvVaw2FXwdcfCz1RZbndLyQSlBe) * TrackR-CNN * TensorFlow 1.13.1: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FVisualComputingInstitute%2FTrackR-CNN&sa=D&sntz=1&usg=AOvVaw3i3kALogli3ZyH93E3zhy5) * Tracktor++ * PyTorch 1.3.1: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fphil-bergmann%2Ftracking_wo_bnw&sa=D&sntz=1&usg=AOvVaw2xvx7s_sfJOiwLiwhmhqR8) * JDE * PyTorch ≥ 1.2.0: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FZhongdao%2FTowards-Realtime-MOT&sa=D&sntz=1&usg=AOvVaw0PcQNbW8igbtHaJ1x8IOVO) * [MCMOT: One-shot multi-class multi-object tracking](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FCaptainEven%2FMCMOT&sa=D&sntz=1&usg=AOvVaw2nrFmqa-Eh4-FoWPWBXrJn) # Self collected datasets ## [Video labeling](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fheartexlabs%2Fawesome- data-labeling&sa=D&sntz=1&usg=AOvVaw1Eb2rubldhdODxp4c4_Xnc) * [VATIC](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fcvondrick%2Fvatic&sa=D&sntz=1&usg=AOvVaw3dbkWnFtLPwO3znnZZz3Jy) * [UltimateLabeling](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Falexandre01%2FUltimateLabeling&sa=D&sntz=1&usg=AOvVaw2CpIp2ojyJ4dgWdKVlYC_r) * [https://github.com/pirahansiah/UltimateLabeling](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2FUltimateLabeling&sa=D&sntz=1&usg=AOvVaw2mQJcLjtHwG4L8YcUkET64) # Reference 1. Vision Meets Drones: Past, Present and Future 2. [https://blog.netcetera.com/object-detection-and-tracking-in-2020-f10fb6ff9af3](https://www.google.com/url?q=https%3A%2F%2Fblog.netcetera.com%2Fobject-detection-and-tracking-in-2020-f10fb6ff9af3&sa=D&sntz=1&usg=AOvVaw1eVxYhtOaKsaR5fPlbFyv6) 3. 4. [https://pythonawesome.com/yolo-rcnn-object-detection-and-multi-object-tracking/](https://www.google.com/url?q=https%3A%2F%2Fpythonawesome.com%2Fyolo-rcnn-object-detection-and-multi-object-tracking%2F&sa=D&sntz=1&usg=AOvVaw0mC4uPk0uEFfs41gzkjf2R) 5. [https://cv-tricks.com/object-tracking/quick-guide-mdnet-goturn-rolo/](https://www.google.com/url?q=https%3A%2F%2Fcv-tricks.com%2Fobject-tracking%2Fquick-guide-mdnet-goturn-rolo%2F&sa=D&sntz=1&usg=AOvVaw08eNSdqkFWRxKgChSpTmGv) 6. Deep Learning in Video Multi-Object Tracking: A Survey [https://arxiv.org/abs/1907.12740](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1907.12740&sa=D&sntz=1&usg=AOvVaw3UJVcBbBuZ6mdflbt5vdp8) 7. **HOTA: A Higher Order Metric for Evaluating Multi-object Tracking** [https://link.springer.com/article/10.1007/s11263-020-01375-2](https://www.google.com/url?q=https%3A%2F%2Flink.springer.com%2Farticle%2F10.1007%2Fs11263-020-01375-2&sa=D&sntz=1&usg=AOvVaw3Nnnf1zeZD4vmMHhiGaMVC) 8. some examples Endeavor to summarize MOT: The best methods running on GPU. The versioning of different deep learning frameworks are crucial. For example the latest version of OS for Jetson Nano "Jetpack" use CUDA 11 but the Pytorch only support up to 10.1 now. So we need to install lower Jetpack version on Jetson Nano or compile the Pytorch. I compile Pytorch and it takes few hours with a lot of issue to solve. For the Ubuntu 20 there is not support for CUDA 10 you need install CUDA 11 and compile the Pytorch with a lot of library. install CUDA 10.x on Ubuntu 20 is possible but takes time to solve conflicts also supporting eGPU is another issue for lower ubuntu. On MacOS installing everything is easy because of not supporting GPU but many library and frameworks of the source codes of tracking require GPU version. Even install CPU version of all library does not grantee to run tracking methods. Another aspect is speed. Running tracking even on GPU is very slow based on my experience using Yolo version 3 which is one of the fastest object detection on GTX 2070 may run in real time. Methods on tracking is very different. First generation, it is completely based on computer vision. The second generation combining machine learning, Kalman filter and advanced computer vision (SIFT), the third generation using deep learning and some of the methods of previous generation like Kalman filter. The fourth generation using combination of two deep learning methods. And the latest generation using complete end to end models with RNN. Object tracking works with all combination of environments such as, moving objects, moving objects and camera in dynamic environments. As long as object appear in the frame until disappeared it the tracking can track and identification as one objects. No mater how many FPS. In around 130 videos of the course of Multiple Object Tracking on EDEX means this topic is huge and require more attention for the more research and development. Running MOT on Jetson nano is tricky and hacky in many way. First, the cup is arm based and not many package are build for it. Datasets for Tracking: MOTChallenge MOT15 MOT16/17 MOT19 KITTI UA-DETRAC tracking benchmark _metrics_ * _Mostly Tracked_ (MT) trajectories: number of ground-truth trajectories that are correctly tracked in at least 80% of the frames. * _Fragments_ : trajectory hypotheses which cover at most 80% of a ground truth trajectory. Observe that a true trajectory can be covered by more than one fragment. * _Mostly Lost_ (ML) trajectories: number of ground-truth trajectories that are correctly tracked in less than 20% of the frames. _False trajectories_ : predicted trajectories which do not correspond to a real object (i.e. to a ground truth trajectory). * _ID switches_ : number of times when the object is correctly tracked, but the associated ID for the object is mistakenly changed. Test: [https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD) Only Ubuntu, Not mac, can based on GPU, webcam not working [https://github.com/tianweiy/CenterPoint](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Ftianweiy%2FCenterPoint&sa=D&sntz=1&usg=AOvVaw1VC56JGagYnHa4SCXM2hFp) Only GPU YouTube: OpenCV [Tracking Objects | OpenCV Python Tutorials for Beginners 2020](https://www.youtube.com/watch?v=1FJWXOO1SRI&ab_channel=Murtaza%27sWorkshop- RoboticsandAI) Multiple Object Tracking [Python: Real-time Multiple Object Tracking (MOT) with Yolov3, Tensorflow and Deep SORT [FULL COURSE]](https://www.youtube.com/watch?v=zi-62z-3c4U&ab_channel=eMasterClassAcademy) There are at least 7 types of tracker algorithms that can be[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.pyimagesearch.com%2F2019%2F12%2F02%2Fopencv- vehicle-detection-tracking-and-speed- estimation%2F&sa=D&sntz=1&usg=AOvVaw1bSowsM3cTzPpHyDtqvvTH)[used](https://www.google.com/url?q=https%3A%2F%2Fwww.pyimagesearch.com%2F2019%2F12%2F02%2Fopencv- vehicle-detection-tracking-and-speed- estimation%2F&sa=D&sntz=1&usg=AOvVaw1bSowsM3cTzPpHyDtqvvTH) in[ ](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd2%2Fd0a%2Ftutorial_introduction_to_tracker.html&sa=D&sntz=1&usg=AOvVaw2-nw055tVNmJbtGsqDgyDC)[OpenCV](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd2%2Fd0a%2Ftutorial_introduction_to_tracker.html&sa=D&sntz=1&usg=AOvVaw2-nw055tVNmJbtGsqDgyDC): not[ ](https://www.google.com/url?q=https%3A%2F%2Fblog.netcetera.com%2Fobject- detection-and-tracking- in-2020-f10fb6ff9af3&sa=D&sntz=1&usg=AOvVaw1eVxYhtOaKsaR5fPlbFyv6)[DL](https://www.google.com/url?q=https%3A%2F%2Fblog.netcetera.com%2Fobject- detection-and-tracking- in-2020-f10fb6ff9af3&sa=D&sntz=1&usg=AOvVaw1eVxYhtOaKsaR5fPlbFyv6) * MIL * BOOSTING * MEDIANFLOW * TLD * KCF * GOTURN * MOSSE Kalman filtering, sparse and dense optical flow are[ ](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1602.00763&sa=D&sntz=1&usg=AOvVaw3wgxS3anU0zhzcI30qN- xK)[Simple Online and Realtime Tracking (SORT)](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1602.00763&sa=D&sntz=1&usg=AOvVaw3wgxS3anU0zhzcI30qN- xK), which uses a combination of the Hungarian algorithm and Kalman filter to achieve decent object tracking. R-CNN around 2000 region proposals [selective search](http://www.google.com/url?q=http%3A%2F%2Fhuppelen.nl%2Fpublications%2FselectiveSearchDraft.pdf&sa=D&sntz=1&usg=AOvVaw0SePhTa1eR5orNEduks3o7) share colors and textures, lightning conditions slow to train and test Fast R-CNN computes a convolutional feature map for the entire input image in a single forward pass of the network architecture is trained end-to-end with a multi-task loss [https://github.com/ZQPei/deep_sort_pytorch](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FZQPei%2Fdeep_sort_pytorch&sa=D&sntz=1&usg=AOvVaw2ZTw3IYJO4rvuF2hUPjCpH) Simple Online and Realtime Tracking with a Deep Association Metric. 2017 [https://arxiv.org/pdf/1703.07402](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fpdf%2F1703.07402&sa=D&sntz=1&usg=AOvVaw2FicrkTufsUcJdkQyC7MEp) [https://mcv-m6-video.github.io/deepvideo-2019/](https://www.google.com/url?q=https%3A%2F%2Fmcv-m6-video.github.io%2Fdeepvideo-2019%2F&sa=D&sntz=1&usg=AOvVaw0NRE06tCEyly2eOp8bmxhg) # [ **The online course about multiple object tracking in Edx:**](https://www.google.com/url?q=https%3A%2F%2Fwww.edx.org%2Fcourse%2Fmulti- object-tracking-for-automotive- systems%3Futm_source%3Dsailthru%26utm_medium%3Demail%26utm_campaign%3Dtriggered_shareit%2520&sa=D&sntz=1&usg=AOvVaw2h6dAyUrPoPcNJBKhz_eql) Course Section 0: Welcome and Introduction ' Part 1: Introduction to Multiple Object Tracking (MOT): good ; many definition and definitions: 15 videos [Introductory examples](https://www.youtube.com/watch?v=ay_QLAHcZLY&list=PLadnyz93xCLhSlm2tMYJSKaik39EZV_Uk) Is about the accurate perception of the driving environment Avoid collisions at the airport Crowd surveillance Crowd behavior Planning of emergency procedures Pedestrian tracking using LIDAR Tracking based on detections Group behavior Part 2: Single Object Tracking in clutter (SOT): Many math; basic methods, 23 videos [Introduction to SOT in Clutter](https://www.youtube.com/watch?v=UpXpUjgqhTw&list=PLadnyz93xCLiHWjLcLFdzc- SidNL1kRF7) Pruning and merging Pruning : remove hypotheses with small weights (and renormalize) Merging: approximate a mixture of densities by a single density (often Gaussian) Gating: technique to disregard unreasonable detections [pruning] SOT * Gaussian densities * Nearest neighbour (NN) filter [pruning] * Probabilistic data association (PDA) filter [merging] * Gaussian mixture densites * Gaussian sum filter (GSF) [pruning/merging] Part 3: Tracking a known number of objects in clutter 30 3.3.6 Predicting the n object density **3.4.1 Introduction to data association** Part 4: Random Finite Sets 24 Part 5: Multiple Object Tracking using conjugate priors 25 [only in YouTube] Part 6: Outlook - what is next? 18 [only in YouTube] Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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The versioning of different deep learning frameworks are crucial. For example the latest version of OS for Jetson Nano [Jetpack](https://www.google.com/url?q=https%3A%2F%2Fdeveloper.nvidia.com%2Fembedded%2Fjetpack&sa=D&sntz=1&usg=AOvVaw12B66A0ktALSyHLcxN- Xx0) which use latest CUDA but the Pytorch only support up to 10.1 now. So we need to install lower Jetpack version on Jetson Nano or compile the Pytorch. I compile Pytorch and it takes few hours with a lot of issue to solve. For the Ubuntu 20 there is not support for CUDA 10 you need install CUDA 11 and compile the Pytorch with a lot of library. install CUDA 10.x on Ubuntu 20 is possible but takes time to solve conflicts also supporting eGPU is another issue for lower ubuntu. On MacOS installing everything is easy because of not supporting GPU but many library and frameworks of the source codes of tracking require GPU version. Even install CPU version of all library does not grantee to run tracking methods. Another aspect is speed. Running tracking even on GPU is very slow based on my experience using Yolo version 3 which is one of the fastest object detection on GTX 2070 can process up to 15 FPS with Full HD videos. Methods on tracking is very different. First generation, it is completely based on computer vision. The second generation combining Kalman filter and advanced computer vision (SIFT), the third generation using deep learning and some of the methods of previous generation like Kalman filter. The fourth generation using combination of two deep learning methods. And the latest generation using complete end to end models like RNN. Object tracking works with all combination of environments such as, moving objects, moving objects and camera in dynamic environments. As long as object appear in the frame until disappeared it the tracking can track and identification as one objects. No mater how many FPS. ![](https://lh6.googleusercontent.com/GrlgOPkT- YjCOfCPVqdMTFh7YATdG8i43aySblQ32f5lYbF4L5uKYWK8e8j9lE- xzlS5H_z3BmPDB0jdpRqg8VAl-2Obz5iscyVN2BbOW1W2OG10Pl1SFTqnTvpHYfqrPg=w1280) ![](https://lh5.googleusercontent.com/3OpYYQA0JUTtvpYBE2uba5FtI3o1e45ieUeM02heJZKobWLMg5LfsbaHCjHnbAVC1WKVjKvZTUJWSjb5286-gAavbKQPohSRNAAHEwZXkC31bHCtWhGalnOepFCb_HPczw=w1280) ![](https://lh6.googleusercontent.com/MPWSDbFeFhPaIpTPufkx- Lfub5XMg3CpemQwDcs4p1RWgPFuNDkhpESbVYPHmAt5VOxITo5IvFlNNZZyxt3QiJpG54qABsGfqombmsOnb1GGt3t8cFriQ7AQRClIddZQgQ=w1280) ![](https://lh5.googleusercontent.com/MkiPU_u3aULSjsA6-ZM7thrVjM- ZGAoJbwbnLRXpUqp- lXGZavIuijln0eZBIvR0nWshaThulIm48lY8KYm9RLajGyYp5ge5teMUBgbZ2Hy7sMC-TnpVSzrqW- dKQl8f=w1280) ![](https://lh5.googleusercontent.com/9PuWB3svPA63SBkzP9-TU9TiV_Pn8ppe8Cjmx76PQFTidX4E-8V7VPcXzBpcPW1pOqsoR0 --oOc_H88gSea0iyXyxzOO2MWuAAhWZNt6eU6mb2aemB2c-UooXzjQ0EGatA=w1280) ![](https://lh5.googleusercontent.com/s333ScrmCHiimrwEMBB0zcJQu- Pd8VjpDFN0mrmXoZIJ0ppxmNRLcFPJrX5qdA0f2h3a6bUclrk4KUZF0CLgK8ahaGw6cbVDUHpFB5Aj_BQy9SqtTvCpUv3a7lmea-F0Dw=w1280) # Tracking * Classic object tracking * * * classic feature detection (SIFT and SURF), combined with a machine learning algorithm like KNN or SVM for classification, or with a description matcher like FLANN for object detection. * Kalman filtering, sparse and dense optical flow, * Example: Simple Online and Realtime Tracking (SORT), which uses a combination of the Hungarian algorithm and Kalman filter * SOT is a hot topic in the last decade. Early visual tracking methods rely on extracting hand-crafted features of candidate target regions, and use matching algorithms or hand-crafted discriminative classifiers to generate tracking results. * The MOT track aims to recover the trajectories of objects in video sequences, which is an important problem in computer vision with many applications, such as surveillance, activity analysis, and sport video analysis. * Video object detection datasets. The video object detection task aims to detect objects of different categories in video sequences. * Multi-object tracking datasets * large-scale benchmark Multi-Class Multi-object tracking datasets * VisDrone datasets is captured in various unconstrained scenes, focusing on four core problems in computer vision fields, i.e., image object detection, video object detection, single object tracking, and multi- object tracking. * the accuracy of detection methods suffers from degenerated object appearances in videos such as motion blur, pose variations, and video de-focus. Exploiting temporal coherence and aggregating features in consecutive frames might to be two effective ways to handle such issue. * Temporal coherence. A feasible way to exploit temporal coherence is using object trackers * Feature aggregation. Aggregating features in consecutive frames is also a useful way to improve the performance. * ### List of Datasets * **MOT20** * KITTI Tracking * MOTChallenge 2015 * UA-DETRAC Tracking * DukeMTMC * Campus * MOT17 * UAVDT-MOT * VisDrone ### Source code * ROLO * TensorFlow: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FGuanghan%2FROLO&sa=D&sntz=1&usg=AOvVaw2amdiMCe2vunOIdlpvqghX) * SiamMask * PyTorch 0.4.1: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Ffoolwood%2FSiamMask&sa=D&sntz=1&usg=AOvVaw1oXduEQoxzhx-VIMIMQ9Rn) * Deep SORT * PyTorch ≥ 0.4.0: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FZQPei%2Fdeep_sort_pytorch&sa=D&sntz=1&usg=AOvVaw2ZTw3IYJO4rvuF2hUPjCpH) * TensorFlow ≥ 1.0: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fnwojke%2Fdeep_sort&sa=D&sntz=1&usg=AOvVaw2FXwdcfCz1RZbndLyQSlBe) * TrackR-CNN * TensorFlow 1.13.1: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FVisualComputingInstitute%2FTrackR-CNN&sa=D&sntz=1&usg=AOvVaw3i3kALogli3ZyH93E3zhy5) * Tracktor++ * PyTorch 1.3.1: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fphil-bergmann%2Ftracking_wo_bnw&sa=D&sntz=1&usg=AOvVaw2xvx7s_sfJOiwLiwhmhqR8) * JDE * PyTorch ≥ 1.2.0: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FZhongdao%2FTowards-Realtime-MOT&sa=D&sntz=1&usg=AOvVaw0PcQNbW8igbtHaJ1x8IOVO) * [MCMOT: One-shot multi-class multi-object tracking](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FCaptainEven%2FMCMOT&sa=D&sntz=1&usg=AOvVaw2nrFmqa-Eh4-FoWPWBXrJn) # Self collected datasets ## [Video labeling](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fheartexlabs%2Fawesome- data-labeling&sa=D&sntz=1&usg=AOvVaw1Eb2rubldhdODxp4c4_Xnc) * [VATIC](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fcvondrick%2Fvatic&sa=D&sntz=1&usg=AOvVaw3dbkWnFtLPwO3znnZZz3Jy) * [UltimateLabeling](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Falexandre01%2FUltimateLabeling&sa=D&sntz=1&usg=AOvVaw2CpIp2ojyJ4dgWdKVlYC_r) * [https://github.com/pirahansiah/UltimateLabeling](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2FUltimateLabeling&sa=D&sntz=1&usg=AOvVaw2mQJcLjtHwG4L8YcUkET64) # Reference 1. Vision Meets Drones: Past, Present and Future 2. [https://blog.netcetera.com/object-detection-and-tracking-in-2020-f10fb6ff9af3](https://www.google.com/url?q=https%3A%2F%2Fblog.netcetera.com%2Fobject-detection-and-tracking-in-2020-f10fb6ff9af3&sa=D&sntz=1&usg=AOvVaw1eVxYhtOaKsaR5fPlbFyv6) 3. 4. [https://pythonawesome.com/yolo-rcnn-object-detection-and-multi-object-tracking/](https://www.google.com/url?q=https%3A%2F%2Fpythonawesome.com%2Fyolo-rcnn-object-detection-and-multi-object-tracking%2F&sa=D&sntz=1&usg=AOvVaw0mC4uPk0uEFfs41gzkjf2R) 5. [https://cv-tricks.com/object-tracking/quick-guide-mdnet-goturn-rolo/](https://www.google.com/url?q=https%3A%2F%2Fcv-tricks.com%2Fobject-tracking%2Fquick-guide-mdnet-goturn-rolo%2F&sa=D&sntz=1&usg=AOvVaw08eNSdqkFWRxKgChSpTmGv) 6. Deep Learning in Video Multi-Object Tracking: A Survey [https://arxiv.org/abs/1907.12740](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1907.12740&sa=D&sntz=1&usg=AOvVaw3UJVcBbBuZ6mdflbt5vdp8) 7. **HOTA: A Higher Order Metric for Evaluating Multi-object Tracking** [https://link.springer.com/article/10.1007/s11263-020-01375-2](https://www.google.com/url?q=https%3A%2F%2Flink.springer.com%2Farticle%2F10.1007%2Fs11263-020-01375-2&sa=D&sntz=1&usg=AOvVaw3Nnnf1zeZD4vmMHhiGaMVC) 8. some examples Endeavor to summarize MOT: The best methods running on GPU. The versioning of different deep learning frameworks are crucial. For example the latest version of OS for Jetson Nano "Jetpack" use CUDA 11 but the Pytorch only support up to 10.1 now. So we need to install lower Jetpack version on Jetson Nano or compile the Pytorch. I compile Pytorch and it takes few hours with a lot of issue to solve. For the Ubuntu 20 there is not support for CUDA 10 you need install CUDA 11 and compile the Pytorch with a lot of library. install CUDA 10.x on Ubuntu 20 is possible but takes time to solve conflicts also supporting eGPU is another issue for lower ubuntu. On MacOS installing everything is easy because of not supporting GPU but many library and frameworks of the source codes of tracking require GPU version. Even install CPU version of all library does not grantee to run tracking methods. Another aspect is speed. Running tracking even on GPU is very slow based on my experience using Yolo version 3 which is one of the fastest object detection on GTX 2070 may run in real time. Methods on tracking is very different. First generation, it is completely based on computer vision. The second generation combining machine learning, Kalman filter and advanced computer vision (SIFT), the third generation using deep learning and some of the methods of previous generation like Kalman filter. The fourth generation using combination of two deep learning methods. And the latest generation using complete end to end models with RNN. Object tracking works with all combination of environments such as, moving objects, moving objects and camera in dynamic environments. As long as object appear in the frame until disappeared it the tracking can track and identification as one objects. No mater how many FPS. In around 130 videos of the course of Multiple Object Tracking on EDEX means this topic is huge and require more attention for the more research and development. Running MOT on Jetson nano is tricky and hacky in many way. First, the cup is arm based and not many package are build for it. Datasets for Tracking: MOTChallenge MOT15 MOT16/17 MOT19 KITTI UA-DETRAC tracking benchmark _metrics_ * _Mostly Tracked_ (MT) trajectories: number of ground-truth trajectories that are correctly tracked in at least 80% of the frames. * _Fragments_ : trajectory hypotheses which cover at most 80% of a ground truth trajectory. Observe that a true trajectory can be covered by more than one fragment. * _Mostly Lost_ (ML) trajectories: number of ground-truth trajectories that are correctly tracked in less than 20% of the frames. _False trajectories_ : predicted trajectories which do not correspond to a real object (i.e. to a ground truth trajectory). * _ID switches_ : number of times when the object is correctly tracked, but the associated ID for the object is mistakenly changed. Test: [https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD) Only Ubuntu, Not mac, can based on GPU, webcam not working [https://github.com/tianweiy/CenterPoint](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Ftianweiy%2FCenterPoint&sa=D&sntz=1&usg=AOvVaw1VC56JGagYnHa4SCXM2hFp) Only GPU YouTube: OpenCV [Tracking Objects | OpenCV Python Tutorials for Beginners 2020](https://www.youtube.com/watch?v=1FJWXOO1SRI&ab_channel=Murtaza%27sWorkshop- RoboticsandAI) Multiple Object Tracking [Python: Real-time Multiple Object Tracking (MOT) with Yolov3, Tensorflow and Deep SORT [FULL COURSE]](https://www.youtube.com/watch?v=zi-62z-3c4U&ab_channel=eMasterClassAcademy) There are at least 7 types of tracker algorithms that can be[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.pyimagesearch.com%2F2019%2F12%2F02%2Fopencv- vehicle-detection-tracking-and-speed- estimation%2F&sa=D&sntz=1&usg=AOvVaw1bSowsM3cTzPpHyDtqvvTH)[used](https://www.google.com/url?q=https%3A%2F%2Fwww.pyimagesearch.com%2F2019%2F12%2F02%2Fopencv- vehicle-detection-tracking-and-speed- estimation%2F&sa=D&sntz=1&usg=AOvVaw1bSowsM3cTzPpHyDtqvvTH) in[ ](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd2%2Fd0a%2Ftutorial_introduction_to_tracker.html&sa=D&sntz=1&usg=AOvVaw2-nw055tVNmJbtGsqDgyDC)[OpenCV](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd2%2Fd0a%2Ftutorial_introduction_to_tracker.html&sa=D&sntz=1&usg=AOvVaw2-nw055tVNmJbtGsqDgyDC): not[ ](https://www.google.com/url?q=https%3A%2F%2Fblog.netcetera.com%2Fobject- detection-and-tracking- in-2020-f10fb6ff9af3&sa=D&sntz=1&usg=AOvVaw1eVxYhtOaKsaR5fPlbFyv6)[DL](https://www.google.com/url?q=https%3A%2F%2Fblog.netcetera.com%2Fobject- detection-and-tracking- in-2020-f10fb6ff9af3&sa=D&sntz=1&usg=AOvVaw1eVxYhtOaKsaR5fPlbFyv6) * MIL * BOOSTING * MEDIANFLOW * TLD * KCF * GOTURN * MOSSE Kalman filtering, sparse and dense optical flow are[ ](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1602.00763&sa=D&sntz=1&usg=AOvVaw3wgxS3anU0zhzcI30qN- xK)[Simple Online and Realtime Tracking (SORT)](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1602.00763&sa=D&sntz=1&usg=AOvVaw3wgxS3anU0zhzcI30qN- xK), which uses a combination of the Hungarian algorithm and Kalman filter to achieve decent object tracking. R-CNN around 2000 region proposals [selective search](http://www.google.com/url?q=http%3A%2F%2Fhuppelen.nl%2Fpublications%2FselectiveSearchDraft.pdf&sa=D&sntz=1&usg=AOvVaw0SePhTa1eR5orNEduks3o7) share colors and textures, lightning conditions slow to train and test Fast R-CNN computes a convolutional feature map for the entire input image in a single forward pass of the network architecture is trained end-to-end with a multi-task loss [https://github.com/ZQPei/deep_sort_pytorch](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FZQPei%2Fdeep_sort_pytorch&sa=D&sntz=1&usg=AOvVaw2ZTw3IYJO4rvuF2hUPjCpH) Simple Online and Realtime Tracking with a Deep Association Metric. 2017 [https://arxiv.org/pdf/1703.07402](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fpdf%2F1703.07402&sa=D&sntz=1&usg=AOvVaw2FicrkTufsUcJdkQyC7MEp) [https://mcv-m6-video.github.io/deepvideo-2019/](https://www.google.com/url?q=https%3A%2F%2Fmcv-m6-video.github.io%2Fdeepvideo-2019%2F&sa=D&sntz=1&usg=AOvVaw0NRE06tCEyly2eOp8bmxhg) # [ **The online course about multiple object tracking in Edx:**](https://www.google.com/url?q=https%3A%2F%2Fwww.edx.org%2Fcourse%2Fmulti- object-tracking-for-automotive- systems%3Futm_source%3Dsailthru%26utm_medium%3Demail%26utm_campaign%3Dtriggered_shareit%2520&sa=D&sntz=1&usg=AOvVaw2h6dAyUrPoPcNJBKhz_eql) Course Section 0: Welcome and Introduction ' Part 1: Introduction to Multiple Object Tracking (MOT): good ; many definition and definitions: 15 videos [Introductory examples](https://www.youtube.com/watch?v=ay_QLAHcZLY&list=PLadnyz93xCLhSlm2tMYJSKaik39EZV_Uk) Is about the accurate perception of the driving environment Avoid collisions at the airport Crowd surveillance Crowd behavior Planning of emergency procedures Pedestrian tracking using LIDAR Tracking based on detections Group behavior Part 2: Single Object Tracking in clutter (SOT): Many math; basic methods, 23 videos [Introduction to SOT in Clutter](https://www.youtube.com/watch?v=UpXpUjgqhTw&list=PLadnyz93xCLiHWjLcLFdzc- SidNL1kRF7) Pruning and merging Pruning : remove hypotheses with small weights (and renormalize) Merging: approximate a mixture of densities by a single density (often Gaussian) Gating: technique to disregard unreasonable detections [pruning] SOT * Gaussian densities * Nearest neighbour (NN) filter [pruning] * Probabilistic data association (PDA) filter [merging] * Gaussian mixture densites * Gaussian sum filter (GSF) [pruning/merging] Part 3: Tracking a known number of objects in clutter 30 3.3.6 Predicting the n object density **3.4.1 Introduction to data association** Part 4: Random Finite Sets 24 Part 5: Multiple Object Tracking using conjugate priors 25 [only in YouTube] Part 6: Outlook - what is next? 18 [only in YouTube] Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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The versioning of different deep learning frameworks are crucial. For example the latest version of OS for Jetson Nano [Jetpack](https://www.google.com/url?q=https%3A%2F%2Fdeveloper.nvidia.com%2Fembedded%2Fjetpack&sa=D&sntz=1&usg=AOvVaw12B66A0ktALSyHLcxN- Xx0) which use latest CUDA but the Pytorch only support up to 10.1 now. So we need to install lower Jetpack version on Jetson Nano or compile the Pytorch. I compile Pytorch and it takes few hours with a lot of issue to solve. For the Ubuntu 20 there is not support for CUDA 10 you need install CUDA 11 and compile the Pytorch with a lot of library. install CUDA 10.x on Ubuntu 20 is possible but takes time to solve conflicts also supporting eGPU is another issue for lower ubuntu. On MacOS installing everything is easy because of not supporting GPU but many library and frameworks of the source codes of tracking require GPU version. Even install CPU version of all library does not grantee to run tracking methods. Another aspect is speed. Running tracking even on GPU is very slow based on my experience using Yolo version 3 which is one of the fastest object detection on GTX 2070 can process up to 15 FPS with Full HD videos. Methods on tracking is very different. First generation, it is completely based on computer vision. The second generation combining Kalman filter and advanced computer vision (SIFT), the third generation using deep learning and some of the methods of previous generation like Kalman filter. The fourth generation using combination of two deep learning methods. And the latest generation using complete end to end models like RNN. Object tracking works with all combination of environments such as, moving objects, moving objects and camera in dynamic environments. As long as object appear in the frame until disappeared it the tracking can track and identification as one objects. No mater how many FPS. ![](https://lh6.googleusercontent.com/GrlgOPkT- YjCOfCPVqdMTFh7YATdG8i43aySblQ32f5lYbF4L5uKYWK8e8j9lE- xzlS5H_z3BmPDB0jdpRqg8VAl-2Obz5iscyVN2BbOW1W2OG10Pl1SFTqnTvpHYfqrPg=w1280) ![](https://lh5.googleusercontent.com/3OpYYQA0JUTtvpYBE2uba5FtI3o1e45ieUeM02heJZKobWLMg5LfsbaHCjHnbAVC1WKVjKvZTUJWSjb5286-gAavbKQPohSRNAAHEwZXkC31bHCtWhGalnOepFCb_HPczw=w1280) ![](https://lh6.googleusercontent.com/MPWSDbFeFhPaIpTPufkx- Lfub5XMg3CpemQwDcs4p1RWgPFuNDkhpESbVYPHmAt5VOxITo5IvFlNNZZyxt3QiJpG54qABsGfqombmsOnb1GGt3t8cFriQ7AQRClIddZQgQ=w1280) ![](https://lh5.googleusercontent.com/MkiPU_u3aULSjsA6-ZM7thrVjM- ZGAoJbwbnLRXpUqp- lXGZavIuijln0eZBIvR0nWshaThulIm48lY8KYm9RLajGyYp5ge5teMUBgbZ2Hy7sMC-TnpVSzrqW- dKQl8f=w1280) ![](https://lh5.googleusercontent.com/9PuWB3svPA63SBkzP9-TU9TiV_Pn8ppe8Cjmx76PQFTidX4E-8V7VPcXzBpcPW1pOqsoR0 --oOc_H88gSea0iyXyxzOO2MWuAAhWZNt6eU6mb2aemB2c-UooXzjQ0EGatA=w1280) ![](https://lh5.googleusercontent.com/s333ScrmCHiimrwEMBB0zcJQu- Pd8VjpDFN0mrmXoZIJ0ppxmNRLcFPJrX5qdA0f2h3a6bUclrk4KUZF0CLgK8ahaGw6cbVDUHpFB5Aj_BQy9SqtTvCpUv3a7lmea-F0Dw=w1280) # Tracking * Classic object tracking * * * classic feature detection (SIFT and SURF), combined with a machine learning algorithm like KNN or SVM for classification, or with a description matcher like FLANN for object detection. * Kalman filtering, sparse and dense optical flow, * Example: Simple Online and Realtime Tracking (SORT), which uses a combination of the Hungarian algorithm and Kalman filter * SOT is a hot topic in the last decade. Early visual tracking methods rely on extracting hand-crafted features of candidate target regions, and use matching algorithms or hand-crafted discriminative classifiers to generate tracking results. * The MOT track aims to recover the trajectories of objects in video sequences, which is an important problem in computer vision with many applications, such as surveillance, activity analysis, and sport video analysis. * Video object detection datasets. The video object detection task aims to detect objects of different categories in video sequences. * Multi-object tracking datasets * large-scale benchmark Multi-Class Multi-object tracking datasets * VisDrone datasets is captured in various unconstrained scenes, focusing on four core problems in computer vision fields, i.e., image object detection, video object detection, single object tracking, and multi- object tracking. * the accuracy of detection methods suffers from degenerated object appearances in videos such as motion blur, pose variations, and video de-focus. Exploiting temporal coherence and aggregating features in consecutive frames might to be two effective ways to handle such issue. * Temporal coherence. A feasible way to exploit temporal coherence is using object trackers * Feature aggregation. Aggregating features in consecutive frames is also a useful way to improve the performance. * ### List of Datasets * **MOT20** * KITTI Tracking * MOTChallenge 2015 * UA-DETRAC Tracking * DukeMTMC * Campus * MOT17 * UAVDT-MOT * VisDrone ### Source code * ROLO * TensorFlow: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FGuanghan%2FROLO&sa=D&sntz=1&usg=AOvVaw2amdiMCe2vunOIdlpvqghX) * SiamMask * PyTorch 0.4.1: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Ffoolwood%2FSiamMask&sa=D&sntz=1&usg=AOvVaw1oXduEQoxzhx-VIMIMQ9Rn) * Deep SORT * PyTorch ≥ 0.4.0: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FZQPei%2Fdeep_sort_pytorch&sa=D&sntz=1&usg=AOvVaw2ZTw3IYJO4rvuF2hUPjCpH) * TensorFlow ≥ 1.0: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fnwojke%2Fdeep_sort&sa=D&sntz=1&usg=AOvVaw2FXwdcfCz1RZbndLyQSlBe) * TrackR-CNN * TensorFlow 1.13.1: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FVisualComputingInstitute%2FTrackR-CNN&sa=D&sntz=1&usg=AOvVaw3i3kALogli3ZyH93E3zhy5) * Tracktor++ * PyTorch 1.3.1: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fphil-bergmann%2Ftracking_wo_bnw&sa=D&sntz=1&usg=AOvVaw2xvx7s_sfJOiwLiwhmhqR8) * JDE * PyTorch ≥ 1.2.0: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FZhongdao%2FTowards-Realtime-MOT&sa=D&sntz=1&usg=AOvVaw0PcQNbW8igbtHaJ1x8IOVO) * [MCMOT: One-shot multi-class multi-object tracking](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FCaptainEven%2FMCMOT&sa=D&sntz=1&usg=AOvVaw2nrFmqa-Eh4-FoWPWBXrJn) # Self collected datasets ## [Video labeling](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fheartexlabs%2Fawesome- data-labeling&sa=D&sntz=1&usg=AOvVaw1Eb2rubldhdODxp4c4_Xnc) * [VATIC](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fcvondrick%2Fvatic&sa=D&sntz=1&usg=AOvVaw3dbkWnFtLPwO3znnZZz3Jy) * [UltimateLabeling](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Falexandre01%2FUltimateLabeling&sa=D&sntz=1&usg=AOvVaw2CpIp2ojyJ4dgWdKVlYC_r) * [https://github.com/pirahansiah/UltimateLabeling](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2FUltimateLabeling&sa=D&sntz=1&usg=AOvVaw2mQJcLjtHwG4L8YcUkET64) # Reference 1. Vision Meets Drones: Past, Present and Future 2. [https://blog.netcetera.com/object-detection-and-tracking-in-2020-f10fb6ff9af3](https://www.google.com/url?q=https%3A%2F%2Fblog.netcetera.com%2Fobject-detection-and-tracking-in-2020-f10fb6ff9af3&sa=D&sntz=1&usg=AOvVaw1eVxYhtOaKsaR5fPlbFyv6) 3. 4. [https://pythonawesome.com/yolo-rcnn-object-detection-and-multi-object-tracking/](https://www.google.com/url?q=https%3A%2F%2Fpythonawesome.com%2Fyolo-rcnn-object-detection-and-multi-object-tracking%2F&sa=D&sntz=1&usg=AOvVaw0mC4uPk0uEFfs41gzkjf2R) 5. [https://cv-tricks.com/object-tracking/quick-guide-mdnet-goturn-rolo/](https://www.google.com/url?q=https%3A%2F%2Fcv-tricks.com%2Fobject-tracking%2Fquick-guide-mdnet-goturn-rolo%2F&sa=D&sntz=1&usg=AOvVaw08eNSdqkFWRxKgChSpTmGv) 6. Deep Learning in Video Multi-Object Tracking: A Survey [https://arxiv.org/abs/1907.12740](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1907.12740&sa=D&sntz=1&usg=AOvVaw3UJVcBbBuZ6mdflbt5vdp8) 7. **HOTA: A Higher Order Metric for Evaluating Multi-object Tracking** [https://link.springer.com/article/10.1007/s11263-020-01375-2](https://www.google.com/url?q=https%3A%2F%2Flink.springer.com%2Farticle%2F10.1007%2Fs11263-020-01375-2&sa=D&sntz=1&usg=AOvVaw3Nnnf1zeZD4vmMHhiGaMVC) 8. some examples Endeavor to summarize MOT: The best methods running on GPU. The versioning of different deep learning frameworks are crucial. For example the latest version of OS for Jetson Nano "Jetpack" use CUDA 11 but the Pytorch only support up to 10.1 now. So we need to install lower Jetpack version on Jetson Nano or compile the Pytorch. I compile Pytorch and it takes few hours with a lot of issue to solve. For the Ubuntu 20 there is not support for CUDA 10 you need install CUDA 11 and compile the Pytorch with a lot of library. install CUDA 10.x on Ubuntu 20 is possible but takes time to solve conflicts also supporting eGPU is another issue for lower ubuntu. On MacOS installing everything is easy because of not supporting GPU but many library and frameworks of the source codes of tracking require GPU version. Even install CPU version of all library does not grantee to run tracking methods. Another aspect is speed. Running tracking even on GPU is very slow based on my experience using Yolo version 3 which is one of the fastest object detection on GTX 2070 may run in real time. Methods on tracking is very different. First generation, it is completely based on computer vision. The second generation combining machine learning, Kalman filter and advanced computer vision (SIFT), the third generation using deep learning and some of the methods of previous generation like Kalman filter. The fourth generation using combination of two deep learning methods. And the latest generation using complete end to end models with RNN. Object tracking works with all combination of environments such as, moving objects, moving objects and camera in dynamic environments. As long as object appear in the frame until disappeared it the tracking can track and identification as one objects. No mater how many FPS. In around 130 videos of the course of Multiple Object Tracking on EDEX means this topic is huge and require more attention for the more research and development. Running MOT on Jetson nano is tricky and hacky in many way. First, the cup is arm based and not many package are build for it. Datasets for Tracking: MOTChallenge MOT15 MOT16/17 MOT19 KITTI UA-DETRAC tracking benchmark _metrics_ * _Mostly Tracked_ (MT) trajectories: number of ground-truth trajectories that are correctly tracked in at least 80% of the frames. * _Fragments_ : trajectory hypotheses which cover at most 80% of a ground truth trajectory. Observe that a true trajectory can be covered by more than one fragment. * _Mostly Lost_ (ML) trajectories: number of ground-truth trajectories that are correctly tracked in less than 20% of the frames. _False trajectories_ : predicted trajectories which do not correspond to a real object (i.e. to a ground truth trajectory). * _ID switches_ : number of times when the object is correctly tracked, but the associated ID for the object is mistakenly changed. Test: [https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD) Only Ubuntu, Not mac, can based on GPU, webcam not working [https://github.com/tianweiy/CenterPoint](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Ftianweiy%2FCenterPoint&sa=D&sntz=1&usg=AOvVaw1VC56JGagYnHa4SCXM2hFp) Only GPU YouTube: OpenCV [Tracking Objects | OpenCV Python Tutorials for Beginners 2020](https://www.youtube.com/watch?v=1FJWXOO1SRI&ab_channel=Murtaza%27sWorkshop- RoboticsandAI) Multiple Object Tracking [Python: Real-time Multiple Object Tracking (MOT) with Yolov3, Tensorflow and Deep SORT [FULL COURSE]](https://www.youtube.com/watch?v=zi-62z-3c4U&ab_channel=eMasterClassAcademy) There are at least 7 types of tracker algorithms that can be[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.pyimagesearch.com%2F2019%2F12%2F02%2Fopencv- vehicle-detection-tracking-and-speed- estimation%2F&sa=D&sntz=1&usg=AOvVaw1bSowsM3cTzPpHyDtqvvTH)[used](https://www.google.com/url?q=https%3A%2F%2Fwww.pyimagesearch.com%2F2019%2F12%2F02%2Fopencv- vehicle-detection-tracking-and-speed- estimation%2F&sa=D&sntz=1&usg=AOvVaw1bSowsM3cTzPpHyDtqvvTH) in[ ](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd2%2Fd0a%2Ftutorial_introduction_to_tracker.html&sa=D&sntz=1&usg=AOvVaw2-nw055tVNmJbtGsqDgyDC)[OpenCV](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd2%2Fd0a%2Ftutorial_introduction_to_tracker.html&sa=D&sntz=1&usg=AOvVaw2-nw055tVNmJbtGsqDgyDC): not[ ](https://www.google.com/url?q=https%3A%2F%2Fblog.netcetera.com%2Fobject- detection-and-tracking- in-2020-f10fb6ff9af3&sa=D&sntz=1&usg=AOvVaw1eVxYhtOaKsaR5fPlbFyv6)[DL](https://www.google.com/url?q=https%3A%2F%2Fblog.netcetera.com%2Fobject- detection-and-tracking- in-2020-f10fb6ff9af3&sa=D&sntz=1&usg=AOvVaw1eVxYhtOaKsaR5fPlbFyv6) * MIL * BOOSTING * MEDIANFLOW * TLD * KCF * GOTURN * MOSSE Kalman filtering, sparse and dense optical flow are[ ](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1602.00763&sa=D&sntz=1&usg=AOvVaw3wgxS3anU0zhzcI30qN- xK)[Simple Online and Realtime Tracking (SORT)](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1602.00763&sa=D&sntz=1&usg=AOvVaw3wgxS3anU0zhzcI30qN- xK), which uses a combination of the Hungarian algorithm and Kalman filter to achieve decent object tracking. R-CNN around 2000 region proposals [selective search](http://www.google.com/url?q=http%3A%2F%2Fhuppelen.nl%2Fpublications%2FselectiveSearchDraft.pdf&sa=D&sntz=1&usg=AOvVaw0SePhTa1eR5orNEduks3o7) share colors and textures, lightning conditions slow to train and test Fast R-CNN computes a convolutional feature map for the entire input image in a single forward pass of the network architecture is trained end-to-end with a multi-task loss [https://github.com/ZQPei/deep_sort_pytorch](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FZQPei%2Fdeep_sort_pytorch&sa=D&sntz=1&usg=AOvVaw2ZTw3IYJO4rvuF2hUPjCpH) Simple Online and Realtime Tracking with a Deep Association Metric. 2017 [https://arxiv.org/pdf/1703.07402](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fpdf%2F1703.07402&sa=D&sntz=1&usg=AOvVaw2FicrkTufsUcJdkQyC7MEp) [https://mcv-m6-video.github.io/deepvideo-2019/](https://www.google.com/url?q=https%3A%2F%2Fmcv-m6-video.github.io%2Fdeepvideo-2019%2F&sa=D&sntz=1&usg=AOvVaw0NRE06tCEyly2eOp8bmxhg) # [ **The online course about multiple object tracking in Edx:**](https://www.google.com/url?q=https%3A%2F%2Fwww.edx.org%2Fcourse%2Fmulti- object-tracking-for-automotive- systems%3Futm_source%3Dsailthru%26utm_medium%3Demail%26utm_campaign%3Dtriggered_shareit%2520&sa=D&sntz=1&usg=AOvVaw2h6dAyUrPoPcNJBKhz_eql) Course Section 0: Welcome and Introduction ' Part 1: Introduction to Multiple Object Tracking (MOT): good ; many definition and definitions: 15 videos [Introductory examples](https://www.youtube.com/watch?v=ay_QLAHcZLY&list=PLadnyz93xCLhSlm2tMYJSKaik39EZV_Uk) Is about the accurate perception of the driving environment Avoid collisions at the airport Crowd surveillance Crowd behavior Planning of emergency procedures Pedestrian tracking using LIDAR Tracking based on detections Group behavior Part 2: Single Object Tracking in clutter (SOT): Many math; basic methods, 23 videos [Introduction to SOT in Clutter](https://www.youtube.com/watch?v=UpXpUjgqhTw&list=PLadnyz93xCLiHWjLcLFdzc- SidNL1kRF7) Pruning and merging Pruning : remove hypotheses with small weights (and renormalize) Merging: approximate a mixture of densities by a single density (often Gaussian) Gating: technique to disregard unreasonable detections [pruning] SOT * Gaussian densities * Nearest neighbour (NN) filter [pruning] * Probabilistic data association (PDA) filter [merging] * Gaussian mixture densites * Gaussian sum filter (GSF) [pruning/merging] Part 3: Tracking a known number of objects in clutter 30 3.3.6 Predicting the n object density **3.4.1 Introduction to data association** Part 4: Random Finite Sets 24 Part 5: Multiple Object Tracking using conjugate priors 25 [only in YouTube] Part 6: Outlook - what is next? 18 [only in YouTube] Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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The versioning of different deep learning frameworks are crucial. For example the latest version of OS for Jetson Nano [Jetpack](https://www.google.com/url?q=https%3A%2F%2Fdeveloper.nvidia.com%2Fembedded%2Fjetpack&sa=D&sntz=1&usg=AOvVaw12B66A0ktALSyHLcxN- Xx0) which use latest CUDA but the Pytorch only support up to 10.1 now. So we need to install lower Jetpack version on Jetson Nano or compile the Pytorch. I compile Pytorch and it takes few hours with a lot of issue to solve. For the Ubuntu 20 there is not support for CUDA 10 you need install CUDA 11 and compile the Pytorch with a lot of library. install CUDA 10.x on Ubuntu 20 is possible but takes time to solve conflicts also supporting eGPU is another issue for lower ubuntu. On MacOS installing everything is easy because of not supporting GPU but many library and frameworks of the source codes of tracking require GPU version. Even install CPU version of all library does not grantee to run tracking methods. Another aspect is speed. Running tracking even on GPU is very slow based on my experience using Yolo version 3 which is one of the fastest object detection on GTX 2070 can process up to 15 FPS with Full HD videos. Methods on tracking is very different. First generation, it is completely based on computer vision. The second generation combining Kalman filter and advanced computer vision (SIFT), the third generation using deep learning and some of the methods of previous generation like Kalman filter. The fourth generation using combination of two deep learning methods. And the latest generation using complete end to end models like RNN. Object tracking works with all combination of environments such as, moving objects, moving objects and camera in dynamic environments. As long as object appear in the frame until disappeared it the tracking can track and identification as one objects. No mater how many FPS. ![](https://lh6.googleusercontent.com/GrlgOPkT- YjCOfCPVqdMTFh7YATdG8i43aySblQ32f5lYbF4L5uKYWK8e8j9lE- xzlS5H_z3BmPDB0jdpRqg8VAl-2Obz5iscyVN2BbOW1W2OG10Pl1SFTqnTvpHYfqrPg=w1280) ![](https://lh5.googleusercontent.com/3OpYYQA0JUTtvpYBE2uba5FtI3o1e45ieUeM02heJZKobWLMg5LfsbaHCjHnbAVC1WKVjKvZTUJWSjb5286-gAavbKQPohSRNAAHEwZXkC31bHCtWhGalnOepFCb_HPczw=w1280) ![](https://lh6.googleusercontent.com/MPWSDbFeFhPaIpTPufkx- Lfub5XMg3CpemQwDcs4p1RWgPFuNDkhpESbVYPHmAt5VOxITo5IvFlNNZZyxt3QiJpG54qABsGfqombmsOnb1GGt3t8cFriQ7AQRClIddZQgQ=w1280) ![](https://lh5.googleusercontent.com/MkiPU_u3aULSjsA6-ZM7thrVjM- ZGAoJbwbnLRXpUqp- lXGZavIuijln0eZBIvR0nWshaThulIm48lY8KYm9RLajGyYp5ge5teMUBgbZ2Hy7sMC-TnpVSzrqW- dKQl8f=w1280) ![](https://lh5.googleusercontent.com/9PuWB3svPA63SBkzP9-TU9TiV_Pn8ppe8Cjmx76PQFTidX4E-8V7VPcXzBpcPW1pOqsoR0 --oOc_H88gSea0iyXyxzOO2MWuAAhWZNt6eU6mb2aemB2c-UooXzjQ0EGatA=w1280) ![](https://lh5.googleusercontent.com/s333ScrmCHiimrwEMBB0zcJQu- Pd8VjpDFN0mrmXoZIJ0ppxmNRLcFPJrX5qdA0f2h3a6bUclrk4KUZF0CLgK8ahaGw6cbVDUHpFB5Aj_BQy9SqtTvCpUv3a7lmea-F0Dw=w1280) # Tracking * Classic object tracking * * * classic feature detection (SIFT and SURF), combined with a machine learning algorithm like KNN or SVM for classification, or with a description matcher like FLANN for object detection. * Kalman filtering, sparse and dense optical flow, * Example: Simple Online and Realtime Tracking (SORT), which uses a combination of the Hungarian algorithm and Kalman filter * SOT is a hot topic in the last decade. Early visual tracking methods rely on extracting hand-crafted features of candidate target regions, and use matching algorithms or hand-crafted discriminative classifiers to generate tracking results. * The MOT track aims to recover the trajectories of objects in video sequences, which is an important problem in computer vision with many applications, such as surveillance, activity analysis, and sport video analysis. * Video object detection datasets. The video object detection task aims to detect objects of different categories in video sequences. * Multi-object tracking datasets * large-scale benchmark Multi-Class Multi-object tracking datasets * VisDrone datasets is captured in various unconstrained scenes, focusing on four core problems in computer vision fields, i.e., image object detection, video object detection, single object tracking, and multi- object tracking. * the accuracy of detection methods suffers from degenerated object appearances in videos such as motion blur, pose variations, and video de-focus. Exploiting temporal coherence and aggregating features in consecutive frames might to be two effective ways to handle such issue. * Temporal coherence. A feasible way to exploit temporal coherence is using object trackers * Feature aggregation. Aggregating features in consecutive frames is also a useful way to improve the performance. * ### List of Datasets * **MOT20** * KITTI Tracking * MOTChallenge 2015 * UA-DETRAC Tracking * DukeMTMC * Campus * MOT17 * UAVDT-MOT * VisDrone ### Source code * ROLO * TensorFlow: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FGuanghan%2FROLO&sa=D&sntz=1&usg=AOvVaw2amdiMCe2vunOIdlpvqghX) * SiamMask * PyTorch 0.4.1: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Ffoolwood%2FSiamMask&sa=D&sntz=1&usg=AOvVaw1oXduEQoxzhx-VIMIMQ9Rn) * Deep SORT * PyTorch ≥ 0.4.0: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FZQPei%2Fdeep_sort_pytorch&sa=D&sntz=1&usg=AOvVaw2ZTw3IYJO4rvuF2hUPjCpH) * TensorFlow ≥ 1.0: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fnwojke%2Fdeep_sort&sa=D&sntz=1&usg=AOvVaw2FXwdcfCz1RZbndLyQSlBe) * TrackR-CNN * TensorFlow 1.13.1: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FVisualComputingInstitute%2FTrackR-CNN&sa=D&sntz=1&usg=AOvVaw3i3kALogli3ZyH93E3zhy5) * Tracktor++ * PyTorch 1.3.1: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fphil-bergmann%2Ftracking_wo_bnw&sa=D&sntz=1&usg=AOvVaw2xvx7s_sfJOiwLiwhmhqR8) * JDE * PyTorch ≥ 1.2.0: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FZhongdao%2FTowards-Realtime-MOT&sa=D&sntz=1&usg=AOvVaw0PcQNbW8igbtHaJ1x8IOVO) * [MCMOT: One-shot multi-class multi-object tracking](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FCaptainEven%2FMCMOT&sa=D&sntz=1&usg=AOvVaw2nrFmqa-Eh4-FoWPWBXrJn) # Self collected datasets ## [Video labeling](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fheartexlabs%2Fawesome- data-labeling&sa=D&sntz=1&usg=AOvVaw1Eb2rubldhdODxp4c4_Xnc) * [VATIC](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fcvondrick%2Fvatic&sa=D&sntz=1&usg=AOvVaw3dbkWnFtLPwO3znnZZz3Jy) * [UltimateLabeling](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Falexandre01%2FUltimateLabeling&sa=D&sntz=1&usg=AOvVaw2CpIp2ojyJ4dgWdKVlYC_r) * [https://github.com/pirahansiah/UltimateLabeling](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2FUltimateLabeling&sa=D&sntz=1&usg=AOvVaw2mQJcLjtHwG4L8YcUkET64) # Reference 1. Vision Meets Drones: Past, Present and Future 2. [https://blog.netcetera.com/object-detection-and-tracking-in-2020-f10fb6ff9af3](https://www.google.com/url?q=https%3A%2F%2Fblog.netcetera.com%2Fobject-detection-and-tracking-in-2020-f10fb6ff9af3&sa=D&sntz=1&usg=AOvVaw1eVxYhtOaKsaR5fPlbFyv6) 3. 4. [https://pythonawesome.com/yolo-rcnn-object-detection-and-multi-object-tracking/](https://www.google.com/url?q=https%3A%2F%2Fpythonawesome.com%2Fyolo-rcnn-object-detection-and-multi-object-tracking%2F&sa=D&sntz=1&usg=AOvVaw0mC4uPk0uEFfs41gzkjf2R) 5. [https://cv-tricks.com/object-tracking/quick-guide-mdnet-goturn-rolo/](https://www.google.com/url?q=https%3A%2F%2Fcv-tricks.com%2Fobject-tracking%2Fquick-guide-mdnet-goturn-rolo%2F&sa=D&sntz=1&usg=AOvVaw08eNSdqkFWRxKgChSpTmGv) 6. Deep Learning in Video Multi-Object Tracking: A Survey [https://arxiv.org/abs/1907.12740](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1907.12740&sa=D&sntz=1&usg=AOvVaw3UJVcBbBuZ6mdflbt5vdp8) 7. **HOTA: A Higher Order Metric for Evaluating Multi-object Tracking** [https://link.springer.com/article/10.1007/s11263-020-01375-2](https://www.google.com/url?q=https%3A%2F%2Flink.springer.com%2Farticle%2F10.1007%2Fs11263-020-01375-2&sa=D&sntz=1&usg=AOvVaw3Nnnf1zeZD4vmMHhiGaMVC) 8. some examples Endeavor to summarize MOT: The best methods running on GPU. The versioning of different deep learning frameworks are crucial. For example the latest version of OS for Jetson Nano "Jetpack" use CUDA 11 but the Pytorch only support up to 10.1 now. So we need to install lower Jetpack version on Jetson Nano or compile the Pytorch. I compile Pytorch and it takes few hours with a lot of issue to solve. For the Ubuntu 20 there is not support for CUDA 10 you need install CUDA 11 and compile the Pytorch with a lot of library. install CUDA 10.x on Ubuntu 20 is possible but takes time to solve conflicts also supporting eGPU is another issue for lower ubuntu. On MacOS installing everything is easy because of not supporting GPU but many library and frameworks of the source codes of tracking require GPU version. Even install CPU version of all library does not grantee to run tracking methods. Another aspect is speed. Running tracking even on GPU is very slow based on my experience using Yolo version 3 which is one of the fastest object detection on GTX 2070 may run in real time. Methods on tracking is very different. First generation, it is completely based on computer vision. The second generation combining machine learning, Kalman filter and advanced computer vision (SIFT), the third generation using deep learning and some of the methods of previous generation like Kalman filter. The fourth generation using combination of two deep learning methods. And the latest generation using complete end to end models with RNN. Object tracking works with all combination of environments such as, moving objects, moving objects and camera in dynamic environments. As long as object appear in the frame until disappeared it the tracking can track and identification as one objects. No mater how many FPS. In around 130 videos of the course of Multiple Object Tracking on EDEX means this topic is huge and require more attention for the more research and development. Running MOT on Jetson nano is tricky and hacky in many way. First, the cup is arm based and not many package are build for it. Datasets for Tracking: MOTChallenge MOT15 MOT16/17 MOT19 KITTI UA-DETRAC tracking benchmark _metrics_ * _Mostly Tracked_ (MT) trajectories: number of ground-truth trajectories that are correctly tracked in at least 80% of the frames. * _Fragments_ : trajectory hypotheses which cover at most 80% of a ground truth trajectory. Observe that a true trajectory can be covered by more than one fragment. * _Mostly Lost_ (ML) trajectories: number of ground-truth trajectories that are correctly tracked in less than 20% of the frames. _False trajectories_ : predicted trajectories which do not correspond to a real object (i.e. to a ground truth trajectory). * _ID switches_ : number of times when the object is correctly tracked, but the associated ID for the object is mistakenly changed. Test: [https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD) Only Ubuntu, Not mac, can based on GPU, webcam not working [https://github.com/tianweiy/CenterPoint](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Ftianweiy%2FCenterPoint&sa=D&sntz=1&usg=AOvVaw1VC56JGagYnHa4SCXM2hFp) Only GPU YouTube: OpenCV [Tracking Objects | OpenCV Python Tutorials for Beginners 2020](https://www.youtube.com/watch?v=1FJWXOO1SRI&ab_channel=Murtaza%27sWorkshop- RoboticsandAI) Multiple Object Tracking [Python: Real-time Multiple Object Tracking (MOT) with Yolov3, Tensorflow and Deep SORT [FULL COURSE]](https://www.youtube.com/watch?v=zi-62z-3c4U&ab_channel=eMasterClassAcademy) There are at least 7 types of tracker algorithms that can be[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.pyimagesearch.com%2F2019%2F12%2F02%2Fopencv- vehicle-detection-tracking-and-speed- estimation%2F&sa=D&sntz=1&usg=AOvVaw1bSowsM3cTzPpHyDtqvvTH)[used](https://www.google.com/url?q=https%3A%2F%2Fwww.pyimagesearch.com%2F2019%2F12%2F02%2Fopencv- vehicle-detection-tracking-and-speed- estimation%2F&sa=D&sntz=1&usg=AOvVaw1bSowsM3cTzPpHyDtqvvTH) in[ ](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd2%2Fd0a%2Ftutorial_introduction_to_tracker.html&sa=D&sntz=1&usg=AOvVaw2-nw055tVNmJbtGsqDgyDC)[OpenCV](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd2%2Fd0a%2Ftutorial_introduction_to_tracker.html&sa=D&sntz=1&usg=AOvVaw2-nw055tVNmJbtGsqDgyDC): not[ ](https://www.google.com/url?q=https%3A%2F%2Fblog.netcetera.com%2Fobject- detection-and-tracking- in-2020-f10fb6ff9af3&sa=D&sntz=1&usg=AOvVaw1eVxYhtOaKsaR5fPlbFyv6)[DL](https://www.google.com/url?q=https%3A%2F%2Fblog.netcetera.com%2Fobject- detection-and-tracking- in-2020-f10fb6ff9af3&sa=D&sntz=1&usg=AOvVaw1eVxYhtOaKsaR5fPlbFyv6) * MIL * BOOSTING * MEDIANFLOW * TLD * KCF * GOTURN * MOSSE Kalman filtering, sparse and dense optical flow are[ ](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1602.00763&sa=D&sntz=1&usg=AOvVaw3wgxS3anU0zhzcI30qN- xK)[Simple Online and Realtime Tracking (SORT)](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1602.00763&sa=D&sntz=1&usg=AOvVaw3wgxS3anU0zhzcI30qN- xK), which uses a combination of the Hungarian algorithm and Kalman filter to achieve decent object tracking. R-CNN around 2000 region proposals [selective search](http://www.google.com/url?q=http%3A%2F%2Fhuppelen.nl%2Fpublications%2FselectiveSearchDraft.pdf&sa=D&sntz=1&usg=AOvVaw0SePhTa1eR5orNEduks3o7) share colors and textures, lightning conditions slow to train and test Fast R-CNN computes a convolutional feature map for the entire input image in a single forward pass of the network architecture is trained end-to-end with a multi-task loss [https://github.com/ZQPei/deep_sort_pytorch](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FZQPei%2Fdeep_sort_pytorch&sa=D&sntz=1&usg=AOvVaw2ZTw3IYJO4rvuF2hUPjCpH) Simple Online and Realtime Tracking with a Deep Association Metric. 2017 [https://arxiv.org/pdf/1703.07402](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fpdf%2F1703.07402&sa=D&sntz=1&usg=AOvVaw2FicrkTufsUcJdkQyC7MEp) [https://mcv-m6-video.github.io/deepvideo-2019/](https://www.google.com/url?q=https%3A%2F%2Fmcv-m6-video.github.io%2Fdeepvideo-2019%2F&sa=D&sntz=1&usg=AOvVaw0NRE06tCEyly2eOp8bmxhg) # [ **The online course about multiple object tracking in Edx:**](https://www.google.com/url?q=https%3A%2F%2Fwww.edx.org%2Fcourse%2Fmulti- object-tracking-for-automotive- systems%3Futm_source%3Dsailthru%26utm_medium%3Demail%26utm_campaign%3Dtriggered_shareit%2520&sa=D&sntz=1&usg=AOvVaw2h6dAyUrPoPcNJBKhz_eql) Course Section 0: Welcome and Introduction ' Part 1: Introduction to Multiple Object Tracking (MOT): good ; many definition and definitions: 15 videos [Introductory examples](https://www.youtube.com/watch?v=ay_QLAHcZLY&list=PLadnyz93xCLhSlm2tMYJSKaik39EZV_Uk) Is about the accurate perception of the driving environment Avoid collisions at the airport Crowd surveillance Crowd behavior Planning of emergency procedures Pedestrian tracking using LIDAR Tracking based on detections Group behavior Part 2: Single Object Tracking in clutter (SOT): Many math; basic methods, 23 videos [Introduction to SOT in Clutter](https://www.youtube.com/watch?v=UpXpUjgqhTw&list=PLadnyz93xCLiHWjLcLFdzc- SidNL1kRF7) Pruning and merging Pruning : remove hypotheses with small weights (and renormalize) Merging: approximate a mixture of densities by a single density (often Gaussian) Gating: technique to disregard unreasonable detections [pruning] SOT * Gaussian densities * Nearest neighbour (NN) filter [pruning] * Probabilistic data association (PDA) filter [merging] * Gaussian mixture densites * Gaussian sum filter (GSF) [pruning/merging] Part 3: Tracking a known number of objects in clutter 30 3.3.6 Predicting the n object density **3.4.1 Introduction to data association** Part 4: Random Finite Sets 24 Part 5: Multiple Object Tracking using conjugate priors 25 [only in YouTube] Part 6: Outlook - what is next? 18 [only in YouTube] Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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The versioning of different deep learning frameworks are crucial. For example the latest version of OS for Jetson Nano [Jetpack](https://www.google.com/url?q=https%3A%2F%2Fdeveloper.nvidia.com%2Fembedded%2Fjetpack&sa=D&sntz=1&usg=AOvVaw12B66A0ktALSyHLcxN- Xx0) which use latest CUDA but the Pytorch only support up to 10.1 now. So we need to install lower Jetpack version on Jetson Nano or compile the Pytorch. I compile Pytorch and it takes few hours with a lot of issue to solve. For the Ubuntu 20 there is not support for CUDA 10 you need install CUDA 11 and compile the Pytorch with a lot of library. install CUDA 10.x on Ubuntu 20 is possible but takes time to solve conflicts also supporting eGPU is another issue for lower ubuntu. On MacOS installing everything is easy because of not supporting GPU but many library and frameworks of the source codes of tracking require GPU version. Even install CPU version of all library does not grantee to run tracking methods. Another aspect is speed. Running tracking even on GPU is very slow based on my experience using Yolo version 3 which is one of the fastest object detection on GTX 2070 can process up to 15 FPS with Full HD videos. Methods on tracking is very different. First generation, it is completely based on computer vision. The second generation combining Kalman filter and advanced computer vision (SIFT), the third generation using deep learning and some of the methods of previous generation like Kalman filter. The fourth generation using combination of two deep learning methods. And the latest generation using complete end to end models like RNN. Object tracking works with all combination of environments such as, moving objects, moving objects and camera in dynamic environments. As long as object appear in the frame until disappeared it the tracking can track and identification as one objects. No mater how many FPS. ![](https://lh6.googleusercontent.com/GrlgOPkT- YjCOfCPVqdMTFh7YATdG8i43aySblQ32f5lYbF4L5uKYWK8e8j9lE- xzlS5H_z3BmPDB0jdpRqg8VAl-2Obz5iscyVN2BbOW1W2OG10Pl1SFTqnTvpHYfqrPg=w1280) ![](https://lh5.googleusercontent.com/3OpYYQA0JUTtvpYBE2uba5FtI3o1e45ieUeM02heJZKobWLMg5LfsbaHCjHnbAVC1WKVjKvZTUJWSjb5286-gAavbKQPohSRNAAHEwZXkC31bHCtWhGalnOepFCb_HPczw=w1280) ![](https://lh6.googleusercontent.com/MPWSDbFeFhPaIpTPufkx- Lfub5XMg3CpemQwDcs4p1RWgPFuNDkhpESbVYPHmAt5VOxITo5IvFlNNZZyxt3QiJpG54qABsGfqombmsOnb1GGt3t8cFriQ7AQRClIddZQgQ=w1280) ![](https://lh5.googleusercontent.com/MkiPU_u3aULSjsA6-ZM7thrVjM- ZGAoJbwbnLRXpUqp- lXGZavIuijln0eZBIvR0nWshaThulIm48lY8KYm9RLajGyYp5ge5teMUBgbZ2Hy7sMC-TnpVSzrqW- dKQl8f=w1280) ![](https://lh5.googleusercontent.com/9PuWB3svPA63SBkzP9-TU9TiV_Pn8ppe8Cjmx76PQFTidX4E-8V7VPcXzBpcPW1pOqsoR0 --oOc_H88gSea0iyXyxzOO2MWuAAhWZNt6eU6mb2aemB2c-UooXzjQ0EGatA=w1280) ![](https://lh5.googleusercontent.com/s333ScrmCHiimrwEMBB0zcJQu- Pd8VjpDFN0mrmXoZIJ0ppxmNRLcFPJrX5qdA0f2h3a6bUclrk4KUZF0CLgK8ahaGw6cbVDUHpFB5Aj_BQy9SqtTvCpUv3a7lmea-F0Dw=w1280) # Tracking * Classic object tracking * * * classic feature detection (SIFT and SURF), combined with a machine learning algorithm like KNN or SVM for classification, or with a description matcher like FLANN for object detection. * Kalman filtering, sparse and dense optical flow, * Example: Simple Online and Realtime Tracking (SORT), which uses a combination of the Hungarian algorithm and Kalman filter * SOT is a hot topic in the last decade. Early visual tracking methods rely on extracting hand-crafted features of candidate target regions, and use matching algorithms or hand-crafted discriminative classifiers to generate tracking results. * The MOT track aims to recover the trajectories of objects in video sequences, which is an important problem in computer vision with many applications, such as surveillance, activity analysis, and sport video analysis. * Video object detection datasets. The video object detection task aims to detect objects of different categories in video sequences. * Multi-object tracking datasets * large-scale benchmark Multi-Class Multi-object tracking datasets * VisDrone datasets is captured in various unconstrained scenes, focusing on four core problems in computer vision fields, i.e., image object detection, video object detection, single object tracking, and multi- object tracking. * the accuracy of detection methods suffers from degenerated object appearances in videos such as motion blur, pose variations, and video de-focus. Exploiting temporal coherence and aggregating features in consecutive frames might to be two effective ways to handle such issue. * Temporal coherence. A feasible way to exploit temporal coherence is using object trackers * Feature aggregation. Aggregating features in consecutive frames is also a useful way to improve the performance. * ### List of Datasets * **MOT20** * KITTI Tracking * MOTChallenge 2015 * UA-DETRAC Tracking * DukeMTMC * Campus * MOT17 * UAVDT-MOT * VisDrone ### Source code * ROLO * TensorFlow: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FGuanghan%2FROLO&sa=D&sntz=1&usg=AOvVaw2amdiMCe2vunOIdlpvqghX) * SiamMask * PyTorch 0.4.1: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Ffoolwood%2FSiamMask&sa=D&sntz=1&usg=AOvVaw1oXduEQoxzhx-VIMIMQ9Rn) * Deep SORT * PyTorch ≥ 0.4.0: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FZQPei%2Fdeep_sort_pytorch&sa=D&sntz=1&usg=AOvVaw2ZTw3IYJO4rvuF2hUPjCpH) * TensorFlow ≥ 1.0: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fnwojke%2Fdeep_sort&sa=D&sntz=1&usg=AOvVaw2FXwdcfCz1RZbndLyQSlBe) * TrackR-CNN * TensorFlow 1.13.1: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FVisualComputingInstitute%2FTrackR-CNN&sa=D&sntz=1&usg=AOvVaw3i3kALogli3ZyH93E3zhy5) * Tracktor++ * PyTorch 1.3.1: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fphil-bergmann%2Ftracking_wo_bnw&sa=D&sntz=1&usg=AOvVaw2xvx7s_sfJOiwLiwhmhqR8) * JDE * PyTorch ≥ 1.2.0: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FZhongdao%2FTowards-Realtime-MOT&sa=D&sntz=1&usg=AOvVaw0PcQNbW8igbtHaJ1x8IOVO) * [MCMOT: One-shot multi-class multi-object tracking](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FCaptainEven%2FMCMOT&sa=D&sntz=1&usg=AOvVaw2nrFmqa-Eh4-FoWPWBXrJn) # Self collected datasets ## [Video labeling](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fheartexlabs%2Fawesome- data-labeling&sa=D&sntz=1&usg=AOvVaw1Eb2rubldhdODxp4c4_Xnc) * [VATIC](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fcvondrick%2Fvatic&sa=D&sntz=1&usg=AOvVaw3dbkWnFtLPwO3znnZZz3Jy) * [UltimateLabeling](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Falexandre01%2FUltimateLabeling&sa=D&sntz=1&usg=AOvVaw2CpIp2ojyJ4dgWdKVlYC_r) * [https://github.com/pirahansiah/UltimateLabeling](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2FUltimateLabeling&sa=D&sntz=1&usg=AOvVaw2mQJcLjtHwG4L8YcUkET64) # Reference 1. Vision Meets Drones: Past, Present and Future 2. [https://blog.netcetera.com/object-detection-and-tracking-in-2020-f10fb6ff9af3](https://www.google.com/url?q=https%3A%2F%2Fblog.netcetera.com%2Fobject-detection-and-tracking-in-2020-f10fb6ff9af3&sa=D&sntz=1&usg=AOvVaw1eVxYhtOaKsaR5fPlbFyv6) 3. 4. [https://pythonawesome.com/yolo-rcnn-object-detection-and-multi-object-tracking/](https://www.google.com/url?q=https%3A%2F%2Fpythonawesome.com%2Fyolo-rcnn-object-detection-and-multi-object-tracking%2F&sa=D&sntz=1&usg=AOvVaw0mC4uPk0uEFfs41gzkjf2R) 5. [https://cv-tricks.com/object-tracking/quick-guide-mdnet-goturn-rolo/](https://www.google.com/url?q=https%3A%2F%2Fcv-tricks.com%2Fobject-tracking%2Fquick-guide-mdnet-goturn-rolo%2F&sa=D&sntz=1&usg=AOvVaw08eNSdqkFWRxKgChSpTmGv) 6. Deep Learning in Video Multi-Object Tracking: A Survey [https://arxiv.org/abs/1907.12740](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1907.12740&sa=D&sntz=1&usg=AOvVaw3UJVcBbBuZ6mdflbt5vdp8) 7. **HOTA: A Higher Order Metric for Evaluating Multi-object Tracking** [https://link.springer.com/article/10.1007/s11263-020-01375-2](https://www.google.com/url?q=https%3A%2F%2Flink.springer.com%2Farticle%2F10.1007%2Fs11263-020-01375-2&sa=D&sntz=1&usg=AOvVaw3Nnnf1zeZD4vmMHhiGaMVC) 8. some examples Endeavor to summarize MOT: The best methods running on GPU. The versioning of different deep learning frameworks are crucial. For example the latest version of OS for Jetson Nano "Jetpack" use CUDA 11 but the Pytorch only support up to 10.1 now. So we need to install lower Jetpack version on Jetson Nano or compile the Pytorch. I compile Pytorch and it takes few hours with a lot of issue to solve. For the Ubuntu 20 there is not support for CUDA 10 you need install CUDA 11 and compile the Pytorch with a lot of library. install CUDA 10.x on Ubuntu 20 is possible but takes time to solve conflicts also supporting eGPU is another issue for lower ubuntu. On MacOS installing everything is easy because of not supporting GPU but many library and frameworks of the source codes of tracking require GPU version. Even install CPU version of all library does not grantee to run tracking methods. Another aspect is speed. Running tracking even on GPU is very slow based on my experience using Yolo version 3 which is one of the fastest object detection on GTX 2070 may run in real time. Methods on tracking is very different. First generation, it is completely based on computer vision. The second generation combining machine learning, Kalman filter and advanced computer vision (SIFT), the third generation using deep learning and some of the methods of previous generation like Kalman filter. The fourth generation using combination of two deep learning methods. And the latest generation using complete end to end models with RNN. Object tracking works with all combination of environments such as, moving objects, moving objects and camera in dynamic environments. As long as object appear in the frame until disappeared it the tracking can track and identification as one objects. No mater how many FPS. In around 130 videos of the course of Multiple Object Tracking on EDEX means this topic is huge and require more attention for the more research and development. Running MOT on Jetson nano is tricky and hacky in many way. First, the cup is arm based and not many package are build for it. Datasets for Tracking: MOTChallenge MOT15 MOT16/17 MOT19 KITTI UA-DETRAC tracking benchmark _metrics_ * _Mostly Tracked_ (MT) trajectories: number of ground-truth trajectories that are correctly tracked in at least 80% of the frames. * _Fragments_ : trajectory hypotheses which cover at most 80% of a ground truth trajectory. Observe that a true trajectory can be covered by more than one fragment. * _Mostly Lost_ (ML) trajectories: number of ground-truth trajectories that are correctly tracked in less than 20% of the frames. _False trajectories_ : predicted trajectories which do not correspond to a real object (i.e. to a ground truth trajectory). * _ID switches_ : number of times when the object is correctly tracked, but the associated ID for the object is mistakenly changed. Test: [https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD) Only Ubuntu, Not mac, can based on GPU, webcam not working [https://github.com/tianweiy/CenterPoint](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Ftianweiy%2FCenterPoint&sa=D&sntz=1&usg=AOvVaw1VC56JGagYnHa4SCXM2hFp) Only GPU YouTube: OpenCV [Tracking Objects | OpenCV Python Tutorials for Beginners 2020](https://www.youtube.com/watch?v=1FJWXOO1SRI&ab_channel=Murtaza%27sWorkshop- RoboticsandAI) Multiple Object Tracking [Python: Real-time Multiple Object Tracking (MOT) with Yolov3, Tensorflow and Deep SORT [FULL COURSE]](https://www.youtube.com/watch?v=zi-62z-3c4U&ab_channel=eMasterClassAcademy) There are at least 7 types of tracker algorithms that can be[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.pyimagesearch.com%2F2019%2F12%2F02%2Fopencv- vehicle-detection-tracking-and-speed- estimation%2F&sa=D&sntz=1&usg=AOvVaw1bSowsM3cTzPpHyDtqvvTH)[used](https://www.google.com/url?q=https%3A%2F%2Fwww.pyimagesearch.com%2F2019%2F12%2F02%2Fopencv- vehicle-detection-tracking-and-speed- estimation%2F&sa=D&sntz=1&usg=AOvVaw1bSowsM3cTzPpHyDtqvvTH) in[ ](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd2%2Fd0a%2Ftutorial_introduction_to_tracker.html&sa=D&sntz=1&usg=AOvVaw2-nw055tVNmJbtGsqDgyDC)[OpenCV](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd2%2Fd0a%2Ftutorial_introduction_to_tracker.html&sa=D&sntz=1&usg=AOvVaw2-nw055tVNmJbtGsqDgyDC): not[ ](https://www.google.com/url?q=https%3A%2F%2Fblog.netcetera.com%2Fobject- detection-and-tracking- in-2020-f10fb6ff9af3&sa=D&sntz=1&usg=AOvVaw1eVxYhtOaKsaR5fPlbFyv6)[DL](https://www.google.com/url?q=https%3A%2F%2Fblog.netcetera.com%2Fobject- detection-and-tracking- in-2020-f10fb6ff9af3&sa=D&sntz=1&usg=AOvVaw1eVxYhtOaKsaR5fPlbFyv6) * MIL * BOOSTING * MEDIANFLOW * TLD * KCF * GOTURN * MOSSE Kalman filtering, sparse and dense optical flow are[ ](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1602.00763&sa=D&sntz=1&usg=AOvVaw3wgxS3anU0zhzcI30qN- xK)[Simple Online and Realtime Tracking (SORT)](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1602.00763&sa=D&sntz=1&usg=AOvVaw3wgxS3anU0zhzcI30qN- xK), which uses a combination of the Hungarian algorithm and Kalman filter to achieve decent object tracking. R-CNN around 2000 region proposals [selective search](http://www.google.com/url?q=http%3A%2F%2Fhuppelen.nl%2Fpublications%2FselectiveSearchDraft.pdf&sa=D&sntz=1&usg=AOvVaw0SePhTa1eR5orNEduks3o7) share colors and textures, lightning conditions slow to train and test Fast R-CNN computes a convolutional feature map for the entire input image in a single forward pass of the network architecture is trained end-to-end with a multi-task loss [https://github.com/ZQPei/deep_sort_pytorch](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FZQPei%2Fdeep_sort_pytorch&sa=D&sntz=1&usg=AOvVaw2ZTw3IYJO4rvuF2hUPjCpH) Simple Online and Realtime Tracking with a Deep Association Metric. 2017 [https://arxiv.org/pdf/1703.07402](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fpdf%2F1703.07402&sa=D&sntz=1&usg=AOvVaw2FicrkTufsUcJdkQyC7MEp) [https://mcv-m6-video.github.io/deepvideo-2019/](https://www.google.com/url?q=https%3A%2F%2Fmcv-m6-video.github.io%2Fdeepvideo-2019%2F&sa=D&sntz=1&usg=AOvVaw0NRE06tCEyly2eOp8bmxhg) # [ **The online course about multiple object tracking in Edx:**](https://www.google.com/url?q=https%3A%2F%2Fwww.edx.org%2Fcourse%2Fmulti- object-tracking-for-automotive- systems%3Futm_source%3Dsailthru%26utm_medium%3Demail%26utm_campaign%3Dtriggered_shareit%2520&sa=D&sntz=1&usg=AOvVaw2h6dAyUrPoPcNJBKhz_eql) Course Section 0: Welcome and Introduction ' Part 1: Introduction to Multiple Object Tracking (MOT): good ; many definition and definitions: 15 videos [Introductory examples](https://www.youtube.com/watch?v=ay_QLAHcZLY&list=PLadnyz93xCLhSlm2tMYJSKaik39EZV_Uk) Is about the accurate perception of the driving environment Avoid collisions at the airport Crowd surveillance Crowd behavior Planning of emergency procedures Pedestrian tracking using LIDAR Tracking based on detections Group behavior Part 2: Single Object Tracking in clutter (SOT): Many math; basic methods, 23 videos [Introduction to SOT in Clutter](https://www.youtube.com/watch?v=UpXpUjgqhTw&list=PLadnyz93xCLiHWjLcLFdzc- SidNL1kRF7) Pruning and merging Pruning : remove hypotheses with small weights (and renormalize) Merging: approximate a mixture of densities by a single density (often Gaussian) Gating: technique to disregard unreasonable detections [pruning] SOT * Gaussian densities * Nearest neighbour (NN) filter [pruning] * Probabilistic data association (PDA) filter [merging] * Gaussian mixture densites * Gaussian sum filter (GSF) [pruning/merging] Part 3: Tracking a known number of objects in clutter 30 3.3.6 Predicting the n object density **3.4.1 Introduction to data association** Part 4: Random Finite Sets 24 Part 5: Multiple Object Tracking using conjugate priors 25 [only in YouTube] Part 6: Outlook - what is next? 18 [only in YouTube] Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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The versioning of different deep learning frameworks are crucial. For example the latest version of OS for Jetson Nano [Jetpack](https://www.google.com/url?q=https%3A%2F%2Fdeveloper.nvidia.com%2Fembedded%2Fjetpack&sa=D&sntz=1&usg=AOvVaw12B66A0ktALSyHLcxN- Xx0) which use latest CUDA but the Pytorch only support up to 10.1 now. So we need to install lower Jetpack version on Jetson Nano or compile the Pytorch. I compile Pytorch and it takes few hours with a lot of issue to solve. For the Ubuntu 20 there is not support for CUDA 10 you need install CUDA 11 and compile the Pytorch with a lot of library. install CUDA 10.x on Ubuntu 20 is possible but takes time to solve conflicts also supporting eGPU is another issue for lower ubuntu. On MacOS installing everything is easy because of not supporting GPU but many library and frameworks of the source codes of tracking require GPU version. Even install CPU version of all library does not grantee to run tracking methods. Another aspect is speed. Running tracking even on GPU is very slow based on my experience using Yolo version 3 which is one of the fastest object detection on GTX 2070 can process up to 15 FPS with Full HD videos. Methods on tracking is very different. First generation, it is completely based on computer vision. The second generation combining Kalman filter and advanced computer vision (SIFT), the third generation using deep learning and some of the methods of previous generation like Kalman filter. The fourth generation using combination of two deep learning methods. And the latest generation using complete end to end models like RNN. Object tracking works with all combination of environments such as, moving objects, moving objects and camera in dynamic environments. As long as object appear in the frame until disappeared it the tracking can track and identification as one objects. No mater how many FPS. ![](https://lh6.googleusercontent.com/GrlgOPkT- YjCOfCPVqdMTFh7YATdG8i43aySblQ32f5lYbF4L5uKYWK8e8j9lE- xzlS5H_z3BmPDB0jdpRqg8VAl-2Obz5iscyVN2BbOW1W2OG10Pl1SFTqnTvpHYfqrPg=w1280) ![](https://lh5.googleusercontent.com/3OpYYQA0JUTtvpYBE2uba5FtI3o1e45ieUeM02heJZKobWLMg5LfsbaHCjHnbAVC1WKVjKvZTUJWSjb5286-gAavbKQPohSRNAAHEwZXkC31bHCtWhGalnOepFCb_HPczw=w1280) ![](https://lh6.googleusercontent.com/MPWSDbFeFhPaIpTPufkx- Lfub5XMg3CpemQwDcs4p1RWgPFuNDkhpESbVYPHmAt5VOxITo5IvFlNNZZyxt3QiJpG54qABsGfqombmsOnb1GGt3t8cFriQ7AQRClIddZQgQ=w1280) ![](https://lh5.googleusercontent.com/MkiPU_u3aULSjsA6-ZM7thrVjM- ZGAoJbwbnLRXpUqp- lXGZavIuijln0eZBIvR0nWshaThulIm48lY8KYm9RLajGyYp5ge5teMUBgbZ2Hy7sMC-TnpVSzrqW- dKQl8f=w1280) ![](https://lh5.googleusercontent.com/9PuWB3svPA63SBkzP9-TU9TiV_Pn8ppe8Cjmx76PQFTidX4E-8V7VPcXzBpcPW1pOqsoR0 --oOc_H88gSea0iyXyxzOO2MWuAAhWZNt6eU6mb2aemB2c-UooXzjQ0EGatA=w1280) ![](https://lh5.googleusercontent.com/s333ScrmCHiimrwEMBB0zcJQu- Pd8VjpDFN0mrmXoZIJ0ppxmNRLcFPJrX5qdA0f2h3a6bUclrk4KUZF0CLgK8ahaGw6cbVDUHpFB5Aj_BQy9SqtTvCpUv3a7lmea-F0Dw=w1280) # Tracking * Classic object tracking * * * classic feature detection (SIFT and SURF), combined with a machine learning algorithm like KNN or SVM for classification, or with a description matcher like FLANN for object detection. * Kalman filtering, sparse and dense optical flow, * Example: Simple Online and Realtime Tracking (SORT), which uses a combination of the Hungarian algorithm and Kalman filter * SOT is a hot topic in the last decade. Early visual tracking methods rely on extracting hand-crafted features of candidate target regions, and use matching algorithms or hand-crafted discriminative classifiers to generate tracking results. * The MOT track aims to recover the trajectories of objects in video sequences, which is an important problem in computer vision with many applications, such as surveillance, activity analysis, and sport video analysis. * Video object detection datasets. The video object detection task aims to detect objects of different categories in video sequences. * Multi-object tracking datasets * large-scale benchmark Multi-Class Multi-object tracking datasets * VisDrone datasets is captured in various unconstrained scenes, focusing on four core problems in computer vision fields, i.e., image object detection, video object detection, single object tracking, and multi- object tracking. * the accuracy of detection methods suffers from degenerated object appearances in videos such as motion blur, pose variations, and video de-focus. Exploiting temporal coherence and aggregating features in consecutive frames might to be two effective ways to handle such issue. * Temporal coherence. A feasible way to exploit temporal coherence is using object trackers * Feature aggregation. Aggregating features in consecutive frames is also a useful way to improve the performance. * ### List of Datasets * **MOT20** * KITTI Tracking * MOTChallenge 2015 * UA-DETRAC Tracking * DukeMTMC * Campus * MOT17 * UAVDT-MOT * VisDrone ### Source code * ROLO * TensorFlow: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FGuanghan%2FROLO&sa=D&sntz=1&usg=AOvVaw2amdiMCe2vunOIdlpvqghX) * SiamMask * PyTorch 0.4.1: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Ffoolwood%2FSiamMask&sa=D&sntz=1&usg=AOvVaw1oXduEQoxzhx-VIMIMQ9Rn) * Deep SORT * PyTorch ≥ 0.4.0: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FZQPei%2Fdeep_sort_pytorch&sa=D&sntz=1&usg=AOvVaw2ZTw3IYJO4rvuF2hUPjCpH) * TensorFlow ≥ 1.0: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fnwojke%2Fdeep_sort&sa=D&sntz=1&usg=AOvVaw2FXwdcfCz1RZbndLyQSlBe) * TrackR-CNN * TensorFlow 1.13.1: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FVisualComputingInstitute%2FTrackR-CNN&sa=D&sntz=1&usg=AOvVaw3i3kALogli3ZyH93E3zhy5) * Tracktor++ * PyTorch 1.3.1: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fphil-bergmann%2Ftracking_wo_bnw&sa=D&sntz=1&usg=AOvVaw2xvx7s_sfJOiwLiwhmhqR8) * JDE * PyTorch ≥ 1.2.0: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FZhongdao%2FTowards-Realtime-MOT&sa=D&sntz=1&usg=AOvVaw0PcQNbW8igbtHaJ1x8IOVO) * [MCMOT: One-shot multi-class multi-object tracking](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FCaptainEven%2FMCMOT&sa=D&sntz=1&usg=AOvVaw2nrFmqa-Eh4-FoWPWBXrJn) # Self collected datasets ## [Video labeling](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fheartexlabs%2Fawesome- data-labeling&sa=D&sntz=1&usg=AOvVaw1Eb2rubldhdODxp4c4_Xnc) * [VATIC](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fcvondrick%2Fvatic&sa=D&sntz=1&usg=AOvVaw3dbkWnFtLPwO3znnZZz3Jy) * [UltimateLabeling](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Falexandre01%2FUltimateLabeling&sa=D&sntz=1&usg=AOvVaw2CpIp2ojyJ4dgWdKVlYC_r) * [https://github.com/pirahansiah/UltimateLabeling](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2FUltimateLabeling&sa=D&sntz=1&usg=AOvVaw2mQJcLjtHwG4L8YcUkET64) # Reference 1. Vision Meets Drones: Past, Present and Future 2. [https://blog.netcetera.com/object-detection-and-tracking-in-2020-f10fb6ff9af3](https://www.google.com/url?q=https%3A%2F%2Fblog.netcetera.com%2Fobject-detection-and-tracking-in-2020-f10fb6ff9af3&sa=D&sntz=1&usg=AOvVaw1eVxYhtOaKsaR5fPlbFyv6) 3. 4. [https://pythonawesome.com/yolo-rcnn-object-detection-and-multi-object-tracking/](https://www.google.com/url?q=https%3A%2F%2Fpythonawesome.com%2Fyolo-rcnn-object-detection-and-multi-object-tracking%2F&sa=D&sntz=1&usg=AOvVaw0mC4uPk0uEFfs41gzkjf2R) 5. [https://cv-tricks.com/object-tracking/quick-guide-mdnet-goturn-rolo/](https://www.google.com/url?q=https%3A%2F%2Fcv-tricks.com%2Fobject-tracking%2Fquick-guide-mdnet-goturn-rolo%2F&sa=D&sntz=1&usg=AOvVaw08eNSdqkFWRxKgChSpTmGv) 6. Deep Learning in Video Multi-Object Tracking: A Survey [https://arxiv.org/abs/1907.12740](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1907.12740&sa=D&sntz=1&usg=AOvVaw3UJVcBbBuZ6mdflbt5vdp8) 7. **HOTA: A Higher Order Metric for Evaluating Multi-object Tracking** [https://link.springer.com/article/10.1007/s11263-020-01375-2](https://www.google.com/url?q=https%3A%2F%2Flink.springer.com%2Farticle%2F10.1007%2Fs11263-020-01375-2&sa=D&sntz=1&usg=AOvVaw3Nnnf1zeZD4vmMHhiGaMVC) 8. some examples Endeavor to summarize MOT: The best methods running on GPU. The versioning of different deep learning frameworks are crucial. For example the latest version of OS for Jetson Nano "Jetpack" use CUDA 11 but the Pytorch only support up to 10.1 now. So we need to install lower Jetpack version on Jetson Nano or compile the Pytorch. I compile Pytorch and it takes few hours with a lot of issue to solve. For the Ubuntu 20 there is not support for CUDA 10 you need install CUDA 11 and compile the Pytorch with a lot of library. install CUDA 10.x on Ubuntu 20 is possible but takes time to solve conflicts also supporting eGPU is another issue for lower ubuntu. On MacOS installing everything is easy because of not supporting GPU but many library and frameworks of the source codes of tracking require GPU version. Even install CPU version of all library does not grantee to run tracking methods. Another aspect is speed. Running tracking even on GPU is very slow based on my experience using Yolo version 3 which is one of the fastest object detection on GTX 2070 may run in real time. Methods on tracking is very different. First generation, it is completely based on computer vision. The second generation combining machine learning, Kalman filter and advanced computer vision (SIFT), the third generation using deep learning and some of the methods of previous generation like Kalman filter. The fourth generation using combination of two deep learning methods. And the latest generation using complete end to end models with RNN. Object tracking works with all combination of environments such as, moving objects, moving objects and camera in dynamic environments. As long as object appear in the frame until disappeared it the tracking can track and identification as one objects. No mater how many FPS. In around 130 videos of the course of Multiple Object Tracking on EDEX means this topic is huge and require more attention for the more research and development. Running MOT on Jetson nano is tricky and hacky in many way. First, the cup is arm based and not many package are build for it. Datasets for Tracking: MOTChallenge MOT15 MOT16/17 MOT19 KITTI UA-DETRAC tracking benchmark _metrics_ * _Mostly Tracked_ (MT) trajectories: number of ground-truth trajectories that are correctly tracked in at least 80% of the frames. * _Fragments_ : trajectory hypotheses which cover at most 80% of a ground truth trajectory. Observe that a true trajectory can be covered by more than one fragment. * _Mostly Lost_ (ML) trajectories: number of ground-truth trajectories that are correctly tracked in less than 20% of the frames. _False trajectories_ : predicted trajectories which do not correspond to a real object (i.e. to a ground truth trajectory). * _ID switches_ : number of times when the object is correctly tracked, but the associated ID for the object is mistakenly changed. Test: [https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD) Only Ubuntu, Not mac, can based on GPU, webcam not working [https://github.com/tianweiy/CenterPoint](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Ftianweiy%2FCenterPoint&sa=D&sntz=1&usg=AOvVaw1VC56JGagYnHa4SCXM2hFp) Only GPU YouTube: OpenCV [Tracking Objects | OpenCV Python Tutorials for Beginners 2020](https://www.youtube.com/watch?v=1FJWXOO1SRI&ab_channel=Murtaza%27sWorkshop- RoboticsandAI) Multiple Object Tracking [Python: Real-time Multiple Object Tracking (MOT) with Yolov3, Tensorflow and Deep SORT [FULL COURSE]](https://www.youtube.com/watch?v=zi-62z-3c4U&ab_channel=eMasterClassAcademy) There are at least 7 types of tracker algorithms that can be[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.pyimagesearch.com%2F2019%2F12%2F02%2Fopencv- vehicle-detection-tracking-and-speed- estimation%2F&sa=D&sntz=1&usg=AOvVaw1bSowsM3cTzPpHyDtqvvTH)[used](https://www.google.com/url?q=https%3A%2F%2Fwww.pyimagesearch.com%2F2019%2F12%2F02%2Fopencv- vehicle-detection-tracking-and-speed- estimation%2F&sa=D&sntz=1&usg=AOvVaw1bSowsM3cTzPpHyDtqvvTH) in[ ](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd2%2Fd0a%2Ftutorial_introduction_to_tracker.html&sa=D&sntz=1&usg=AOvVaw2-nw055tVNmJbtGsqDgyDC)[OpenCV](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd2%2Fd0a%2Ftutorial_introduction_to_tracker.html&sa=D&sntz=1&usg=AOvVaw2-nw055tVNmJbtGsqDgyDC): not[ ](https://www.google.com/url?q=https%3A%2F%2Fblog.netcetera.com%2Fobject- detection-and-tracking- in-2020-f10fb6ff9af3&sa=D&sntz=1&usg=AOvVaw1eVxYhtOaKsaR5fPlbFyv6)[DL](https://www.google.com/url?q=https%3A%2F%2Fblog.netcetera.com%2Fobject- detection-and-tracking- in-2020-f10fb6ff9af3&sa=D&sntz=1&usg=AOvVaw1eVxYhtOaKsaR5fPlbFyv6) * MIL * BOOSTING * MEDIANFLOW * TLD * KCF * GOTURN * MOSSE Kalman filtering, sparse and dense optical flow are[ ](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1602.00763&sa=D&sntz=1&usg=AOvVaw3wgxS3anU0zhzcI30qN- xK)[Simple Online and Realtime Tracking (SORT)](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1602.00763&sa=D&sntz=1&usg=AOvVaw3wgxS3anU0zhzcI30qN- xK), which uses a combination of the Hungarian algorithm and Kalman filter to achieve decent object tracking. R-CNN around 2000 region proposals [selective search](http://www.google.com/url?q=http%3A%2F%2Fhuppelen.nl%2Fpublications%2FselectiveSearchDraft.pdf&sa=D&sntz=1&usg=AOvVaw0SePhTa1eR5orNEduks3o7) share colors and textures, lightning conditions slow to train and test Fast R-CNN computes a convolutional feature map for the entire input image in a single forward pass of the network architecture is trained end-to-end with a multi-task loss [https://github.com/ZQPei/deep_sort_pytorch](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FZQPei%2Fdeep_sort_pytorch&sa=D&sntz=1&usg=AOvVaw2ZTw3IYJO4rvuF2hUPjCpH) Simple Online and Realtime Tracking with a Deep Association Metric. 2017 [https://arxiv.org/pdf/1703.07402](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fpdf%2F1703.07402&sa=D&sntz=1&usg=AOvVaw2FicrkTufsUcJdkQyC7MEp) [https://mcv-m6-video.github.io/deepvideo-2019/](https://www.google.com/url?q=https%3A%2F%2Fmcv-m6-video.github.io%2Fdeepvideo-2019%2F&sa=D&sntz=1&usg=AOvVaw0NRE06tCEyly2eOp8bmxhg) # [ **The online course about multiple object tracking in Edx:**](https://www.google.com/url?q=https%3A%2F%2Fwww.edx.org%2Fcourse%2Fmulti- object-tracking-for-automotive- systems%3Futm_source%3Dsailthru%26utm_medium%3Demail%26utm_campaign%3Dtriggered_shareit%2520&sa=D&sntz=1&usg=AOvVaw2h6dAyUrPoPcNJBKhz_eql) Course Section 0: Welcome and Introduction ' Part 1: Introduction to Multiple Object Tracking (MOT): good ; many definition and definitions: 15 videos [Introductory examples](https://www.youtube.com/watch?v=ay_QLAHcZLY&list=PLadnyz93xCLhSlm2tMYJSKaik39EZV_Uk) Is about the accurate perception of the driving environment Avoid collisions at the airport Crowd surveillance Crowd behavior Planning of emergency procedures Pedestrian tracking using LIDAR Tracking based on detections Group behavior Part 2: Single Object Tracking in clutter (SOT): Many math; basic methods, 23 videos [Introduction to SOT in Clutter](https://www.youtube.com/watch?v=UpXpUjgqhTw&list=PLadnyz93xCLiHWjLcLFdzc- SidNL1kRF7) Pruning and merging Pruning : remove hypotheses with small weights (and renormalize) Merging: approximate a mixture of densities by a single density (often Gaussian) Gating: technique to disregard unreasonable detections [pruning] SOT * Gaussian densities * Nearest neighbour (NN) filter [pruning] * Probabilistic data association (PDA) filter [merging] * Gaussian mixture densites * Gaussian sum filter (GSF) [pruning/merging] Part 3: Tracking a known number of objects in clutter 30 3.3.6 Predicting the n object density **3.4.1 Introduction to data association** Part 4: Random Finite Sets 24 Part 5: Multiple Object Tracking using conjugate priors 25 [only in YouTube] Part 6: Outlook - what is next? 18 [only in YouTube] Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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The versioning of different deep learning frameworks are crucial. For example the latest version of OS for Jetson Nano [Jetpack](https://www.google.com/url?q=https%3A%2F%2Fdeveloper.nvidia.com%2Fembedded%2Fjetpack&sa=D&sntz=1&usg=AOvVaw12B66A0ktALSyHLcxN- Xx0) which use latest CUDA but the Pytorch only support up to 10.1 now. So we need to install lower Jetpack version on Jetson Nano or compile the Pytorch. I compile Pytorch and it takes few hours with a lot of issue to solve. For the Ubuntu 20 there is not support for CUDA 10 you need install CUDA 11 and compile the Pytorch with a lot of library. install CUDA 10.x on Ubuntu 20 is possible but takes time to solve conflicts also supporting eGPU is another issue for lower ubuntu. On MacOS installing everything is easy because of not supporting GPU but many library and frameworks of the source codes of tracking require GPU version. Even install CPU version of all library does not grantee to run tracking methods. Another aspect is speed. Running tracking even on GPU is very slow based on my experience using Yolo version 3 which is one of the fastest object detection on GTX 2070 can process up to 15 FPS with Full HD videos. Methods on tracking is very different. First generation, it is completely based on computer vision. The second generation combining Kalman filter and advanced computer vision (SIFT), the third generation using deep learning and some of the methods of previous generation like Kalman filter. The fourth generation using combination of two deep learning methods. And the latest generation using complete end to end models like RNN. Object tracking works with all combination of environments such as, moving objects, moving objects and camera in dynamic environments. As long as object appear in the frame until disappeared it the tracking can track and identification as one objects. No mater how many FPS. ![](https://lh5.googleusercontent.com/frEao1LhgV5UF45sMbN1pGLAShMYMfQUSgrtMBqCu7L0xemepsHxFTK2FsvclW1vAHcjneJNS0vYymDcA8b6Hj7LFcIsgIa8DUyKotlBkc9T4EiqeT8ypBtzo4Zl7knwlw=w1280) ![](https://lh4.googleusercontent.com/wvkhJIEynfOJqi2WZaNJHLoHAUnzIsBXGhx6mnTvxtUWIAzN1ANg_QHl0BEEVLpX1DhW_LjdCe3k4YzvOUAOuqN2A08oQAhtHwwn7BLSEtNDqUzPOjr0VMvJFn1ps6E1-w=w1280) ![](https://lh6.googleusercontent.com/dsntK-A_QJ- _nsf1FmXG_GkFGwt4SRIYe5GRkhcTZJEh7FJg6s-_cblsjAkUFMd78WgTwx6nJhYgnptaVKqPEsp44ShLANFfCpY2zZWRn9ZSVp2aJJs2zdmTAyh4DeVFDA=w1280) ![](https://lh5.googleusercontent.com/RwHP2qaZ30lnf0Li0577PkSPRnFAv2wc1DgLPZf1MHXEa3gYE1eWXpvMCv0jnuwsfPDviXLHxDB3Jgfo_JX2-8zRar3Xv34ooHpXJJahEu7KH- YteHc1x4Kx4UohB4fx=w1280) ![](https://lh6.googleusercontent.com/LHlOjPBDmbjev7ToYCU6tGnYHebHUwdl5DhLjQfR0TxfwzKzN2dwAPwTBGRGK_bpisBPaU2LBxpyAqAqwY5yxyEQC5ySvsVRCg7LrMgeYLXFMRkPdS5uuPpzoNSLYzzElw=w1280) ![](https://lh6.googleusercontent.com/wyq972e8Eaw- nQDqndWJ_WMS7jem5z72ORPzgdPqPxOmTer73H64HP_bg25cX3N7drHeVdqUN- Ib3kmUqcUbSH9OZxiPNARnA8vhtdJg3YPSroFX_6xXAR8P3pQFuXRhMQ=w1280) # Tracking * Classic object tracking * * * classic feature detection (SIFT and SURF), combined with a machine learning algorithm like KNN or SVM for classification, or with a description matcher like FLANN for object detection. * Kalman filtering, sparse and dense optical flow, * Example: Simple Online and Realtime Tracking (SORT), which uses a combination of the Hungarian algorithm and Kalman filter * SOT is a hot topic in the last decade. Early visual tracking methods rely on extracting hand-crafted features of candidate target regions, and use matching algorithms or hand-crafted discriminative classifiers to generate tracking results. * The MOT track aims to recover the trajectories of objects in video sequences, which is an important problem in computer vision with many applications, such as surveillance, activity analysis, and sport video analysis. * Video object detection datasets. The video object detection task aims to detect objects of different categories in video sequences. * Multi-object tracking datasets * large-scale benchmark Multi-Class Multi-object tracking datasets * VisDrone datasets is captured in various unconstrained scenes, focusing on four core problems in computer vision fields, i.e., image object detection, video object detection, single object tracking, and multi- object tracking. * the accuracy of detection methods suffers from degenerated object appearances in videos such as motion blur, pose variations, and video de-focus. Exploiting temporal coherence and aggregating features in consecutive frames might to be two effective ways to handle such issue. * Temporal coherence. A feasible way to exploit temporal coherence is using object trackers * Feature aggregation. Aggregating features in consecutive frames is also a useful way to improve the performance. * ### List of Datasets * **MOT20** * KITTI Tracking * MOTChallenge 2015 * UA-DETRAC Tracking * DukeMTMC * Campus * MOT17 * UAVDT-MOT * VisDrone ### Source code * ROLO * TensorFlow: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FGuanghan%2FROLO&sa=D&sntz=1&usg=AOvVaw2amdiMCe2vunOIdlpvqghX) * SiamMask * PyTorch 0.4.1: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Ffoolwood%2FSiamMask&sa=D&sntz=1&usg=AOvVaw1oXduEQoxzhx-VIMIMQ9Rn) * Deep SORT * PyTorch ≥ 0.4.0: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FZQPei%2Fdeep_sort_pytorch&sa=D&sntz=1&usg=AOvVaw2ZTw3IYJO4rvuF2hUPjCpH) * TensorFlow ≥ 1.0: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fnwojke%2Fdeep_sort&sa=D&sntz=1&usg=AOvVaw2FXwdcfCz1RZbndLyQSlBe) * TrackR-CNN * TensorFlow 1.13.1: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FVisualComputingInstitute%2FTrackR-CNN&sa=D&sntz=1&usg=AOvVaw3i3kALogli3ZyH93E3zhy5) * Tracktor++ * PyTorch 1.3.1: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fphil-bergmann%2Ftracking_wo_bnw&sa=D&sntz=1&usg=AOvVaw2xvx7s_sfJOiwLiwhmhqR8) * JDE * PyTorch ≥ 1.2.0: [link](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FZhongdao%2FTowards-Realtime-MOT&sa=D&sntz=1&usg=AOvVaw0PcQNbW8igbtHaJ1x8IOVO) * [MCMOT: One-shot multi-class multi-object tracking](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FCaptainEven%2FMCMOT&sa=D&sntz=1&usg=AOvVaw2nrFmqa-Eh4-FoWPWBXrJn) # Self collected datasets ## [Video labeling](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fheartexlabs%2Fawesome- data-labeling&sa=D&sntz=1&usg=AOvVaw1Eb2rubldhdODxp4c4_Xnc) * [VATIC](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fcvondrick%2Fvatic&sa=D&sntz=1&usg=AOvVaw3dbkWnFtLPwO3znnZZz3Jy) * [UltimateLabeling](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Falexandre01%2FUltimateLabeling&sa=D&sntz=1&usg=AOvVaw2CpIp2ojyJ4dgWdKVlYC_r) * [https://github.com/pirahansiah/UltimateLabeling](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2FUltimateLabeling&sa=D&sntz=1&usg=AOvVaw2mQJcLjtHwG4L8YcUkET64) # Reference 1. Vision Meets Drones: Past, Present and Future 2. [https://blog.netcetera.com/object-detection-and-tracking-in-2020-f10fb6ff9af3](https://www.google.com/url?q=https%3A%2F%2Fblog.netcetera.com%2Fobject-detection-and-tracking-in-2020-f10fb6ff9af3&sa=D&sntz=1&usg=AOvVaw1eVxYhtOaKsaR5fPlbFyv6) 3. 4. [https://pythonawesome.com/yolo-rcnn-object-detection-and-multi-object-tracking/](https://www.google.com/url?q=https%3A%2F%2Fpythonawesome.com%2Fyolo-rcnn-object-detection-and-multi-object-tracking%2F&sa=D&sntz=1&usg=AOvVaw0mC4uPk0uEFfs41gzkjf2R) 5. [https://cv-tricks.com/object-tracking/quick-guide-mdnet-goturn-rolo/](https://www.google.com/url?q=https%3A%2F%2Fcv-tricks.com%2Fobject-tracking%2Fquick-guide-mdnet-goturn-rolo%2F&sa=D&sntz=1&usg=AOvVaw08eNSdqkFWRxKgChSpTmGv) 6. Deep Learning in Video Multi-Object Tracking: A Survey [https://arxiv.org/abs/1907.12740](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1907.12740&sa=D&sntz=1&usg=AOvVaw3UJVcBbBuZ6mdflbt5vdp8) 7. **HOTA: A Higher Order Metric for Evaluating Multi-object Tracking** [https://link.springer.com/article/10.1007/s11263-020-01375-2](https://www.google.com/url?q=https%3A%2F%2Flink.springer.com%2Farticle%2F10.1007%2Fs11263-020-01375-2&sa=D&sntz=1&usg=AOvVaw3Nnnf1zeZD4vmMHhiGaMVC) 8. some examples Endeavor to summarize MOT: The best methods running on GPU. The versioning of different deep learning frameworks are crucial. For example the latest version of OS for Jetson Nano "Jetpack" use CUDA 11 but the Pytorch only support up to 10.1 now. So we need to install lower Jetpack version on Jetson Nano or compile the Pytorch. I compile Pytorch and it takes few hours with a lot of issue to solve. For the Ubuntu 20 there is not support for CUDA 10 you need install CUDA 11 and compile the Pytorch with a lot of library. install CUDA 10.x on Ubuntu 20 is possible but takes time to solve conflicts also supporting eGPU is another issue for lower ubuntu. On MacOS installing everything is easy because of not supporting GPU but many library and frameworks of the source codes of tracking require GPU version. Even install CPU version of all library does not grantee to run tracking methods. Another aspect is speed. Running tracking even on GPU is very slow based on my experience using Yolo version 3 which is one of the fastest object detection on GTX 2070 may run in real time. Methods on tracking is very different. First generation, it is completely based on computer vision. The second generation combining machine learning, Kalman filter and advanced computer vision (SIFT), the third generation using deep learning and some of the methods of previous generation like Kalman filter. The fourth generation using combination of two deep learning methods. And the latest generation using complete end to end models with RNN. Object tracking works with all combination of environments such as, moving objects, moving objects and camera in dynamic environments. As long as object appear in the frame until disappeared it the tracking can track and identification as one objects. No mater how many FPS. In around 130 videos of the course of Multiple Object Tracking on EDEX means this topic is huge and require more attention for the more research and development. Running MOT on Jetson nano is tricky and hacky in many way. First, the cup is arm based and not many package are build for it. Datasets for Tracking: MOTChallenge MOT15 MOT16/17 MOT19 KITTI UA-DETRAC tracking benchmark _metrics_ * _Mostly Tracked_ (MT) trajectories: number of ground-truth trajectories that are correctly tracked in at least 80% of the frames. * _Fragments_ : trajectory hypotheses which cover at most 80% of a ground truth trajectory. Observe that a true trajectory can be covered by more than one fragment. * _Mostly Lost_ (ML) trajectories: number of ground-truth trajectories that are correctly tracked in less than 20% of the frames. _False trajectories_ : predicted trajectories which do not correspond to a real object (i.e. to a ground truth trajectory). * _ID switches_ : number of times when the object is correctly tracked, but the associated ID for the object is mistakenly changed. Test: [https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD) Only Ubuntu, Not mac, can based on GPU, webcam not working [https://github.com/tianweiy/CenterPoint](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Ftianweiy%2FCenterPoint&sa=D&sntz=1&usg=AOvVaw1VC56JGagYnHa4SCXM2hFp) Only GPU YouTube: OpenCV [Tracking Objects | OpenCV Python Tutorials for Beginners 2020](https://www.youtube.com/watch?v=1FJWXOO1SRI&ab_channel=Murtaza%27sWorkshop- RoboticsandAI) Multiple Object Tracking [Python: Real-time Multiple Object Tracking (MOT) with Yolov3, Tensorflow and Deep SORT [FULL COURSE]](https://www.youtube.com/watch?v=zi-62z-3c4U&ab_channel=eMasterClassAcademy) There are at least 7 types of tracker algorithms that can be[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.pyimagesearch.com%2F2019%2F12%2F02%2Fopencv- vehicle-detection-tracking-and-speed- estimation%2F&sa=D&sntz=1&usg=AOvVaw1bSowsM3cTzPpHyDtqvvTH)[used](https://www.google.com/url?q=https%3A%2F%2Fwww.pyimagesearch.com%2F2019%2F12%2F02%2Fopencv- vehicle-detection-tracking-and-speed- estimation%2F&sa=D&sntz=1&usg=AOvVaw1bSowsM3cTzPpHyDtqvvTH) in[ ](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd2%2Fd0a%2Ftutorial_introduction_to_tracker.html&sa=D&sntz=1&usg=AOvVaw2-nw055tVNmJbtGsqDgyDC)[OpenCV](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd2%2Fd0a%2Ftutorial_introduction_to_tracker.html&sa=D&sntz=1&usg=AOvVaw2-nw055tVNmJbtGsqDgyDC): not[ ](https://www.google.com/url?q=https%3A%2F%2Fblog.netcetera.com%2Fobject- detection-and-tracking- in-2020-f10fb6ff9af3&sa=D&sntz=1&usg=AOvVaw1eVxYhtOaKsaR5fPlbFyv6)[DL](https://www.google.com/url?q=https%3A%2F%2Fblog.netcetera.com%2Fobject- detection-and-tracking- in-2020-f10fb6ff9af3&sa=D&sntz=1&usg=AOvVaw1eVxYhtOaKsaR5fPlbFyv6) * MIL * BOOSTING * MEDIANFLOW * TLD * KCF * GOTURN * MOSSE Kalman filtering, sparse and dense optical flow are[ ](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1602.00763&sa=D&sntz=1&usg=AOvVaw3wgxS3anU0zhzcI30qN- xK)[Simple Online and Realtime Tracking (SORT)](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F1602.00763&sa=D&sntz=1&usg=AOvVaw3wgxS3anU0zhzcI30qN- xK), which uses a combination of the Hungarian algorithm and Kalman filter to achieve decent object tracking. R-CNN around 2000 region proposals [selective search](http://www.google.com/url?q=http%3A%2F%2Fhuppelen.nl%2Fpublications%2FselectiveSearchDraft.pdf&sa=D&sntz=1&usg=AOvVaw0SePhTa1eR5orNEduks3o7) share colors and textures, lightning conditions slow to train and test Fast R-CNN computes a convolutional feature map for the entire input image in a single forward pass of the network architecture is trained end-to-end with a multi-task loss [https://github.com/ZQPei/deep_sort_pytorch](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FZQPei%2Fdeep_sort_pytorch&sa=D&sntz=1&usg=AOvVaw2ZTw3IYJO4rvuF2hUPjCpH) Simple Online and Realtime Tracking with a Deep Association Metric. 2017 [https://arxiv.org/pdf/1703.07402](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fpdf%2F1703.07402&sa=D&sntz=1&usg=AOvVaw2FicrkTufsUcJdkQyC7MEp) [https://mcv-m6-video.github.io/deepvideo-2019/](https://www.google.com/url?q=https%3A%2F%2Fmcv-m6-video.github.io%2Fdeepvideo-2019%2F&sa=D&sntz=1&usg=AOvVaw0NRE06tCEyly2eOp8bmxhg) # [ **The online course about multiple object tracking in Edx:**](https://www.google.com/url?q=https%3A%2F%2Fwww.edx.org%2Fcourse%2Fmulti- object-tracking-for-automotive- systems%3Futm_source%3Dsailthru%26utm_medium%3Demail%26utm_campaign%3Dtriggered_shareit%2520&sa=D&sntz=1&usg=AOvVaw2h6dAyUrPoPcNJBKhz_eql) Course Section 0: Welcome and Introduction ' Part 1: Introduction to Multiple Object Tracking (MOT): good ; many definition and definitions: 15 videos [Introductory examples](https://www.youtube.com/watch?v=ay_QLAHcZLY&list=PLadnyz93xCLhSlm2tMYJSKaik39EZV_Uk) Is about the accurate perception of the driving environment Avoid collisions at the airport Crowd surveillance Crowd behavior Planning of emergency procedures Pedestrian tracking using LIDAR Tracking based on detections Group behavior Part 2: Single Object Tracking in clutter (SOT): Many math; basic methods, 23 videos [Introduction to SOT in Clutter](https://www.youtube.com/watch?v=UpXpUjgqhTw&list=PLadnyz93xCLiHWjLcLFdzc- SidNL1kRF7) Pruning and merging Pruning : remove hypotheses with small weights (and renormalize) Merging: approximate a mixture of densities by a single density (often Gaussian) Gating: technique to disregard unreasonable detections [pruning] SOT * Gaussian densities * Nearest neighbour (NN) filter [pruning] * Probabilistic data association (PDA) filter [merging] * Gaussian mixture densites * Gaussian sum filter (GSF) [pruning/merging] Part 3: Tracking a known number of objects in clutter 30 3.3.6 Predicting the n object density **3.4.1 Introduction to data association** Part 4: Random Finite Sets 24 Part 5: Multiple Object Tracking using conjugate priors 25 [only in YouTube] Part 6: Outlook - what is next? 18 [only in YouTube] Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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Let's partner up to take your project to the next level! pip install mlc-ai-nightly -f https://mlc.ai/wheels https://mlc.ai/ https://mlc.ai/summer22/ Day 1: Introduction to Unity: TVMScript Introduction to Unity: Relax and PyTorch TVM BYOC in Practice Get Started with TVM on Adreno GPU Introduction to Unity: Metaschedule How to Bring microTVM to a custom IDE Day 2: Community Keynote PyTorch 2.0: the journey to bringing compiler technologies to the core of PyTorch Support QNN Dialect for TVM with MediaTek Neuron and Devise the Scheduler for Acceleration On-Device Training Under 256KB Memory AMD Tutorial TVM at TI: Accelerating inference using the C7x/MMA Adreno GPU: 4x speed-up and upstreaming to TVM mainline Transfer-Tuning: Reusing Auto-Schedules for Efficient Tensor Program Code Generation Improvement in the TVM OpenCL codegen to autogenerate optimal convolution kernels for Adreno GPUs TVM Unity: Pass Infrastructure and BYOC Renesas Hardware accelerators with Apache TVM Introduction on 4th Gen Intel Xeon processor and BF16 support with TVM Hidet: Task Mapping Programming Paradigm for Deep Learning Tensor Programs Towards Building a Responsible Data Economy Optimizing SYCL Device Kernels with AKG Adreno GPU Performance Enhancements using TVM Improvements to CMSIS-NN integration in TVM UMA: Universal Modular Accelerator Interface Day 3: TVM Unity for Dynamic Models Empower Tensorflow serving with backend TVM Enabling Conditional Computing on Hexagon target Decoupled Model Schedule for Large Deep Learning Model Training Using TVM to bring Bayesian neural networks to embedded hardware Efficient Support of TVM Scan OP on RISC-V Vector Extension Improvements to Ethos-U55 support in TVM including CI on Alif Semiconductor boards Compiling Dynamic Shapes TVM Packaging in 2023: delivering TVM to end users Cross-Platform Training Using Automatic Differentiation on Relax IR AutoTVM: Reducing tuning space by cross axis filtering SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning Analytical Tensorization and Fusion for Compute-intensive Operators CUTLASS 3.0: Next Generation Composable and Reusable GPU Linear Algebra Library Enabling Data Movement and Computation Pipelining in Deep Learning Compiler Automating DL Compiler Bug Finding with NNSmith TVM at NIO TVM at Tencent Integrating the Andes RISC-V Processors into TVM Alpa: A Compiler for Distributed Deep Learning ACRoBat: Compiler and Runtime Techniques for Efficient Auto-Batching of Dynamic Deep Learning Computations Channel Folding: a Transform Pass for Optimizing Mobilenets ========================================================================Day 1: ************************ Introduction to Unity: TVMScript [https://github.com/cyx-6/TVM- Demo/blob/main/tvmscript.ipynb](https://github.com/cyx-6/TVM- Demo/blob/main/tvmscript.ipynb) Gan NN show us some hidden patter in history we can not see before. “I always have a slip of paper at hand, on which I note down the ideas of certain pages. On the backside I write down the bibliographic details. After finishing the book I go through my notes and think how these notes might be relevant for already written notes in the slip-box. It means that I always read with an eye towards possible connections in the slip-box.” (Luhmann et al., 1987, 150) Deep representation learning Model evaluation. Camera cheaper lidar Point cloud because of we need 3d Capturing reality 1\. 𝐀𝐝𝐝/𝐂𝐨𝐦𝐦𝐢𝐭 𝐀𝐥𝐥 Standard way: git add . git commit -m "Message" Another way: git commit -a -m "Message" 𝟐\. 𝐀𝐥𝐢𝐚𝐬𝐞𝐬 With aliases, you can write your own Git commands that do anything you want. Eg: git config --global alias.ac '!git add -A && git commit -m' (alias called ac, git add -A && git commit -m will do the full add and commit) 𝟑\. 𝐑𝐞𝐯𝐞𝐫𝐭 The revert command simply allows us to undo any commit on the current branch. Eg: git revert 486bdb2 Another way: git revert HEAD (for recent commits) 𝟒\. 𝐑𝐞𝐟𝐥𝐨𝐠 This command lets you easily see the recent commits, pulls, resets, pushes, etc on your local machine. Eg: git reflog 𝟓\. 𝐏𝐫𝐞𝐭𝐭𝐲 𝐋𝐨𝐠𝐬 Gives you the ability to print out a pretty log of your commits/branches. Eg: git log --graph --decorate --oneline 𝟔\. 𝐒𝐞𝐚𝐫𝐜𝐡𝐢𝐧𝐠 𝐋𝐨𝐠𝐬 One can also use the log command to search for specific changes in the code. Eg: git log -S "A promise in JavaScript is very similar" 𝟕\. 𝐒𝐭𝐚𝐬𝐡 This command will stash (store them locally) all your code changes but does not actually commit them. Eg: git stash 𝟖\. 𝐑𝐞𝐦𝐨𝐯𝐞 𝐃𝐞𝐚𝐝 𝐁𝐫𝐚𝐧𝐜𝐡𝐞𝐬 This command will delete all the tracking information for branches that are on your local machine that are not in the remote repository, but it does not delete your local branches. Eg: git remote update --prune 𝟗\. 𝐁𝐢𝐬𝐞𝐜𝐭 For finding which commits caused certain bugs Eg: git bisect start git bisect bad git bisect good 48c86d6 𝟏𝟎\. 𝐃𝐞𝐬𝐭𝐫𝐨𝐲 𝐋𝐨𝐜𝐚𝐥 𝐂𝐡𝐚𝐧𝐠𝐞𝐬 One can wipe out all changes on your local branch to exactly what is in the remote branch. Eg: git reset --hard origin/main Don’t trust your devices IoT. software and hardware are together for better business. Newsletter investing every 3 months 1\. Prototyping. New bie 2\. Patent. Website. ( list of investors) 3\. Pre seed. First founding 1M VC, inistution, anjel capital. 400 000 preseed. Quveribel. Equtible rund convertible non agreement Template. Convertabel lone 1\. Germ standar inistitude 2\. 4\. Equity. Venture builder. 20% 200 000 5\. 100 000 per year to become unocorn in less than 10 years 6\. Soniy corn 100k unicorn 1M 7\. 360 euro per years for database of investor 8\. Convertable loan: Pay interst rate 5% to 8% = 18 months later (2M found in 10M) convert on based . 9\. Invester Never act as co-founder = full time = 20% 10\. Project profit, 11\. Full time after foun rising Make a plan for your business; take your time to make calculations by creating a target audience. Your target audience determines how you approach your business plan. By studying your target audience, you are making empirical research and collecting information from them Then, secure a good partnership if need be, and get enough capital to start up. * * What the people need * Why people need it * When the people need it * It's affordability * It's ease of use * It's maintenance and revenue Pair programming The SB7 Framework harnesses the influence of stories. The structure describes the 7 most common story elements: • Character • Problem • Guide • Plan • Calls to action • Failure • Success Dear [Hiring Manager’s Name], I am writing to apply for the position of computer vision for IoT and cloud at [Company Name]. I am a highly skilled and experienced computer vision engineer with a strong background in IoT and cloud technologies. I believe that my skills and experience make me an ideal candidate for this position and I am excited about the opportunity to contribute to the success of your organization. I have a solid understanding of computer vision algorithms and techniques, as well as experience in developing and implementing computer vision systems. I am proficient in programming languages such as Python, C++, and Java, and have experience with popular computer vision libraries such as OpenCV, TensorFlow, and PyTorch. In addition, I have a strong background in IoT and cloud technologies, including experience with IoT platforms such as AWS IoT, Azure IoT, and Google Cloud IoT. I am familiar with cloud computing technologies such as AWS, Azure, and Google Cloud, and have experience with deploying and managing computer vision systems on these platforms. I am also a team player and have excellent communication skills. I am able to work with cross-functional teams and can effectively communicate with both technical and non-technical stakeholders. I am also highly motivated, and I am always looking for ways to improve my skills and stay up-to-date with the latest technologies. I am excited about the opportunity to join [Company Name] and to contribute to the development of cutting-edge computer vision systems for IoT and cloud. I am confident that my skills and experience make me a strong candidate for this position, and I look forward to discussing how I can contribute to your organization. Thank you for considering my application. I look forward to hearing from you soon. Sincerely, Title: "Unlocking the Power of Computer Vision for IoT and Cloud" Introduction: * Hi, and welcome to our video on the topic of computer vision for IoT and cloud. In this video, we're going to explore how computer vision technology can be used to enhance IoT and cloud-based systems, and how it can be used to unlock new possibilities for businesses and consumers alike. Body: * First, let's talk about what computer vision is and how it works. Essentially, computer vision is the technology that enables computers to understand and interpret visual information from the world around us. This can include things like images, videos, and even 3D models. * One of the key ways that computer vision can be used to enhance IoT and cloud-based systems is by enabling devices to better understand and interact with their environment. For example, a computer vision-enabled camera could be used to monitor a manufacturing facility and identify when a machine is in need of maintenance or when an employee is working in an unsafe manner. * Another way that computer vision can be used to enhance IoT and cloud-based systems is by enabling devices to better understand and interact with people. For example, a computer vision-enabled security camera could be used to identify individuals and track their movements, or a computer vision-enabled smart home system could be used to detect when someone is in the room and adjust the lighting or temperature accordingly. * Additionally, computer vision can also be used to enhance cloud-based systems by providing more accurate data and insights. For example, a computer vision-enabled drone could be used to collect data on crops and provide farmers with more accurate information about the health and growth of their crops. Conclusion: * Overall, computer vision technology has the potential to unlock new possibilities for businesses and consumers alike, by enabling IoT and cloud-based systems to better understand and interact with their environment and people. We hope this video has provided you with a better understanding of the potential of computer vision for IoT and cloud, and we look forward to seeing the new possibilities that will be created as this technology continues to evolve. Excited to share my latest project using computer vision and IoT to improve efficiency in manufacturing. I used a combination of machine learning algorithms and cloud computing to analyze data from cameras and sensors in real-time, resulting in a 20% increase in production speed. This was a challenging project but I enjoyed every step of it! I am always looking for new opportunities to apply my skills in computer vision and IoT to help companies improve their operations. Let's connect if you are working on a similar project or if you are looking for a developer with these skills. #computervision #IoT #cloudcomputing #manufacturingefficiency #machinelearning #developer" In this post, you briefly mention your experience and skills in computer vision and IoT, and you provide a specific example of a project you worked on that demonstrates your abilities. You also make it clear that you are open to new opportunities, and you invite others to connect with you. Using relevant hashtags such as #computervision #IoT #cloudcomputing can help your post reach a wider audience Exciting news! I just published a paper on a new object detection algorithm that I developed. The algorithm uses a combination of deep learning and computer vision techniques to improve accuracy and speed of object detection in real-world scenarios. This is a big step forward in the field of computer vision and I am proud to have contributed to it. I will be presenting my research at the Computer Vision Conference next month, if you're attending be sure to stop by and say hi! #computervision #objectdetection #deeplearning #research" In this post, you briefly explain the main findings and contributions of your research, and you express your excitement and pride in your work. You also mention the upcoming conference where you will be presenting your research, inviting your friends and colleagues to meet you in person. Also using relevant hashtags such as #computervision #objectdetection #deeplearning can help reach a wider audience interested in the field. Features stores 1\. Car parts detection 2\. Resize keep aspects ration 3\. 3.1 Perform damage detection 4\. 3.2Semantic segregation 5\. Transfer to original coordinates 1 class imbalance 2 class definition Maybe Class in between 3 inconstant annotations Color augmentation 1\. RGB shift 2\. Random brithness and contrast 3\. Sharpen 4\. Hue saturation value Why manually data augmented Becasu control of data. Not too rotate or change something Photogrammetry model Neural radiance fields (NeRF) NeRF in the wild \ [GitHub - google-research/tuning_playbook: A playbook for systematically maximizing the performance of deep learning models.](https://github.com/google-research/tuning_playbook) Yocto and Machine Learning + OpenCV: [https://www.yoctoproject.org](https://www.yoctoproject.org) [https://www.hackster.io/monica/running-machine-learning-on-maaxboard-s-yocto- image-part-1-6a4796](https://www.hackster.io/monica/running-machine-learning- on-maaxboard-s-yocto-image-part-1-6a4796) Bard Google: [https://blog.google/technology/ai/bard-google-ai-search- updates/](https://blog.google/technology/ai/bard-google-ai-search-updates/) [https://mustang.ir/questions/question/راه-اندازی-پروژه-های-گیت-هاب-با-git- pages](https://mustang.ir/questions/question/%D8%B1%D8%A7%D9%87-%D8%A7%D9%86%D8%AF%D8%A7%D8%B2%DB%8C-%D9%BE%D8%B1%D9%88%DA%98%D9%87-%D9%87%D8%A7%DB%8C-%DA%AF%DB%8C%D8%AA-%D9%87%D8%A7%D8%A8-%D8%A8%D8%A7-git- pages) Book: Project Management for Non-Project Managers [https://fa.wikipedia.org/wiki/علی_اکبرپور](https://fa.wikipedia.org/wiki/%D8%B9%D9%84%DB%8C_%D8%A7%DA%A9%D8%A8%D8%B1%D9%BE%D9%88%D8%B1) [https://www.kingorama.com](https://www.kingorama.com) شاهنامه سه بعدی [Accelerate deep learning model development with cloud custom environments - AWS Online Tech Talks - YouTube](https://m.youtube.com/watch?v=2Wt2zlkMtKI&noapp=1) [بخش هایی از کتاب Refactoring (نسخه رایگان)](https://www.developit.ir/refactoring/free.html#f7) [Performance Notes Of PyTorch Support for M1 and M2 GPUs - Lightning AI](https://lightning.ai/pages/community/community-discussions/performance- notes-of-pytorch-support-for-m1-and-m2-gpus/) [Investopedia Academy](https://academy.investopedia.com/) [HandBrake updated with AV1 and VP9 10-bit video encoding](https://9to5mac.com/2022/12/29/handbrake-support-av1-and- vp9-10-bit/) [How to Start Your Sole Proprietorship in 6 Simple Steps](https://qonto.com/en/blog/creators/administrative/sole-proprietorship- in-germany) [Duolingo English Test](https://englishtest.duolingo.com/applicants) [چالش‌های تولید محتوا برای مارکت اروپا و آمریکا - YouTube](https://m.youtube.com/watch?v=wW0HZdubuWQ) [PyTorch for Deep Learning & Machine Learning – Full Course - YouTube](https://m.youtube.com/watch?v=V_xro1bcAuA#dialog) [Why passive investing makes less sense in the current environment | Financial Times](https://archive.ph/0VucZ) [GitHub - google-research/tuning_playbook: A playbook for systematically maximizing the performance of deep learning models.](https://github.com/google-research/tuning_playbook) [GitHub - mgechev/google-interview-preparation-problems: leetcode problems I solved to prepare for my Google interview.](https://github.com/mgechev/google- interview-preparation-problems) [Bayesian Neural Networks and Variational Dropout](https://dmittov.github.io/variational_dropout/#/maximum-likelihood) [One machine learning question every day - bnomial](https://today.bnomial.com/?ref=email) Git remote add orgine Asynchronous Operation Anomaly detection Use experience. Personalizes. Prediction manage society mobility Personalization Covenant Platform. OpenMMLab Wordtune - AI-powered Writing Companion tree -v -I '*.png' -I '*.jpg' \--charset utf-8 >list2.txt 3D object using triangular mesh need vertices point cloud underlying surface of some 3D object, faster Definition of Done User Story complete Code\Implementation complete Code\Implementation Peer Reviews) approved Unit tests complete (if required) Testing Notes complete (if required) User Story Acceptance criteria defined and verified Backend: Python, Redis, Postgres, Celery Frontend: React, Redux, TypeScript DevOps: Terraform, Kubernetes, GitHub, Docker, AWS Data: Python (Data Science), Kafka, Fastapi, MLFlow, AWS SageMaker ML: Selcond core, Kubeflow, … [Sharpness](https://en.wikipedia.org/wiki/Sharpness_%28visual%29) ,[Noise](https://en.wikipedia.org/wiki/Image_noise), [Dynamic range](https://en.wikipedia.org/wiki/Dynamic_range), [Tone reproduction](https://en.wikipedia.org/wiki/Tone_reproduction) , [Contrast](https://en.wikipedia.org/wiki/Contrast_%28vision%29), [Color](https://en.wikipedia.org/wiki/Color), [Distortion](https://en.wikipedia.org/wiki/Distortion_%28optics%29) , [DSLR lenses](https://en.wikipedia.org/wiki/Lenses_for_SLR_and_DSLR_cameras), [Vignetting](https://en.wikipedia.org/wiki/Vignetting), [Exposure](https://en.wikipedia.org/wiki/Exposure_%28photography%29), Lateral [chromatic aberration](https://en.wikipedia.org/wiki/Chromatic_aberration) (LCA), [Lens flare](https://en.wikipedia.org/wiki/Lens_flare), Color, [Artifacts](https://en.wikipedia.org/wiki/Compression_artifact) ۱\. جهت انتخاب کلمه مورد نظرتان، دو بار روی آن تپ کنید. ۲\. برای انتخاب کل یک پاراگراف، کافیست چهار با روی آن تپ کنید. ۳\. یک انگشت را در ابتدا و انگشت دیگر را در آخر یک محدود گذاشته و کمی نگه دارید. متن میان دو انگشت انتخاب خواهد شد. ۴\. روی ابتدای محدوده ای دلخواه دو بار تپ کرده و بلافاصله با درگ کردن (کشیدن) پین محدوده ی انتخاب شده را گسترش دهید. (انگشت خود را پس از دومین تپ جدا نکنید) ۵\. برای انتخاب کل پاراگراف، به جز استفاده از مورد ۲، می توانید با دو انگشت، یک بار روی آن تپ کنید. namely motion estimation, motion smoothing, and image warping. Motion estimation algorithms often use a similarity transform to handle camera translations, rotations, and zooming. The tricky part is getting these algorithms to lock onto the background motion, 0\. video frames captured during fast motion are often blurry. Their appearance can be improved either using deblurring techniques (Section 10.3) or stealing sharper pixels from other frames with less motion or better focus (Matsushita, Ofek, Ge et al. 2006). Exercise 8.3 has you implement and test some of these ideas. 1\. Background subtraction 2\. Motion estimation 3\. Motion smoothing 4\. Image warping. image warping can result in missing borders around the image, which must be cropped, filled using information from other frames, or hallucinated using inpainting techniques (Section 10.5.1). Vision stabilization There is much recent work on Multi-view 3D reconstruction is a central research topic in computer vision that is driven in many different directions There are many available methods that can handle the noisy image completion problem In the case of surveillance using a fixed camera, there is no desired motion. In the case of most robotic applications, horizontal and vertical motions are desired, but rotation is not. In some cases of ground vehicles where the terrain is known to have many incline changes, or with aerial vehicles undergoing complicated maneuvers where the vehicle’s body is meant to be in varying orientations, rotation might be desired as the robot is meant to be at an angle at times. In robotics applications, computational complexity is extremely important due to the need for real-time operation. Also, it is likely that the center of rotation will not lie in the center of the image frame because the camera is rarely mounted at the robot’s center of mass. This first assumption is made in many video stabilization algorithms, and is a convenient way to seed the correct features with higher trust values. It is not an unreasonable assumption to make. Depending on the application, there is often a large portion of frames where local motion does not occur. In some situations, such as monitoring of steady traffic, there is no guarantee that local motion will not occur. This situation has not been tested, nor has our algorithm been designed to handle it. The second assumption comes from a combination of common sense, and the experience of many computer vision researchers. It makes sense that an object in the scene which does not move will be recognized more easily and more often. Being recognized consistently and consecutively is considered stable. On the other hand, objects which have local motion are less likely to be recognized as often. They might move through shadows, change orientation, or even move completely out of the scene. These possibilities all lead to a less stable class of features. It is likely that, more often than not, there are more background features than foreground features. Moving objects generally cover a small portion of the screen, which usually yields fewer features. Although uncommon, we did not want to make the assumption that this would occur in every frame. Certain scenes will consist of a large portion of local motion, or an object will move very close to the camera, consuming a much larger portion of the scene than the background. As long as some background features are discovered in each frame, our stabilization algorithm should succeed. # image processing tips: * the image size and kernel size need to depended. the best way is to use the one variable to define the size of the image and kernel together. * the coordinate of the image start at top left of the image/display * in order to change it to the normal coordinate you can use * grid of points; two matrix to X , Y coordinate * subtract half of W, H from X, Y in order to have normal coordinate system for our image * now we have cartesian coordinate * * cartesian coordinate to polar coordinate * تبدیل فضای کارتزین به پولار در خیلی از برنامه های پردازش تصویر کارایی دارد. برای پیدا کردن ترشلد ها هم می توان استفاده کرد * in MATLAB we can use ":"for example MatrixA(:) which means all entity of the matrix no mater how many dimensions we have but if we want to implemented in Python we can use numpy.flatten(). * in the MATLAB the round is different from python. if you want same result you need implement the rand function by yourself. * imge_mask=np.ones_like(image_source)*255 * imge_mask=imge_mask.astype(np.uint8) * imge_mask=imge_mask.flatten() ??? .ravel() * .asarray * np.logical_and( 1, 2) * indexes=[index for index in range(len(array1)) if array1[index] == True] * cv2.bitwise_not(yyy) * "olive" editor remove silence ![](https://lh5.googleusercontent.com/nILOXEoEKiANosdHjTOC05i7h8b-84246iAmayzrsrwyQtrN_ZG776o1GnXEFO0E0yH9lMQqIokQWJJgFxAvIzsUdQG6vzewTBzTMKkc1A4J4Lq94r_tVjMgcij_2Nj3DQ=w1280) Questions: How to train model to add new classes? How to add a new class to an existing classifier in deep learning? Adding new Class to One Shot Learning trained model Is it possible to train a neural network as new classes are given? Merging all several models that detection system for all these tasks. Answer 1: There are several ways to add new classes to the trained model, which require just training for the new classes. * Incremental training ([GitHub](https://github.com/khurramjaved96/incremental-learning)) * continuously learn a stream of data ([GitHub](https://github.com/creme-ml/creme)) * online machine learning ([GitHub](https://github.com/GMvandeVen/continual-learning)) * Transfer Learning Twice * Continual learning approaches (Regularization, Expansion, Rehearsal) ([GitHub](https://github.com/facebookresearch/Adversarial-Continual-Learning)) Answer 2: Online learning is a term used to refer to a model which takes a continual or sequential stream of input data while training, in contrast to offline learning (also called batch learning), where the model is pre-trained on a static predefined dataset. Continual learning (also called incremental, continuous, lifelong learning) refers to a branch of ML working in an online learning context where models are designed to learn new tasks while maintaining performance on historic tasks. It can be applied to multiple problem paradigms (including Class- incremental learning, where each new task presents new class labels for an ever expanding super-classification problem). Do I need to train my whole model again on all four classes or is there any way I can just train my model on new class? Naively re-training the model on the updated dataset is indeed a solution. Continual learning seeks to address contexts where access to historic data (i.e. the original 3 classes) is not possible, or when retraining on an increasingly large dataset is impractical (for efficiency, space, privacy etc concerns). Multiple such models using different underlying architectures have been proposed, but almost all examples exclusively deal with image classification problems. Answer 3: You could use transfer learning (i.e. use a pre-trained model, then change its last layer to accommodate the new classes, and re-train this slightly modified model, maybe with a lower learning rate) to achieve that, but transfer learning does not necessarily attempt to retain any of the previously acquired information (especially if you don't use very small learning rates, you keep on training and you do not freeze the weights of the convolutional layers), but only to speed up training or when your new dataset is not big enough, by starting from a model that has already learned general features that are supposedly similar to the features needed for your specific task. There is also the related domain adaptation problem. There are more suitable approaches to perform incremental class learning (which is what you are asking for!), which directly address the [catastrophic forgetting problem](https://ai.stackexchange.com/a/13293/2444). For instance, you can take a look at this paper [Class-incremental Learning via Deep Model Consolidation](https://arxiv.org/pdf/1903.07864.pdf), which proposes the Deep Model Consolidation (DMC) approach. There are other continual/incremental learning approaches, many of them are described [here](https://ai.stackexchange.com/a/24529/2444) or in more detail [here](https://reader.elsevier.com/reader/sd/pii/S0893608019300231). Answer 4: by using Continual learning approaches to trained without losing the original classes. It has 3 categories: Regularization Expansion Rehearsal Answer 5: if you access to the dataset then you can download it and add all you new classes when you have " 'N' COCO Classes + 'M' New classes " after that you can fine tune model based on new dataset. you do not need all of the dataset just same number of image for all class enough. [https://learnopencv.com/stanford-mrnet-challenge-classifying-knee- mris/](https://learnopencv.com/stanford-mrnet-challenge-classifying-knee- mris/) Before start your machine learning project ask these questions and preparation: What is your inference hardware? specify the use case. specify model interface. how would we monitor performance after deployment? how can we approximate post-deployment monitoring before deployment? build a model and iteratively improve it. How to deploy the model at the end? monitor performance after deployment. what is your metric? How do you split your data (training and validation)? ### Preparation ML Project Workflow * [What is your hardware ?](/topics-and-projects/hardware) * specify the use case * specify model interface * how would we monitor performance after deployment? * how can we approximate post-deployment monitoring before deployment? * build a model and iteratively improve it * deploy the model * monitor performance * what is your are metric? * How do you split your data? ### Before Training deep learning model * using large model to train because * it is faster to train with lower overfit and faster converge due to best training * it is easier and higher compress in the final stage * model compression and acceleration: reducing parameters without significantly decreasing the model performance * Data: How to have good data for training deep learning models; How to Build and Enhance A Good Data Set For Your Deep Learning Project: using same config and data for training and inference, removing redundant (delete data which you don't need), get more data, Handle missing data, using data augmentation techniques or GAN to generate more data, re-scale/balance data, Transform your data (Change data types), Feature selection based on data-set and use case * * The data you don't need: removing redundant samples * get more data * Invent more data * data augmentation * Re-scale data * balance datasets * Transform your data * Feature selection based on dataset and use case * ML-Augmented Video Object Tracking: By applying and evaluating multiple algorithmic models, enhanced ability to scale object tracking in high-density video compositions. ### Training deep learning model * automated hyper-parameters * Using Hyperparameter tuning / Hyperparameter optimization tools * AutoML * genetic algorithm * population based training * bayesian optimization * You need to set some parameters and config for training * * Diagnostics * Weight Initialization * Learning rate * Activation function * Network Topology * Batches and Epochs * Regularization * Optimization and Loss * Early Stopping ### Continuous delivery * evolve with latest detection models * more data (no labels) * semi-supervised learning: big self-supervised models are strong semi-supervised learners ### After Training deep learning model * Parameter pruning * model pruning: reducing redundant parameters which are not sensitive to the performance. * aim: remove all connections with absolute weights below a threshold * Quantization * compresses by reducing the number of bits used to represent the weights * quantization effectively constraints the number of different weights we can use inside our kernels * per-channel quantization for weights, which improves performance by model compression and latency reduction. * Low rank matrix factorization (LRMF) * there exists latent structures in the data, by uncovering which we can obtain a compressed representation of the data * LRMF factorizes the original matrix into lower rank matrices while preserving latent structures and addressing the issue of sparseness * Compact convolutional filters (Video/CNN) * designing special structural convolutional filters to save parameters * replace over parametric filters with compact filters to achieve overall speedup while maintaining comparable accuracy * Knowledge distillation * training a compact neural network with distilled knowledge of a large model * distillation (knowledge transfer) from an ensemble of big networks into a much smaller network which learns directly from the cumbersome model's outputs, that is lighter to deploy * Binarized Neural Networks (BNNs) * Apache TVM (incubating) is a compiler stack for deep learning systems * Neural Networks Compression Framework (NNCF) ### Deep learning model in production * security: controls access to model(s) through secure packaging and execution * Test * auto training * using parallel processing and library such as GStreamer # Technology Docker AWS Flask Django # My Keynote (February 2021) 1. introduction 2. Machine Learning/ Deep Learning Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed 3. supervised Machine Learning 1. Deep Convolutional Neural Networks (DCNN) Architecture 2. Visualizing and Understanding Convolutional Networks 3. Object Detection by Deep Learning 4. [Video Tracking](/topics-and-projects/video-tracking) 5. Style Transfer 4. semi-supervised Machine Learning/ Deep Reinforcement learning (DRL) 1. Google 2. [Deep Reinforcement learning (DRL)](/topics-and-projects/drl) 5. unsupervised Machine Learning 1. Auto Encoder 6. Generative Adversarial Networks (GANs) 7. Tools 8. Pre trained model 9. Effect of Augmented Datasets to Train DCNNs 10. Training for more classes 11. Optimization 12. [Hardware](/topics-and-projects/hardware) 13. Production setup 14. post development 15. business , Gartner, Hype Cycle for emerging technologies, 2025 ### Advanced and practical 1. Inside CNN 1. Deep Convolutional Neural Networks Architecture 2. Convolution 3. Convolution Layer 4. Conv/FC Filters 5. Activation Functions 6. Layer Activations 7. Pooling Layer 8. Dropout ; L2 pooling 9. Why 1. Max-pooling is useful 2. How to see inside each layer and find important features * Visualizing and Understanding Convolutional Networks * [https://tensorspace.org/](https://tensorspace.org/) * [https://www.youtube.com/watch?v=AgkfIQ4IGaM](https://www.youtube.com/watch?v=AgkfIQ4IGaM) 2. Hands on python for deep learning 3. Fundamental deep learning 4. Installation: TensorFlow, PyTorch 5. [Using PC+eGPU for training video tracking](/topics-and-projects/source-code/compile) Summary of the summit * AI Hardware Europe Summit (July 2020) * [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit) * Apache TVM And Deep Learning Compilation Conference (December 2020) * [RISC-V Summit (December 2020) ](/workshops-and-events/risc-v) [https://www.inspectar.com/demo](https://www.inspectar.com/demo) for rasp # Face * Effective and precise face detection based on color and depth data * [https://www.sciencedirect.com/science/article/pii/S221083271400009X](https://www.sciencedirect.com/science/article/pii/S221083271400009X) * containing or not containing a face * Eigenface, Fisherface, waveletface, PCA (Principal Component Analysis), LDA (Linear Dis-criminant Analysis), Haar wavelet transform, and so on. * Viola–Jones detector * illumination changes and occlusion * depthinformation is used to filter the regions of the image where a candidate face regionis found by the Viola–Jones (VJ) detector * \- the first filtering rule is defined on the color of the region; since some false positiveshave colors not compatible with the face (e.g. shadows on jeans) a skin detector isapplied to remove the candidate face regions that do not contain skin pixels; * \- the second filtering rule is defined on the size of the face: using the depth mapit is quite easy to calculate the size of the candidate face region, which is use-ful to discard smallest and largest faces from the final result set; * \- the third filtering rule is defined on the depth map to discard flat objects (e.g.candidate faces found in a wall) or uneven objects (e.g. candidate face foundin the leaves of a tree). Combining color and depth data the candidate faceregion can be extracted from the background and measures of depth and reg-ularity are used for filtering out false positives. * The size criteria simply remove the candidate faces not included in a fixed rangesize ([12.5,30] cm). The size of a candidate face region is extracted from the depthmap according to the following approach. * image below * Gaussian mixture 3D morphable face model * [https://www.sciencedirect.com/science/article/pii/S0031320317303527](https://www.sciencedirect.com/science/article/pii/S0031320317303527) * * * Face Synthesis for Eyeglass-Robust Face Recognition * [https://paperswithcode.com/paper/face-synthesis-for-eyeglass-robust-face](https://paperswithcode.com/paper/face-synthesis-for-eyeglass-robust-face) * GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data * [https://paperswithcode.com/paper/genegan-learning-object-transfiguration-and](https://paperswithcode.com/paper/genegan-learning-object-transfiguration-and) * FacePoseNet: Making a Case for Landmark-Free Face Alignment * [https://paperswithcode.com/paper/faceposenet-making-a-case-for-landmark-free](https://paperswithcode.com/paper/faceposenet-making-a-case-for-landmark-free) * Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision * [https://paperswithcode.com/paper/learning-to-regress-3d-face-shape-and](https://paperswithcode.com/paper/learning-to-regress-3d-face-shape-and) * Unsupervised Eyeglasses Removal in the Wild * [https://paperswithcode.com/paper/unsupervised-eyeglasses-removal-in-the-wild](https://paperswithcode.com/paper/unsupervised-eyeglasses-removal-in-the-wild) * How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks) * [https://arxiv.org/pdf/1703.07332v3.pdf](https://arxiv.org/pdf/1703.07332v3.pdf) * (a) we construct, for the first time, a very strong baseline by combining a state-of-the-art architecture for landmark localization with a state-of-the-art residual block, train it on a very large yet synthetically expanded 2D facial landmark dataset and fi- nally evaluate it on all other 2D facial landmark datasets. * (b) We create a guided by 2D landmarks network which con- verts 2D landmark annotations to 3D and unifies all exist- ing datasets, leading to the creation of LS3D-W, the largest and most challenging 3D facial landmark dataset to date (~230,000 images). * (c) Following that, we train a neural network for 3D face alignment and evaluate it on the newly introduced LS3D-W. * (d) We further look into the effect of all “traditional” factors affecting face alignment performance like large pose, initialization and resolution, and introduce a “new” one, namely the size of the network. * (e) We show that both 2D and 3D face alignment networks achieve per- formance of remarkable accuracy which is probably close to saturating the datasets used. * Training and testing code as well as the dataset can be downloaded from https: //[www.adrianbulat.com/face-alignment/](http://www.adrianbulat.com/face-alignment/) ![](https://lh6.googleusercontent.com/d8ABZ3w_DsDnuxD_X_PaSGPK9sxYEZhuyrYuZLCcmgFLMmTmheY4FDHRb3Cbhg- lYHPf4AdNHufhU04dxPdG3_pjwCOx9l7BZM9gLwwest05tq8ELg9sNocjKkjnMe6h=w1280) 19.Sep.2021 [Medium](https://medium.com/p/626019137fa9/edit) [https://fi.co/madlibs](https://fi.co/madlibs) [https://orcid.org/0000-0001-8382-1389](https://orcid.org/0000-0001-8382-1389) Dreyer's English (learn write English) #book story Greek Mythology Explained: A Deeper Look at Classical Greek Lore and Myth **Papers:** CALTag: High Precision Fiducial Markers for Camera Diatom Autofocusing in Brightfield Microscopy: a Comparative Study :implementation variation of the laplacian Analysis of focus measure operators in shape-from-focus: why laplacian? Blure detection? Iqaf? Optical flow modeling and computation: A survey Toward general type 2 fuzzy logic systems based on zSlices \-------------------------------------------------------------------- Lost in space The OA Film:[ https://en.wikipedia.org/wiki/Shark_Tank](https://en.wikipedia.org/wiki/Shark_Tank) Movie Serial billons monk serial movies Python async Highly decoupled microservice Edex RIS-V , Self-car RISC-V Magazine Road map Game: over/under [https://www.sporcle.com/games/Hejman/underwhelmed](https://www.sporcle.com/games/Hejman/underwhelmed) \-------------------------------------------------------------------- \-------------------------------------------------------------------- GDPR in IoT The EU General Data Protection Regulation (GDPR) and Face Images in IoT The GDPR (General Data Protection Regulation), taking effect in May 2018, introduces strict requirements for personal data protection and the privacy rights of individuals. The EU regulations will set a new global standard for privacy rights and change the way organizations worldwide store and process personal data. The GDPR brings the importance of preserving the privacy of personal information to the forefront, yet the importance of face images within this context is often overlooked. The purpose of this paper is to introduce a solution that helps companies protect face images in IoT devices which record or process image by camera, to strengthen compliance with the GDPR. Our Face is our Identity Our face is the most fundamental and highly visible element of our identity. People recognize us when they see our face or a photo of our face. Recent years have seen exponential increase in the use, storage and dissemination of face images in both private and public sectors - in social networks, corporate databases, IoT, smart-city deployments, digital media, government applications, and nearly every organization’s databases. \--------------------- $(aws-okta env stage) aws s3 cp s3://dataset/archive.tar.gz /Users/a.zip aws s3 ls images | tail -n 100 aws s3 cp staging-images/test.jpg /Users/test.jpg \--------------------- screen -rD k get pods Docker RUN chmod +x /tmp/run.sh Can run docker in terminal and run code line by line docker run -it --rm debian:stable-slim bash apt-get update apt-get installl -y \-------------------------------- brew install awscli aws-okta kubectx kubernetes-cli tfenv touch ~/.aws/config \-------------------------------------------------------------------- docker image rm TETSTDFSAFDSADF docker image ls docker system prune docker run -p 5000:5000 nameDocker:latest docker build . -t nameDocker:latest docker container stop number-docker-name docker container ls * docker pull quay.io/test:v0.0.1 * docker run --rm -p 5000:5000 -it quay.io/test:v0.0.1 * curl --header "Content-Type: application/json" \--request POST --data '[{"fixed":7.4, "a":0, "b":0.56, "c":9.4}]'[ http://127.0.0.1:5000/predict](https://meet.google.com/linkredirect?authuser=0&dest=http%3A%2F%2F127.0.0.1%3A5000%2Fpredict) * docker run --rm -v /home/.aws/credentials:/root/.aws/credentials -it quay.io/test /bin/sh aws s3 ls --profile=test \-------------------------------- Cloud software engineer and consultant focusing on building highly available, scalable and fully automated infrastructure environments on top of Amazon Web Services and Microsoft Azure clouds. My goal is always to make my customers happy in the cloud. \---------------- Search google for 3d = tiger - iPhone show AR/VR \--------------- brew install youtube-dl \---------------------------- List: Collection bucket : 1 for week 2 for month 3 for future \-------------------------------------------------------------------- **• Per frame operation** – Detection – Classification – Segmentation – Feature extraction – Recognition **• Across frames ** – Tracking – Counting **• High level** – Intention – Relations – Analyzing ============================= Deep compression Pruning deep learning Hash table neural network Dl compression Deep compression =================================== Mini PCI-e slot * What have I learned so far: * Problem-based learning * real life scenarios * index card (answer , idea) * Think-Pair-Share * Leverage flip charts * Summarizing \-------------------------------------------------------------------- Self \\\ Advancing Self-Supervised and Semi-Supervised Learning with SimCLR \cite{Chen2020} %https://github.com/google-research/simclr first pretraining on a large unlabeled dataset and then fine-tuning on a smaller labeled dataset pretraining on large unlabeled image datasets, as demonstrated by Exemplar- CNN, Instance Discrimination, CPC, AMDIM, CMC, MoCo and others. “A Simple Framework for Contrastive Learning of Visual Representations”, 85.8\% top-5 accuracy using 1\% of labeled images on the ImageNet dataset contrastive learning algorithms linear evaluation protocol (Zhang et al., 2016; Oord et al.,2018; Bachman et al., 2019; Kolesnikov et al., 2019) unsupervised learning benefits more from bigger models than its supervised counterpart. \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- Some of optimization algorithms ======================== Swarm Algorithm =============== 1\. Ant Colony Optimization (ACO) was inspired by the research on the behavior of ant colonies 2\. Firefly Algorithm based on insects called fireflies 3\. Marriage in Honey Bees Optimization Algorithm (MBO algorithm) is inspired by the process of reproduction of Honey Bee 4\. Artificial Bee Colony Algorithm (ABC) is based on the recollection of the Honey Bees 5\. Wasp Swarm Algorithm was inspired on the Parasitic wasps 6\. Bee Collecting Pollen Algorithm (BCPA) 7\. Termite Algorithm 8\. Mosquito swarms Algorithm (MSA) 9\. zooplankton swarms Algorithm (ZSA) 10\. Bumblebees Swarms Algorithm (BSA) 11\. Fish Swarm Algorithm (FSA) 12\. Bacteria Foraging Algorithm (BFA) 13\. Particle Swarm Optimization (PSO) 14\. Cuckoo Search 15\. Bat Algorithm (BA) 16\. Accelerated PSO 17\. Bee System 18\. Beehive Algorithm 19\. Cat Swarm 20\. Consultant-guided search 21\. Eagle Strategy 22\. Fast Backterial swarming algorithm 23\. Good lattice swarm optimization 24\. Glowworm swarm optimization 25\. Hierarchical swarm model 26\. Krill Herd 27\. Monkey Search 28\. Virtual ant algorithm 29\. Virtual bees 30\. Weighted Swarm Algorithm 31\. Wisdom of Artificial Crowd algorithm 32\. Prey-predator algorithm 33\. Memetic algorithm 34\. Lion Optimization Algorithm 35\. Chicken Swarm Optimization 36\. Ant Lion Optimizer 37\. Compact Particle Swarm Optimization 38\. Fruit Fly Optimization Algorithm 39\. marine propeller optimization algorithm 40\. The Whale Optimization Algorithm 41\. virus colony search algorithm 42\. Slime mould optimization algorithm Ecology Inspired Algorithm ========================== 1\. Biogeography-based Optimization 2\. Invasive Weed Optimization 3\. Symbiosis-Inspired Optimization - PS2O 4\. Atmosphere Clouds Model 5\. Brain Storm Optimization 6\. Dolphin echolocation 7\. Japanese Tree Frog Calling algorithm 8\. Eco-inspired evolutionary algorithm 9\. Egyptian Vulture 10\. Fish School search 11\. Flower Pollination algorithm 12\. Gene Expression 13\. Great Salmon Run 14\. Group Search Optimizer 15\. Human Inspired Algorithm 16\. Roach Infestation algorithm 17\. Queen-bee algorithm 18\. Shuffled frog leaping algorithm 19\. Forest Optimization Algorithm 20\. coral reefs optimization algorithm 21\. cultural evolution algorithm 22\. Grey Wolf Optimizer 23\. probabilistic pso 24\. omicron aco algorithm 25\. shark smell optimization 26\. social spider algorithm 27\. sosial insects behavior algorithm 28\. sperm whale algorithm Evolutionary Optimization ========================= 1\. Genetic Algorithm 2\. Genetic Programming 3\. Evolutionary Strategies 4\. Differential Evolution 5\. Paddy Field Algorithm 6\. Queen-bee Evolution 7\. Quantum Inspired Social Evolution Physic and Chemistry inspired algorithm ======================================= 1\. Big bang-Big Crunch 2\. Block hole algorithm 3\. Central force optimization 4\. Charged System search 5\. Electro-magnetism optimization 6\. Galaxy based search algorithm 7\. Gravitational search 8\. Harmony search algorithm 9\. Intelligent water drop algorithm 10\. River formation algorithm 11\. Self-propelled dynamics 12\. Simulated Annealing 13\. Stachastic diffusion search 14\. Spiral optimization 15\. Water Cycle algorithm 16\. Artificial Physics optimization 17\. Binary Gravitational search algorithm 18\. Continous quantum ant colony optimization 19\. Extended artificial physics optimization 20\. Extended Central force optimization 21\. Electromagnetism-like heuristic 22\. Gravitational Interaction optimization 23\. Hysteristetic Optimization algorithm 24\. Hybrid quantum-inspired GA 25\. Immune gravitational inspired algorithm 26\. Improved quantum evolutinary algorithm 27\. Linear programming 28\. Quantum-inspired bacterial swarming 29\. Quantum-inspired evolutionary algorithm 30\. Quantum-inspired genetic algorithm 31\. Quantum-behaved PSO 32\. Unified big bang-chaotic big crunch 33\. Vector model of artificial physics 34\. Versatile quantum-inspired evolutionary algorithm 35\. Space Gravitational Algorithm 36\. Ion Motion Algorithm 37\. Light Ray Optimization Algorithm 38\. Ray Optimization 39\. Photosynthetic Algorithms 40\. floorplanning algorithm 41\. Gases Brownian Motion Optimization 42\. gradient-type optimization 43\. mean-variance optimization 44\. Mine blast algorithm 45\. moth flame optimization 46\. multi battalion search algorithm 47\. music inspired optimization 48\. no free lunch theorems algorithm 49\. Optics inspired optimization 50\. runner-root algorithm 51\. sine cosine algorithm 52\. pitch tracking algorithm 53\. Stochastic Fractal Search algorithm 54\. stroke volume optimization 55\. Stud krill herd algorithm 56\. The Great Deluge Algorithm 57\. Water Evaporation Optimization 58\. water wave optimization algorithm 59\. Island model algorithm 60\. 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Let's partner up to take your project to the next level! pip install mlc-ai-nightly -f https://mlc.ai/wheels https://mlc.ai/ https://mlc.ai/summer22/ Day 1: Introduction to Unity: TVMScript Introduction to Unity: Relax and PyTorch TVM BYOC in Practice Get Started with TVM on Adreno GPU Introduction to Unity: Metaschedule How to Bring microTVM to a custom IDE Day 2: Community Keynote PyTorch 2.0: the journey to bringing compiler technologies to the core of PyTorch Support QNN Dialect for TVM with MediaTek Neuron and Devise the Scheduler for Acceleration On-Device Training Under 256KB Memory AMD Tutorial TVM at TI: Accelerating inference using the C7x/MMA Adreno GPU: 4x speed-up and upstreaming to TVM mainline Transfer-Tuning: Reusing Auto-Schedules for Efficient Tensor Program Code Generation Improvement in the TVM OpenCL codegen to autogenerate optimal convolution kernels for Adreno GPUs TVM Unity: Pass Infrastructure and BYOC Renesas Hardware accelerators with Apache TVM Introduction on 4th Gen Intel Xeon processor and BF16 support with TVM Hidet: Task Mapping Programming Paradigm for Deep Learning Tensor Programs Towards Building a Responsible Data Economy Optimizing SYCL Device Kernels with AKG Adreno GPU Performance Enhancements using TVM Improvements to CMSIS-NN integration in TVM UMA: Universal Modular Accelerator Interface Day 3: TVM Unity for Dynamic Models Empower Tensorflow serving with backend TVM Enabling Conditional Computing on Hexagon target Decoupled Model Schedule for Large Deep Learning Model Training Using TVM to bring Bayesian neural networks to embedded hardware Efficient Support of TVM Scan OP on RISC-V Vector Extension Improvements to Ethos-U55 support in TVM including CI on Alif Semiconductor boards Compiling Dynamic Shapes TVM Packaging in 2023: delivering TVM to end users Cross-Platform Training Using Automatic Differentiation on Relax IR AutoTVM: Reducing tuning space by cross axis filtering SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning Analytical Tensorization and Fusion for Compute-intensive Operators CUTLASS 3.0: Next Generation Composable and Reusable GPU Linear Algebra Library Enabling Data Movement and Computation Pipelining in Deep Learning Compiler Automating DL Compiler Bug Finding with NNSmith TVM at NIO TVM at Tencent Integrating the Andes RISC-V Processors into TVM Alpa: A Compiler for Distributed Deep Learning ACRoBat: Compiler and Runtime Techniques for Efficient Auto-Batching of Dynamic Deep Learning Computations Channel Folding: a Transform Pass for Optimizing Mobilenets ========================================================================Day 1: ************************ Introduction to Unity: TVMScript [https://github.com/cyx-6/TVM- Demo/blob/main/tvmscript.ipynb](https://github.com/cyx-6/TVM- Demo/blob/main/tvmscript.ipynb) Gan NN show us some hidden patter in history we can not see before. “I always have a slip of paper at hand, on which I note down the ideas of certain pages. On the backside I write down the bibliographic details. After finishing the book I go through my notes and think how these notes might be relevant for already written notes in the slip-box. It means that I always read with an eye towards possible connections in the slip-box.” (Luhmann et al., 1987, 150) Deep representation learning Model evaluation. Camera cheaper lidar Point cloud because of we need 3d Capturing reality 1\. 𝐀𝐝𝐝/𝐂𝐨𝐦𝐦𝐢𝐭 𝐀𝐥𝐥 Standard way: git add . git commit -m "Message" Another way: git commit -a -m "Message" 𝟐\. 𝐀𝐥𝐢𝐚𝐬𝐞𝐬 With aliases, you can write your own Git commands that do anything you want. Eg: git config --global alias.ac '!git add -A && git commit -m' (alias called ac, git add -A && git commit -m will do the full add and commit) 𝟑\. 𝐑𝐞𝐯𝐞𝐫𝐭 The revert command simply allows us to undo any commit on the current branch. Eg: git revert 486bdb2 Another way: git revert HEAD (for recent commits) 𝟒\. 𝐑𝐞𝐟𝐥𝐨𝐠 This command lets you easily see the recent commits, pulls, resets, pushes, etc on your local machine. Eg: git reflog 𝟓\. 𝐏𝐫𝐞𝐭𝐭𝐲 𝐋𝐨𝐠𝐬 Gives you the ability to print out a pretty log of your commits/branches. Eg: git log --graph --decorate --oneline 𝟔\. 𝐒𝐞𝐚𝐫𝐜𝐡𝐢𝐧𝐠 𝐋𝐨𝐠𝐬 One can also use the log command to search for specific changes in the code. Eg: git log -S "A promise in JavaScript is very similar" 𝟕\. 𝐒𝐭𝐚𝐬𝐡 This command will stash (store them locally) all your code changes but does not actually commit them. Eg: git stash 𝟖\. 𝐑𝐞𝐦𝐨𝐯𝐞 𝐃𝐞𝐚𝐝 𝐁𝐫𝐚𝐧𝐜𝐡𝐞𝐬 This command will delete all the tracking information for branches that are on your local machine that are not in the remote repository, but it does not delete your local branches. Eg: git remote update --prune 𝟗\. 𝐁𝐢𝐬𝐞𝐜𝐭 For finding which commits caused certain bugs Eg: git bisect start git bisect bad git bisect good 48c86d6 𝟏𝟎\. 𝐃𝐞𝐬𝐭𝐫𝐨𝐲 𝐋𝐨𝐜𝐚𝐥 𝐂𝐡𝐚𝐧𝐠𝐞𝐬 One can wipe out all changes on your local branch to exactly what is in the remote branch. Eg: git reset --hard origin/main Don’t trust your devices IoT. software and hardware are together for better business. Newsletter investing every 3 months 1\. Prototyping. New bie 2\. Patent. Website. ( list of investors) 3\. Pre seed. First founding 1M VC, inistution, anjel capital. 400 000 preseed. Quveribel. Equtible rund convertible non agreement Template. Convertabel lone 1\. Germ standar inistitude 2\. 4\. Equity. Venture builder. 20% 200 000 5\. 100 000 per year to become unocorn in less than 10 years 6\. Soniy corn 100k unicorn 1M 7\. 360 euro per years for database of investor 8\. Convertable loan: Pay interst rate 5% to 8% = 18 months later (2M found in 10M) convert on based . 9\. Invester Never act as co-founder = full time = 20% 10\. Project profit, 11\. Full time after foun rising Make a plan for your business; take your time to make calculations by creating a target audience. Your target audience determines how you approach your business plan. By studying your target audience, you are making empirical research and collecting information from them Then, secure a good partnership if need be, and get enough capital to start up. * * What the people need * Why people need it * When the people need it * It's affordability * It's ease of use * It's maintenance and revenue Pair programming The SB7 Framework harnesses the influence of stories. The structure describes the 7 most common story elements: • Character • Problem • Guide • Plan • Calls to action • Failure • Success Dear [Hiring Manager’s Name], I am writing to apply for the position of computer vision for IoT and cloud at [Company Name]. I am a highly skilled and experienced computer vision engineer with a strong background in IoT and cloud technologies. I believe that my skills and experience make me an ideal candidate for this position and I am excited about the opportunity to contribute to the success of your organization. I have a solid understanding of computer vision algorithms and techniques, as well as experience in developing and implementing computer vision systems. I am proficient in programming languages such as Python, C++, and Java, and have experience with popular computer vision libraries such as OpenCV, TensorFlow, and PyTorch. In addition, I have a strong background in IoT and cloud technologies, including experience with IoT platforms such as AWS IoT, Azure IoT, and Google Cloud IoT. I am familiar with cloud computing technologies such as AWS, Azure, and Google Cloud, and have experience with deploying and managing computer vision systems on these platforms. I am also a team player and have excellent communication skills. I am able to work with cross-functional teams and can effectively communicate with both technical and non-technical stakeholders. I am also highly motivated, and I am always looking for ways to improve my skills and stay up-to-date with the latest technologies. I am excited about the opportunity to join [Company Name] and to contribute to the development of cutting-edge computer vision systems for IoT and cloud. I am confident that my skills and experience make me a strong candidate for this position, and I look forward to discussing how I can contribute to your organization. Thank you for considering my application. I look forward to hearing from you soon. Sincerely, Title: "Unlocking the Power of Computer Vision for IoT and Cloud" Introduction: * Hi, and welcome to our video on the topic of computer vision for IoT and cloud. In this video, we're going to explore how computer vision technology can be used to enhance IoT and cloud-based systems, and how it can be used to unlock new possibilities for businesses and consumers alike. Body: * First, let's talk about what computer vision is and how it works. Essentially, computer vision is the technology that enables computers to understand and interpret visual information from the world around us. This can include things like images, videos, and even 3D models. * One of the key ways that computer vision can be used to enhance IoT and cloud-based systems is by enabling devices to better understand and interact with their environment. For example, a computer vision-enabled camera could be used to monitor a manufacturing facility and identify when a machine is in need of maintenance or when an employee is working in an unsafe manner. * Another way that computer vision can be used to enhance IoT and cloud-based systems is by enabling devices to better understand and interact with people. For example, a computer vision-enabled security camera could be used to identify individuals and track their movements, or a computer vision-enabled smart home system could be used to detect when someone is in the room and adjust the lighting or temperature accordingly. * Additionally, computer vision can also be used to enhance cloud-based systems by providing more accurate data and insights. For example, a computer vision-enabled drone could be used to collect data on crops and provide farmers with more accurate information about the health and growth of their crops. Conclusion: * Overall, computer vision technology has the potential to unlock new possibilities for businesses and consumers alike, by enabling IoT and cloud-based systems to better understand and interact with their environment and people. We hope this video has provided you with a better understanding of the potential of computer vision for IoT and cloud, and we look forward to seeing the new possibilities that will be created as this technology continues to evolve. Excited to share my latest project using computer vision and IoT to improve efficiency in manufacturing. I used a combination of machine learning algorithms and cloud computing to analyze data from cameras and sensors in real-time, resulting in a 20% increase in production speed. This was a challenging project but I enjoyed every step of it! I am always looking for new opportunities to apply my skills in computer vision and IoT to help companies improve their operations. Let's connect if you are working on a similar project or if you are looking for a developer with these skills. #computervision #IoT #cloudcomputing #manufacturingefficiency #machinelearning #developer" In this post, you briefly mention your experience and skills in computer vision and IoT, and you provide a specific example of a project you worked on that demonstrates your abilities. You also make it clear that you are open to new opportunities, and you invite others to connect with you. Using relevant hashtags such as #computervision #IoT #cloudcomputing can help your post reach a wider audience Exciting news! I just published a paper on a new object detection algorithm that I developed. The algorithm uses a combination of deep learning and computer vision techniques to improve accuracy and speed of object detection in real-world scenarios. This is a big step forward in the field of computer vision and I am proud to have contributed to it. I will be presenting my research at the Computer Vision Conference next month, if you're attending be sure to stop by and say hi! #computervision #objectdetection #deeplearning #research" In this post, you briefly explain the main findings and contributions of your research, and you express your excitement and pride in your work. You also mention the upcoming conference where you will be presenting your research, inviting your friends and colleagues to meet you in person. Also using relevant hashtags such as #computervision #objectdetection #deeplearning can help reach a wider audience interested in the field. Features stores 1\. Car parts detection 2\. Resize keep aspects ration 3\. 3.1 Perform damage detection 4\. 3.2Semantic segregation 5\. Transfer to original coordinates 1 class imbalance 2 class definition Maybe Class in between 3 inconstant annotations Color augmentation 1\. RGB shift 2\. Random brithness and contrast 3\. Sharpen 4\. Hue saturation value Why manually data augmented Becasu control of data. Not too rotate or change something Photogrammetry model Neural radiance fields (NeRF) NeRF in the wild \ [GitHub - google-research/tuning_playbook: A playbook for systematically maximizing the performance of deep learning models.](https://github.com/google-research/tuning_playbook) Yocto and Machine Learning + OpenCV: [https://www.yoctoproject.org](https://www.yoctoproject.org) [https://www.hackster.io/monica/running-machine-learning-on-maaxboard-s-yocto- image-part-1-6a4796](https://www.hackster.io/monica/running-machine-learning- on-maaxboard-s-yocto-image-part-1-6a4796) Bard Google: [https://blog.google/technology/ai/bard-google-ai-search- updates/](https://blog.google/technology/ai/bard-google-ai-search-updates/) [https://mustang.ir/questions/question/راه-اندازی-پروژه-های-گیت-هاب-با-git- pages](https://mustang.ir/questions/question/%D8%B1%D8%A7%D9%87-%D8%A7%D9%86%D8%AF%D8%A7%D8%B2%DB%8C-%D9%BE%D8%B1%D9%88%DA%98%D9%87-%D9%87%D8%A7%DB%8C-%DA%AF%DB%8C%D8%AA-%D9%87%D8%A7%D8%A8-%D8%A8%D8%A7-git- pages) Book: Project Management for Non-Project Managers [https://fa.wikipedia.org/wiki/علی_اکبرپور](https://fa.wikipedia.org/wiki/%D8%B9%D9%84%DB%8C_%D8%A7%DA%A9%D8%A8%D8%B1%D9%BE%D9%88%D8%B1) [https://www.kingorama.com](https://www.kingorama.com) شاهنامه سه بعدی [Accelerate deep learning model development with cloud custom environments - AWS Online Tech Talks - YouTube](https://m.youtube.com/watch?v=2Wt2zlkMtKI&noapp=1) [بخش هایی از کتاب Refactoring (نسخه رایگان)](https://www.developit.ir/refactoring/free.html#f7) [Performance Notes Of PyTorch Support for M1 and M2 GPUs - Lightning AI](https://lightning.ai/pages/community/community-discussions/performance- notes-of-pytorch-support-for-m1-and-m2-gpus/) [Investopedia Academy](https://academy.investopedia.com/) [HandBrake updated with AV1 and VP9 10-bit video encoding](https://9to5mac.com/2022/12/29/handbrake-support-av1-and- vp9-10-bit/) [How to Start Your Sole Proprietorship in 6 Simple Steps](https://qonto.com/en/blog/creators/administrative/sole-proprietorship- in-germany) [Duolingo English Test](https://englishtest.duolingo.com/applicants) [چالش‌های تولید محتوا برای مارکت اروپا و آمریکا - YouTube](https://m.youtube.com/watch?v=wW0HZdubuWQ) [PyTorch for Deep Learning & Machine Learning – Full Course - YouTube](https://m.youtube.com/watch?v=V_xro1bcAuA#dialog) [Why passive investing makes less sense in the current environment | Financial Times](https://archive.ph/0VucZ) [GitHub - google-research/tuning_playbook: A playbook for systematically maximizing the performance of deep learning models.](https://github.com/google-research/tuning_playbook) [GitHub - mgechev/google-interview-preparation-problems: leetcode problems I solved to prepare for my Google interview.](https://github.com/mgechev/google- interview-preparation-problems) [Bayesian Neural Networks and Variational Dropout](https://dmittov.github.io/variational_dropout/#/maximum-likelihood) [One machine learning question every day - bnomial](https://today.bnomial.com/?ref=email) Git remote add orgine Asynchronous Operation Anomaly detection Use experience. Personalizes. Prediction manage society mobility Personalization Covenant Platform. OpenMMLab Wordtune - AI-powered Writing Companion tree -v -I '*.png' -I '*.jpg' \--charset utf-8 >list2.txt 3D object using triangular mesh need vertices point cloud underlying surface of some 3D object, faster Definition of Done User Story complete Code\Implementation complete Code\Implementation Peer Reviews) approved Unit tests complete (if required) Testing Notes complete (if required) User Story Acceptance criteria defined and verified Backend: Python, Redis, Postgres, Celery Frontend: React, Redux, TypeScript DevOps: Terraform, Kubernetes, GitHub, Docker, AWS Data: Python (Data Science), Kafka, Fastapi, MLFlow, AWS SageMaker ML: Selcond core, Kubeflow, … [Sharpness](https://en.wikipedia.org/wiki/Sharpness_%28visual%29) ,[Noise](https://en.wikipedia.org/wiki/Image_noise), [Dynamic range](https://en.wikipedia.org/wiki/Dynamic_range), [Tone reproduction](https://en.wikipedia.org/wiki/Tone_reproduction) , [Contrast](https://en.wikipedia.org/wiki/Contrast_%28vision%29), [Color](https://en.wikipedia.org/wiki/Color), [Distortion](https://en.wikipedia.org/wiki/Distortion_%28optics%29) , [DSLR lenses](https://en.wikipedia.org/wiki/Lenses_for_SLR_and_DSLR_cameras), [Vignetting](https://en.wikipedia.org/wiki/Vignetting), [Exposure](https://en.wikipedia.org/wiki/Exposure_%28photography%29), Lateral [chromatic aberration](https://en.wikipedia.org/wiki/Chromatic_aberration) (LCA), [Lens flare](https://en.wikipedia.org/wiki/Lens_flare), Color, [Artifacts](https://en.wikipedia.org/wiki/Compression_artifact) ۱\. جهت انتخاب کلمه مورد نظرتان، دو بار روی آن تپ کنید. ۲\. برای انتخاب کل یک پاراگراف، کافیست چهار با روی آن تپ کنید. ۳\. یک انگشت را در ابتدا و انگشت دیگر را در آخر یک محدود گذاشته و کمی نگه دارید. متن میان دو انگشت انتخاب خواهد شد. ۴\. روی ابتدای محدوده ای دلخواه دو بار تپ کرده و بلافاصله با درگ کردن (کشیدن) پین محدوده ی انتخاب شده را گسترش دهید. (انگشت خود را پس از دومین تپ جدا نکنید) ۵\. برای انتخاب کل پاراگراف، به جز استفاده از مورد ۲، می توانید با دو انگشت، یک بار روی آن تپ کنید. namely motion estimation, motion smoothing, and image warping. Motion estimation algorithms often use a similarity transform to handle camera translations, rotations, and zooming. The tricky part is getting these algorithms to lock onto the background motion, 0\. video frames captured during fast motion are often blurry. Their appearance can be improved either using deblurring techniques (Section 10.3) or stealing sharper pixels from other frames with less motion or better focus (Matsushita, Ofek, Ge et al. 2006). Exercise 8.3 has you implement and test some of these ideas. 1\. Background subtraction 2\. Motion estimation 3\. Motion smoothing 4\. Image warping. image warping can result in missing borders around the image, which must be cropped, filled using information from other frames, or hallucinated using inpainting techniques (Section 10.5.1). Vision stabilization There is much recent work on Multi-view 3D reconstruction is a central research topic in computer vision that is driven in many different directions There are many available methods that can handle the noisy image completion problem In the case of surveillance using a fixed camera, there is no desired motion. In the case of most robotic applications, horizontal and vertical motions are desired, but rotation is not. In some cases of ground vehicles where the terrain is known to have many incline changes, or with aerial vehicles undergoing complicated maneuvers where the vehicle’s body is meant to be in varying orientations, rotation might be desired as the robot is meant to be at an angle at times. In robotics applications, computational complexity is extremely important due to the need for real-time operation. Also, it is likely that the center of rotation will not lie in the center of the image frame because the camera is rarely mounted at the robot’s center of mass. This first assumption is made in many video stabilization algorithms, and is a convenient way to seed the correct features with higher trust values. It is not an unreasonable assumption to make. Depending on the application, there is often a large portion of frames where local motion does not occur. In some situations, such as monitoring of steady traffic, there is no guarantee that local motion will not occur. This situation has not been tested, nor has our algorithm been designed to handle it. The second assumption comes from a combination of common sense, and the experience of many computer vision researchers. It makes sense that an object in the scene which does not move will be recognized more easily and more often. Being recognized consistently and consecutively is considered stable. On the other hand, objects which have local motion are less likely to be recognized as often. They might move through shadows, change orientation, or even move completely out of the scene. These possibilities all lead to a less stable class of features. It is likely that, more often than not, there are more background features than foreground features. Moving objects generally cover a small portion of the screen, which usually yields fewer features. Although uncommon, we did not want to make the assumption that this would occur in every frame. Certain scenes will consist of a large portion of local motion, or an object will move very close to the camera, consuming a much larger portion of the scene than the background. As long as some background features are discovered in each frame, our stabilization algorithm should succeed. # image processing tips: * the image size and kernel size need to depended. the best way is to use the one variable to define the size of the image and kernel together. * the coordinate of the image start at top left of the image/display * in order to change it to the normal coordinate you can use * grid of points; two matrix to X , Y coordinate * subtract half of W, H from X, Y in order to have normal coordinate system for our image * now we have cartesian coordinate * * cartesian coordinate to polar coordinate * تبدیل فضای کارتزین به پولار در خیلی از برنامه های پردازش تصویر کارایی دارد. برای پیدا کردن ترشلد ها هم می توان استفاده کرد * in MATLAB we can use ":"for example MatrixA(:) which means all entity of the matrix no mater how many dimensions we have but if we want to implemented in Python we can use numpy.flatten(). * in the MATLAB the round is different from python. if you want same result you need implement the rand function by yourself. * imge_mask=np.ones_like(image_source)*255 * imge_mask=imge_mask.astype(np.uint8) * imge_mask=imge_mask.flatten() ??? .ravel() * .asarray * np.logical_and( 1, 2) * indexes=[index for index in range(len(array1)) if array1[index] == True] * cv2.bitwise_not(yyy) * "olive" editor remove silence ![](https://lh5.googleusercontent.com/nILOXEoEKiANosdHjTOC05i7h8b-84246iAmayzrsrwyQtrN_ZG776o1GnXEFO0E0yH9lMQqIokQWJJgFxAvIzsUdQG6vzewTBzTMKkc1A4J4Lq94r_tVjMgcij_2Nj3DQ=w1280) Questions: How to train model to add new classes? How to add a new class to an existing classifier in deep learning? Adding new Class to One Shot Learning trained model Is it possible to train a neural network as new classes are given? Merging all several models that detection system for all these tasks. Answer 1: There are several ways to add new classes to the trained model, which require just training for the new classes. * Incremental training ([GitHub](https://github.com/khurramjaved96/incremental-learning)) * continuously learn a stream of data ([GitHub](https://github.com/creme-ml/creme)) * online machine learning ([GitHub](https://github.com/GMvandeVen/continual-learning)) * Transfer Learning Twice * Continual learning approaches (Regularization, Expansion, Rehearsal) ([GitHub](https://github.com/facebookresearch/Adversarial-Continual-Learning)) Answer 2: Online learning is a term used to refer to a model which takes a continual or sequential stream of input data while training, in contrast to offline learning (also called batch learning), where the model is pre-trained on a static predefined dataset. Continual learning (also called incremental, continuous, lifelong learning) refers to a branch of ML working in an online learning context where models are designed to learn new tasks while maintaining performance on historic tasks. It can be applied to multiple problem paradigms (including Class- incremental learning, where each new task presents new class labels for an ever expanding super-classification problem). Do I need to train my whole model again on all four classes or is there any way I can just train my model on new class? Naively re-training the model on the updated dataset is indeed a solution. Continual learning seeks to address contexts where access to historic data (i.e. the original 3 classes) is not possible, or when retraining on an increasingly large dataset is impractical (for efficiency, space, privacy etc concerns). Multiple such models using different underlying architectures have been proposed, but almost all examples exclusively deal with image classification problems. Answer 3: You could use transfer learning (i.e. use a pre-trained model, then change its last layer to accommodate the new classes, and re-train this slightly modified model, maybe with a lower learning rate) to achieve that, but transfer learning does not necessarily attempt to retain any of the previously acquired information (especially if you don't use very small learning rates, you keep on training and you do not freeze the weights of the convolutional layers), but only to speed up training or when your new dataset is not big enough, by starting from a model that has already learned general features that are supposedly similar to the features needed for your specific task. There is also the related domain adaptation problem. There are more suitable approaches to perform incremental class learning (which is what you are asking for!), which directly address the [catastrophic forgetting problem](https://ai.stackexchange.com/a/13293/2444). For instance, you can take a look at this paper [Class-incremental Learning via Deep Model Consolidation](https://arxiv.org/pdf/1903.07864.pdf), which proposes the Deep Model Consolidation (DMC) approach. There are other continual/incremental learning approaches, many of them are described [here](https://ai.stackexchange.com/a/24529/2444) or in more detail [here](https://reader.elsevier.com/reader/sd/pii/S0893608019300231). Answer 4: by using Continual learning approaches to trained without losing the original classes. It has 3 categories: Regularization Expansion Rehearsal Answer 5: if you access to the dataset then you can download it and add all you new classes when you have " 'N' COCO Classes + 'M' New classes " after that you can fine tune model based on new dataset. you do not need all of the dataset just same number of image for all class enough. [https://learnopencv.com/stanford-mrnet-challenge-classifying-knee- mris/](https://learnopencv.com/stanford-mrnet-challenge-classifying-knee- mris/) Before start your machine learning project ask these questions and preparation: What is your inference hardware? specify the use case. specify model interface. how would we monitor performance after deployment? how can we approximate post-deployment monitoring before deployment? build a model and iteratively improve it. How to deploy the model at the end? monitor performance after deployment. what is your metric? How do you split your data (training and validation)? ### Preparation ML Project Workflow * [What is your hardware ?](/topics-and-projects/hardware) * specify the use case * specify model interface * how would we monitor performance after deployment? * how can we approximate post-deployment monitoring before deployment? * build a model and iteratively improve it * deploy the model * monitor performance * what is your are metric? * How do you split your data? ### Before Training deep learning model * using large model to train because * it is faster to train with lower overfit and faster converge due to best training * it is easier and higher compress in the final stage * model compression and acceleration: reducing parameters without significantly decreasing the model performance * Data: How to have good data for training deep learning models; How to Build and Enhance A Good Data Set For Your Deep Learning Project: using same config and data for training and inference, removing redundant (delete data which you don't need), get more data, Handle missing data, using data augmentation techniques or GAN to generate more data, re-scale/balance data, Transform your data (Change data types), Feature selection based on data-set and use case * * The data you don't need: removing redundant samples * get more data * Invent more data * data augmentation * Re-scale data * balance datasets * Transform your data * Feature selection based on dataset and use case * ML-Augmented Video Object Tracking: By applying and evaluating multiple algorithmic models, enhanced ability to scale object tracking in high-density video compositions. ### Training deep learning model * automated hyper-parameters * Using Hyperparameter tuning / Hyperparameter optimization tools * AutoML * genetic algorithm * population based training * bayesian optimization * You need to set some parameters and config for training * * Diagnostics * Weight Initialization * Learning rate * Activation function * Network Topology * Batches and Epochs * Regularization * Optimization and Loss * Early Stopping ### Continuous delivery * evolve with latest detection models * more data (no labels) * semi-supervised learning: big self-supervised models are strong semi-supervised learners ### After Training deep learning model * Parameter pruning * model pruning: reducing redundant parameters which are not sensitive to the performance. * aim: remove all connections with absolute weights below a threshold * Quantization * compresses by reducing the number of bits used to represent the weights * quantization effectively constraints the number of different weights we can use inside our kernels * per-channel quantization for weights, which improves performance by model compression and latency reduction. * Low rank matrix factorization (LRMF) * there exists latent structures in the data, by uncovering which we can obtain a compressed representation of the data * LRMF factorizes the original matrix into lower rank matrices while preserving latent structures and addressing the issue of sparseness * Compact convolutional filters (Video/CNN) * designing special structural convolutional filters to save parameters * replace over parametric filters with compact filters to achieve overall speedup while maintaining comparable accuracy * Knowledge distillation * training a compact neural network with distilled knowledge of a large model * distillation (knowledge transfer) from an ensemble of big networks into a much smaller network which learns directly from the cumbersome model's outputs, that is lighter to deploy * Binarized Neural Networks (BNNs) * Apache TVM (incubating) is a compiler stack for deep learning systems * Neural Networks Compression Framework (NNCF) ### Deep learning model in production * security: controls access to model(s) through secure packaging and execution * Test * auto training * using parallel processing and library such as GStreamer # Technology Docker AWS Flask Django # My Keynote (February 2021) 1. introduction 2. Machine Learning/ Deep Learning Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed 3. supervised Machine Learning 1. Deep Convolutional Neural Networks (DCNN) Architecture 2. Visualizing and Understanding Convolutional Networks 3. Object Detection by Deep Learning 4. [Video Tracking](/topics-and-projects/video-tracking) 5. Style Transfer 4. semi-supervised Machine Learning/ Deep Reinforcement learning (DRL) 1. Google 2. [Deep Reinforcement learning (DRL)](/topics-and-projects/drl) 5. unsupervised Machine Learning 1. Auto Encoder 6. Generative Adversarial Networks (GANs) 7. Tools 8. Pre trained model 9. Effect of Augmented Datasets to Train DCNNs 10. Training for more classes 11. Optimization 12. [Hardware](/topics-and-projects/hardware) 13. Production setup 14. post development 15. business , Gartner, Hype Cycle for emerging technologies, 2025 ### Advanced and practical 1. Inside CNN 1. Deep Convolutional Neural Networks Architecture 2. Convolution 3. Convolution Layer 4. Conv/FC Filters 5. Activation Functions 6. Layer Activations 7. Pooling Layer 8. Dropout ; L2 pooling 9. Why 1. Max-pooling is useful 2. How to see inside each layer and find important features * Visualizing and Understanding Convolutional Networks * [https://tensorspace.org/](https://tensorspace.org/) * [https://www.youtube.com/watch?v=AgkfIQ4IGaM](https://www.youtube.com/watch?v=AgkfIQ4IGaM) 2. Hands on python for deep learning 3. Fundamental deep learning 4. Installation: TensorFlow, PyTorch 5. [Using PC+eGPU for training video tracking](/topics-and-projects/source-code/compile) Summary of the summit * AI Hardware Europe Summit (July 2020) * [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit) * Apache TVM And Deep Learning Compilation Conference (December 2020) * [RISC-V Summit (December 2020) ](/workshops-and-events/risc-v) [https://www.inspectar.com/demo](https://www.inspectar.com/demo) for rasp # Face * Effective and precise face detection based on color and depth data * [https://www.sciencedirect.com/science/article/pii/S221083271400009X](https://www.sciencedirect.com/science/article/pii/S221083271400009X) * containing or not containing a face * Eigenface, Fisherface, waveletface, PCA (Principal Component Analysis), LDA (Linear Dis-criminant Analysis), Haar wavelet transform, and so on. * Viola–Jones detector * illumination changes and occlusion * depthinformation is used to filter the regions of the image where a candidate face regionis found by the Viola–Jones (VJ) detector * \- the first filtering rule is defined on the color of the region; since some false positiveshave colors not compatible with the face (e.g. shadows on jeans) a skin detector isapplied to remove the candidate face regions that do not contain skin pixels; * \- the second filtering rule is defined on the size of the face: using the depth mapit is quite easy to calculate the size of the candidate face region, which is use-ful to discard smallest and largest faces from the final result set; * \- the third filtering rule is defined on the depth map to discard flat objects (e.g.candidate faces found in a wall) or uneven objects (e.g. candidate face foundin the leaves of a tree). Combining color and depth data the candidate faceregion can be extracted from the background and measures of depth and reg-ularity are used for filtering out false positives. * The size criteria simply remove the candidate faces not included in a fixed rangesize ([12.5,30] cm). The size of a candidate face region is extracted from the depthmap according to the following approach. * image below * Gaussian mixture 3D morphable face model * [https://www.sciencedirect.com/science/article/pii/S0031320317303527](https://www.sciencedirect.com/science/article/pii/S0031320317303527) * * * Face Synthesis for Eyeglass-Robust Face Recognition * [https://paperswithcode.com/paper/face-synthesis-for-eyeglass-robust-face](https://paperswithcode.com/paper/face-synthesis-for-eyeglass-robust-face) * GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data * [https://paperswithcode.com/paper/genegan-learning-object-transfiguration-and](https://paperswithcode.com/paper/genegan-learning-object-transfiguration-and) * FacePoseNet: Making a Case for Landmark-Free Face Alignment * [https://paperswithcode.com/paper/faceposenet-making-a-case-for-landmark-free](https://paperswithcode.com/paper/faceposenet-making-a-case-for-landmark-free) * Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision * [https://paperswithcode.com/paper/learning-to-regress-3d-face-shape-and](https://paperswithcode.com/paper/learning-to-regress-3d-face-shape-and) * Unsupervised Eyeglasses Removal in the Wild * [https://paperswithcode.com/paper/unsupervised-eyeglasses-removal-in-the-wild](https://paperswithcode.com/paper/unsupervised-eyeglasses-removal-in-the-wild) * How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks) * [https://arxiv.org/pdf/1703.07332v3.pdf](https://arxiv.org/pdf/1703.07332v3.pdf) * (a) we construct, for the first time, a very strong baseline by combining a state-of-the-art architecture for landmark localization with a state-of-the-art residual block, train it on a very large yet synthetically expanded 2D facial landmark dataset and fi- nally evaluate it on all other 2D facial landmark datasets. * (b) We create a guided by 2D landmarks network which con- verts 2D landmark annotations to 3D and unifies all exist- ing datasets, leading to the creation of LS3D-W, the largest and most challenging 3D facial landmark dataset to date (~230,000 images). * (c) Following that, we train a neural network for 3D face alignment and evaluate it on the newly introduced LS3D-W. * (d) We further look into the effect of all “traditional” factors affecting face alignment performance like large pose, initialization and resolution, and introduce a “new” one, namely the size of the network. * (e) We show that both 2D and 3D face alignment networks achieve per- formance of remarkable accuracy which is probably close to saturating the datasets used. * Training and testing code as well as the dataset can be downloaded from https: //[www.adrianbulat.com/face-alignment/](http://www.adrianbulat.com/face-alignment/) ![](https://lh6.googleusercontent.com/d8ABZ3w_DsDnuxD_X_PaSGPK9sxYEZhuyrYuZLCcmgFLMmTmheY4FDHRb3Cbhg- lYHPf4AdNHufhU04dxPdG3_pjwCOx9l7BZM9gLwwest05tq8ELg9sNocjKkjnMe6h=w1280) 19.Sep.2021 [Medium](https://medium.com/p/626019137fa9/edit) [https://fi.co/madlibs](https://fi.co/madlibs) [https://orcid.org/0000-0001-8382-1389](https://orcid.org/0000-0001-8382-1389) Dreyer's English (learn write English) #book story Greek Mythology Explained: A Deeper Look at Classical Greek Lore and Myth **Papers:** CALTag: High Precision Fiducial Markers for Camera Diatom Autofocusing in Brightfield Microscopy: a Comparative Study :implementation variation of the laplacian Analysis of focus measure operators in shape-from-focus: why laplacian? Blure detection? Iqaf? Optical flow modeling and computation: A survey Toward general type 2 fuzzy logic systems based on zSlices \-------------------------------------------------------------------- Lost in space The OA Film:[ https://en.wikipedia.org/wiki/Shark_Tank](https://en.wikipedia.org/wiki/Shark_Tank) Movie Serial billons monk serial movies Python async Highly decoupled microservice Edex RIS-V , Self-car RISC-V Magazine Road map Game: over/under [https://www.sporcle.com/games/Hejman/underwhelmed](https://www.sporcle.com/games/Hejman/underwhelmed) \-------------------------------------------------------------------- \-------------------------------------------------------------------- GDPR in IoT The EU General Data Protection Regulation (GDPR) and Face Images in IoT The GDPR (General Data Protection Regulation), taking effect in May 2018, introduces strict requirements for personal data protection and the privacy rights of individuals. The EU regulations will set a new global standard for privacy rights and change the way organizations worldwide store and process personal data. The GDPR brings the importance of preserving the privacy of personal information to the forefront, yet the importance of face images within this context is often overlooked. The purpose of this paper is to introduce a solution that helps companies protect face images in IoT devices which record or process image by camera, to strengthen compliance with the GDPR. Our Face is our Identity Our face is the most fundamental and highly visible element of our identity. People recognize us when they see our face or a photo of our face. Recent years have seen exponential increase in the use, storage and dissemination of face images in both private and public sectors - in social networks, corporate databases, IoT, smart-city deployments, digital media, government applications, and nearly every organization’s databases. \--------------------- $(aws-okta env stage) aws s3 cp s3://dataset/archive.tar.gz /Users/a.zip aws s3 ls images | tail -n 100 aws s3 cp staging-images/test.jpg /Users/test.jpg \--------------------- screen -rD k get pods Docker RUN chmod +x /tmp/run.sh Can run docker in terminal and run code line by line docker run -it --rm debian:stable-slim bash apt-get update apt-get installl -y \-------------------------------- brew install awscli aws-okta kubectx kubernetes-cli tfenv touch ~/.aws/config \-------------------------------------------------------------------- docker image rm TETSTDFSAFDSADF docker image ls docker system prune docker run -p 5000:5000 nameDocker:latest docker build . -t nameDocker:latest docker container stop number-docker-name docker container ls * docker pull quay.io/test:v0.0.1 * docker run --rm -p 5000:5000 -it quay.io/test:v0.0.1 * curl --header "Content-Type: application/json" \--request POST --data '[{"fixed":7.4, "a":0, "b":0.56, "c":9.4}]'[ http://127.0.0.1:5000/predict](https://meet.google.com/linkredirect?authuser=0&dest=http%3A%2F%2F127.0.0.1%3A5000%2Fpredict) * docker run --rm -v /home/.aws/credentials:/root/.aws/credentials -it quay.io/test /bin/sh aws s3 ls --profile=test \-------------------------------- Cloud software engineer and consultant focusing on building highly available, scalable and fully automated infrastructure environments on top of Amazon Web Services and Microsoft Azure clouds. My goal is always to make my customers happy in the cloud. \---------------- Search google for 3d = tiger - iPhone show AR/VR \--------------- brew install youtube-dl \---------------------------- List: Collection bucket : 1 for week 2 for month 3 for future \-------------------------------------------------------------------- **• Per frame operation** – Detection – Classification – Segmentation – Feature extraction – Recognition **• Across frames ** – Tracking – Counting **• High level** – Intention – Relations – Analyzing ============================= Deep compression Pruning deep learning Hash table neural network Dl compression Deep compression =================================== Mini PCI-e slot * What have I learned so far: * Problem-based learning * real life scenarios * index card (answer , idea) * Think-Pair-Share * Leverage flip charts * Summarizing \-------------------------------------------------------------------- Self \\\ Advancing Self-Supervised and Semi-Supervised Learning with SimCLR \cite{Chen2020} %https://github.com/google-research/simclr first pretraining on a large unlabeled dataset and then fine-tuning on a smaller labeled dataset pretraining on large unlabeled image datasets, as demonstrated by Exemplar- CNN, Instance Discrimination, CPC, AMDIM, CMC, MoCo and others. “A Simple Framework for Contrastive Learning of Visual Representations”, 85.8\% top-5 accuracy using 1\% of labeled images on the ImageNet dataset contrastive learning algorithms linear evaluation protocol (Zhang et al., 2016; Oord et al.,2018; Bachman et al., 2019; Kolesnikov et al., 2019) unsupervised learning benefits more from bigger models than its supervised counterpart. \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- Some of optimization algorithms ======================== Swarm Algorithm =============== 1\. Ant Colony Optimization (ACO) was inspired by the research on the behavior of ant colonies 2\. Firefly Algorithm based on insects called fireflies 3\. Marriage in Honey Bees Optimization Algorithm (MBO algorithm) is inspired by the process of reproduction of Honey Bee 4\. Artificial Bee Colony Algorithm (ABC) is based on the recollection of the Honey Bees 5\. Wasp Swarm Algorithm was inspired on the Parasitic wasps 6\. Bee Collecting Pollen Algorithm (BCPA) 7\. Termite Algorithm 8\. Mosquito swarms Algorithm (MSA) 9\. zooplankton swarms Algorithm (ZSA) 10\. Bumblebees Swarms Algorithm (BSA) 11\. Fish Swarm Algorithm (FSA) 12\. Bacteria Foraging Algorithm (BFA) 13\. Particle Swarm Optimization (PSO) 14\. Cuckoo Search 15\. Bat Algorithm (BA) 16\. Accelerated PSO 17\. Bee System 18\. Beehive Algorithm 19\. Cat Swarm 20\. Consultant-guided search 21\. Eagle Strategy 22\. Fast Backterial swarming algorithm 23\. Good lattice swarm optimization 24\. Glowworm swarm optimization 25\. Hierarchical swarm model 26\. Krill Herd 27\. Monkey Search 28\. Virtual ant algorithm 29\. Virtual bees 30\. Weighted Swarm Algorithm 31\. Wisdom of Artificial Crowd algorithm 32\. Prey-predator algorithm 33\. Memetic algorithm 34\. Lion Optimization Algorithm 35\. Chicken Swarm Optimization 36\. Ant Lion Optimizer 37\. Compact Particle Swarm Optimization 38\. Fruit Fly Optimization Algorithm 39\. marine propeller optimization algorithm 40\. The Whale Optimization Algorithm 41\. virus colony search algorithm 42\. Slime mould optimization algorithm Ecology Inspired Algorithm ========================== 1\. Biogeography-based Optimization 2\. Invasive Weed Optimization 3\. Symbiosis-Inspired Optimization - PS2O 4\. Atmosphere Clouds Model 5\. Brain Storm Optimization 6\. Dolphin echolocation 7\. Japanese Tree Frog Calling algorithm 8\. Eco-inspired evolutionary algorithm 9\. Egyptian Vulture 10\. Fish School search 11\. Flower Pollination algorithm 12\. Gene Expression 13\. Great Salmon Run 14\. Group Search Optimizer 15\. Human Inspired Algorithm 16\. Roach Infestation algorithm 17\. Queen-bee algorithm 18\. Shuffled frog leaping algorithm 19\. Forest Optimization Algorithm 20\. coral reefs optimization algorithm 21\. cultural evolution algorithm 22\. Grey Wolf Optimizer 23\. probabilistic pso 24\. omicron aco algorithm 25\. shark smell optimization 26\. social spider algorithm 27\. sosial insects behavior algorithm 28\. sperm whale algorithm Evolutionary Optimization ========================= 1\. Genetic Algorithm 2\. Genetic Programming 3\. Evolutionary Strategies 4\. Differential Evolution 5\. Paddy Field Algorithm 6\. Queen-bee Evolution 7\. Quantum Inspired Social Evolution Physic and Chemistry inspired algorithm ======================================= 1\. Big bang-Big Crunch 2\. Block hole algorithm 3\. Central force optimization 4\. Charged System search 5\. Electro-magnetism optimization 6\. Galaxy based search algorithm 7\. Gravitational search 8\. Harmony search algorithm 9\. Intelligent water drop algorithm 10\. River formation algorithm 11\. Self-propelled dynamics 12\. Simulated Annealing 13\. Stachastic diffusion search 14\. Spiral optimization 15\. Water Cycle algorithm 16\. Artificial Physics optimization 17\. Binary Gravitational search algorithm 18\. Continous quantum ant colony optimization 19\. Extended artificial physics optimization 20\. Extended Central force optimization 21\. Electromagnetism-like heuristic 22\. Gravitational Interaction optimization 23\. Hysteristetic Optimization algorithm 24\. Hybrid quantum-inspired GA 25\. Immune gravitational inspired algorithm 26\. Improved quantum evolutinary algorithm 27\. Linear programming 28\. Quantum-inspired bacterial swarming 29\. Quantum-inspired evolutionary algorithm 30\. Quantum-inspired genetic algorithm 31\. Quantum-behaved PSO 32\. Unified big bang-chaotic big crunch 33\. Vector model of artificial physics 34\. Versatile quantum-inspired evolutionary algorithm 35\. Space Gravitational Algorithm 36\. Ion Motion Algorithm 37\. Light Ray Optimization Algorithm 38\. Ray Optimization 39\. Photosynthetic Algorithms 40\. floorplanning algorithm 41\. Gases Brownian Motion Optimization 42\. gradient-type optimization 43\. mean-variance optimization 44\. Mine blast algorithm 45\. moth flame optimization 46\. multi battalion search algorithm 47\. music inspired optimization 48\. no free lunch theorems algorithm 49\. Optics inspired optimization 50\. runner-root algorithm 51\. sine cosine algorithm 52\. pitch tracking algorithm 53\. Stochastic Fractal Search algorithm 54\. stroke volume optimization 55\. Stud krill herd algorithm 56\. The Great Deluge Algorithm 57\. Water Evaporation Optimization 58\. water wave optimization algorithm 59\. Island model algorithm 60\. 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Let's partner up to take your project to the next level! pip install mlc-ai-nightly -f https://mlc.ai/wheels https://mlc.ai/ https://mlc.ai/summer22/ Day 1: Introduction to Unity: TVMScript Introduction to Unity: Relax and PyTorch TVM BYOC in Practice Get Started with TVM on Adreno GPU Introduction to Unity: Metaschedule How to Bring microTVM to a custom IDE Day 2: Community Keynote PyTorch 2.0: the journey to bringing compiler technologies to the core of PyTorch Support QNN Dialect for TVM with MediaTek Neuron and Devise the Scheduler for Acceleration On-Device Training Under 256KB Memory AMD Tutorial TVM at TI: Accelerating inference using the C7x/MMA Adreno GPU: 4x speed-up and upstreaming to TVM mainline Transfer-Tuning: Reusing Auto-Schedules for Efficient Tensor Program Code Generation Improvement in the TVM OpenCL codegen to autogenerate optimal convolution kernels for Adreno GPUs TVM Unity: Pass Infrastructure and BYOC Renesas Hardware accelerators with Apache TVM Introduction on 4th Gen Intel Xeon processor and BF16 support with TVM Hidet: Task Mapping Programming Paradigm for Deep Learning Tensor Programs Towards Building a Responsible Data Economy Optimizing SYCL Device Kernels with AKG Adreno GPU Performance Enhancements using TVM Improvements to CMSIS-NN integration in TVM UMA: Universal Modular Accelerator Interface Day 3: TVM Unity for Dynamic Models Empower Tensorflow serving with backend TVM Enabling Conditional Computing on Hexagon target Decoupled Model Schedule for Large Deep Learning Model Training Using TVM to bring Bayesian neural networks to embedded hardware Efficient Support of TVM Scan OP on RISC-V Vector Extension Improvements to Ethos-U55 support in TVM including CI on Alif Semiconductor boards Compiling Dynamic Shapes TVM Packaging in 2023: delivering TVM to end users Cross-Platform Training Using Automatic Differentiation on Relax IR AutoTVM: Reducing tuning space by cross axis filtering SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning Analytical Tensorization and Fusion for Compute-intensive Operators CUTLASS 3.0: Next Generation Composable and Reusable GPU Linear Algebra Library Enabling Data Movement and Computation Pipelining in Deep Learning Compiler Automating DL Compiler Bug Finding with NNSmith TVM at NIO TVM at Tencent Integrating the Andes RISC-V Processors into TVM Alpa: A Compiler for Distributed Deep Learning ACRoBat: Compiler and Runtime Techniques for Efficient Auto-Batching of Dynamic Deep Learning Computations Channel Folding: a Transform Pass for Optimizing Mobilenets ========================================================================Day 1: ************************ Introduction to Unity: TVMScript [https://github.com/cyx-6/TVM- Demo/blob/main/tvmscript.ipynb](https://github.com/cyx-6/TVM- Demo/blob/main/tvmscript.ipynb) Gan NN show us some hidden patter in history we can not see before. “I always have a slip of paper at hand, on which I note down the ideas of certain pages. On the backside I write down the bibliographic details. After finishing the book I go through my notes and think how these notes might be relevant for already written notes in the slip-box. It means that I always read with an eye towards possible connections in the slip-box.” (Luhmann et al., 1987, 150) Deep representation learning Model evaluation. Camera cheaper lidar Point cloud because of we need 3d Capturing reality 1\. 𝐀𝐝𝐝/𝐂𝐨𝐦𝐦𝐢𝐭 𝐀𝐥𝐥 Standard way: git add . git commit -m "Message" Another way: git commit -a -m "Message" 𝟐\. 𝐀𝐥𝐢𝐚𝐬𝐞𝐬 With aliases, you can write your own Git commands that do anything you want. Eg: git config --global alias.ac '!git add -A && git commit -m' (alias called ac, git add -A && git commit -m will do the full add and commit) 𝟑\. 𝐑𝐞𝐯𝐞𝐫𝐭 The revert command simply allows us to undo any commit on the current branch. Eg: git revert 486bdb2 Another way: git revert HEAD (for recent commits) 𝟒\. 𝐑𝐞𝐟𝐥𝐨𝐠 This command lets you easily see the recent commits, pulls, resets, pushes, etc on your local machine. Eg: git reflog 𝟓\. 𝐏𝐫𝐞𝐭𝐭𝐲 𝐋𝐨𝐠𝐬 Gives you the ability to print out a pretty log of your commits/branches. Eg: git log --graph --decorate --oneline 𝟔\. 𝐒𝐞𝐚𝐫𝐜𝐡𝐢𝐧𝐠 𝐋𝐨𝐠𝐬 One can also use the log command to search for specific changes in the code. Eg: git log -S "A promise in JavaScript is very similar" 𝟕\. 𝐒𝐭𝐚𝐬𝐡 This command will stash (store them locally) all your code changes but does not actually commit them. Eg: git stash 𝟖\. 𝐑𝐞𝐦𝐨𝐯𝐞 𝐃𝐞𝐚𝐝 𝐁𝐫𝐚𝐧𝐜𝐡𝐞𝐬 This command will delete all the tracking information for branches that are on your local machine that are not in the remote repository, but it does not delete your local branches. Eg: git remote update --prune 𝟗\. 𝐁𝐢𝐬𝐞𝐜𝐭 For finding which commits caused certain bugs Eg: git bisect start git bisect bad git bisect good 48c86d6 𝟏𝟎\. 𝐃𝐞𝐬𝐭𝐫𝐨𝐲 𝐋𝐨𝐜𝐚𝐥 𝐂𝐡𝐚𝐧𝐠𝐞𝐬 One can wipe out all changes on your local branch to exactly what is in the remote branch. Eg: git reset --hard origin/main Don’t trust your devices IoT. software and hardware are together for better business. Newsletter investing every 3 months 1\. Prototyping. New bie 2\. Patent. Website. ( list of investors) 3\. Pre seed. First founding 1M VC, inistution, anjel capital. 400 000 preseed. Quveribel. Equtible rund convertible non agreement Template. Convertabel lone 1\. Germ standar inistitude 2\. 4\. Equity. Venture builder. 20% 200 000 5\. 100 000 per year to become unocorn in less than 10 years 6\. Soniy corn 100k unicorn 1M 7\. 360 euro per years for database of investor 8\. Convertable loan: Pay interst rate 5% to 8% = 18 months later (2M found in 10M) convert on based . 9\. Invester Never act as co-founder = full time = 20% 10\. Project profit, 11\. Full time after foun rising Make a plan for your business; take your time to make calculations by creating a target audience. Your target audience determines how you approach your business plan. By studying your target audience, you are making empirical research and collecting information from them Then, secure a good partnership if need be, and get enough capital to start up. * * What the people need * Why people need it * When the people need it * It's affordability * It's ease of use * It's maintenance and revenue Pair programming The SB7 Framework harnesses the influence of stories. The structure describes the 7 most common story elements: • Character • Problem • Guide • Plan • Calls to action • Failure • Success Dear [Hiring Manager’s Name], I am writing to apply for the position of computer vision for IoT and cloud at [Company Name]. I am a highly skilled and experienced computer vision engineer with a strong background in IoT and cloud technologies. I believe that my skills and experience make me an ideal candidate for this position and I am excited about the opportunity to contribute to the success of your organization. I have a solid understanding of computer vision algorithms and techniques, as well as experience in developing and implementing computer vision systems. I am proficient in programming languages such as Python, C++, and Java, and have experience with popular computer vision libraries such as OpenCV, TensorFlow, and PyTorch. In addition, I have a strong background in IoT and cloud technologies, including experience with IoT platforms such as AWS IoT, Azure IoT, and Google Cloud IoT. I am familiar with cloud computing technologies such as AWS, Azure, and Google Cloud, and have experience with deploying and managing computer vision systems on these platforms. I am also a team player and have excellent communication skills. I am able to work with cross-functional teams and can effectively communicate with both technical and non-technical stakeholders. I am also highly motivated, and I am always looking for ways to improve my skills and stay up-to-date with the latest technologies. I am excited about the opportunity to join [Company Name] and to contribute to the development of cutting-edge computer vision systems for IoT and cloud. I am confident that my skills and experience make me a strong candidate for this position, and I look forward to discussing how I can contribute to your organization. Thank you for considering my application. I look forward to hearing from you soon. Sincerely, Title: "Unlocking the Power of Computer Vision for IoT and Cloud" Introduction: * Hi, and welcome to our video on the topic of computer vision for IoT and cloud. In this video, we're going to explore how computer vision technology can be used to enhance IoT and cloud-based systems, and how it can be used to unlock new possibilities for businesses and consumers alike. Body: * First, let's talk about what computer vision is and how it works. Essentially, computer vision is the technology that enables computers to understand and interpret visual information from the world around us. This can include things like images, videos, and even 3D models. * One of the key ways that computer vision can be used to enhance IoT and cloud-based systems is by enabling devices to better understand and interact with their environment. For example, a computer vision-enabled camera could be used to monitor a manufacturing facility and identify when a machine is in need of maintenance or when an employee is working in an unsafe manner. * Another way that computer vision can be used to enhance IoT and cloud-based systems is by enabling devices to better understand and interact with people. For example, a computer vision-enabled security camera could be used to identify individuals and track their movements, or a computer vision-enabled smart home system could be used to detect when someone is in the room and adjust the lighting or temperature accordingly. * Additionally, computer vision can also be used to enhance cloud-based systems by providing more accurate data and insights. For example, a computer vision-enabled drone could be used to collect data on crops and provide farmers with more accurate information about the health and growth of their crops. Conclusion: * Overall, computer vision technology has the potential to unlock new possibilities for businesses and consumers alike, by enabling IoT and cloud-based systems to better understand and interact with their environment and people. We hope this video has provided you with a better understanding of the potential of computer vision for IoT and cloud, and we look forward to seeing the new possibilities that will be created as this technology continues to evolve. Excited to share my latest project using computer vision and IoT to improve efficiency in manufacturing. I used a combination of machine learning algorithms and cloud computing to analyze data from cameras and sensors in real-time, resulting in a 20% increase in production speed. This was a challenging project but I enjoyed every step of it! I am always looking for new opportunities to apply my skills in computer vision and IoT to help companies improve their operations. Let's connect if you are working on a similar project or if you are looking for a developer with these skills. #computervision #IoT #cloudcomputing #manufacturingefficiency #machinelearning #developer" In this post, you briefly mention your experience and skills in computer vision and IoT, and you provide a specific example of a project you worked on that demonstrates your abilities. You also make it clear that you are open to new opportunities, and you invite others to connect with you. Using relevant hashtags such as #computervision #IoT #cloudcomputing can help your post reach a wider audience Exciting news! I just published a paper on a new object detection algorithm that I developed. The algorithm uses a combination of deep learning and computer vision techniques to improve accuracy and speed of object detection in real-world scenarios. This is a big step forward in the field of computer vision and I am proud to have contributed to it. I will be presenting my research at the Computer Vision Conference next month, if you're attending be sure to stop by and say hi! #computervision #objectdetection #deeplearning #research" In this post, you briefly explain the main findings and contributions of your research, and you express your excitement and pride in your work. You also mention the upcoming conference where you will be presenting your research, inviting your friends and colleagues to meet you in person. Also using relevant hashtags such as #computervision #objectdetection #deeplearning can help reach a wider audience interested in the field. Features stores 1\. Car parts detection 2\. Resize keep aspects ration 3\. 3.1 Perform damage detection 4\. 3.2Semantic segregation 5\. Transfer to original coordinates 1 class imbalance 2 class definition Maybe Class in between 3 inconstant annotations Color augmentation 1\. RGB shift 2\. Random brithness and contrast 3\. Sharpen 4\. Hue saturation value Why manually data augmented Becasu control of data. Not too rotate or change something Photogrammetry model Neural radiance fields (NeRF) NeRF in the wild \ [GitHub - google-research/tuning_playbook: A playbook for systematically maximizing the performance of deep learning models.](https://github.com/google-research/tuning_playbook) Yocto and Machine Learning + OpenCV: [https://www.yoctoproject.org](https://www.yoctoproject.org) [https://www.hackster.io/monica/running-machine-learning-on-maaxboard-s-yocto- image-part-1-6a4796](https://www.hackster.io/monica/running-machine-learning- on-maaxboard-s-yocto-image-part-1-6a4796) Bard Google: [https://blog.google/technology/ai/bard-google-ai-search- updates/](https://blog.google/technology/ai/bard-google-ai-search-updates/) [https://mustang.ir/questions/question/راه-اندازی-پروژه-های-گیت-هاب-با-git- pages](https://mustang.ir/questions/question/%D8%B1%D8%A7%D9%87-%D8%A7%D9%86%D8%AF%D8%A7%D8%B2%DB%8C-%D9%BE%D8%B1%D9%88%DA%98%D9%87-%D9%87%D8%A7%DB%8C-%DA%AF%DB%8C%D8%AA-%D9%87%D8%A7%D8%A8-%D8%A8%D8%A7-git- pages) Book: Project Management for Non-Project Managers [https://fa.wikipedia.org/wiki/علی_اکبرپور](https://fa.wikipedia.org/wiki/%D8%B9%D9%84%DB%8C_%D8%A7%DA%A9%D8%A8%D8%B1%D9%BE%D9%88%D8%B1) [https://www.kingorama.com](https://www.kingorama.com) شاهنامه سه بعدی [Accelerate deep learning model development with cloud custom environments - AWS Online Tech Talks - YouTube](https://m.youtube.com/watch?v=2Wt2zlkMtKI&noapp=1) [بخش هایی از کتاب Refactoring (نسخه رایگان)](https://www.developit.ir/refactoring/free.html#f7) [Performance Notes Of PyTorch Support for M1 and M2 GPUs - Lightning AI](https://lightning.ai/pages/community/community-discussions/performance- notes-of-pytorch-support-for-m1-and-m2-gpus/) [Investopedia Academy](https://academy.investopedia.com/) [HandBrake updated with AV1 and VP9 10-bit video encoding](https://9to5mac.com/2022/12/29/handbrake-support-av1-and- vp9-10-bit/) [How to Start Your Sole Proprietorship in 6 Simple Steps](https://qonto.com/en/blog/creators/administrative/sole-proprietorship- in-germany) [Duolingo English Test](https://englishtest.duolingo.com/applicants) [چالش‌های تولید محتوا برای مارکت اروپا و آمریکا - YouTube](https://m.youtube.com/watch?v=wW0HZdubuWQ) [PyTorch for Deep Learning & Machine Learning – Full Course - YouTube](https://m.youtube.com/watch?v=V_xro1bcAuA#dialog) [Why passive investing makes less sense in the current environment | Financial Times](https://archive.ph/0VucZ) [GitHub - google-research/tuning_playbook: A playbook for systematically maximizing the performance of deep learning models.](https://github.com/google-research/tuning_playbook) [GitHub - mgechev/google-interview-preparation-problems: leetcode problems I solved to prepare for my Google interview.](https://github.com/mgechev/google- interview-preparation-problems) [Bayesian Neural Networks and Variational Dropout](https://dmittov.github.io/variational_dropout/#/maximum-likelihood) [One machine learning question every day - bnomial](https://today.bnomial.com/?ref=email) Git remote add orgine Asynchronous Operation Anomaly detection Use experience. Personalizes. Prediction manage society mobility Personalization Covenant Platform. OpenMMLab Wordtune - AI-powered Writing Companion tree -v -I '*.png' -I '*.jpg' \--charset utf-8 >list2.txt 3D object using triangular mesh need vertices point cloud underlying surface of some 3D object, faster Definition of Done User Story complete Code\Implementation complete Code\Implementation Peer Reviews) approved Unit tests complete (if required) Testing Notes complete (if required) User Story Acceptance criteria defined and verified Backend: Python, Redis, Postgres, Celery Frontend: React, Redux, TypeScript DevOps: Terraform, Kubernetes, GitHub, Docker, AWS Data: Python (Data Science), Kafka, Fastapi, MLFlow, AWS SageMaker ML: Selcond core, Kubeflow, … [Sharpness](https://en.wikipedia.org/wiki/Sharpness_%28visual%29) ,[Noise](https://en.wikipedia.org/wiki/Image_noise), [Dynamic range](https://en.wikipedia.org/wiki/Dynamic_range), [Tone reproduction](https://en.wikipedia.org/wiki/Tone_reproduction) , [Contrast](https://en.wikipedia.org/wiki/Contrast_%28vision%29), [Color](https://en.wikipedia.org/wiki/Color), [Distortion](https://en.wikipedia.org/wiki/Distortion_%28optics%29) , [DSLR lenses](https://en.wikipedia.org/wiki/Lenses_for_SLR_and_DSLR_cameras), [Vignetting](https://en.wikipedia.org/wiki/Vignetting), [Exposure](https://en.wikipedia.org/wiki/Exposure_%28photography%29), Lateral [chromatic aberration](https://en.wikipedia.org/wiki/Chromatic_aberration) (LCA), [Lens flare](https://en.wikipedia.org/wiki/Lens_flare), Color, [Artifacts](https://en.wikipedia.org/wiki/Compression_artifact) ۱\. جهت انتخاب کلمه مورد نظرتان، دو بار روی آن تپ کنید. ۲\. برای انتخاب کل یک پاراگراف، کافیست چهار با روی آن تپ کنید. ۳\. یک انگشت را در ابتدا و انگشت دیگر را در آخر یک محدود گذاشته و کمی نگه دارید. متن میان دو انگشت انتخاب خواهد شد. ۴\. روی ابتدای محدوده ای دلخواه دو بار تپ کرده و بلافاصله با درگ کردن (کشیدن) پین محدوده ی انتخاب شده را گسترش دهید. (انگشت خود را پس از دومین تپ جدا نکنید) ۵\. برای انتخاب کل پاراگراف، به جز استفاده از مورد ۲، می توانید با دو انگشت، یک بار روی آن تپ کنید. namely motion estimation, motion smoothing, and image warping. Motion estimation algorithms often use a similarity transform to handle camera translations, rotations, and zooming. The tricky part is getting these algorithms to lock onto the background motion, 0\. video frames captured during fast motion are often blurry. Their appearance can be improved either using deblurring techniques (Section 10.3) or stealing sharper pixels from other frames with less motion or better focus (Matsushita, Ofek, Ge et al. 2006). Exercise 8.3 has you implement and test some of these ideas. 1\. Background subtraction 2\. Motion estimation 3\. Motion smoothing 4\. Image warping. image warping can result in missing borders around the image, which must be cropped, filled using information from other frames, or hallucinated using inpainting techniques (Section 10.5.1). Vision stabilization There is much recent work on Multi-view 3D reconstruction is a central research topic in computer vision that is driven in many different directions There are many available methods that can handle the noisy image completion problem In the case of surveillance using a fixed camera, there is no desired motion. In the case of most robotic applications, horizontal and vertical motions are desired, but rotation is not. In some cases of ground vehicles where the terrain is known to have many incline changes, or with aerial vehicles undergoing complicated maneuvers where the vehicle’s body is meant to be in varying orientations, rotation might be desired as the robot is meant to be at an angle at times. In robotics applications, computational complexity is extremely important due to the need for real-time operation. Also, it is likely that the center of rotation will not lie in the center of the image frame because the camera is rarely mounted at the robot’s center of mass. This first assumption is made in many video stabilization algorithms, and is a convenient way to seed the correct features with higher trust values. It is not an unreasonable assumption to make. Depending on the application, there is often a large portion of frames where local motion does not occur. In some situations, such as monitoring of steady traffic, there is no guarantee that local motion will not occur. This situation has not been tested, nor has our algorithm been designed to handle it. The second assumption comes from a combination of common sense, and the experience of many computer vision researchers. It makes sense that an object in the scene which does not move will be recognized more easily and more often. Being recognized consistently and consecutively is considered stable. On the other hand, objects which have local motion are less likely to be recognized as often. They might move through shadows, change orientation, or even move completely out of the scene. These possibilities all lead to a less stable class of features. It is likely that, more often than not, there are more background features than foreground features. Moving objects generally cover a small portion of the screen, which usually yields fewer features. Although uncommon, we did not want to make the assumption that this would occur in every frame. Certain scenes will consist of a large portion of local motion, or an object will move very close to the camera, consuming a much larger portion of the scene than the background. As long as some background features are discovered in each frame, our stabilization algorithm should succeed. # image processing tips: * the image size and kernel size need to depended. the best way is to use the one variable to define the size of the image and kernel together. * the coordinate of the image start at top left of the image/display * in order to change it to the normal coordinate you can use * grid of points; two matrix to X , Y coordinate * subtract half of W, H from X, Y in order to have normal coordinate system for our image * now we have cartesian coordinate * * cartesian coordinate to polar coordinate * تبدیل فضای کارتزین به پولار در خیلی از برنامه های پردازش تصویر کارایی دارد. برای پیدا کردن ترشلد ها هم می توان استفاده کرد * in MATLAB we can use ":"for example MatrixA(:) which means all entity of the matrix no mater how many dimensions we have but if we want to implemented in Python we can use numpy.flatten(). * in the MATLAB the round is different from python. if you want same result you need implement the rand function by yourself. * imge_mask=np.ones_like(image_source)*255 * imge_mask=imge_mask.astype(np.uint8) * imge_mask=imge_mask.flatten() ??? .ravel() * .asarray * np.logical_and( 1, 2) * indexes=[index for index in range(len(array1)) if array1[index] == True] * cv2.bitwise_not(yyy) * "olive" editor remove silence ![](https://lh5.googleusercontent.com/nILOXEoEKiANosdHjTOC05i7h8b-84246iAmayzrsrwyQtrN_ZG776o1GnXEFO0E0yH9lMQqIokQWJJgFxAvIzsUdQG6vzewTBzTMKkc1A4J4Lq94r_tVjMgcij_2Nj3DQ=w1280) Questions: How to train model to add new classes? How to add a new class to an existing classifier in deep learning? Adding new Class to One Shot Learning trained model Is it possible to train a neural network as new classes are given? Merging all several models that detection system for all these tasks. Answer 1: There are several ways to add new classes to the trained model, which require just training for the new classes. * Incremental training ([GitHub](https://github.com/khurramjaved96/incremental-learning)) * continuously learn a stream of data ([GitHub](https://github.com/creme-ml/creme)) * online machine learning ([GitHub](https://github.com/GMvandeVen/continual-learning)) * Transfer Learning Twice * Continual learning approaches (Regularization, Expansion, Rehearsal) ([GitHub](https://github.com/facebookresearch/Adversarial-Continual-Learning)) Answer 2: Online learning is a term used to refer to a model which takes a continual or sequential stream of input data while training, in contrast to offline learning (also called batch learning), where the model is pre-trained on a static predefined dataset. Continual learning (also called incremental, continuous, lifelong learning) refers to a branch of ML working in an online learning context where models are designed to learn new tasks while maintaining performance on historic tasks. It can be applied to multiple problem paradigms (including Class- incremental learning, where each new task presents new class labels for an ever expanding super-classification problem). Do I need to train my whole model again on all four classes or is there any way I can just train my model on new class? Naively re-training the model on the updated dataset is indeed a solution. Continual learning seeks to address contexts where access to historic data (i.e. the original 3 classes) is not possible, or when retraining on an increasingly large dataset is impractical (for efficiency, space, privacy etc concerns). Multiple such models using different underlying architectures have been proposed, but almost all examples exclusively deal with image classification problems. Answer 3: You could use transfer learning (i.e. use a pre-trained model, then change its last layer to accommodate the new classes, and re-train this slightly modified model, maybe with a lower learning rate) to achieve that, but transfer learning does not necessarily attempt to retain any of the previously acquired information (especially if you don't use very small learning rates, you keep on training and you do not freeze the weights of the convolutional layers), but only to speed up training or when your new dataset is not big enough, by starting from a model that has already learned general features that are supposedly similar to the features needed for your specific task. There is also the related domain adaptation problem. There are more suitable approaches to perform incremental class learning (which is what you are asking for!), which directly address the [catastrophic forgetting problem](https://ai.stackexchange.com/a/13293/2444). For instance, you can take a look at this paper [Class-incremental Learning via Deep Model Consolidation](https://arxiv.org/pdf/1903.07864.pdf), which proposes the Deep Model Consolidation (DMC) approach. There are other continual/incremental learning approaches, many of them are described [here](https://ai.stackexchange.com/a/24529/2444) or in more detail [here](https://reader.elsevier.com/reader/sd/pii/S0893608019300231). Answer 4: by using Continual learning approaches to trained without losing the original classes. It has 3 categories: Regularization Expansion Rehearsal Answer 5: if you access to the dataset then you can download it and add all you new classes when you have " 'N' COCO Classes + 'M' New classes " after that you can fine tune model based on new dataset. you do not need all of the dataset just same number of image for all class enough. [https://learnopencv.com/stanford-mrnet-challenge-classifying-knee- mris/](https://learnopencv.com/stanford-mrnet-challenge-classifying-knee- mris/) Before start your machine learning project ask these questions and preparation: What is your inference hardware? specify the use case. specify model interface. how would we monitor performance after deployment? how can we approximate post-deployment monitoring before deployment? build a model and iteratively improve it. How to deploy the model at the end? monitor performance after deployment. what is your metric? How do you split your data (training and validation)? ### Preparation ML Project Workflow * [What is your hardware ?](/topics-and-projects/hardware) * specify the use case * specify model interface * how would we monitor performance after deployment? * how can we approximate post-deployment monitoring before deployment? * build a model and iteratively improve it * deploy the model * monitor performance * what is your are metric? * How do you split your data? ### Before Training deep learning model * using large model to train because * it is faster to train with lower overfit and faster converge due to best training * it is easier and higher compress in the final stage * model compression and acceleration: reducing parameters without significantly decreasing the model performance * Data: How to have good data for training deep learning models; How to Build and Enhance A Good Data Set For Your Deep Learning Project: using same config and data for training and inference, removing redundant (delete data which you don't need), get more data, Handle missing data, using data augmentation techniques or GAN to generate more data, re-scale/balance data, Transform your data (Change data types), Feature selection based on data-set and use case * * The data you don't need: removing redundant samples * get more data * Invent more data * data augmentation * Re-scale data * balance datasets * Transform your data * Feature selection based on dataset and use case * ML-Augmented Video Object Tracking: By applying and evaluating multiple algorithmic models, enhanced ability to scale object tracking in high-density video compositions. ### Training deep learning model * automated hyper-parameters * Using Hyperparameter tuning / Hyperparameter optimization tools * AutoML * genetic algorithm * population based training * bayesian optimization * You need to set some parameters and config for training * * Diagnostics * Weight Initialization * Learning rate * Activation function * Network Topology * Batches and Epochs * Regularization * Optimization and Loss * Early Stopping ### Continuous delivery * evolve with latest detection models * more data (no labels) * semi-supervised learning: big self-supervised models are strong semi-supervised learners ### After Training deep learning model * Parameter pruning * model pruning: reducing redundant parameters which are not sensitive to the performance. * aim: remove all connections with absolute weights below a threshold * Quantization * compresses by reducing the number of bits used to represent the weights * quantization effectively constraints the number of different weights we can use inside our kernels * per-channel quantization for weights, which improves performance by model compression and latency reduction. * Low rank matrix factorization (LRMF) * there exists latent structures in the data, by uncovering which we can obtain a compressed representation of the data * LRMF factorizes the original matrix into lower rank matrices while preserving latent structures and addressing the issue of sparseness * Compact convolutional filters (Video/CNN) * designing special structural convolutional filters to save parameters * replace over parametric filters with compact filters to achieve overall speedup while maintaining comparable accuracy * Knowledge distillation * training a compact neural network with distilled knowledge of a large model * distillation (knowledge transfer) from an ensemble of big networks into a much smaller network which learns directly from the cumbersome model's outputs, that is lighter to deploy * Binarized Neural Networks (BNNs) * Apache TVM (incubating) is a compiler stack for deep learning systems * Neural Networks Compression Framework (NNCF) ### Deep learning model in production * security: controls access to model(s) through secure packaging and execution * Test * auto training * using parallel processing and library such as GStreamer # Technology Docker AWS Flask Django # My Keynote (February 2021) 1. introduction 2. Machine Learning/ Deep Learning Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed 3. supervised Machine Learning 1. Deep Convolutional Neural Networks (DCNN) Architecture 2. Visualizing and Understanding Convolutional Networks 3. Object Detection by Deep Learning 4. [Video Tracking](/topics-and-projects/video-tracking) 5. Style Transfer 4. semi-supervised Machine Learning/ Deep Reinforcement learning (DRL) 1. Google 2. [Deep Reinforcement learning (DRL)](/topics-and-projects/drl) 5. unsupervised Machine Learning 1. Auto Encoder 6. Generative Adversarial Networks (GANs) 7. Tools 8. Pre trained model 9. Effect of Augmented Datasets to Train DCNNs 10. Training for more classes 11. Optimization 12. [Hardware](/topics-and-projects/hardware) 13. Production setup 14. post development 15. business , Gartner, Hype Cycle for emerging technologies, 2025 ### Advanced and practical 1. Inside CNN 1. Deep Convolutional Neural Networks Architecture 2. Convolution 3. Convolution Layer 4. Conv/FC Filters 5. Activation Functions 6. Layer Activations 7. Pooling Layer 8. Dropout ; L2 pooling 9. Why 1. Max-pooling is useful 2. How to see inside each layer and find important features * Visualizing and Understanding Convolutional Networks * [https://tensorspace.org/](https://tensorspace.org/) * [https://www.youtube.com/watch?v=AgkfIQ4IGaM](https://www.youtube.com/watch?v=AgkfIQ4IGaM) 2. Hands on python for deep learning 3. Fundamental deep learning 4. Installation: TensorFlow, PyTorch 5. [Using PC+eGPU for training video tracking](/topics-and-projects/source-code/compile) Summary of the summit * AI Hardware Europe Summit (July 2020) * [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit) * Apache TVM And Deep Learning Compilation Conference (December 2020) * [RISC-V Summit (December 2020) ](/workshops-and-events/risc-v) [https://www.inspectar.com/demo](https://www.inspectar.com/demo) for rasp # Face * Effective and precise face detection based on color and depth data * [https://www.sciencedirect.com/science/article/pii/S221083271400009X](https://www.sciencedirect.com/science/article/pii/S221083271400009X) * containing or not containing a face * Eigenface, Fisherface, waveletface, PCA (Principal Component Analysis), LDA (Linear Dis-criminant Analysis), Haar wavelet transform, and so on. * Viola–Jones detector * illumination changes and occlusion * depthinformation is used to filter the regions of the image where a candidate face regionis found by the Viola–Jones (VJ) detector * \- the first filtering rule is defined on the color of the region; since some false positiveshave colors not compatible with the face (e.g. shadows on jeans) a skin detector isapplied to remove the candidate face regions that do not contain skin pixels; * \- the second filtering rule is defined on the size of the face: using the depth mapit is quite easy to calculate the size of the candidate face region, which is use-ful to discard smallest and largest faces from the final result set; * \- the third filtering rule is defined on the depth map to discard flat objects (e.g.candidate faces found in a wall) or uneven objects (e.g. candidate face foundin the leaves of a tree). Combining color and depth data the candidate faceregion can be extracted from the background and measures of depth and reg-ularity are used for filtering out false positives. * The size criteria simply remove the candidate faces not included in a fixed rangesize ([12.5,30] cm). The size of a candidate face region is extracted from the depthmap according to the following approach. * image below * Gaussian mixture 3D morphable face model * [https://www.sciencedirect.com/science/article/pii/S0031320317303527](https://www.sciencedirect.com/science/article/pii/S0031320317303527) * * * Face Synthesis for Eyeglass-Robust Face Recognition * [https://paperswithcode.com/paper/face-synthesis-for-eyeglass-robust-face](https://paperswithcode.com/paper/face-synthesis-for-eyeglass-robust-face) * GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data * [https://paperswithcode.com/paper/genegan-learning-object-transfiguration-and](https://paperswithcode.com/paper/genegan-learning-object-transfiguration-and) * FacePoseNet: Making a Case for Landmark-Free Face Alignment * [https://paperswithcode.com/paper/faceposenet-making-a-case-for-landmark-free](https://paperswithcode.com/paper/faceposenet-making-a-case-for-landmark-free) * Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision * [https://paperswithcode.com/paper/learning-to-regress-3d-face-shape-and](https://paperswithcode.com/paper/learning-to-regress-3d-face-shape-and) * Unsupervised Eyeglasses Removal in the Wild * [https://paperswithcode.com/paper/unsupervised-eyeglasses-removal-in-the-wild](https://paperswithcode.com/paper/unsupervised-eyeglasses-removal-in-the-wild) * How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks) * [https://arxiv.org/pdf/1703.07332v3.pdf](https://arxiv.org/pdf/1703.07332v3.pdf) * (a) we construct, for the first time, a very strong baseline by combining a state-of-the-art architecture for landmark localization with a state-of-the-art residual block, train it on a very large yet synthetically expanded 2D facial landmark dataset and fi- nally evaluate it on all other 2D facial landmark datasets. * (b) We create a guided by 2D landmarks network which con- verts 2D landmark annotations to 3D and unifies all exist- ing datasets, leading to the creation of LS3D-W, the largest and most challenging 3D facial landmark dataset to date (~230,000 images). * (c) Following that, we train a neural network for 3D face alignment and evaluate it on the newly introduced LS3D-W. * (d) We further look into the effect of all “traditional” factors affecting face alignment performance like large pose, initialization and resolution, and introduce a “new” one, namely the size of the network. * (e) We show that both 2D and 3D face alignment networks achieve per- formance of remarkable accuracy which is probably close to saturating the datasets used. * Training and testing code as well as the dataset can be downloaded from https: //[www.adrianbulat.com/face-alignment/](http://www.adrianbulat.com/face-alignment/) ![](https://lh6.googleusercontent.com/d8ABZ3w_DsDnuxD_X_PaSGPK9sxYEZhuyrYuZLCcmgFLMmTmheY4FDHRb3Cbhg- lYHPf4AdNHufhU04dxPdG3_pjwCOx9l7BZM9gLwwest05tq8ELg9sNocjKkjnMe6h=w1280) 19.Sep.2021 [Medium](https://medium.com/p/626019137fa9/edit) [https://fi.co/madlibs](https://fi.co/madlibs) [https://orcid.org/0000-0001-8382-1389](https://orcid.org/0000-0001-8382-1389) Dreyer's English (learn write English) #book story Greek Mythology Explained: A Deeper Look at Classical Greek Lore and Myth **Papers:** CALTag: High Precision Fiducial Markers for Camera Diatom Autofocusing in Brightfield Microscopy: a Comparative Study :implementation variation of the laplacian Analysis of focus measure operators in shape-from-focus: why laplacian? Blure detection? Iqaf? Optical flow modeling and computation: A survey Toward general type 2 fuzzy logic systems based on zSlices \-------------------------------------------------------------------- Lost in space The OA Film:[ https://en.wikipedia.org/wiki/Shark_Tank](https://en.wikipedia.org/wiki/Shark_Tank) Movie Serial billons monk serial movies Python async Highly decoupled microservice Edex RIS-V , Self-car RISC-V Magazine Road map Game: over/under [https://www.sporcle.com/games/Hejman/underwhelmed](https://www.sporcle.com/games/Hejman/underwhelmed) \-------------------------------------------------------------------- \-------------------------------------------------------------------- GDPR in IoT The EU General Data Protection Regulation (GDPR) and Face Images in IoT The GDPR (General Data Protection Regulation), taking effect in May 2018, introduces strict requirements for personal data protection and the privacy rights of individuals. The EU regulations will set a new global standard for privacy rights and change the way organizations worldwide store and process personal data. The GDPR brings the importance of preserving the privacy of personal information to the forefront, yet the importance of face images within this context is often overlooked. The purpose of this paper is to introduce a solution that helps companies protect face images in IoT devices which record or process image by camera, to strengthen compliance with the GDPR. Our Face is our Identity Our face is the most fundamental and highly visible element of our identity. People recognize us when they see our face or a photo of our face. Recent years have seen exponential increase in the use, storage and dissemination of face images in both private and public sectors - in social networks, corporate databases, IoT, smart-city deployments, digital media, government applications, and nearly every organization’s databases. \--------------------- $(aws-okta env stage) aws s3 cp s3://dataset/archive.tar.gz /Users/a.zip aws s3 ls images | tail -n 100 aws s3 cp staging-images/test.jpg /Users/test.jpg \--------------------- screen -rD k get pods Docker RUN chmod +x /tmp/run.sh Can run docker in terminal and run code line by line docker run -it --rm debian:stable-slim bash apt-get update apt-get installl -y \-------------------------------- brew install awscli aws-okta kubectx kubernetes-cli tfenv touch ~/.aws/config \-------------------------------------------------------------------- docker image rm TETSTDFSAFDSADF docker image ls docker system prune docker run -p 5000:5000 nameDocker:latest docker build . -t nameDocker:latest docker container stop number-docker-name docker container ls * docker pull quay.io/test:v0.0.1 * docker run --rm -p 5000:5000 -it quay.io/test:v0.0.1 * curl --header "Content-Type: application/json" \--request POST --data '[{"fixed":7.4, "a":0, "b":0.56, "c":9.4}]'[ http://127.0.0.1:5000/predict](https://meet.google.com/linkredirect?authuser=0&dest=http%3A%2F%2F127.0.0.1%3A5000%2Fpredict) * docker run --rm -v /home/.aws/credentials:/root/.aws/credentials -it quay.io/test /bin/sh aws s3 ls --profile=test \-------------------------------- Cloud software engineer and consultant focusing on building highly available, scalable and fully automated infrastructure environments on top of Amazon Web Services and Microsoft Azure clouds. My goal is always to make my customers happy in the cloud. \---------------- Search google for 3d = tiger - iPhone show AR/VR \--------------- brew install youtube-dl \---------------------------- List: Collection bucket : 1 for week 2 for month 3 for future \-------------------------------------------------------------------- **• Per frame operation** – Detection – Classification – Segmentation – Feature extraction – Recognition **• Across frames ** – Tracking – Counting **• High level** – Intention – Relations – Analyzing ============================= Deep compression Pruning deep learning Hash table neural network Dl compression Deep compression =================================== Mini PCI-e slot * What have I learned so far: * Problem-based learning * real life scenarios * index card (answer , idea) * Think-Pair-Share * Leverage flip charts * Summarizing \-------------------------------------------------------------------- Self \\\ Advancing Self-Supervised and Semi-Supervised Learning with SimCLR \cite{Chen2020} %https://github.com/google-research/simclr first pretraining on a large unlabeled dataset and then fine-tuning on a smaller labeled dataset pretraining on large unlabeled image datasets, as demonstrated by Exemplar- CNN, Instance Discrimination, CPC, AMDIM, CMC, MoCo and others. “A Simple Framework for Contrastive Learning of Visual Representations”, 85.8\% top-5 accuracy using 1\% of labeled images on the ImageNet dataset contrastive learning algorithms linear evaluation protocol (Zhang et al., 2016; Oord et al.,2018; Bachman et al., 2019; Kolesnikov et al., 2019) unsupervised learning benefits more from bigger models than its supervised counterpart. \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- Some of optimization algorithms ======================== Swarm Algorithm =============== 1\. Ant Colony Optimization (ACO) was inspired by the research on the behavior of ant colonies 2\. Firefly Algorithm based on insects called fireflies 3\. Marriage in Honey Bees Optimization Algorithm (MBO algorithm) is inspired by the process of reproduction of Honey Bee 4\. Artificial Bee Colony Algorithm (ABC) is based on the recollection of the Honey Bees 5\. Wasp Swarm Algorithm was inspired on the Parasitic wasps 6\. Bee Collecting Pollen Algorithm (BCPA) 7\. Termite Algorithm 8\. Mosquito swarms Algorithm (MSA) 9\. zooplankton swarms Algorithm (ZSA) 10\. Bumblebees Swarms Algorithm (BSA) 11\. Fish Swarm Algorithm (FSA) 12\. Bacteria Foraging Algorithm (BFA) 13\. Particle Swarm Optimization (PSO) 14\. Cuckoo Search 15\. Bat Algorithm (BA) 16\. Accelerated PSO 17\. Bee System 18\. Beehive Algorithm 19\. Cat Swarm 20\. Consultant-guided search 21\. Eagle Strategy 22\. Fast Backterial swarming algorithm 23\. Good lattice swarm optimization 24\. Glowworm swarm optimization 25\. Hierarchical swarm model 26\. Krill Herd 27\. Monkey Search 28\. Virtual ant algorithm 29\. Virtual bees 30\. Weighted Swarm Algorithm 31\. Wisdom of Artificial Crowd algorithm 32\. Prey-predator algorithm 33\. Memetic algorithm 34\. Lion Optimization Algorithm 35\. Chicken Swarm Optimization 36\. Ant Lion Optimizer 37\. Compact Particle Swarm Optimization 38\. Fruit Fly Optimization Algorithm 39\. marine propeller optimization algorithm 40\. The Whale Optimization Algorithm 41\. virus colony search algorithm 42\. Slime mould optimization algorithm Ecology Inspired Algorithm ========================== 1\. Biogeography-based Optimization 2\. Invasive Weed Optimization 3\. Symbiosis-Inspired Optimization - PS2O 4\. Atmosphere Clouds Model 5\. Brain Storm Optimization 6\. Dolphin echolocation 7\. Japanese Tree Frog Calling algorithm 8\. Eco-inspired evolutionary algorithm 9\. Egyptian Vulture 10\. Fish School search 11\. Flower Pollination algorithm 12\. Gene Expression 13\. Great Salmon Run 14\. Group Search Optimizer 15\. Human Inspired Algorithm 16\. Roach Infestation algorithm 17\. Queen-bee algorithm 18\. Shuffled frog leaping algorithm 19\. Forest Optimization Algorithm 20\. coral reefs optimization algorithm 21\. cultural evolution algorithm 22\. Grey Wolf Optimizer 23\. probabilistic pso 24\. omicron aco algorithm 25\. shark smell optimization 26\. social spider algorithm 27\. sosial insects behavior algorithm 28\. sperm whale algorithm Evolutionary Optimization ========================= 1\. Genetic Algorithm 2\. Genetic Programming 3\. Evolutionary Strategies 4\. Differential Evolution 5\. Paddy Field Algorithm 6\. Queen-bee Evolution 7\. Quantum Inspired Social Evolution Physic and Chemistry inspired algorithm ======================================= 1\. Big bang-Big Crunch 2\. Block hole algorithm 3\. Central force optimization 4\. Charged System search 5\. Electro-magnetism optimization 6\. Galaxy based search algorithm 7\. Gravitational search 8\. Harmony search algorithm 9\. Intelligent water drop algorithm 10\. River formation algorithm 11\. Self-propelled dynamics 12\. Simulated Annealing 13\. Stachastic diffusion search 14\. Spiral optimization 15\. Water Cycle algorithm 16\. Artificial Physics optimization 17\. Binary Gravitational search algorithm 18\. Continous quantum ant colony optimization 19\. Extended artificial physics optimization 20\. Extended Central force optimization 21\. Electromagnetism-like heuristic 22\. Gravitational Interaction optimization 23\. Hysteristetic Optimization algorithm 24\. Hybrid quantum-inspired GA 25\. Immune gravitational inspired algorithm 26\. Improved quantum evolutinary algorithm 27\. Linear programming 28\. Quantum-inspired bacterial swarming 29\. Quantum-inspired evolutionary algorithm 30\. Quantum-inspired genetic algorithm 31\. Quantum-behaved PSO 32\. Unified big bang-chaotic big crunch 33\. Vector model of artificial physics 34\. Versatile quantum-inspired evolutionary algorithm 35\. Space Gravitational Algorithm 36\. Ion Motion Algorithm 37\. Light Ray Optimization Algorithm 38\. Ray Optimization 39\. Photosynthetic Algorithms 40\. floorplanning algorithm 41\. Gases Brownian Motion Optimization 42\. gradient-type optimization 43\. mean-variance optimization 44\. Mine blast algorithm 45\. moth flame optimization 46\. multi battalion search algorithm 47\. music inspired optimization 48\. no free lunch theorems algorithm 49\. Optics inspired optimization 50\. runner-root algorithm 51\. sine cosine algorithm 52\. pitch tracking algorithm 53\. Stochastic Fractal Search algorithm 54\. stroke volume optimization 55\. Stud krill herd algorithm 56\. The Great Deluge Algorithm 57\. Water Evaporation Optimization 58\. water wave optimization algorithm 59\. Island model algorithm 60\. 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Let's partner up to take your project to the next level! pip install mlc-ai-nightly -f https://mlc.ai/wheels https://mlc.ai/ https://mlc.ai/summer22/ Day 1: Introduction to Unity: TVMScript Introduction to Unity: Relax and PyTorch TVM BYOC in Practice Get Started with TVM on Adreno GPU Introduction to Unity: Metaschedule How to Bring microTVM to a custom IDE Day 2: Community Keynote PyTorch 2.0: the journey to bringing compiler technologies to the core of PyTorch Support QNN Dialect for TVM with MediaTek Neuron and Devise the Scheduler for Acceleration On-Device Training Under 256KB Memory AMD Tutorial TVM at TI: Accelerating inference using the C7x/MMA Adreno GPU: 4x speed-up and upstreaming to TVM mainline Transfer-Tuning: Reusing Auto-Schedules for Efficient Tensor Program Code Generation Improvement in the TVM OpenCL codegen to autogenerate optimal convolution kernels for Adreno GPUs TVM Unity: Pass Infrastructure and BYOC Renesas Hardware accelerators with Apache TVM Introduction on 4th Gen Intel Xeon processor and BF16 support with TVM Hidet: Task Mapping Programming Paradigm for Deep Learning Tensor Programs Towards Building a Responsible Data Economy Optimizing SYCL Device Kernels with AKG Adreno GPU Performance Enhancements using TVM Improvements to CMSIS-NN integration in TVM UMA: Universal Modular Accelerator Interface Day 3: TVM Unity for Dynamic Models Empower Tensorflow serving with backend TVM Enabling Conditional Computing on Hexagon target Decoupled Model Schedule for Large Deep Learning Model Training Using TVM to bring Bayesian neural networks to embedded hardware Efficient Support of TVM Scan OP on RISC-V Vector Extension Improvements to Ethos-U55 support in TVM including CI on Alif Semiconductor boards Compiling Dynamic Shapes TVM Packaging in 2023: delivering TVM to end users Cross-Platform Training Using Automatic Differentiation on Relax IR AutoTVM: Reducing tuning space by cross axis filtering SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning Analytical Tensorization and Fusion for Compute-intensive Operators CUTLASS 3.0: Next Generation Composable and Reusable GPU Linear Algebra Library Enabling Data Movement and Computation Pipelining in Deep Learning Compiler Automating DL Compiler Bug Finding with NNSmith TVM at NIO TVM at Tencent Integrating the Andes RISC-V Processors into TVM Alpa: A Compiler for Distributed Deep Learning ACRoBat: Compiler and Runtime Techniques for Efficient Auto-Batching of Dynamic Deep Learning Computations Channel Folding: a Transform Pass for Optimizing Mobilenets ========================================================================Day 1: ************************ Introduction to Unity: TVMScript [https://github.com/cyx-6/TVM- Demo/blob/main/tvmscript.ipynb](https://github.com/cyx-6/TVM- Demo/blob/main/tvmscript.ipynb) Gan NN show us some hidden patter in history we can not see before. “I always have a slip of paper at hand, on which I note down the ideas of certain pages. On the backside I write down the bibliographic details. After finishing the book I go through my notes and think how these notes might be relevant for already written notes in the slip-box. It means that I always read with an eye towards possible connections in the slip-box.” (Luhmann et al., 1987, 150) Deep representation learning Model evaluation. Camera cheaper lidar Point cloud because of we need 3d Capturing reality 1\. 𝐀𝐝𝐝/𝐂𝐨𝐦𝐦𝐢𝐭 𝐀𝐥𝐥 Standard way: git add . git commit -m "Message" Another way: git commit -a -m "Message" 𝟐\. 𝐀𝐥𝐢𝐚𝐬𝐞𝐬 With aliases, you can write your own Git commands that do anything you want. Eg: git config --global alias.ac '!git add -A && git commit -m' (alias called ac, git add -A && git commit -m will do the full add and commit) 𝟑\. 𝐑𝐞𝐯𝐞𝐫𝐭 The revert command simply allows us to undo any commit on the current branch. Eg: git revert 486bdb2 Another way: git revert HEAD (for recent commits) 𝟒\. 𝐑𝐞𝐟𝐥𝐨𝐠 This command lets you easily see the recent commits, pulls, resets, pushes, etc on your local machine. Eg: git reflog 𝟓\. 𝐏𝐫𝐞𝐭𝐭𝐲 𝐋𝐨𝐠𝐬 Gives you the ability to print out a pretty log of your commits/branches. Eg: git log --graph --decorate --oneline 𝟔\. 𝐒𝐞𝐚𝐫𝐜𝐡𝐢𝐧𝐠 𝐋𝐨𝐠𝐬 One can also use the log command to search for specific changes in the code. Eg: git log -S "A promise in JavaScript is very similar" 𝟕\. 𝐒𝐭𝐚𝐬𝐡 This command will stash (store them locally) all your code changes but does not actually commit them. Eg: git stash 𝟖\. 𝐑𝐞𝐦𝐨𝐯𝐞 𝐃𝐞𝐚𝐝 𝐁𝐫𝐚𝐧𝐜𝐡𝐞𝐬 This command will delete all the tracking information for branches that are on your local machine that are not in the remote repository, but it does not delete your local branches. Eg: git remote update --prune 𝟗\. 𝐁𝐢𝐬𝐞𝐜𝐭 For finding which commits caused certain bugs Eg: git bisect start git bisect bad git bisect good 48c86d6 𝟏𝟎\. 𝐃𝐞𝐬𝐭𝐫𝐨𝐲 𝐋𝐨𝐜𝐚𝐥 𝐂𝐡𝐚𝐧𝐠𝐞𝐬 One can wipe out all changes on your local branch to exactly what is in the remote branch. Eg: git reset --hard origin/main Don’t trust your devices IoT. software and hardware are together for better business. Newsletter investing every 3 months 1\. Prototyping. New bie 2\. Patent. Website. ( list of investors) 3\. Pre seed. First founding 1M VC, inistution, anjel capital. 400 000 preseed. Quveribel. Equtible rund convertible non agreement Template. Convertabel lone 1\. Germ standar inistitude 2\. 4\. Equity. Venture builder. 20% 200 000 5\. 100 000 per year to become unocorn in less than 10 years 6\. Soniy corn 100k unicorn 1M 7\. 360 euro per years for database of investor 8\. Convertable loan: Pay interst rate 5% to 8% = 18 months later (2M found in 10M) convert on based . 9\. Invester Never act as co-founder = full time = 20% 10\. Project profit, 11\. Full time after foun rising Make a plan for your business; take your time to make calculations by creating a target audience. Your target audience determines how you approach your business plan. By studying your target audience, you are making empirical research and collecting information from them Then, secure a good partnership if need be, and get enough capital to start up. * * What the people need * Why people need it * When the people need it * It's affordability * It's ease of use * It's maintenance and revenue Pair programming The SB7 Framework harnesses the influence of stories. The structure describes the 7 most common story elements: • Character • Problem • Guide • Plan • Calls to action • Failure • Success Dear [Hiring Manager’s Name], I am writing to apply for the position of computer vision for IoT and cloud at [Company Name]. I am a highly skilled and experienced computer vision engineer with a strong background in IoT and cloud technologies. I believe that my skills and experience make me an ideal candidate for this position and I am excited about the opportunity to contribute to the success of your organization. I have a solid understanding of computer vision algorithms and techniques, as well as experience in developing and implementing computer vision systems. I am proficient in programming languages such as Python, C++, and Java, and have experience with popular computer vision libraries such as OpenCV, TensorFlow, and PyTorch. In addition, I have a strong background in IoT and cloud technologies, including experience with IoT platforms such as AWS IoT, Azure IoT, and Google Cloud IoT. I am familiar with cloud computing technologies such as AWS, Azure, and Google Cloud, and have experience with deploying and managing computer vision systems on these platforms. I am also a team player and have excellent communication skills. I am able to work with cross-functional teams and can effectively communicate with both technical and non-technical stakeholders. I am also highly motivated, and I am always looking for ways to improve my skills and stay up-to-date with the latest technologies. I am excited about the opportunity to join [Company Name] and to contribute to the development of cutting-edge computer vision systems for IoT and cloud. I am confident that my skills and experience make me a strong candidate for this position, and I look forward to discussing how I can contribute to your organization. Thank you for considering my application. I look forward to hearing from you soon. Sincerely, Title: "Unlocking the Power of Computer Vision for IoT and Cloud" Introduction: * Hi, and welcome to our video on the topic of computer vision for IoT and cloud. In this video, we're going to explore how computer vision technology can be used to enhance IoT and cloud-based systems, and how it can be used to unlock new possibilities for businesses and consumers alike. Body: * First, let's talk about what computer vision is and how it works. Essentially, computer vision is the technology that enables computers to understand and interpret visual information from the world around us. This can include things like images, videos, and even 3D models. * One of the key ways that computer vision can be used to enhance IoT and cloud-based systems is by enabling devices to better understand and interact with their environment. For example, a computer vision-enabled camera could be used to monitor a manufacturing facility and identify when a machine is in need of maintenance or when an employee is working in an unsafe manner. * Another way that computer vision can be used to enhance IoT and cloud-based systems is by enabling devices to better understand and interact with people. For example, a computer vision-enabled security camera could be used to identify individuals and track their movements, or a computer vision-enabled smart home system could be used to detect when someone is in the room and adjust the lighting or temperature accordingly. * Additionally, computer vision can also be used to enhance cloud-based systems by providing more accurate data and insights. For example, a computer vision-enabled drone could be used to collect data on crops and provide farmers with more accurate information about the health and growth of their crops. Conclusion: * Overall, computer vision technology has the potential to unlock new possibilities for businesses and consumers alike, by enabling IoT and cloud-based systems to better understand and interact with their environment and people. We hope this video has provided you with a better understanding of the potential of computer vision for IoT and cloud, and we look forward to seeing the new possibilities that will be created as this technology continues to evolve. Excited to share my latest project using computer vision and IoT to improve efficiency in manufacturing. I used a combination of machine learning algorithms and cloud computing to analyze data from cameras and sensors in real-time, resulting in a 20% increase in production speed. This was a challenging project but I enjoyed every step of it! I am always looking for new opportunities to apply my skills in computer vision and IoT to help companies improve their operations. Let's connect if you are working on a similar project or if you are looking for a developer with these skills. #computervision #IoT #cloudcomputing #manufacturingefficiency #machinelearning #developer" In this post, you briefly mention your experience and skills in computer vision and IoT, and you provide a specific example of a project you worked on that demonstrates your abilities. You also make it clear that you are open to new opportunities, and you invite others to connect with you. Using relevant hashtags such as #computervision #IoT #cloudcomputing can help your post reach a wider audience Exciting news! I just published a paper on a new object detection algorithm that I developed. The algorithm uses a combination of deep learning and computer vision techniques to improve accuracy and speed of object detection in real-world scenarios. This is a big step forward in the field of computer vision and I am proud to have contributed to it. I will be presenting my research at the Computer Vision Conference next month, if you're attending be sure to stop by and say hi! #computervision #objectdetection #deeplearning #research" In this post, you briefly explain the main findings and contributions of your research, and you express your excitement and pride in your work. You also mention the upcoming conference where you will be presenting your research, inviting your friends and colleagues to meet you in person. Also using relevant hashtags such as #computervision #objectdetection #deeplearning can help reach a wider audience interested in the field. Features stores 1\. Car parts detection 2\. Resize keep aspects ration 3\. 3.1 Perform damage detection 4\. 3.2Semantic segregation 5\. Transfer to original coordinates 1 class imbalance 2 class definition Maybe Class in between 3 inconstant annotations Color augmentation 1\. RGB shift 2\. Random brithness and contrast 3\. Sharpen 4\. Hue saturation value Why manually data augmented Becasu control of data. Not too rotate or change something Photogrammetry model Neural radiance fields (NeRF) NeRF in the wild \ [GitHub - google-research/tuning_playbook: A playbook for systematically maximizing the performance of deep learning models.](https://github.com/google-research/tuning_playbook) Yocto and Machine Learning + OpenCV: [https://www.yoctoproject.org](https://www.yoctoproject.org) [https://www.hackster.io/monica/running-machine-learning-on-maaxboard-s-yocto- image-part-1-6a4796](https://www.hackster.io/monica/running-machine-learning- on-maaxboard-s-yocto-image-part-1-6a4796) Bard Google: [https://blog.google/technology/ai/bard-google-ai-search- updates/](https://blog.google/technology/ai/bard-google-ai-search-updates/) [https://mustang.ir/questions/question/راه-اندازی-پروژه-های-گیت-هاب-با-git- pages](https://mustang.ir/questions/question/%D8%B1%D8%A7%D9%87-%D8%A7%D9%86%D8%AF%D8%A7%D8%B2%DB%8C-%D9%BE%D8%B1%D9%88%DA%98%D9%87-%D9%87%D8%A7%DB%8C-%DA%AF%DB%8C%D8%AA-%D9%87%D8%A7%D8%A8-%D8%A8%D8%A7-git- pages) Book: Project Management for Non-Project Managers [https://fa.wikipedia.org/wiki/علی_اکبرپور](https://fa.wikipedia.org/wiki/%D8%B9%D9%84%DB%8C_%D8%A7%DA%A9%D8%A8%D8%B1%D9%BE%D9%88%D8%B1) [https://www.kingorama.com](https://www.kingorama.com) شاهنامه سه بعدی [Accelerate deep learning model development with cloud custom environments - AWS Online Tech Talks - YouTube](https://m.youtube.com/watch?v=2Wt2zlkMtKI&noapp=1) [بخش هایی از کتاب Refactoring (نسخه رایگان)](https://www.developit.ir/refactoring/free.html#f7) [Performance Notes Of PyTorch Support for M1 and M2 GPUs - Lightning AI](https://lightning.ai/pages/community/community-discussions/performance- notes-of-pytorch-support-for-m1-and-m2-gpus/) [Investopedia Academy](https://academy.investopedia.com/) [HandBrake updated with AV1 and VP9 10-bit video encoding](https://9to5mac.com/2022/12/29/handbrake-support-av1-and- vp9-10-bit/) [How to Start Your Sole Proprietorship in 6 Simple Steps](https://qonto.com/en/blog/creators/administrative/sole-proprietorship- in-germany) [Duolingo English Test](https://englishtest.duolingo.com/applicants) [چالش‌های تولید محتوا برای مارکت اروپا و آمریکا - YouTube](https://m.youtube.com/watch?v=wW0HZdubuWQ) [PyTorch for Deep Learning & Machine Learning – Full Course - YouTube](https://m.youtube.com/watch?v=V_xro1bcAuA#dialog) [Why passive investing makes less sense in the current environment | Financial Times](https://archive.ph/0VucZ) [GitHub - google-research/tuning_playbook: A playbook for systematically maximizing the performance of deep learning models.](https://github.com/google-research/tuning_playbook) [GitHub - mgechev/google-interview-preparation-problems: leetcode problems I solved to prepare for my Google interview.](https://github.com/mgechev/google- interview-preparation-problems) [Bayesian Neural Networks and Variational Dropout](https://dmittov.github.io/variational_dropout/#/maximum-likelihood) [One machine learning question every day - bnomial](https://today.bnomial.com/?ref=email) Git remote add orgine Asynchronous Operation Anomaly detection Use experience. Personalizes. Prediction manage society mobility Personalization Covenant Platform. OpenMMLab Wordtune - AI-powered Writing Companion tree -v -I '*.png' -I '*.jpg' \--charset utf-8 >list2.txt 3D object using triangular mesh need vertices point cloud underlying surface of some 3D object, faster Definition of Done User Story complete Code\Implementation complete Code\Implementation Peer Reviews) approved Unit tests complete (if required) Testing Notes complete (if required) User Story Acceptance criteria defined and verified Backend: Python, Redis, Postgres, Celery Frontend: React, Redux, TypeScript DevOps: Terraform, Kubernetes, GitHub, Docker, AWS Data: Python (Data Science), Kafka, Fastapi, MLFlow, AWS SageMaker ML: Selcond core, Kubeflow, … [Sharpness](https://en.wikipedia.org/wiki/Sharpness_%28visual%29) ,[Noise](https://en.wikipedia.org/wiki/Image_noise), [Dynamic range](https://en.wikipedia.org/wiki/Dynamic_range), [Tone reproduction](https://en.wikipedia.org/wiki/Tone_reproduction) , [Contrast](https://en.wikipedia.org/wiki/Contrast_%28vision%29), [Color](https://en.wikipedia.org/wiki/Color), [Distortion](https://en.wikipedia.org/wiki/Distortion_%28optics%29) , [DSLR lenses](https://en.wikipedia.org/wiki/Lenses_for_SLR_and_DSLR_cameras), [Vignetting](https://en.wikipedia.org/wiki/Vignetting), [Exposure](https://en.wikipedia.org/wiki/Exposure_%28photography%29), Lateral [chromatic aberration](https://en.wikipedia.org/wiki/Chromatic_aberration) (LCA), [Lens flare](https://en.wikipedia.org/wiki/Lens_flare), Color, [Artifacts](https://en.wikipedia.org/wiki/Compression_artifact) ۱\. جهت انتخاب کلمه مورد نظرتان، دو بار روی آن تپ کنید. ۲\. برای انتخاب کل یک پاراگراف، کافیست چهار با روی آن تپ کنید. ۳\. یک انگشت را در ابتدا و انگشت دیگر را در آخر یک محدود گذاشته و کمی نگه دارید. متن میان دو انگشت انتخاب خواهد شد. ۴\. روی ابتدای محدوده ای دلخواه دو بار تپ کرده و بلافاصله با درگ کردن (کشیدن) پین محدوده ی انتخاب شده را گسترش دهید. (انگشت خود را پس از دومین تپ جدا نکنید) ۵\. برای انتخاب کل پاراگراف، به جز استفاده از مورد ۲، می توانید با دو انگشت، یک بار روی آن تپ کنید. namely motion estimation, motion smoothing, and image warping. Motion estimation algorithms often use a similarity transform to handle camera translations, rotations, and zooming. The tricky part is getting these algorithms to lock onto the background motion, 0\. video frames captured during fast motion are often blurry. Their appearance can be improved either using deblurring techniques (Section 10.3) or stealing sharper pixels from other frames with less motion or better focus (Matsushita, Ofek, Ge et al. 2006). Exercise 8.3 has you implement and test some of these ideas. 1\. Background subtraction 2\. Motion estimation 3\. Motion smoothing 4\. Image warping. image warping can result in missing borders around the image, which must be cropped, filled using information from other frames, or hallucinated using inpainting techniques (Section 10.5.1). Vision stabilization There is much recent work on Multi-view 3D reconstruction is a central research topic in computer vision that is driven in many different directions There are many available methods that can handle the noisy image completion problem In the case of surveillance using a fixed camera, there is no desired motion. In the case of most robotic applications, horizontal and vertical motions are desired, but rotation is not. In some cases of ground vehicles where the terrain is known to have many incline changes, or with aerial vehicles undergoing complicated maneuvers where the vehicle’s body is meant to be in varying orientations, rotation might be desired as the robot is meant to be at an angle at times. In robotics applications, computational complexity is extremely important due to the need for real-time operation. Also, it is likely that the center of rotation will not lie in the center of the image frame because the camera is rarely mounted at the robot’s center of mass. This first assumption is made in many video stabilization algorithms, and is a convenient way to seed the correct features with higher trust values. It is not an unreasonable assumption to make. Depending on the application, there is often a large portion of frames where local motion does not occur. In some situations, such as monitoring of steady traffic, there is no guarantee that local motion will not occur. This situation has not been tested, nor has our algorithm been designed to handle it. The second assumption comes from a combination of common sense, and the experience of many computer vision researchers. It makes sense that an object in the scene which does not move will be recognized more easily and more often. Being recognized consistently and consecutively is considered stable. On the other hand, objects which have local motion are less likely to be recognized as often. They might move through shadows, change orientation, or even move completely out of the scene. These possibilities all lead to a less stable class of features. It is likely that, more often than not, there are more background features than foreground features. Moving objects generally cover a small portion of the screen, which usually yields fewer features. Although uncommon, we did not want to make the assumption that this would occur in every frame. Certain scenes will consist of a large portion of local motion, or an object will move very close to the camera, consuming a much larger portion of the scene than the background. As long as some background features are discovered in each frame, our stabilization algorithm should succeed. # image processing tips: * the image size and kernel size need to depended. the best way is to use the one variable to define the size of the image and kernel together. * the coordinate of the image start at top left of the image/display * in order to change it to the normal coordinate you can use * grid of points; two matrix to X , Y coordinate * subtract half of W, H from X, Y in order to have normal coordinate system for our image * now we have cartesian coordinate * * cartesian coordinate to polar coordinate * تبدیل فضای کارتزین به پولار در خیلی از برنامه های پردازش تصویر کارایی دارد. برای پیدا کردن ترشلد ها هم می توان استفاده کرد * in MATLAB we can use ":"for example MatrixA(:) which means all entity of the matrix no mater how many dimensions we have but if we want to implemented in Python we can use numpy.flatten(). * in the MATLAB the round is different from python. if you want same result you need implement the rand function by yourself. * imge_mask=np.ones_like(image_source)*255 * imge_mask=imge_mask.astype(np.uint8) * imge_mask=imge_mask.flatten() ??? .ravel() * .asarray * np.logical_and( 1, 2) * indexes=[index for index in range(len(array1)) if array1[index] == True] * cv2.bitwise_not(yyy) * "olive" editor remove silence ![](https://lh5.googleusercontent.com/nILOXEoEKiANosdHjTOC05i7h8b-84246iAmayzrsrwyQtrN_ZG776o1GnXEFO0E0yH9lMQqIokQWJJgFxAvIzsUdQG6vzewTBzTMKkc1A4J4Lq94r_tVjMgcij_2Nj3DQ=w1280) Questions: How to train model to add new classes? How to add a new class to an existing classifier in deep learning? Adding new Class to One Shot Learning trained model Is it possible to train a neural network as new classes are given? Merging all several models that detection system for all these tasks. Answer 1: There are several ways to add new classes to the trained model, which require just training for the new classes. * Incremental training ([GitHub](https://github.com/khurramjaved96/incremental-learning)) * continuously learn a stream of data ([GitHub](https://github.com/creme-ml/creme)) * online machine learning ([GitHub](https://github.com/GMvandeVen/continual-learning)) * Transfer Learning Twice * Continual learning approaches (Regularization, Expansion, Rehearsal) ([GitHub](https://github.com/facebookresearch/Adversarial-Continual-Learning)) Answer 2: Online learning is a term used to refer to a model which takes a continual or sequential stream of input data while training, in contrast to offline learning (also called batch learning), where the model is pre-trained on a static predefined dataset. Continual learning (also called incremental, continuous, lifelong learning) refers to a branch of ML working in an online learning context where models are designed to learn new tasks while maintaining performance on historic tasks. It can be applied to multiple problem paradigms (including Class- incremental learning, where each new task presents new class labels for an ever expanding super-classification problem). Do I need to train my whole model again on all four classes or is there any way I can just train my model on new class? Naively re-training the model on the updated dataset is indeed a solution. Continual learning seeks to address contexts where access to historic data (i.e. the original 3 classes) is not possible, or when retraining on an increasingly large dataset is impractical (for efficiency, space, privacy etc concerns). Multiple such models using different underlying architectures have been proposed, but almost all examples exclusively deal with image classification problems. Answer 3: You could use transfer learning (i.e. use a pre-trained model, then change its last layer to accommodate the new classes, and re-train this slightly modified model, maybe with a lower learning rate) to achieve that, but transfer learning does not necessarily attempt to retain any of the previously acquired information (especially if you don't use very small learning rates, you keep on training and you do not freeze the weights of the convolutional layers), but only to speed up training or when your new dataset is not big enough, by starting from a model that has already learned general features that are supposedly similar to the features needed for your specific task. There is also the related domain adaptation problem. There are more suitable approaches to perform incremental class learning (which is what you are asking for!), which directly address the [catastrophic forgetting problem](https://ai.stackexchange.com/a/13293/2444). For instance, you can take a look at this paper [Class-incremental Learning via Deep Model Consolidation](https://arxiv.org/pdf/1903.07864.pdf), which proposes the Deep Model Consolidation (DMC) approach. There are other continual/incremental learning approaches, many of them are described [here](https://ai.stackexchange.com/a/24529/2444) or in more detail [here](https://reader.elsevier.com/reader/sd/pii/S0893608019300231). Answer 4: by using Continual learning approaches to trained without losing the original classes. It has 3 categories: Regularization Expansion Rehearsal Answer 5: if you access to the dataset then you can download it and add all you new classes when you have " 'N' COCO Classes + 'M' New classes " after that you can fine tune model based on new dataset. you do not need all of the dataset just same number of image for all class enough. [https://learnopencv.com/stanford-mrnet-challenge-classifying-knee- mris/](https://learnopencv.com/stanford-mrnet-challenge-classifying-knee- mris/) Before start your machine learning project ask these questions and preparation: What is your inference hardware? specify the use case. specify model interface. how would we monitor performance after deployment? how can we approximate post-deployment monitoring before deployment? build a model and iteratively improve it. How to deploy the model at the end? monitor performance after deployment. what is your metric? How do you split your data (training and validation)? ### Preparation ML Project Workflow * [What is your hardware ?](/topics-and-projects/hardware) * specify the use case * specify model interface * how would we monitor performance after deployment? * how can we approximate post-deployment monitoring before deployment? * build a model and iteratively improve it * deploy the model * monitor performance * what is your are metric? * How do you split your data? ### Before Training deep learning model * using large model to train because * it is faster to train with lower overfit and faster converge due to best training * it is easier and higher compress in the final stage * model compression and acceleration: reducing parameters without significantly decreasing the model performance * Data: How to have good data for training deep learning models; How to Build and Enhance A Good Data Set For Your Deep Learning Project: using same config and data for training and inference, removing redundant (delete data which you don't need), get more data, Handle missing data, using data augmentation techniques or GAN to generate more data, re-scale/balance data, Transform your data (Change data types), Feature selection based on data-set and use case * * The data you don't need: removing redundant samples * get more data * Invent more data * data augmentation * Re-scale data * balance datasets * Transform your data * Feature selection based on dataset and use case * ML-Augmented Video Object Tracking: By applying and evaluating multiple algorithmic models, enhanced ability to scale object tracking in high-density video compositions. ### Training deep learning model * automated hyper-parameters * Using Hyperparameter tuning / Hyperparameter optimization tools * AutoML * genetic algorithm * population based training * bayesian optimization * You need to set some parameters and config for training * * Diagnostics * Weight Initialization * Learning rate * Activation function * Network Topology * Batches and Epochs * Regularization * Optimization and Loss * Early Stopping ### Continuous delivery * evolve with latest detection models * more data (no labels) * semi-supervised learning: big self-supervised models are strong semi-supervised learners ### After Training deep learning model * Parameter pruning * model pruning: reducing redundant parameters which are not sensitive to the performance. * aim: remove all connections with absolute weights below a threshold * Quantization * compresses by reducing the number of bits used to represent the weights * quantization effectively constraints the number of different weights we can use inside our kernels * per-channel quantization for weights, which improves performance by model compression and latency reduction. * Low rank matrix factorization (LRMF) * there exists latent structures in the data, by uncovering which we can obtain a compressed representation of the data * LRMF factorizes the original matrix into lower rank matrices while preserving latent structures and addressing the issue of sparseness * Compact convolutional filters (Video/CNN) * designing special structural convolutional filters to save parameters * replace over parametric filters with compact filters to achieve overall speedup while maintaining comparable accuracy * Knowledge distillation * training a compact neural network with distilled knowledge of a large model * distillation (knowledge transfer) from an ensemble of big networks into a much smaller network which learns directly from the cumbersome model's outputs, that is lighter to deploy * Binarized Neural Networks (BNNs) * Apache TVM (incubating) is a compiler stack for deep learning systems * Neural Networks Compression Framework (NNCF) ### Deep learning model in production * security: controls access to model(s) through secure packaging and execution * Test * auto training * using parallel processing and library such as GStreamer # Technology Docker AWS Flask Django # My Keynote (February 2021) 1. introduction 2. Machine Learning/ Deep Learning Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed 3. supervised Machine Learning 1. Deep Convolutional Neural Networks (DCNN) Architecture 2. Visualizing and Understanding Convolutional Networks 3. Object Detection by Deep Learning 4. [Video Tracking](/topics-and-projects/video-tracking) 5. Style Transfer 4. semi-supervised Machine Learning/ Deep Reinforcement learning (DRL) 1. Google 2. [Deep Reinforcement learning (DRL)](/topics-and-projects/drl) 5. unsupervised Machine Learning 1. Auto Encoder 6. Generative Adversarial Networks (GANs) 7. Tools 8. Pre trained model 9. Effect of Augmented Datasets to Train DCNNs 10. Training for more classes 11. Optimization 12. [Hardware](/topics-and-projects/hardware) 13. Production setup 14. post development 15. business , Gartner, Hype Cycle for emerging technologies, 2025 ### Advanced and practical 1. Inside CNN 1. Deep Convolutional Neural Networks Architecture 2. Convolution 3. Convolution Layer 4. Conv/FC Filters 5. Activation Functions 6. Layer Activations 7. Pooling Layer 8. Dropout ; L2 pooling 9. Why 1. Max-pooling is useful 2. How to see inside each layer and find important features * Visualizing and Understanding Convolutional Networks * [https://tensorspace.org/](https://tensorspace.org/) * [https://www.youtube.com/watch?v=AgkfIQ4IGaM](https://www.youtube.com/watch?v=AgkfIQ4IGaM) 2. Hands on python for deep learning 3. Fundamental deep learning 4. Installation: TensorFlow, PyTorch 5. [Using PC+eGPU for training video tracking](/topics-and-projects/source-code/compile) Summary of the summit * AI Hardware Europe Summit (July 2020) * [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit) * Apache TVM And Deep Learning Compilation Conference (December 2020) * [RISC-V Summit (December 2020) ](/workshops-and-events/risc-v) [https://www.inspectar.com/demo](https://www.inspectar.com/demo) for rasp # Face * Effective and precise face detection based on color and depth data * [https://www.sciencedirect.com/science/article/pii/S221083271400009X](https://www.sciencedirect.com/science/article/pii/S221083271400009X) * containing or not containing a face * Eigenface, Fisherface, waveletface, PCA (Principal Component Analysis), LDA (Linear Dis-criminant Analysis), Haar wavelet transform, and so on. * Viola–Jones detector * illumination changes and occlusion * depthinformation is used to filter the regions of the image where a candidate face regionis found by the Viola–Jones (VJ) detector * \- the first filtering rule is defined on the color of the region; since some false positiveshave colors not compatible with the face (e.g. shadows on jeans) a skin detector isapplied to remove the candidate face regions that do not contain skin pixels; * \- the second filtering rule is defined on the size of the face: using the depth mapit is quite easy to calculate the size of the candidate face region, which is use-ful to discard smallest and largest faces from the final result set; * \- the third filtering rule is defined on the depth map to discard flat objects (e.g.candidate faces found in a wall) or uneven objects (e.g. candidate face foundin the leaves of a tree). Combining color and depth data the candidate faceregion can be extracted from the background and measures of depth and reg-ularity are used for filtering out false positives. * The size criteria simply remove the candidate faces not included in a fixed rangesize ([12.5,30] cm). The size of a candidate face region is extracted from the depthmap according to the following approach. * image below * Gaussian mixture 3D morphable face model * [https://www.sciencedirect.com/science/article/pii/S0031320317303527](https://www.sciencedirect.com/science/article/pii/S0031320317303527) * * * Face Synthesis for Eyeglass-Robust Face Recognition * [https://paperswithcode.com/paper/face-synthesis-for-eyeglass-robust-face](https://paperswithcode.com/paper/face-synthesis-for-eyeglass-robust-face) * GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data * [https://paperswithcode.com/paper/genegan-learning-object-transfiguration-and](https://paperswithcode.com/paper/genegan-learning-object-transfiguration-and) * FacePoseNet: Making a Case for Landmark-Free Face Alignment * [https://paperswithcode.com/paper/faceposenet-making-a-case-for-landmark-free](https://paperswithcode.com/paper/faceposenet-making-a-case-for-landmark-free) * Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision * [https://paperswithcode.com/paper/learning-to-regress-3d-face-shape-and](https://paperswithcode.com/paper/learning-to-regress-3d-face-shape-and) * Unsupervised Eyeglasses Removal in the Wild * [https://paperswithcode.com/paper/unsupervised-eyeglasses-removal-in-the-wild](https://paperswithcode.com/paper/unsupervised-eyeglasses-removal-in-the-wild) * How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks) * [https://arxiv.org/pdf/1703.07332v3.pdf](https://arxiv.org/pdf/1703.07332v3.pdf) * (a) we construct, for the first time, a very strong baseline by combining a state-of-the-art architecture for landmark localization with a state-of-the-art residual block, train it on a very large yet synthetically expanded 2D facial landmark dataset and fi- nally evaluate it on all other 2D facial landmark datasets. * (b) We create a guided by 2D landmarks network which con- verts 2D landmark annotations to 3D and unifies all exist- ing datasets, leading to the creation of LS3D-W, the largest and most challenging 3D facial landmark dataset to date (~230,000 images). * (c) Following that, we train a neural network for 3D face alignment and evaluate it on the newly introduced LS3D-W. * (d) We further look into the effect of all “traditional” factors affecting face alignment performance like large pose, initialization and resolution, and introduce a “new” one, namely the size of the network. * (e) We show that both 2D and 3D face alignment networks achieve per- formance of remarkable accuracy which is probably close to saturating the datasets used. * Training and testing code as well as the dataset can be downloaded from https: //[www.adrianbulat.com/face-alignment/](http://www.adrianbulat.com/face-alignment/) ![](https://lh6.googleusercontent.com/d8ABZ3w_DsDnuxD_X_PaSGPK9sxYEZhuyrYuZLCcmgFLMmTmheY4FDHRb3Cbhg- lYHPf4AdNHufhU04dxPdG3_pjwCOx9l7BZM9gLwwest05tq8ELg9sNocjKkjnMe6h=w1280) 19.Sep.2021 [Medium](https://medium.com/p/626019137fa9/edit) [https://fi.co/madlibs](https://fi.co/madlibs) [https://orcid.org/0000-0001-8382-1389](https://orcid.org/0000-0001-8382-1389) Dreyer's English (learn write English) #book story Greek Mythology Explained: A Deeper Look at Classical Greek Lore and Myth **Papers:** CALTag: High Precision Fiducial Markers for Camera Diatom Autofocusing in Brightfield Microscopy: a Comparative Study :implementation variation of the laplacian Analysis of focus measure operators in shape-from-focus: why laplacian? Blure detection? Iqaf? Optical flow modeling and computation: A survey Toward general type 2 fuzzy logic systems based on zSlices \-------------------------------------------------------------------- Lost in space The OA Film:[ https://en.wikipedia.org/wiki/Shark_Tank](https://en.wikipedia.org/wiki/Shark_Tank) Movie Serial billons monk serial movies Python async Highly decoupled microservice Edex RIS-V , Self-car RISC-V Magazine Road map Game: over/under [https://www.sporcle.com/games/Hejman/underwhelmed](https://www.sporcle.com/games/Hejman/underwhelmed) \-------------------------------------------------------------------- \-------------------------------------------------------------------- GDPR in IoT The EU General Data Protection Regulation (GDPR) and Face Images in IoT The GDPR (General Data Protection Regulation), taking effect in May 2018, introduces strict requirements for personal data protection and the privacy rights of individuals. The EU regulations will set a new global standard for privacy rights and change the way organizations worldwide store and process personal data. The GDPR brings the importance of preserving the privacy of personal information to the forefront, yet the importance of face images within this context is often overlooked. The purpose of this paper is to introduce a solution that helps companies protect face images in IoT devices which record or process image by camera, to strengthen compliance with the GDPR. Our Face is our Identity Our face is the most fundamental and highly visible element of our identity. People recognize us when they see our face or a photo of our face. Recent years have seen exponential increase in the use, storage and dissemination of face images in both private and public sectors - in social networks, corporate databases, IoT, smart-city deployments, digital media, government applications, and nearly every organization’s databases. \--------------------- $(aws-okta env stage) aws s3 cp s3://dataset/archive.tar.gz /Users/a.zip aws s3 ls images | tail -n 100 aws s3 cp staging-images/test.jpg /Users/test.jpg \--------------------- screen -rD k get pods Docker RUN chmod +x /tmp/run.sh Can run docker in terminal and run code line by line docker run -it --rm debian:stable-slim bash apt-get update apt-get installl -y \-------------------------------- brew install awscli aws-okta kubectx kubernetes-cli tfenv touch ~/.aws/config \-------------------------------------------------------------------- docker image rm TETSTDFSAFDSADF docker image ls docker system prune docker run -p 5000:5000 nameDocker:latest docker build . -t nameDocker:latest docker container stop number-docker-name docker container ls * docker pull quay.io/test:v0.0.1 * docker run --rm -p 5000:5000 -it quay.io/test:v0.0.1 * curl --header "Content-Type: application/json" \--request POST --data '[{"fixed":7.4, "a":0, "b":0.56, "c":9.4}]'[ http://127.0.0.1:5000/predict](https://meet.google.com/linkredirect?authuser=0&dest=http%3A%2F%2F127.0.0.1%3A5000%2Fpredict) * docker run --rm -v /home/.aws/credentials:/root/.aws/credentials -it quay.io/test /bin/sh aws s3 ls --profile=test \-------------------------------- Cloud software engineer and consultant focusing on building highly available, scalable and fully automated infrastructure environments on top of Amazon Web Services and Microsoft Azure clouds. My goal is always to make my customers happy in the cloud. \---------------- Search google for 3d = tiger - iPhone show AR/VR \--------------- brew install youtube-dl \---------------------------- List: Collection bucket : 1 for week 2 for month 3 for future \-------------------------------------------------------------------- **• Per frame operation** – Detection – Classification – Segmentation – Feature extraction – Recognition **• Across frames ** – Tracking – Counting **• High level** – Intention – Relations – Analyzing ============================= Deep compression Pruning deep learning Hash table neural network Dl compression Deep compression =================================== Mini PCI-e slot * What have I learned so far: * Problem-based learning * real life scenarios * index card (answer , idea) * Think-Pair-Share * Leverage flip charts * Summarizing \-------------------------------------------------------------------- Self \\\ Advancing Self-Supervised and Semi-Supervised Learning with SimCLR \cite{Chen2020} %https://github.com/google-research/simclr first pretraining on a large unlabeled dataset and then fine-tuning on a smaller labeled dataset pretraining on large unlabeled image datasets, as demonstrated by Exemplar- CNN, Instance Discrimination, CPC, AMDIM, CMC, MoCo and others. “A Simple Framework for Contrastive Learning of Visual Representations”, 85.8\% top-5 accuracy using 1\% of labeled images on the ImageNet dataset contrastive learning algorithms linear evaluation protocol (Zhang et al., 2016; Oord et al.,2018; Bachman et al., 2019; Kolesnikov et al., 2019) unsupervised learning benefits more from bigger models than its supervised counterpart. \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- Some of optimization algorithms ======================== Swarm Algorithm =============== 1\. Ant Colony Optimization (ACO) was inspired by the research on the behavior of ant colonies 2\. Firefly Algorithm based on insects called fireflies 3\. Marriage in Honey Bees Optimization Algorithm (MBO algorithm) is inspired by the process of reproduction of Honey Bee 4\. Artificial Bee Colony Algorithm (ABC) is based on the recollection of the Honey Bees 5\. Wasp Swarm Algorithm was inspired on the Parasitic wasps 6\. Bee Collecting Pollen Algorithm (BCPA) 7\. Termite Algorithm 8\. Mosquito swarms Algorithm (MSA) 9\. zooplankton swarms Algorithm (ZSA) 10\. Bumblebees Swarms Algorithm (BSA) 11\. Fish Swarm Algorithm (FSA) 12\. Bacteria Foraging Algorithm (BFA) 13\. Particle Swarm Optimization (PSO) 14\. Cuckoo Search 15\. Bat Algorithm (BA) 16\. Accelerated PSO 17\. Bee System 18\. Beehive Algorithm 19\. Cat Swarm 20\. Consultant-guided search 21\. Eagle Strategy 22\. Fast Backterial swarming algorithm 23\. Good lattice swarm optimization 24\. Glowworm swarm optimization 25\. Hierarchical swarm model 26\. Krill Herd 27\. Monkey Search 28\. Virtual ant algorithm 29\. Virtual bees 30\. Weighted Swarm Algorithm 31\. Wisdom of Artificial Crowd algorithm 32\. Prey-predator algorithm 33\. Memetic algorithm 34\. Lion Optimization Algorithm 35\. Chicken Swarm Optimization 36\. Ant Lion Optimizer 37\. Compact Particle Swarm Optimization 38\. Fruit Fly Optimization Algorithm 39\. marine propeller optimization algorithm 40\. The Whale Optimization Algorithm 41\. virus colony search algorithm 42\. Slime mould optimization algorithm Ecology Inspired Algorithm ========================== 1\. Biogeography-based Optimization 2\. Invasive Weed Optimization 3\. Symbiosis-Inspired Optimization - PS2O 4\. Atmosphere Clouds Model 5\. Brain Storm Optimization 6\. Dolphin echolocation 7\. Japanese Tree Frog Calling algorithm 8\. Eco-inspired evolutionary algorithm 9\. Egyptian Vulture 10\. Fish School search 11\. Flower Pollination algorithm 12\. Gene Expression 13\. Great Salmon Run 14\. Group Search Optimizer 15\. Human Inspired Algorithm 16\. Roach Infestation algorithm 17\. Queen-bee algorithm 18\. Shuffled frog leaping algorithm 19\. Forest Optimization Algorithm 20\. coral reefs optimization algorithm 21\. cultural evolution algorithm 22\. Grey Wolf Optimizer 23\. probabilistic pso 24\. omicron aco algorithm 25\. shark smell optimization 26\. social spider algorithm 27\. sosial insects behavior algorithm 28\. sperm whale algorithm Evolutionary Optimization ========================= 1\. Genetic Algorithm 2\. Genetic Programming 3\. Evolutionary Strategies 4\. Differential Evolution 5\. Paddy Field Algorithm 6\. Queen-bee Evolution 7\. Quantum Inspired Social Evolution Physic and Chemistry inspired algorithm ======================================= 1\. Big bang-Big Crunch 2\. Block hole algorithm 3\. Central force optimization 4\. Charged System search 5\. Electro-magnetism optimization 6\. Galaxy based search algorithm 7\. Gravitational search 8\. Harmony search algorithm 9\. Intelligent water drop algorithm 10\. River formation algorithm 11\. Self-propelled dynamics 12\. Simulated Annealing 13\. Stachastic diffusion search 14\. Spiral optimization 15\. Water Cycle algorithm 16\. Artificial Physics optimization 17\. Binary Gravitational search algorithm 18\. Continous quantum ant colony optimization 19\. Extended artificial physics optimization 20\. Extended Central force optimization 21\. Electromagnetism-like heuristic 22\. Gravitational Interaction optimization 23\. Hysteristetic Optimization algorithm 24\. Hybrid quantum-inspired GA 25\. Immune gravitational inspired algorithm 26\. Improved quantum evolutinary algorithm 27\. Linear programming 28\. Quantum-inspired bacterial swarming 29\. Quantum-inspired evolutionary algorithm 30\. Quantum-inspired genetic algorithm 31\. Quantum-behaved PSO 32\. Unified big bang-chaotic big crunch 33\. Vector model of artificial physics 34\. Versatile quantum-inspired evolutionary algorithm 35\. Space Gravitational Algorithm 36\. Ion Motion Algorithm 37\. Light Ray Optimization Algorithm 38\. Ray Optimization 39\. Photosynthetic Algorithms 40\. floorplanning algorithm 41\. Gases Brownian Motion Optimization 42\. gradient-type optimization 43\. mean-variance optimization 44\. Mine blast algorithm 45\. moth flame optimization 46\. multi battalion search algorithm 47\. music inspired optimization 48\. no free lunch theorems algorithm 49\. Optics inspired optimization 50\. runner-root algorithm 51\. sine cosine algorithm 52\. pitch tracking algorithm 53\. Stochastic Fractal Search algorithm 54\. stroke volume optimization 55\. Stud krill herd algorithm 56\. The Great Deluge Algorithm 57\. Water Evaporation Optimization 58\. water wave optimization algorithm 59\. Island model algorithm 60\. 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Let's partner up to take your project to the next level! pip install mlc-ai-nightly -f https://mlc.ai/wheels https://mlc.ai/ https://mlc.ai/summer22/ Day 1: Introduction to Unity: TVMScript Introduction to Unity: Relax and PyTorch TVM BYOC in Practice Get Started with TVM on Adreno GPU Introduction to Unity: Metaschedule How to Bring microTVM to a custom IDE Day 2: Community Keynote PyTorch 2.0: the journey to bringing compiler technologies to the core of PyTorch Support QNN Dialect for TVM with MediaTek Neuron and Devise the Scheduler for Acceleration On-Device Training Under 256KB Memory AMD Tutorial TVM at TI: Accelerating inference using the C7x/MMA Adreno GPU: 4x speed-up and upstreaming to TVM mainline Transfer-Tuning: Reusing Auto-Schedules for Efficient Tensor Program Code Generation Improvement in the TVM OpenCL codegen to autogenerate optimal convolution kernels for Adreno GPUs TVM Unity: Pass Infrastructure and BYOC Renesas Hardware accelerators with Apache TVM Introduction on 4th Gen Intel Xeon processor and BF16 support with TVM Hidet: Task Mapping Programming Paradigm for Deep Learning Tensor Programs Towards Building a Responsible Data Economy Optimizing SYCL Device Kernels with AKG Adreno GPU Performance Enhancements using TVM Improvements to CMSIS-NN integration in TVM UMA: Universal Modular Accelerator Interface Day 3: TVM Unity for Dynamic Models Empower Tensorflow serving with backend TVM Enabling Conditional Computing on Hexagon target Decoupled Model Schedule for Large Deep Learning Model Training Using TVM to bring Bayesian neural networks to embedded hardware Efficient Support of TVM Scan OP on RISC-V Vector Extension Improvements to Ethos-U55 support in TVM including CI on Alif Semiconductor boards Compiling Dynamic Shapes TVM Packaging in 2023: delivering TVM to end users Cross-Platform Training Using Automatic Differentiation on Relax IR AutoTVM: Reducing tuning space by cross axis filtering SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning Analytical Tensorization and Fusion for Compute-intensive Operators CUTLASS 3.0: Next Generation Composable and Reusable GPU Linear Algebra Library Enabling Data Movement and Computation Pipelining in Deep Learning Compiler Automating DL Compiler Bug Finding with NNSmith TVM at NIO TVM at Tencent Integrating the Andes RISC-V Processors into TVM Alpa: A Compiler for Distributed Deep Learning ACRoBat: Compiler and Runtime Techniques for Efficient Auto-Batching of Dynamic Deep Learning Computations Channel Folding: a Transform Pass for Optimizing Mobilenets ========================================================================Day 1: ************************ Introduction to Unity: TVMScript [https://github.com/cyx-6/TVM- Demo/blob/main/tvmscript.ipynb](https://github.com/cyx-6/TVM- Demo/blob/main/tvmscript.ipynb) Gan NN show us some hidden patter in history we can not see before. “I always have a slip of paper at hand, on which I note down the ideas of certain pages. On the backside I write down the bibliographic details. After finishing the book I go through my notes and think how these notes might be relevant for already written notes in the slip-box. It means that I always read with an eye towards possible connections in the slip-box.” (Luhmann et al., 1987, 150) Deep representation learning Model evaluation. Camera cheaper lidar Point cloud because of we need 3d Capturing reality 1\. 𝐀𝐝𝐝/𝐂𝐨𝐦𝐦𝐢𝐭 𝐀𝐥𝐥 Standard way: git add . git commit -m "Message" Another way: git commit -a -m "Message" 𝟐\. 𝐀𝐥𝐢𝐚𝐬𝐞𝐬 With aliases, you can write your own Git commands that do anything you want. Eg: git config --global alias.ac '!git add -A && git commit -m' (alias called ac, git add -A && git commit -m will do the full add and commit) 𝟑\. 𝐑𝐞𝐯𝐞𝐫𝐭 The revert command simply allows us to undo any commit on the current branch. Eg: git revert 486bdb2 Another way: git revert HEAD (for recent commits) 𝟒\. 𝐑𝐞𝐟𝐥𝐨𝐠 This command lets you easily see the recent commits, pulls, resets, pushes, etc on your local machine. Eg: git reflog 𝟓\. 𝐏𝐫𝐞𝐭𝐭𝐲 𝐋𝐨𝐠𝐬 Gives you the ability to print out a pretty log of your commits/branches. Eg: git log --graph --decorate --oneline 𝟔\. 𝐒𝐞𝐚𝐫𝐜𝐡𝐢𝐧𝐠 𝐋𝐨𝐠𝐬 One can also use the log command to search for specific changes in the code. Eg: git log -S "A promise in JavaScript is very similar" 𝟕\. 𝐒𝐭𝐚𝐬𝐡 This command will stash (store them locally) all your code changes but does not actually commit them. Eg: git stash 𝟖\. 𝐑𝐞𝐦𝐨𝐯𝐞 𝐃𝐞𝐚𝐝 𝐁𝐫𝐚𝐧𝐜𝐡𝐞𝐬 This command will delete all the tracking information for branches that are on your local machine that are not in the remote repository, but it does not delete your local branches. Eg: git remote update --prune 𝟗\. 𝐁𝐢𝐬𝐞𝐜𝐭 For finding which commits caused certain bugs Eg: git bisect start git bisect bad git bisect good 48c86d6 𝟏𝟎\. 𝐃𝐞𝐬𝐭𝐫𝐨𝐲 𝐋𝐨𝐜𝐚𝐥 𝐂𝐡𝐚𝐧𝐠𝐞𝐬 One can wipe out all changes on your local branch to exactly what is in the remote branch. Eg: git reset --hard origin/main Don’t trust your devices IoT. software and hardware are together for better business. Newsletter investing every 3 months 1\. Prototyping. New bie 2\. Patent. Website. ( list of investors) 3\. Pre seed. First founding 1M VC, inistution, anjel capital. 400 000 preseed. Quveribel. Equtible rund convertible non agreement Template. Convertabel lone 1\. Germ standar inistitude 2\. 4\. Equity. Venture builder. 20% 200 000 5\. 100 000 per year to become unocorn in less than 10 years 6\. Soniy corn 100k unicorn 1M 7\. 360 euro per years for database of investor 8\. Convertable loan: Pay interst rate 5% to 8% = 18 months later (2M found in 10M) convert on based . 9\. Invester Never act as co-founder = full time = 20% 10\. Project profit, 11\. Full time after foun rising Make a plan for your business; take your time to make calculations by creating a target audience. Your target audience determines how you approach your business plan. By studying your target audience, you are making empirical research and collecting information from them Then, secure a good partnership if need be, and get enough capital to start up. * * What the people need * Why people need it * When the people need it * It's affordability * It's ease of use * It's maintenance and revenue Pair programming The SB7 Framework harnesses the influence of stories. The structure describes the 7 most common story elements: • Character • Problem • Guide • Plan • Calls to action • Failure • Success Dear [Hiring Manager’s Name], I am writing to apply for the position of computer vision for IoT and cloud at [Company Name]. I am a highly skilled and experienced computer vision engineer with a strong background in IoT and cloud technologies. I believe that my skills and experience make me an ideal candidate for this position and I am excited about the opportunity to contribute to the success of your organization. I have a solid understanding of computer vision algorithms and techniques, as well as experience in developing and implementing computer vision systems. I am proficient in programming languages such as Python, C++, and Java, and have experience with popular computer vision libraries such as OpenCV, TensorFlow, and PyTorch. In addition, I have a strong background in IoT and cloud technologies, including experience with IoT platforms such as AWS IoT, Azure IoT, and Google Cloud IoT. I am familiar with cloud computing technologies such as AWS, Azure, and Google Cloud, and have experience with deploying and managing computer vision systems on these platforms. I am also a team player and have excellent communication skills. I am able to work with cross-functional teams and can effectively communicate with both technical and non-technical stakeholders. I am also highly motivated, and I am always looking for ways to improve my skills and stay up-to-date with the latest technologies. I am excited about the opportunity to join [Company Name] and to contribute to the development of cutting-edge computer vision systems for IoT and cloud. I am confident that my skills and experience make me a strong candidate for this position, and I look forward to discussing how I can contribute to your organization. Thank you for considering my application. I look forward to hearing from you soon. Sincerely, Title: "Unlocking the Power of Computer Vision for IoT and Cloud" Introduction: * Hi, and welcome to our video on the topic of computer vision for IoT and cloud. In this video, we're going to explore how computer vision technology can be used to enhance IoT and cloud-based systems, and how it can be used to unlock new possibilities for businesses and consumers alike. Body: * First, let's talk about what computer vision is and how it works. Essentially, computer vision is the technology that enables computers to understand and interpret visual information from the world around us. This can include things like images, videos, and even 3D models. * One of the key ways that computer vision can be used to enhance IoT and cloud-based systems is by enabling devices to better understand and interact with their environment. For example, a computer vision-enabled camera could be used to monitor a manufacturing facility and identify when a machine is in need of maintenance or when an employee is working in an unsafe manner. * Another way that computer vision can be used to enhance IoT and cloud-based systems is by enabling devices to better understand and interact with people. For example, a computer vision-enabled security camera could be used to identify individuals and track their movements, or a computer vision-enabled smart home system could be used to detect when someone is in the room and adjust the lighting or temperature accordingly. * Additionally, computer vision can also be used to enhance cloud-based systems by providing more accurate data and insights. For example, a computer vision-enabled drone could be used to collect data on crops and provide farmers with more accurate information about the health and growth of their crops. Conclusion: * Overall, computer vision technology has the potential to unlock new possibilities for businesses and consumers alike, by enabling IoT and cloud-based systems to better understand and interact with their environment and people. We hope this video has provided you with a better understanding of the potential of computer vision for IoT and cloud, and we look forward to seeing the new possibilities that will be created as this technology continues to evolve. Excited to share my latest project using computer vision and IoT to improve efficiency in manufacturing. I used a combination of machine learning algorithms and cloud computing to analyze data from cameras and sensors in real-time, resulting in a 20% increase in production speed. This was a challenging project but I enjoyed every step of it! I am always looking for new opportunities to apply my skills in computer vision and IoT to help companies improve their operations. Let's connect if you are working on a similar project or if you are looking for a developer with these skills. #computervision #IoT #cloudcomputing #manufacturingefficiency #machinelearning #developer" In this post, you briefly mention your experience and skills in computer vision and IoT, and you provide a specific example of a project you worked on that demonstrates your abilities. You also make it clear that you are open to new opportunities, and you invite others to connect with you. Using relevant hashtags such as #computervision #IoT #cloudcomputing can help your post reach a wider audience Exciting news! I just published a paper on a new object detection algorithm that I developed. The algorithm uses a combination of deep learning and computer vision techniques to improve accuracy and speed of object detection in real-world scenarios. This is a big step forward in the field of computer vision and I am proud to have contributed to it. I will be presenting my research at the Computer Vision Conference next month, if you're attending be sure to stop by and say hi! #computervision #objectdetection #deeplearning #research" In this post, you briefly explain the main findings and contributions of your research, and you express your excitement and pride in your work. You also mention the upcoming conference where you will be presenting your research, inviting your friends and colleagues to meet you in person. Also using relevant hashtags such as #computervision #objectdetection #deeplearning can help reach a wider audience interested in the field. Features stores 1\. Car parts detection 2\. Resize keep aspects ration 3\. 3.1 Perform damage detection 4\. 3.2Semantic segregation 5\. Transfer to original coordinates 1 class imbalance 2 class definition Maybe Class in between 3 inconstant annotations Color augmentation 1\. RGB shift 2\. Random brithness and contrast 3\. Sharpen 4\. Hue saturation value Why manually data augmented Becasu control of data. Not too rotate or change something Photogrammetry model Neural radiance fields (NeRF) NeRF in the wild \ [GitHub - google-research/tuning_playbook: A playbook for systematically maximizing the performance of deep learning models.](https://github.com/google-research/tuning_playbook) Yocto and Machine Learning + OpenCV: [https://www.yoctoproject.org](https://www.yoctoproject.org) [https://www.hackster.io/monica/running-machine-learning-on-maaxboard-s-yocto- image-part-1-6a4796](https://www.hackster.io/monica/running-machine-learning- on-maaxboard-s-yocto-image-part-1-6a4796) Bard Google: [https://blog.google/technology/ai/bard-google-ai-search- updates/](https://blog.google/technology/ai/bard-google-ai-search-updates/) [https://mustang.ir/questions/question/راه-اندازی-پروژه-های-گیت-هاب-با-git- pages](https://mustang.ir/questions/question/%D8%B1%D8%A7%D9%87-%D8%A7%D9%86%D8%AF%D8%A7%D8%B2%DB%8C-%D9%BE%D8%B1%D9%88%DA%98%D9%87-%D9%87%D8%A7%DB%8C-%DA%AF%DB%8C%D8%AA-%D9%87%D8%A7%D8%A8-%D8%A8%D8%A7-git- pages) Book: Project Management for Non-Project Managers [https://fa.wikipedia.org/wiki/علی_اکبرپور](https://fa.wikipedia.org/wiki/%D8%B9%D9%84%DB%8C_%D8%A7%DA%A9%D8%A8%D8%B1%D9%BE%D9%88%D8%B1) [https://www.kingorama.com](https://www.kingorama.com) شاهنامه سه بعدی [Accelerate deep learning model development with cloud custom environments - AWS Online Tech Talks - YouTube](https://m.youtube.com/watch?v=2Wt2zlkMtKI&noapp=1) [بخش هایی از کتاب Refactoring (نسخه رایگان)](https://www.developit.ir/refactoring/free.html#f7) [Performance Notes Of PyTorch Support for M1 and M2 GPUs - Lightning AI](https://lightning.ai/pages/community/community-discussions/performance- notes-of-pytorch-support-for-m1-and-m2-gpus/) [Investopedia Academy](https://academy.investopedia.com/) [HandBrake updated with AV1 and VP9 10-bit video encoding](https://9to5mac.com/2022/12/29/handbrake-support-av1-and- vp9-10-bit/) [How to Start Your Sole Proprietorship in 6 Simple Steps](https://qonto.com/en/blog/creators/administrative/sole-proprietorship- in-germany) [Duolingo English Test](https://englishtest.duolingo.com/applicants) [چالش‌های تولید محتوا برای مارکت اروپا و آمریکا - YouTube](https://m.youtube.com/watch?v=wW0HZdubuWQ) [PyTorch for Deep Learning & Machine Learning – Full Course - YouTube](https://m.youtube.com/watch?v=V_xro1bcAuA#dialog) [Why passive investing makes less sense in the current environment | Financial Times](https://archive.ph/0VucZ) [GitHub - google-research/tuning_playbook: A playbook for systematically maximizing the performance of deep learning models.](https://github.com/google-research/tuning_playbook) [GitHub - mgechev/google-interview-preparation-problems: leetcode problems I solved to prepare for my Google interview.](https://github.com/mgechev/google- interview-preparation-problems) [Bayesian Neural Networks and Variational Dropout](https://dmittov.github.io/variational_dropout/#/maximum-likelihood) [One machine learning question every day - bnomial](https://today.bnomial.com/?ref=email) Git remote add orgine Asynchronous Operation Anomaly detection Use experience. Personalizes. Prediction manage society mobility Personalization Covenant Platform. OpenMMLab Wordtune - AI-powered Writing Companion tree -v -I '*.png' -I '*.jpg' \--charset utf-8 >list2.txt 3D object using triangular mesh need vertices point cloud underlying surface of some 3D object, faster Definition of Done User Story complete Code\Implementation complete Code\Implementation Peer Reviews) approved Unit tests complete (if required) Testing Notes complete (if required) User Story Acceptance criteria defined and verified Backend: Python, Redis, Postgres, Celery Frontend: React, Redux, TypeScript DevOps: Terraform, Kubernetes, GitHub, Docker, AWS Data: Python (Data Science), Kafka, Fastapi, MLFlow, AWS SageMaker ML: Selcond core, Kubeflow, … [Sharpness](https://en.wikipedia.org/wiki/Sharpness_%28visual%29) ,[Noise](https://en.wikipedia.org/wiki/Image_noise), [Dynamic range](https://en.wikipedia.org/wiki/Dynamic_range), [Tone reproduction](https://en.wikipedia.org/wiki/Tone_reproduction) , [Contrast](https://en.wikipedia.org/wiki/Contrast_%28vision%29), [Color](https://en.wikipedia.org/wiki/Color), [Distortion](https://en.wikipedia.org/wiki/Distortion_%28optics%29) , [DSLR lenses](https://en.wikipedia.org/wiki/Lenses_for_SLR_and_DSLR_cameras), [Vignetting](https://en.wikipedia.org/wiki/Vignetting), [Exposure](https://en.wikipedia.org/wiki/Exposure_%28photography%29), Lateral [chromatic aberration](https://en.wikipedia.org/wiki/Chromatic_aberration) (LCA), [Lens flare](https://en.wikipedia.org/wiki/Lens_flare), Color, [Artifacts](https://en.wikipedia.org/wiki/Compression_artifact) ۱\. جهت انتخاب کلمه مورد نظرتان، دو بار روی آن تپ کنید. ۲\. برای انتخاب کل یک پاراگراف، کافیست چهار با روی آن تپ کنید. ۳\. یک انگشت را در ابتدا و انگشت دیگر را در آخر یک محدود گذاشته و کمی نگه دارید. متن میان دو انگشت انتخاب خواهد شد. ۴\. روی ابتدای محدوده ای دلخواه دو بار تپ کرده و بلافاصله با درگ کردن (کشیدن) پین محدوده ی انتخاب شده را گسترش دهید. (انگشت خود را پس از دومین تپ جدا نکنید) ۵\. برای انتخاب کل پاراگراف، به جز استفاده از مورد ۲، می توانید با دو انگشت، یک بار روی آن تپ کنید. namely motion estimation, motion smoothing, and image warping. Motion estimation algorithms often use a similarity transform to handle camera translations, rotations, and zooming. The tricky part is getting these algorithms to lock onto the background motion, 0\. video frames captured during fast motion are often blurry. Their appearance can be improved either using deblurring techniques (Section 10.3) or stealing sharper pixels from other frames with less motion or better focus (Matsushita, Ofek, Ge et al. 2006). Exercise 8.3 has you implement and test some of these ideas. 1\. Background subtraction 2\. Motion estimation 3\. Motion smoothing 4\. Image warping. image warping can result in missing borders around the image, which must be cropped, filled using information from other frames, or hallucinated using inpainting techniques (Section 10.5.1). Vision stabilization There is much recent work on Multi-view 3D reconstruction is a central research topic in computer vision that is driven in many different directions There are many available methods that can handle the noisy image completion problem In the case of surveillance using a fixed camera, there is no desired motion. In the case of most robotic applications, horizontal and vertical motions are desired, but rotation is not. In some cases of ground vehicles where the terrain is known to have many incline changes, or with aerial vehicles undergoing complicated maneuvers where the vehicle’s body is meant to be in varying orientations, rotation might be desired as the robot is meant to be at an angle at times. In robotics applications, computational complexity is extremely important due to the need for real-time operation. Also, it is likely that the center of rotation will not lie in the center of the image frame because the camera is rarely mounted at the robot’s center of mass. This first assumption is made in many video stabilization algorithms, and is a convenient way to seed the correct features with higher trust values. It is not an unreasonable assumption to make. Depending on the application, there is often a large portion of frames where local motion does not occur. In some situations, such as monitoring of steady traffic, there is no guarantee that local motion will not occur. This situation has not been tested, nor has our algorithm been designed to handle it. The second assumption comes from a combination of common sense, and the experience of many computer vision researchers. It makes sense that an object in the scene which does not move will be recognized more easily and more often. Being recognized consistently and consecutively is considered stable. On the other hand, objects which have local motion are less likely to be recognized as often. They might move through shadows, change orientation, or even move completely out of the scene. These possibilities all lead to a less stable class of features. It is likely that, more often than not, there are more background features than foreground features. Moving objects generally cover a small portion of the screen, which usually yields fewer features. Although uncommon, we did not want to make the assumption that this would occur in every frame. Certain scenes will consist of a large portion of local motion, or an object will move very close to the camera, consuming a much larger portion of the scene than the background. As long as some background features are discovered in each frame, our stabilization algorithm should succeed. # image processing tips: * the image size and kernel size need to depended. the best way is to use the one variable to define the size of the image and kernel together. * the coordinate of the image start at top left of the image/display * in order to change it to the normal coordinate you can use * grid of points; two matrix to X , Y coordinate * subtract half of W, H from X, Y in order to have normal coordinate system for our image * now we have cartesian coordinate * * cartesian coordinate to polar coordinate * تبدیل فضای کارتزین به پولار در خیلی از برنامه های پردازش تصویر کارایی دارد. برای پیدا کردن ترشلد ها هم می توان استفاده کرد * in MATLAB we can use ":"for example MatrixA(:) which means all entity of the matrix no mater how many dimensions we have but if we want to implemented in Python we can use numpy.flatten(). * in the MATLAB the round is different from python. if you want same result you need implement the rand function by yourself. * imge_mask=np.ones_like(image_source)*255 * imge_mask=imge_mask.astype(np.uint8) * imge_mask=imge_mask.flatten() ??? .ravel() * .asarray * np.logical_and( 1, 2) * indexes=[index for index in range(len(array1)) if array1[index] == True] * cv2.bitwise_not(yyy) * "olive" editor remove silence ![](https://lh5.googleusercontent.com/nILOXEoEKiANosdHjTOC05i7h8b-84246iAmayzrsrwyQtrN_ZG776o1GnXEFO0E0yH9lMQqIokQWJJgFxAvIzsUdQG6vzewTBzTMKkc1A4J4Lq94r_tVjMgcij_2Nj3DQ=w1280) Questions: How to train model to add new classes? How to add a new class to an existing classifier in deep learning? Adding new Class to One Shot Learning trained model Is it possible to train a neural network as new classes are given? Merging all several models that detection system for all these tasks. Answer 1: There are several ways to add new classes to the trained model, which require just training for the new classes. * Incremental training ([GitHub](https://github.com/khurramjaved96/incremental-learning)) * continuously learn a stream of data ([GitHub](https://github.com/creme-ml/creme)) * online machine learning ([GitHub](https://github.com/GMvandeVen/continual-learning)) * Transfer Learning Twice * Continual learning approaches (Regularization, Expansion, Rehearsal) ([GitHub](https://github.com/facebookresearch/Adversarial-Continual-Learning)) Answer 2: Online learning is a term used to refer to a model which takes a continual or sequential stream of input data while training, in contrast to offline learning (also called batch learning), where the model is pre-trained on a static predefined dataset. Continual learning (also called incremental, continuous, lifelong learning) refers to a branch of ML working in an online learning context where models are designed to learn new tasks while maintaining performance on historic tasks. It can be applied to multiple problem paradigms (including Class- incremental learning, where each new task presents new class labels for an ever expanding super-classification problem). Do I need to train my whole model again on all four classes or is there any way I can just train my model on new class? Naively re-training the model on the updated dataset is indeed a solution. Continual learning seeks to address contexts where access to historic data (i.e. the original 3 classes) is not possible, or when retraining on an increasingly large dataset is impractical (for efficiency, space, privacy etc concerns). Multiple such models using different underlying architectures have been proposed, but almost all examples exclusively deal with image classification problems. Answer 3: You could use transfer learning (i.e. use a pre-trained model, then change its last layer to accommodate the new classes, and re-train this slightly modified model, maybe with a lower learning rate) to achieve that, but transfer learning does not necessarily attempt to retain any of the previously acquired information (especially if you don't use very small learning rates, you keep on training and you do not freeze the weights of the convolutional layers), but only to speed up training or when your new dataset is not big enough, by starting from a model that has already learned general features that are supposedly similar to the features needed for your specific task. There is also the related domain adaptation problem. There are more suitable approaches to perform incremental class learning (which is what you are asking for!), which directly address the [catastrophic forgetting problem](https://ai.stackexchange.com/a/13293/2444). For instance, you can take a look at this paper [Class-incremental Learning via Deep Model Consolidation](https://arxiv.org/pdf/1903.07864.pdf), which proposes the Deep Model Consolidation (DMC) approach. There are other continual/incremental learning approaches, many of them are described [here](https://ai.stackexchange.com/a/24529/2444) or in more detail [here](https://reader.elsevier.com/reader/sd/pii/S0893608019300231). Answer 4: by using Continual learning approaches to trained without losing the original classes. It has 3 categories: Regularization Expansion Rehearsal Answer 5: if you access to the dataset then you can download it and add all you new classes when you have " 'N' COCO Classes + 'M' New classes " after that you can fine tune model based on new dataset. you do not need all of the dataset just same number of image for all class enough. [https://learnopencv.com/stanford-mrnet-challenge-classifying-knee- mris/](https://learnopencv.com/stanford-mrnet-challenge-classifying-knee- mris/) Before start your machine learning project ask these questions and preparation: What is your inference hardware? specify the use case. specify model interface. how would we monitor performance after deployment? how can we approximate post-deployment monitoring before deployment? build a model and iteratively improve it. How to deploy the model at the end? monitor performance after deployment. what is your metric? How do you split your data (training and validation)? ### Preparation ML Project Workflow * [What is your hardware ?](/topics-and-projects/hardware) * specify the use case * specify model interface * how would we monitor performance after deployment? * how can we approximate post-deployment monitoring before deployment? * build a model and iteratively improve it * deploy the model * monitor performance * what is your are metric? * How do you split your data? ### Before Training deep learning model * using large model to train because * it is faster to train with lower overfit and faster converge due to best training * it is easier and higher compress in the final stage * model compression and acceleration: reducing parameters without significantly decreasing the model performance * Data: How to have good data for training deep learning models; How to Build and Enhance A Good Data Set For Your Deep Learning Project: using same config and data for training and inference, removing redundant (delete data which you don't need), get more data, Handle missing data, using data augmentation techniques or GAN to generate more data, re-scale/balance data, Transform your data (Change data types), Feature selection based on data-set and use case * * The data you don't need: removing redundant samples * get more data * Invent more data * data augmentation * Re-scale data * balance datasets * Transform your data * Feature selection based on dataset and use case * ML-Augmented Video Object Tracking: By applying and evaluating multiple algorithmic models, enhanced ability to scale object tracking in high-density video compositions. ### Training deep learning model * automated hyper-parameters * Using Hyperparameter tuning / Hyperparameter optimization tools * AutoML * genetic algorithm * population based training * bayesian optimization * You need to set some parameters and config for training * * Diagnostics * Weight Initialization * Learning rate * Activation function * Network Topology * Batches and Epochs * Regularization * Optimization and Loss * Early Stopping ### Continuous delivery * evolve with latest detection models * more data (no labels) * semi-supervised learning: big self-supervised models are strong semi-supervised learners ### After Training deep learning model * Parameter pruning * model pruning: reducing redundant parameters which are not sensitive to the performance. * aim: remove all connections with absolute weights below a threshold * Quantization * compresses by reducing the number of bits used to represent the weights * quantization effectively constraints the number of different weights we can use inside our kernels * per-channel quantization for weights, which improves performance by model compression and latency reduction. * Low rank matrix factorization (LRMF) * there exists latent structures in the data, by uncovering which we can obtain a compressed representation of the data * LRMF factorizes the original matrix into lower rank matrices while preserving latent structures and addressing the issue of sparseness * Compact convolutional filters (Video/CNN) * designing special structural convolutional filters to save parameters * replace over parametric filters with compact filters to achieve overall speedup while maintaining comparable accuracy * Knowledge distillation * training a compact neural network with distilled knowledge of a large model * distillation (knowledge transfer) from an ensemble of big networks into a much smaller network which learns directly from the cumbersome model's outputs, that is lighter to deploy * Binarized Neural Networks (BNNs) * Apache TVM (incubating) is a compiler stack for deep learning systems * Neural Networks Compression Framework (NNCF) ### Deep learning model in production * security: controls access to model(s) through secure packaging and execution * Test * auto training * using parallel processing and library such as GStreamer # Technology Docker AWS Flask Django # My Keynote (February 2021) 1. introduction 2. Machine Learning/ Deep Learning Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed 3. supervised Machine Learning 1. Deep Convolutional Neural Networks (DCNN) Architecture 2. Visualizing and Understanding Convolutional Networks 3. Object Detection by Deep Learning 4. [Video Tracking](/topics-and-projects/video-tracking) 5. Style Transfer 4. semi-supervised Machine Learning/ Deep Reinforcement learning (DRL) 1. Google 2. [Deep Reinforcement learning (DRL)](/topics-and-projects/drl) 5. unsupervised Machine Learning 1. Auto Encoder 6. Generative Adversarial Networks (GANs) 7. Tools 8. Pre trained model 9. Effect of Augmented Datasets to Train DCNNs 10. Training for more classes 11. Optimization 12. [Hardware](/topics-and-projects/hardware) 13. Production setup 14. post development 15. business , Gartner, Hype Cycle for emerging technologies, 2025 ### Advanced and practical 1. Inside CNN 1. Deep Convolutional Neural Networks Architecture 2. Convolution 3. Convolution Layer 4. Conv/FC Filters 5. Activation Functions 6. Layer Activations 7. Pooling Layer 8. Dropout ; L2 pooling 9. Why 1. Max-pooling is useful 2. How to see inside each layer and find important features * Visualizing and Understanding Convolutional Networks * [https://tensorspace.org/](https://tensorspace.org/) * [https://www.youtube.com/watch?v=AgkfIQ4IGaM](https://www.youtube.com/watch?v=AgkfIQ4IGaM) 2. Hands on python for deep learning 3. Fundamental deep learning 4. Installation: TensorFlow, PyTorch 5. [Using PC+eGPU for training video tracking](/topics-and-projects/source-code/compile) Summary of the summit * AI Hardware Europe Summit (July 2020) * [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit) * Apache TVM And Deep Learning Compilation Conference (December 2020) * [RISC-V Summit (December 2020) ](/workshops-and-events/risc-v) [https://www.inspectar.com/demo](https://www.inspectar.com/demo) for rasp # Face * Effective and precise face detection based on color and depth data * [https://www.sciencedirect.com/science/article/pii/S221083271400009X](https://www.sciencedirect.com/science/article/pii/S221083271400009X) * containing or not containing a face * Eigenface, Fisherface, waveletface, PCA (Principal Component Analysis), LDA (Linear Dis-criminant Analysis), Haar wavelet transform, and so on. * Viola–Jones detector * illumination changes and occlusion * depthinformation is used to filter the regions of the image where a candidate face regionis found by the Viola–Jones (VJ) detector * \- the first filtering rule is defined on the color of the region; since some false positiveshave colors not compatible with the face (e.g. shadows on jeans) a skin detector isapplied to remove the candidate face regions that do not contain skin pixels; * \- the second filtering rule is defined on the size of the face: using the depth mapit is quite easy to calculate the size of the candidate face region, which is use-ful to discard smallest and largest faces from the final result set; * \- the third filtering rule is defined on the depth map to discard flat objects (e.g.candidate faces found in a wall) or uneven objects (e.g. candidate face foundin the leaves of a tree). Combining color and depth data the candidate faceregion can be extracted from the background and measures of depth and reg-ularity are used for filtering out false positives. * The size criteria simply remove the candidate faces not included in a fixed rangesize ([12.5,30] cm). The size of a candidate face region is extracted from the depthmap according to the following approach. * image below * Gaussian mixture 3D morphable face model * [https://www.sciencedirect.com/science/article/pii/S0031320317303527](https://www.sciencedirect.com/science/article/pii/S0031320317303527) * * * Face Synthesis for Eyeglass-Robust Face Recognition * [https://paperswithcode.com/paper/face-synthesis-for-eyeglass-robust-face](https://paperswithcode.com/paper/face-synthesis-for-eyeglass-robust-face) * GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data * [https://paperswithcode.com/paper/genegan-learning-object-transfiguration-and](https://paperswithcode.com/paper/genegan-learning-object-transfiguration-and) * FacePoseNet: Making a Case for Landmark-Free Face Alignment * [https://paperswithcode.com/paper/faceposenet-making-a-case-for-landmark-free](https://paperswithcode.com/paper/faceposenet-making-a-case-for-landmark-free) * Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision * [https://paperswithcode.com/paper/learning-to-regress-3d-face-shape-and](https://paperswithcode.com/paper/learning-to-regress-3d-face-shape-and) * Unsupervised Eyeglasses Removal in the Wild * [https://paperswithcode.com/paper/unsupervised-eyeglasses-removal-in-the-wild](https://paperswithcode.com/paper/unsupervised-eyeglasses-removal-in-the-wild) * How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks) * [https://arxiv.org/pdf/1703.07332v3.pdf](https://arxiv.org/pdf/1703.07332v3.pdf) * (a) we construct, for the first time, a very strong baseline by combining a state-of-the-art architecture for landmark localization with a state-of-the-art residual block, train it on a very large yet synthetically expanded 2D facial landmark dataset and fi- nally evaluate it on all other 2D facial landmark datasets. * (b) We create a guided by 2D landmarks network which con- verts 2D landmark annotations to 3D and unifies all exist- ing datasets, leading to the creation of LS3D-W, the largest and most challenging 3D facial landmark dataset to date (~230,000 images). * (c) Following that, we train a neural network for 3D face alignment and evaluate it on the newly introduced LS3D-W. * (d) We further look into the effect of all “traditional” factors affecting face alignment performance like large pose, initialization and resolution, and introduce a “new” one, namely the size of the network. * (e) We show that both 2D and 3D face alignment networks achieve per- formance of remarkable accuracy which is probably close to saturating the datasets used. * Training and testing code as well as the dataset can be downloaded from https: //[www.adrianbulat.com/face-alignment/](http://www.adrianbulat.com/face-alignment/) ![](https://lh6.googleusercontent.com/d8ABZ3w_DsDnuxD_X_PaSGPK9sxYEZhuyrYuZLCcmgFLMmTmheY4FDHRb3Cbhg- lYHPf4AdNHufhU04dxPdG3_pjwCOx9l7BZM9gLwwest05tq8ELg9sNocjKkjnMe6h=w1280) 19.Sep.2021 [Medium](https://medium.com/p/626019137fa9/edit) [https://fi.co/madlibs](https://fi.co/madlibs) [https://orcid.org/0000-0001-8382-1389](https://orcid.org/0000-0001-8382-1389) Dreyer's English (learn write English) #book story Greek Mythology Explained: A Deeper Look at Classical Greek Lore and Myth **Papers:** CALTag: High Precision Fiducial Markers for Camera Diatom Autofocusing in Brightfield Microscopy: a Comparative Study :implementation variation of the laplacian Analysis of focus measure operators in shape-from-focus: why laplacian? Blure detection? Iqaf? Optical flow modeling and computation: A survey Toward general type 2 fuzzy logic systems based on zSlices \-------------------------------------------------------------------- Lost in space The OA Film:[ https://en.wikipedia.org/wiki/Shark_Tank](https://en.wikipedia.org/wiki/Shark_Tank) Movie Serial billons monk serial movies Python async Highly decoupled microservice Edex RIS-V , Self-car RISC-V Magazine Road map Game: over/under [https://www.sporcle.com/games/Hejman/underwhelmed](https://www.sporcle.com/games/Hejman/underwhelmed) \-------------------------------------------------------------------- \-------------------------------------------------------------------- GDPR in IoT The EU General Data Protection Regulation (GDPR) and Face Images in IoT The GDPR (General Data Protection Regulation), taking effect in May 2018, introduces strict requirements for personal data protection and the privacy rights of individuals. The EU regulations will set a new global standard for privacy rights and change the way organizations worldwide store and process personal data. The GDPR brings the importance of preserving the privacy of personal information to the forefront, yet the importance of face images within this context is often overlooked. The purpose of this paper is to introduce a solution that helps companies protect face images in IoT devices which record or process image by camera, to strengthen compliance with the GDPR. Our Face is our Identity Our face is the most fundamental and highly visible element of our identity. People recognize us when they see our face or a photo of our face. Recent years have seen exponential increase in the use, storage and dissemination of face images in both private and public sectors - in social networks, corporate databases, IoT, smart-city deployments, digital media, government applications, and nearly every organization’s databases. \--------------------- $(aws-okta env stage) aws s3 cp s3://dataset/archive.tar.gz /Users/a.zip aws s3 ls images | tail -n 100 aws s3 cp staging-images/test.jpg /Users/test.jpg \--------------------- screen -rD k get pods Docker RUN chmod +x /tmp/run.sh Can run docker in terminal and run code line by line docker run -it --rm debian:stable-slim bash apt-get update apt-get installl -y \-------------------------------- brew install awscli aws-okta kubectx kubernetes-cli tfenv touch ~/.aws/config \-------------------------------------------------------------------- docker image rm TETSTDFSAFDSADF docker image ls docker system prune docker run -p 5000:5000 nameDocker:latest docker build . -t nameDocker:latest docker container stop number-docker-name docker container ls * docker pull quay.io/test:v0.0.1 * docker run --rm -p 5000:5000 -it quay.io/test:v0.0.1 * curl --header "Content-Type: application/json" \--request POST --data '[{"fixed":7.4, "a":0, "b":0.56, "c":9.4}]'[ http://127.0.0.1:5000/predict](https://meet.google.com/linkredirect?authuser=0&dest=http%3A%2F%2F127.0.0.1%3A5000%2Fpredict) * docker run --rm -v /home/.aws/credentials:/root/.aws/credentials -it quay.io/test /bin/sh aws s3 ls --profile=test \-------------------------------- Cloud software engineer and consultant focusing on building highly available, scalable and fully automated infrastructure environments on top of Amazon Web Services and Microsoft Azure clouds. My goal is always to make my customers happy in the cloud. \---------------- Search google for 3d = tiger - iPhone show AR/VR \--------------- brew install youtube-dl \---------------------------- List: Collection bucket : 1 for week 2 for month 3 for future \-------------------------------------------------------------------- **• Per frame operation** – Detection – Classification – Segmentation – Feature extraction – Recognition **• Across frames ** – Tracking – Counting **• High level** – Intention – Relations – Analyzing ============================= Deep compression Pruning deep learning Hash table neural network Dl compression Deep compression =================================== Mini PCI-e slot * What have I learned so far: * Problem-based learning * real life scenarios * index card (answer , idea) * Think-Pair-Share * Leverage flip charts * Summarizing \-------------------------------------------------------------------- Self \\\ Advancing Self-Supervised and Semi-Supervised Learning with SimCLR \cite{Chen2020} %https://github.com/google-research/simclr first pretraining on a large unlabeled dataset and then fine-tuning on a smaller labeled dataset pretraining on large unlabeled image datasets, as demonstrated by Exemplar- CNN, Instance Discrimination, CPC, AMDIM, CMC, MoCo and others. “A Simple Framework for Contrastive Learning of Visual Representations”, 85.8\% top-5 accuracy using 1\% of labeled images on the ImageNet dataset contrastive learning algorithms linear evaluation protocol (Zhang et al., 2016; Oord et al.,2018; Bachman et al., 2019; Kolesnikov et al., 2019) unsupervised learning benefits more from bigger models than its supervised counterpart. \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- Some of optimization algorithms ======================== Swarm Algorithm =============== 1\. Ant Colony Optimization (ACO) was inspired by the research on the behavior of ant colonies 2\. Firefly Algorithm based on insects called fireflies 3\. Marriage in Honey Bees Optimization Algorithm (MBO algorithm) is inspired by the process of reproduction of Honey Bee 4\. Artificial Bee Colony Algorithm (ABC) is based on the recollection of the Honey Bees 5\. Wasp Swarm Algorithm was inspired on the Parasitic wasps 6\. Bee Collecting Pollen Algorithm (BCPA) 7\. Termite Algorithm 8\. Mosquito swarms Algorithm (MSA) 9\. zooplankton swarms Algorithm (ZSA) 10\. Bumblebees Swarms Algorithm (BSA) 11\. Fish Swarm Algorithm (FSA) 12\. Bacteria Foraging Algorithm (BFA) 13\. Particle Swarm Optimization (PSO) 14\. Cuckoo Search 15\. Bat Algorithm (BA) 16\. Accelerated PSO 17\. Bee System 18\. Beehive Algorithm 19\. Cat Swarm 20\. Consultant-guided search 21\. Eagle Strategy 22\. Fast Backterial swarming algorithm 23\. Good lattice swarm optimization 24\. Glowworm swarm optimization 25\. Hierarchical swarm model 26\. Krill Herd 27\. Monkey Search 28\. Virtual ant algorithm 29\. Virtual bees 30\. Weighted Swarm Algorithm 31\. Wisdom of Artificial Crowd algorithm 32\. Prey-predator algorithm 33\. Memetic algorithm 34\. Lion Optimization Algorithm 35\. Chicken Swarm Optimization 36\. Ant Lion Optimizer 37\. Compact Particle Swarm Optimization 38\. Fruit Fly Optimization Algorithm 39\. marine propeller optimization algorithm 40\. The Whale Optimization Algorithm 41\. virus colony search algorithm 42\. Slime mould optimization algorithm Ecology Inspired Algorithm ========================== 1\. Biogeography-based Optimization 2\. Invasive Weed Optimization 3\. Symbiosis-Inspired Optimization - PS2O 4\. Atmosphere Clouds Model 5\. Brain Storm Optimization 6\. Dolphin echolocation 7\. Japanese Tree Frog Calling algorithm 8\. Eco-inspired evolutionary algorithm 9\. Egyptian Vulture 10\. Fish School search 11\. Flower Pollination algorithm 12\. Gene Expression 13\. Great Salmon Run 14\. Group Search Optimizer 15\. Human Inspired Algorithm 16\. Roach Infestation algorithm 17\. Queen-bee algorithm 18\. Shuffled frog leaping algorithm 19\. Forest Optimization Algorithm 20\. coral reefs optimization algorithm 21\. cultural evolution algorithm 22\. Grey Wolf Optimizer 23\. probabilistic pso 24\. omicron aco algorithm 25\. shark smell optimization 26\. social spider algorithm 27\. sosial insects behavior algorithm 28\. sperm whale algorithm Evolutionary Optimization ========================= 1\. Genetic Algorithm 2\. Genetic Programming 3\. Evolutionary Strategies 4\. Differential Evolution 5\. Paddy Field Algorithm 6\. Queen-bee Evolution 7\. Quantum Inspired Social Evolution Physic and Chemistry inspired algorithm ======================================= 1\. Big bang-Big Crunch 2\. Block hole algorithm 3\. Central force optimization 4\. Charged System search 5\. Electro-magnetism optimization 6\. Galaxy based search algorithm 7\. Gravitational search 8\. Harmony search algorithm 9\. Intelligent water drop algorithm 10\. River formation algorithm 11\. Self-propelled dynamics 12\. Simulated Annealing 13\. Stachastic diffusion search 14\. Spiral optimization 15\. Water Cycle algorithm 16\. Artificial Physics optimization 17\. Binary Gravitational search algorithm 18\. Continous quantum ant colony optimization 19\. Extended artificial physics optimization 20\. Extended Central force optimization 21\. Electromagnetism-like heuristic 22\. Gravitational Interaction optimization 23\. Hysteristetic Optimization algorithm 24\. Hybrid quantum-inspired GA 25\. Immune gravitational inspired algorithm 26\. Improved quantum evolutinary algorithm 27\. Linear programming 28\. Quantum-inspired bacterial swarming 29\. Quantum-inspired evolutionary algorithm 30\. Quantum-inspired genetic algorithm 31\. Quantum-behaved PSO 32\. Unified big bang-chaotic big crunch 33\. Vector model of artificial physics 34\. Versatile quantum-inspired evolutionary algorithm 35\. Space Gravitational Algorithm 36\. Ion Motion Algorithm 37\. Light Ray Optimization Algorithm 38\. Ray Optimization 39\. Photosynthetic Algorithms 40\. floorplanning algorithm 41\. Gases Brownian Motion Optimization 42\. gradient-type optimization 43\. mean-variance optimization 44\. Mine blast algorithm 45\. moth flame optimization 46\. multi battalion search algorithm 47\. music inspired optimization 48\. no free lunch theorems algorithm 49\. Optics inspired optimization 50\. runner-root algorithm 51\. sine cosine algorithm 52\. pitch tracking algorithm 53\. Stochastic Fractal Search algorithm 54\. stroke volume optimization 55\. Stud krill herd algorithm 56\. The Great Deluge Algorithm 57\. Water Evaporation Optimization 58\. water wave optimization algorithm 59\. Island model algorithm 60\. 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Let's partner up to take your project to the next level! pip install mlc-ai-nightly -f https://mlc.ai/wheels https://mlc.ai/ https://mlc.ai/summer22/ Day 1: Introduction to Unity: TVMScript Introduction to Unity: Relax and PyTorch TVM BYOC in Practice Get Started with TVM on Adreno GPU Introduction to Unity: Metaschedule How to Bring microTVM to a custom IDE Day 2: Community Keynote PyTorch 2.0: the journey to bringing compiler technologies to the core of PyTorch Support QNN Dialect for TVM with MediaTek Neuron and Devise the Scheduler for Acceleration On-Device Training Under 256KB Memory AMD Tutorial TVM at TI: Accelerating inference using the C7x/MMA Adreno GPU: 4x speed-up and upstreaming to TVM mainline Transfer-Tuning: Reusing Auto-Schedules for Efficient Tensor Program Code Generation Improvement in the TVM OpenCL codegen to autogenerate optimal convolution kernels for Adreno GPUs TVM Unity: Pass Infrastructure and BYOC Renesas Hardware accelerators with Apache TVM Introduction on 4th Gen Intel Xeon processor and BF16 support with TVM Hidet: Task Mapping Programming Paradigm for Deep Learning Tensor Programs Towards Building a Responsible Data Economy Optimizing SYCL Device Kernels with AKG Adreno GPU Performance Enhancements using TVM Improvements to CMSIS-NN integration in TVM UMA: Universal Modular Accelerator Interface Day 3: TVM Unity for Dynamic Models Empower Tensorflow serving with backend TVM Enabling Conditional Computing on Hexagon target Decoupled Model Schedule for Large Deep Learning Model Training Using TVM to bring Bayesian neural networks to embedded hardware Efficient Support of TVM Scan OP on RISC-V Vector Extension Improvements to Ethos-U55 support in TVM including CI on Alif Semiconductor boards Compiling Dynamic Shapes TVM Packaging in 2023: delivering TVM to end users Cross-Platform Training Using Automatic Differentiation on Relax IR AutoTVM: Reducing tuning space by cross axis filtering SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning Analytical Tensorization and Fusion for Compute-intensive Operators CUTLASS 3.0: Next Generation Composable and Reusable GPU Linear Algebra Library Enabling Data Movement and Computation Pipelining in Deep Learning Compiler Automating DL Compiler Bug Finding with NNSmith TVM at NIO TVM at Tencent Integrating the Andes RISC-V Processors into TVM Alpa: A Compiler for Distributed Deep Learning ACRoBat: Compiler and Runtime Techniques for Efficient Auto-Batching of Dynamic Deep Learning Computations Channel Folding: a Transform Pass for Optimizing Mobilenets ========================================================================Day 1: ************************ Introduction to Unity: TVMScript [https://github.com/cyx-6/TVM- Demo/blob/main/tvmscript.ipynb](https://github.com/cyx-6/TVM- Demo/blob/main/tvmscript.ipynb) Gan NN show us some hidden patter in history we can not see before. “I always have a slip of paper at hand, on which I note down the ideas of certain pages. On the backside I write down the bibliographic details. After finishing the book I go through my notes and think how these notes might be relevant for already written notes in the slip-box. It means that I always read with an eye towards possible connections in the slip-box.” (Luhmann et al., 1987, 150) Deep representation learning Model evaluation. Camera cheaper lidar Point cloud because of we need 3d Capturing reality 1\. 𝐀𝐝𝐝/𝐂𝐨𝐦𝐦𝐢𝐭 𝐀𝐥𝐥 Standard way: git add . git commit -m "Message" Another way: git commit -a -m "Message" 𝟐\. 𝐀𝐥𝐢𝐚𝐬𝐞𝐬 With aliases, you can write your own Git commands that do anything you want. Eg: git config --global alias.ac '!git add -A && git commit -m' (alias called ac, git add -A && git commit -m will do the full add and commit) 𝟑\. 𝐑𝐞𝐯𝐞𝐫𝐭 The revert command simply allows us to undo any commit on the current branch. Eg: git revert 486bdb2 Another way: git revert HEAD (for recent commits) 𝟒\. 𝐑𝐞𝐟𝐥𝐨𝐠 This command lets you easily see the recent commits, pulls, resets, pushes, etc on your local machine. Eg: git reflog 𝟓\. 𝐏𝐫𝐞𝐭𝐭𝐲 𝐋𝐨𝐠𝐬 Gives you the ability to print out a pretty log of your commits/branches. Eg: git log --graph --decorate --oneline 𝟔\. 𝐒𝐞𝐚𝐫𝐜𝐡𝐢𝐧𝐠 𝐋𝐨𝐠𝐬 One can also use the log command to search for specific changes in the code. Eg: git log -S "A promise in JavaScript is very similar" 𝟕\. 𝐒𝐭𝐚𝐬𝐡 This command will stash (store them locally) all your code changes but does not actually commit them. Eg: git stash 𝟖\. 𝐑𝐞𝐦𝐨𝐯𝐞 𝐃𝐞𝐚𝐝 𝐁𝐫𝐚𝐧𝐜𝐡𝐞𝐬 This command will delete all the tracking information for branches that are on your local machine that are not in the remote repository, but it does not delete your local branches. Eg: git remote update --prune 𝟗\. 𝐁𝐢𝐬𝐞𝐜𝐭 For finding which commits caused certain bugs Eg: git bisect start git bisect bad git bisect good 48c86d6 𝟏𝟎\. 𝐃𝐞𝐬𝐭𝐫𝐨𝐲 𝐋𝐨𝐜𝐚𝐥 𝐂𝐡𝐚𝐧𝐠𝐞𝐬 One can wipe out all changes on your local branch to exactly what is in the remote branch. Eg: git reset --hard origin/main Don’t trust your devices IoT. software and hardware are together for better business. Newsletter investing every 3 months 1\. Prototyping. New bie 2\. Patent. Website. ( list of investors) 3\. Pre seed. First founding 1M VC, inistution, anjel capital. 400 000 preseed. Quveribel. Equtible rund convertible non agreement Template. Convertabel lone 1\. Germ standar inistitude 2\. 4\. Equity. Venture builder. 20% 200 000 5\. 100 000 per year to become unocorn in less than 10 years 6\. Soniy corn 100k unicorn 1M 7\. 360 euro per years for database of investor 8\. Convertable loan: Pay interst rate 5% to 8% = 18 months later (2M found in 10M) convert on based . 9\. Invester Never act as co-founder = full time = 20% 10\. Project profit, 11\. Full time after foun rising Make a plan for your business; take your time to make calculations by creating a target audience. Your target audience determines how you approach your business plan. By studying your target audience, you are making empirical research and collecting information from them Then, secure a good partnership if need be, and get enough capital to start up. * * What the people need * Why people need it * When the people need it * It's affordability * It's ease of use * It's maintenance and revenue Pair programming The SB7 Framework harnesses the influence of stories. The structure describes the 7 most common story elements: • Character • Problem • Guide • Plan • Calls to action • Failure • Success Dear [Hiring Manager’s Name], I am writing to apply for the position of computer vision for IoT and cloud at [Company Name]. I am a highly skilled and experienced computer vision engineer with a strong background in IoT and cloud technologies. I believe that my skills and experience make me an ideal candidate for this position and I am excited about the opportunity to contribute to the success of your organization. I have a solid understanding of computer vision algorithms and techniques, as well as experience in developing and implementing computer vision systems. I am proficient in programming languages such as Python, C++, and Java, and have experience with popular computer vision libraries such as OpenCV, TensorFlow, and PyTorch. In addition, I have a strong background in IoT and cloud technologies, including experience with IoT platforms such as AWS IoT, Azure IoT, and Google Cloud IoT. I am familiar with cloud computing technologies such as AWS, Azure, and Google Cloud, and have experience with deploying and managing computer vision systems on these platforms. I am also a team player and have excellent communication skills. I am able to work with cross-functional teams and can effectively communicate with both technical and non-technical stakeholders. I am also highly motivated, and I am always looking for ways to improve my skills and stay up-to-date with the latest technologies. I am excited about the opportunity to join [Company Name] and to contribute to the development of cutting-edge computer vision systems for IoT and cloud. I am confident that my skills and experience make me a strong candidate for this position, and I look forward to discussing how I can contribute to your organization. Thank you for considering my application. I look forward to hearing from you soon. Sincerely, Title: "Unlocking the Power of Computer Vision for IoT and Cloud" Introduction: * Hi, and welcome to our video on the topic of computer vision for IoT and cloud. In this video, we're going to explore how computer vision technology can be used to enhance IoT and cloud-based systems, and how it can be used to unlock new possibilities for businesses and consumers alike. Body: * First, let's talk about what computer vision is and how it works. Essentially, computer vision is the technology that enables computers to understand and interpret visual information from the world around us. This can include things like images, videos, and even 3D models. * One of the key ways that computer vision can be used to enhance IoT and cloud-based systems is by enabling devices to better understand and interact with their environment. For example, a computer vision-enabled camera could be used to monitor a manufacturing facility and identify when a machine is in need of maintenance or when an employee is working in an unsafe manner. * Another way that computer vision can be used to enhance IoT and cloud-based systems is by enabling devices to better understand and interact with people. For example, a computer vision-enabled security camera could be used to identify individuals and track their movements, or a computer vision-enabled smart home system could be used to detect when someone is in the room and adjust the lighting or temperature accordingly. * Additionally, computer vision can also be used to enhance cloud-based systems by providing more accurate data and insights. For example, a computer vision-enabled drone could be used to collect data on crops and provide farmers with more accurate information about the health and growth of their crops. Conclusion: * Overall, computer vision technology has the potential to unlock new possibilities for businesses and consumers alike, by enabling IoT and cloud-based systems to better understand and interact with their environment and people. We hope this video has provided you with a better understanding of the potential of computer vision for IoT and cloud, and we look forward to seeing the new possibilities that will be created as this technology continues to evolve. Excited to share my latest project using computer vision and IoT to improve efficiency in manufacturing. I used a combination of machine learning algorithms and cloud computing to analyze data from cameras and sensors in real-time, resulting in a 20% increase in production speed. This was a challenging project but I enjoyed every step of it! I am always looking for new opportunities to apply my skills in computer vision and IoT to help companies improve their operations. Let's connect if you are working on a similar project or if you are looking for a developer with these skills. #computervision #IoT #cloudcomputing #manufacturingefficiency #machinelearning #developer" In this post, you briefly mention your experience and skills in computer vision and IoT, and you provide a specific example of a project you worked on that demonstrates your abilities. You also make it clear that you are open to new opportunities, and you invite others to connect with you. Using relevant hashtags such as #computervision #IoT #cloudcomputing can help your post reach a wider audience Exciting news! I just published a paper on a new object detection algorithm that I developed. The algorithm uses a combination of deep learning and computer vision techniques to improve accuracy and speed of object detection in real-world scenarios. This is a big step forward in the field of computer vision and I am proud to have contributed to it. I will be presenting my research at the Computer Vision Conference next month, if you're attending be sure to stop by and say hi! #computervision #objectdetection #deeplearning #research" In this post, you briefly explain the main findings and contributions of your research, and you express your excitement and pride in your work. You also mention the upcoming conference where you will be presenting your research, inviting your friends and colleagues to meet you in person. Also using relevant hashtags such as #computervision #objectdetection #deeplearning can help reach a wider audience interested in the field. Features stores 1\. Car parts detection 2\. Resize keep aspects ration 3\. 3.1 Perform damage detection 4\. 3.2Semantic segregation 5\. Transfer to original coordinates 1 class imbalance 2 class definition Maybe Class in between 3 inconstant annotations Color augmentation 1\. RGB shift 2\. Random brithness and contrast 3\. Sharpen 4\. Hue saturation value Why manually data augmented Becasu control of data. Not too rotate or change something Photogrammetry model Neural radiance fields (NeRF) NeRF in the wild \ [GitHub - google-research/tuning_playbook: A playbook for systematically maximizing the performance of deep learning models.](https://github.com/google-research/tuning_playbook) Yocto and Machine Learning + OpenCV: [https://www.yoctoproject.org](https://www.yoctoproject.org) [https://www.hackster.io/monica/running-machine-learning-on-maaxboard-s-yocto- image-part-1-6a4796](https://www.hackster.io/monica/running-machine-learning- on-maaxboard-s-yocto-image-part-1-6a4796) Bard Google: [https://blog.google/technology/ai/bard-google-ai-search- updates/](https://blog.google/technology/ai/bard-google-ai-search-updates/) [https://mustang.ir/questions/question/راه-اندازی-پروژه-های-گیت-هاب-با-git- pages](https://mustang.ir/questions/question/%D8%B1%D8%A7%D9%87-%D8%A7%D9%86%D8%AF%D8%A7%D8%B2%DB%8C-%D9%BE%D8%B1%D9%88%DA%98%D9%87-%D9%87%D8%A7%DB%8C-%DA%AF%DB%8C%D8%AA-%D9%87%D8%A7%D8%A8-%D8%A8%D8%A7-git- pages) Book: Project Management for Non-Project Managers [https://fa.wikipedia.org/wiki/علی_اکبرپور](https://fa.wikipedia.org/wiki/%D8%B9%D9%84%DB%8C_%D8%A7%DA%A9%D8%A8%D8%B1%D9%BE%D9%88%D8%B1) [https://www.kingorama.com](https://www.kingorama.com) شاهنامه سه بعدی [Accelerate deep learning model development with cloud custom environments - AWS Online Tech Talks - YouTube](https://m.youtube.com/watch?v=2Wt2zlkMtKI&noapp=1) [بخش هایی از کتاب Refactoring (نسخه رایگان)](https://www.developit.ir/refactoring/free.html#f7) [Performance Notes Of PyTorch Support for M1 and M2 GPUs - Lightning AI](https://lightning.ai/pages/community/community-discussions/performance- notes-of-pytorch-support-for-m1-and-m2-gpus/) [Investopedia Academy](https://academy.investopedia.com/) [HandBrake updated with AV1 and VP9 10-bit video encoding](https://9to5mac.com/2022/12/29/handbrake-support-av1-and- vp9-10-bit/) [How to Start Your Sole Proprietorship in 6 Simple Steps](https://qonto.com/en/blog/creators/administrative/sole-proprietorship- in-germany) [Duolingo English Test](https://englishtest.duolingo.com/applicants) [چالش‌های تولید محتوا برای مارکت اروپا و آمریکا - YouTube](https://m.youtube.com/watch?v=wW0HZdubuWQ) [PyTorch for Deep Learning & Machine Learning – Full Course - YouTube](https://m.youtube.com/watch?v=V_xro1bcAuA#dialog) [Why passive investing makes less sense in the current environment | Financial Times](https://archive.ph/0VucZ) [GitHub - google-research/tuning_playbook: A playbook for systematically maximizing the performance of deep learning models.](https://github.com/google-research/tuning_playbook) [GitHub - mgechev/google-interview-preparation-problems: leetcode problems I solved to prepare for my Google interview.](https://github.com/mgechev/google- interview-preparation-problems) [Bayesian Neural Networks and Variational Dropout](https://dmittov.github.io/variational_dropout/#/maximum-likelihood) [One machine learning question every day - bnomial](https://today.bnomial.com/?ref=email) Git remote add orgine Asynchronous Operation Anomaly detection Use experience. Personalizes. Prediction manage society mobility Personalization Covenant Platform. OpenMMLab Wordtune - AI-powered Writing Companion tree -v -I '*.png' -I '*.jpg' \--charset utf-8 >list2.txt 3D object using triangular mesh need vertices point cloud underlying surface of some 3D object, faster Definition of Done User Story complete Code\Implementation complete Code\Implementation Peer Reviews) approved Unit tests complete (if required) Testing Notes complete (if required) User Story Acceptance criteria defined and verified Backend: Python, Redis, Postgres, Celery Frontend: React, Redux, TypeScript DevOps: Terraform, Kubernetes, GitHub, Docker, AWS Data: Python (Data Science), Kafka, Fastapi, MLFlow, AWS SageMaker ML: Selcond core, Kubeflow, … [Sharpness](https://en.wikipedia.org/wiki/Sharpness_%28visual%29) ,[Noise](https://en.wikipedia.org/wiki/Image_noise), [Dynamic range](https://en.wikipedia.org/wiki/Dynamic_range), [Tone reproduction](https://en.wikipedia.org/wiki/Tone_reproduction) , [Contrast](https://en.wikipedia.org/wiki/Contrast_%28vision%29), [Color](https://en.wikipedia.org/wiki/Color), [Distortion](https://en.wikipedia.org/wiki/Distortion_%28optics%29) , [DSLR lenses](https://en.wikipedia.org/wiki/Lenses_for_SLR_and_DSLR_cameras), [Vignetting](https://en.wikipedia.org/wiki/Vignetting), [Exposure](https://en.wikipedia.org/wiki/Exposure_%28photography%29), Lateral [chromatic aberration](https://en.wikipedia.org/wiki/Chromatic_aberration) (LCA), [Lens flare](https://en.wikipedia.org/wiki/Lens_flare), Color, [Artifacts](https://en.wikipedia.org/wiki/Compression_artifact) ۱\. جهت انتخاب کلمه مورد نظرتان، دو بار روی آن تپ کنید. ۲\. برای انتخاب کل یک پاراگراف، کافیست چهار با روی آن تپ کنید. ۳\. یک انگشت را در ابتدا و انگشت دیگر را در آخر یک محدود گذاشته و کمی نگه دارید. متن میان دو انگشت انتخاب خواهد شد. ۴\. روی ابتدای محدوده ای دلخواه دو بار تپ کرده و بلافاصله با درگ کردن (کشیدن) پین محدوده ی انتخاب شده را گسترش دهید. (انگشت خود را پس از دومین تپ جدا نکنید) ۵\. برای انتخاب کل پاراگراف، به جز استفاده از مورد ۲، می توانید با دو انگشت، یک بار روی آن تپ کنید. namely motion estimation, motion smoothing, and image warping. Motion estimation algorithms often use a similarity transform to handle camera translations, rotations, and zooming. The tricky part is getting these algorithms to lock onto the background motion, 0\. video frames captured during fast motion are often blurry. Their appearance can be improved either using deblurring techniques (Section 10.3) or stealing sharper pixels from other frames with less motion or better focus (Matsushita, Ofek, Ge et al. 2006). Exercise 8.3 has you implement and test some of these ideas. 1\. Background subtraction 2\. Motion estimation 3\. Motion smoothing 4\. Image warping. image warping can result in missing borders around the image, which must be cropped, filled using information from other frames, or hallucinated using inpainting techniques (Section 10.5.1). Vision stabilization There is much recent work on Multi-view 3D reconstruction is a central research topic in computer vision that is driven in many different directions There are many available methods that can handle the noisy image completion problem In the case of surveillance using a fixed camera, there is no desired motion. In the case of most robotic applications, horizontal and vertical motions are desired, but rotation is not. In some cases of ground vehicles where the terrain is known to have many incline changes, or with aerial vehicles undergoing complicated maneuvers where the vehicle’s body is meant to be in varying orientations, rotation might be desired as the robot is meant to be at an angle at times. In robotics applications, computational complexity is extremely important due to the need for real-time operation. Also, it is likely that the center of rotation will not lie in the center of the image frame because the camera is rarely mounted at the robot’s center of mass. This first assumption is made in many video stabilization algorithms, and is a convenient way to seed the correct features with higher trust values. It is not an unreasonable assumption to make. Depending on the application, there is often a large portion of frames where local motion does not occur. In some situations, such as monitoring of steady traffic, there is no guarantee that local motion will not occur. This situation has not been tested, nor has our algorithm been designed to handle it. The second assumption comes from a combination of common sense, and the experience of many computer vision researchers. It makes sense that an object in the scene which does not move will be recognized more easily and more often. Being recognized consistently and consecutively is considered stable. On the other hand, objects which have local motion are less likely to be recognized as often. They might move through shadows, change orientation, or even move completely out of the scene. These possibilities all lead to a less stable class of features. It is likely that, more often than not, there are more background features than foreground features. Moving objects generally cover a small portion of the screen, which usually yields fewer features. Although uncommon, we did not want to make the assumption that this would occur in every frame. Certain scenes will consist of a large portion of local motion, or an object will move very close to the camera, consuming a much larger portion of the scene than the background. As long as some background features are discovered in each frame, our stabilization algorithm should succeed. # image processing tips: * the image size and kernel size need to depended. the best way is to use the one variable to define the size of the image and kernel together. * the coordinate of the image start at top left of the image/display * in order to change it to the normal coordinate you can use * grid of points; two matrix to X , Y coordinate * subtract half of W, H from X, Y in order to have normal coordinate system for our image * now we have cartesian coordinate * * cartesian coordinate to polar coordinate * تبدیل فضای کارتزین به پولار در خیلی از برنامه های پردازش تصویر کارایی دارد. برای پیدا کردن ترشلد ها هم می توان استفاده کرد * in MATLAB we can use ":"for example MatrixA(:) which means all entity of the matrix no mater how many dimensions we have but if we want to implemented in Python we can use numpy.flatten(). * in the MATLAB the round is different from python. if you want same result you need implement the rand function by yourself. * imge_mask=np.ones_like(image_source)*255 * imge_mask=imge_mask.astype(np.uint8) * imge_mask=imge_mask.flatten() ??? .ravel() * .asarray * np.logical_and( 1, 2) * indexes=[index for index in range(len(array1)) if array1[index] == True] * cv2.bitwise_not(yyy) * "olive" editor remove silence ![](https://lh5.googleusercontent.com/nILOXEoEKiANosdHjTOC05i7h8b-84246iAmayzrsrwyQtrN_ZG776o1GnXEFO0E0yH9lMQqIokQWJJgFxAvIzsUdQG6vzewTBzTMKkc1A4J4Lq94r_tVjMgcij_2Nj3DQ=w1280) Questions: How to train model to add new classes? How to add a new class to an existing classifier in deep learning? Adding new Class to One Shot Learning trained model Is it possible to train a neural network as new classes are given? Merging all several models that detection system for all these tasks. Answer 1: There are several ways to add new classes to the trained model, which require just training for the new classes. * Incremental training ([GitHub](https://github.com/khurramjaved96/incremental-learning)) * continuously learn a stream of data ([GitHub](https://github.com/creme-ml/creme)) * online machine learning ([GitHub](https://github.com/GMvandeVen/continual-learning)) * Transfer Learning Twice * Continual learning approaches (Regularization, Expansion, Rehearsal) ([GitHub](https://github.com/facebookresearch/Adversarial-Continual-Learning)) Answer 2: Online learning is a term used to refer to a model which takes a continual or sequential stream of input data while training, in contrast to offline learning (also called batch learning), where the model is pre-trained on a static predefined dataset. Continual learning (also called incremental, continuous, lifelong learning) refers to a branch of ML working in an online learning context where models are designed to learn new tasks while maintaining performance on historic tasks. It can be applied to multiple problem paradigms (including Class- incremental learning, where each new task presents new class labels for an ever expanding super-classification problem). Do I need to train my whole model again on all four classes or is there any way I can just train my model on new class? Naively re-training the model on the updated dataset is indeed a solution. Continual learning seeks to address contexts where access to historic data (i.e. the original 3 classes) is not possible, or when retraining on an increasingly large dataset is impractical (for efficiency, space, privacy etc concerns). Multiple such models using different underlying architectures have been proposed, but almost all examples exclusively deal with image classification problems. Answer 3: You could use transfer learning (i.e. use a pre-trained model, then change its last layer to accommodate the new classes, and re-train this slightly modified model, maybe with a lower learning rate) to achieve that, but transfer learning does not necessarily attempt to retain any of the previously acquired information (especially if you don't use very small learning rates, you keep on training and you do not freeze the weights of the convolutional layers), but only to speed up training or when your new dataset is not big enough, by starting from a model that has already learned general features that are supposedly similar to the features needed for your specific task. There is also the related domain adaptation problem. There are more suitable approaches to perform incremental class learning (which is what you are asking for!), which directly address the [catastrophic forgetting problem](https://ai.stackexchange.com/a/13293/2444). For instance, you can take a look at this paper [Class-incremental Learning via Deep Model Consolidation](https://arxiv.org/pdf/1903.07864.pdf), which proposes the Deep Model Consolidation (DMC) approach. There are other continual/incremental learning approaches, many of them are described [here](https://ai.stackexchange.com/a/24529/2444) or in more detail [here](https://reader.elsevier.com/reader/sd/pii/S0893608019300231). Answer 4: by using Continual learning approaches to trained without losing the original classes. It has 3 categories: Regularization Expansion Rehearsal Answer 5: if you access to the dataset then you can download it and add all you new classes when you have " 'N' COCO Classes + 'M' New classes " after that you can fine tune model based on new dataset. you do not need all of the dataset just same number of image for all class enough. [https://learnopencv.com/stanford-mrnet-challenge-classifying-knee- mris/](https://learnopencv.com/stanford-mrnet-challenge-classifying-knee- mris/) Before start your machine learning project ask these questions and preparation: What is your inference hardware? specify the use case. specify model interface. how would we monitor performance after deployment? how can we approximate post-deployment monitoring before deployment? build a model and iteratively improve it. How to deploy the model at the end? monitor performance after deployment. what is your metric? How do you split your data (training and validation)? ### Preparation ML Project Workflow * [What is your hardware ?](/topics-and-projects/hardware) * specify the use case * specify model interface * how would we monitor performance after deployment? * how can we approximate post-deployment monitoring before deployment? * build a model and iteratively improve it * deploy the model * monitor performance * what is your are metric? * How do you split your data? ### Before Training deep learning model * using large model to train because * it is faster to train with lower overfit and faster converge due to best training * it is easier and higher compress in the final stage * model compression and acceleration: reducing parameters without significantly decreasing the model performance * Data: How to have good data for training deep learning models; How to Build and Enhance A Good Data Set For Your Deep Learning Project: using same config and data for training and inference, removing redundant (delete data which you don't need), get more data, Handle missing data, using data augmentation techniques or GAN to generate more data, re-scale/balance data, Transform your data (Change data types), Feature selection based on data-set and use case * * The data you don't need: removing redundant samples * get more data * Invent more data * data augmentation * Re-scale data * balance datasets * Transform your data * Feature selection based on dataset and use case * ML-Augmented Video Object Tracking: By applying and evaluating multiple algorithmic models, enhanced ability to scale object tracking in high-density video compositions. ### Training deep learning model * automated hyper-parameters * Using Hyperparameter tuning / Hyperparameter optimization tools * AutoML * genetic algorithm * population based training * bayesian optimization * You need to set some parameters and config for training * * Diagnostics * Weight Initialization * Learning rate * Activation function * Network Topology * Batches and Epochs * Regularization * Optimization and Loss * Early Stopping ### Continuous delivery * evolve with latest detection models * more data (no labels) * semi-supervised learning: big self-supervised models are strong semi-supervised learners ### After Training deep learning model * Parameter pruning * model pruning: reducing redundant parameters which are not sensitive to the performance. * aim: remove all connections with absolute weights below a threshold * Quantization * compresses by reducing the number of bits used to represent the weights * quantization effectively constraints the number of different weights we can use inside our kernels * per-channel quantization for weights, which improves performance by model compression and latency reduction. * Low rank matrix factorization (LRMF) * there exists latent structures in the data, by uncovering which we can obtain a compressed representation of the data * LRMF factorizes the original matrix into lower rank matrices while preserving latent structures and addressing the issue of sparseness * Compact convolutional filters (Video/CNN) * designing special structural convolutional filters to save parameters * replace over parametric filters with compact filters to achieve overall speedup while maintaining comparable accuracy * Knowledge distillation * training a compact neural network with distilled knowledge of a large model * distillation (knowledge transfer) from an ensemble of big networks into a much smaller network which learns directly from the cumbersome model's outputs, that is lighter to deploy * Binarized Neural Networks (BNNs) * Apache TVM (incubating) is a compiler stack for deep learning systems * Neural Networks Compression Framework (NNCF) ### Deep learning model in production * security: controls access to model(s) through secure packaging and execution * Test * auto training * using parallel processing and library such as GStreamer # Technology Docker AWS Flask Django # My Keynote (February 2021) 1. introduction 2. Machine Learning/ Deep Learning Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed 3. supervised Machine Learning 1. Deep Convolutional Neural Networks (DCNN) Architecture 2. Visualizing and Understanding Convolutional Networks 3. Object Detection by Deep Learning 4. [Video Tracking](/topics-and-projects/video-tracking) 5. Style Transfer 4. semi-supervised Machine Learning/ Deep Reinforcement learning (DRL) 1. Google 2. [Deep Reinforcement learning (DRL)](/topics-and-projects/drl) 5. unsupervised Machine Learning 1. Auto Encoder 6. Generative Adversarial Networks (GANs) 7. Tools 8. Pre trained model 9. Effect of Augmented Datasets to Train DCNNs 10. Training for more classes 11. Optimization 12. [Hardware](/topics-and-projects/hardware) 13. Production setup 14. post development 15. business , Gartner, Hype Cycle for emerging technologies, 2025 ### Advanced and practical 1. Inside CNN 1. Deep Convolutional Neural Networks Architecture 2. Convolution 3. Convolution Layer 4. Conv/FC Filters 5. Activation Functions 6. Layer Activations 7. Pooling Layer 8. Dropout ; L2 pooling 9. Why 1. Max-pooling is useful 2. How to see inside each layer and find important features * Visualizing and Understanding Convolutional Networks * [https://tensorspace.org/](https://tensorspace.org/) * [https://www.youtube.com/watch?v=AgkfIQ4IGaM](https://www.youtube.com/watch?v=AgkfIQ4IGaM) 2. Hands on python for deep learning 3. Fundamental deep learning 4. Installation: TensorFlow, PyTorch 5. [Using PC+eGPU for training video tracking](/topics-and-projects/source-code/compile) Summary of the summit * AI Hardware Europe Summit (July 2020) * [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit) * Apache TVM And Deep Learning Compilation Conference (December 2020) * [RISC-V Summit (December 2020) ](/workshops-and-events/risc-v) [https://www.inspectar.com/demo](https://www.inspectar.com/demo) for rasp # Face * Effective and precise face detection based on color and depth data * [https://www.sciencedirect.com/science/article/pii/S221083271400009X](https://www.sciencedirect.com/science/article/pii/S221083271400009X) * containing or not containing a face * Eigenface, Fisherface, waveletface, PCA (Principal Component Analysis), LDA (Linear Dis-criminant Analysis), Haar wavelet transform, and so on. * Viola–Jones detector * illumination changes and occlusion * depthinformation is used to filter the regions of the image where a candidate face regionis found by the Viola–Jones (VJ) detector * \- the first filtering rule is defined on the color of the region; since some false positiveshave colors not compatible with the face (e.g. shadows on jeans) a skin detector isapplied to remove the candidate face regions that do not contain skin pixels; * \- the second filtering rule is defined on the size of the face: using the depth mapit is quite easy to calculate the size of the candidate face region, which is use-ful to discard smallest and largest faces from the final result set; * \- the third filtering rule is defined on the depth map to discard flat objects (e.g.candidate faces found in a wall) or uneven objects (e.g. candidate face foundin the leaves of a tree). Combining color and depth data the candidate faceregion can be extracted from the background and measures of depth and reg-ularity are used for filtering out false positives. * The size criteria simply remove the candidate faces not included in a fixed rangesize ([12.5,30] cm). The size of a candidate face region is extracted from the depthmap according to the following approach. * image below * Gaussian mixture 3D morphable face model * [https://www.sciencedirect.com/science/article/pii/S0031320317303527](https://www.sciencedirect.com/science/article/pii/S0031320317303527) * * * Face Synthesis for Eyeglass-Robust Face Recognition * [https://paperswithcode.com/paper/face-synthesis-for-eyeglass-robust-face](https://paperswithcode.com/paper/face-synthesis-for-eyeglass-robust-face) * GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data * [https://paperswithcode.com/paper/genegan-learning-object-transfiguration-and](https://paperswithcode.com/paper/genegan-learning-object-transfiguration-and) * FacePoseNet: Making a Case for Landmark-Free Face Alignment * [https://paperswithcode.com/paper/faceposenet-making-a-case-for-landmark-free](https://paperswithcode.com/paper/faceposenet-making-a-case-for-landmark-free) * Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision * [https://paperswithcode.com/paper/learning-to-regress-3d-face-shape-and](https://paperswithcode.com/paper/learning-to-regress-3d-face-shape-and) * Unsupervised Eyeglasses Removal in the Wild * [https://paperswithcode.com/paper/unsupervised-eyeglasses-removal-in-the-wild](https://paperswithcode.com/paper/unsupervised-eyeglasses-removal-in-the-wild) * How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks) * [https://arxiv.org/pdf/1703.07332v3.pdf](https://arxiv.org/pdf/1703.07332v3.pdf) * (a) we construct, for the first time, a very strong baseline by combining a state-of-the-art architecture for landmark localization with a state-of-the-art residual block, train it on a very large yet synthetically expanded 2D facial landmark dataset and fi- nally evaluate it on all other 2D facial landmark datasets. * (b) We create a guided by 2D landmarks network which con- verts 2D landmark annotations to 3D and unifies all exist- ing datasets, leading to the creation of LS3D-W, the largest and most challenging 3D facial landmark dataset to date (~230,000 images). * (c) Following that, we train a neural network for 3D face alignment and evaluate it on the newly introduced LS3D-W. * (d) We further look into the effect of all “traditional” factors affecting face alignment performance like large pose, initialization and resolution, and introduce a “new” one, namely the size of the network. * (e) We show that both 2D and 3D face alignment networks achieve per- formance of remarkable accuracy which is probably close to saturating the datasets used. * Training and testing code as well as the dataset can be downloaded from https: //[www.adrianbulat.com/face-alignment/](http://www.adrianbulat.com/face-alignment/) ![](https://lh6.googleusercontent.com/d8ABZ3w_DsDnuxD_X_PaSGPK9sxYEZhuyrYuZLCcmgFLMmTmheY4FDHRb3Cbhg- lYHPf4AdNHufhU04dxPdG3_pjwCOx9l7BZM9gLwwest05tq8ELg9sNocjKkjnMe6h=w1280) 19.Sep.2021 [Medium](https://medium.com/p/626019137fa9/edit) [https://fi.co/madlibs](https://fi.co/madlibs) [https://orcid.org/0000-0001-8382-1389](https://orcid.org/0000-0001-8382-1389) Dreyer's English (learn write English) #book story Greek Mythology Explained: A Deeper Look at Classical Greek Lore and Myth **Papers:** CALTag: High Precision Fiducial Markers for Camera Diatom Autofocusing in Brightfield Microscopy: a Comparative Study :implementation variation of the laplacian Analysis of focus measure operators in shape-from-focus: why laplacian? Blure detection? Iqaf? Optical flow modeling and computation: A survey Toward general type 2 fuzzy logic systems based on zSlices \-------------------------------------------------------------------- Lost in space The OA Film:[ https://en.wikipedia.org/wiki/Shark_Tank](https://en.wikipedia.org/wiki/Shark_Tank) Movie Serial billons monk serial movies Python async Highly decoupled microservice Edex RIS-V , Self-car RISC-V Magazine Road map Game: over/under [https://www.sporcle.com/games/Hejman/underwhelmed](https://www.sporcle.com/games/Hejman/underwhelmed) \-------------------------------------------------------------------- \-------------------------------------------------------------------- GDPR in IoT The EU General Data Protection Regulation (GDPR) and Face Images in IoT The GDPR (General Data Protection Regulation), taking effect in May 2018, introduces strict requirements for personal data protection and the privacy rights of individuals. The EU regulations will set a new global standard for privacy rights and change the way organizations worldwide store and process personal data. The GDPR brings the importance of preserving the privacy of personal information to the forefront, yet the importance of face images within this context is often overlooked. The purpose of this paper is to introduce a solution that helps companies protect face images in IoT devices which record or process image by camera, to strengthen compliance with the GDPR. Our Face is our Identity Our face is the most fundamental and highly visible element of our identity. People recognize us when they see our face or a photo of our face. Recent years have seen exponential increase in the use, storage and dissemination of face images in both private and public sectors - in social networks, corporate databases, IoT, smart-city deployments, digital media, government applications, and nearly every organization’s databases. \--------------------- $(aws-okta env stage) aws s3 cp s3://dataset/archive.tar.gz /Users/a.zip aws s3 ls images | tail -n 100 aws s3 cp staging-images/test.jpg /Users/test.jpg \--------------------- screen -rD k get pods Docker RUN chmod +x /tmp/run.sh Can run docker in terminal and run code line by line docker run -it --rm debian:stable-slim bash apt-get update apt-get installl -y \-------------------------------- brew install awscli aws-okta kubectx kubernetes-cli tfenv touch ~/.aws/config \-------------------------------------------------------------------- docker image rm TETSTDFSAFDSADF docker image ls docker system prune docker run -p 5000:5000 nameDocker:latest docker build . -t nameDocker:latest docker container stop number-docker-name docker container ls * docker pull quay.io/test:v0.0.1 * docker run --rm -p 5000:5000 -it quay.io/test:v0.0.1 * curl --header "Content-Type: application/json" \--request POST --data '[{"fixed":7.4, "a":0, "b":0.56, "c":9.4}]'[ http://127.0.0.1:5000/predict](https://meet.google.com/linkredirect?authuser=0&dest=http%3A%2F%2F127.0.0.1%3A5000%2Fpredict) * docker run --rm -v /home/.aws/credentials:/root/.aws/credentials -it quay.io/test /bin/sh aws s3 ls --profile=test \-------------------------------- Cloud software engineer and consultant focusing on building highly available, scalable and fully automated infrastructure environments on top of Amazon Web Services and Microsoft Azure clouds. My goal is always to make my customers happy in the cloud. \---------------- Search google for 3d = tiger - iPhone show AR/VR \--------------- brew install youtube-dl \---------------------------- List: Collection bucket : 1 for week 2 for month 3 for future \-------------------------------------------------------------------- **• Per frame operation** – Detection – Classification – Segmentation – Feature extraction – Recognition **• Across frames ** – Tracking – Counting **• High level** – Intention – Relations – Analyzing ============================= Deep compression Pruning deep learning Hash table neural network Dl compression Deep compression =================================== Mini PCI-e slot * What have I learned so far: * Problem-based learning * real life scenarios * index card (answer , idea) * Think-Pair-Share * Leverage flip charts * Summarizing \-------------------------------------------------------------------- Self \\\ Advancing Self-Supervised and Semi-Supervised Learning with SimCLR \cite{Chen2020} %https://github.com/google-research/simclr first pretraining on a large unlabeled dataset and then fine-tuning on a smaller labeled dataset pretraining on large unlabeled image datasets, as demonstrated by Exemplar- CNN, Instance Discrimination, CPC, AMDIM, CMC, MoCo and others. “A Simple Framework for Contrastive Learning of Visual Representations”, 85.8\% top-5 accuracy using 1\% of labeled images on the ImageNet dataset contrastive learning algorithms linear evaluation protocol (Zhang et al., 2016; Oord et al.,2018; Bachman et al., 2019; Kolesnikov et al., 2019) unsupervised learning benefits more from bigger models than its supervised counterpart. \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- Some of optimization algorithms ======================== Swarm Algorithm =============== 1\. Ant Colony Optimization (ACO) was inspired by the research on the behavior of ant colonies 2\. Firefly Algorithm based on insects called fireflies 3\. Marriage in Honey Bees Optimization Algorithm (MBO algorithm) is inspired by the process of reproduction of Honey Bee 4\. Artificial Bee Colony Algorithm (ABC) is based on the recollection of the Honey Bees 5\. Wasp Swarm Algorithm was inspired on the Parasitic wasps 6\. Bee Collecting Pollen Algorithm (BCPA) 7\. Termite Algorithm 8\. Mosquito swarms Algorithm (MSA) 9\. zooplankton swarms Algorithm (ZSA) 10\. Bumblebees Swarms Algorithm (BSA) 11\. Fish Swarm Algorithm (FSA) 12\. Bacteria Foraging Algorithm (BFA) 13\. Particle Swarm Optimization (PSO) 14\. Cuckoo Search 15\. Bat Algorithm (BA) 16\. Accelerated PSO 17\. Bee System 18\. Beehive Algorithm 19\. Cat Swarm 20\. Consultant-guided search 21\. Eagle Strategy 22\. Fast Backterial swarming algorithm 23\. Good lattice swarm optimization 24\. Glowworm swarm optimization 25\. Hierarchical swarm model 26\. Krill Herd 27\. Monkey Search 28\. Virtual ant algorithm 29\. Virtual bees 30\. Weighted Swarm Algorithm 31\. Wisdom of Artificial Crowd algorithm 32\. Prey-predator algorithm 33\. Memetic algorithm 34\. Lion Optimization Algorithm 35\. Chicken Swarm Optimization 36\. Ant Lion Optimizer 37\. Compact Particle Swarm Optimization 38\. Fruit Fly Optimization Algorithm 39\. marine propeller optimization algorithm 40\. The Whale Optimization Algorithm 41\. virus colony search algorithm 42\. Slime mould optimization algorithm Ecology Inspired Algorithm ========================== 1\. Biogeography-based Optimization 2\. Invasive Weed Optimization 3\. Symbiosis-Inspired Optimization - PS2O 4\. Atmosphere Clouds Model 5\. Brain Storm Optimization 6\. Dolphin echolocation 7\. Japanese Tree Frog Calling algorithm 8\. Eco-inspired evolutionary algorithm 9\. Egyptian Vulture 10\. Fish School search 11\. Flower Pollination algorithm 12\. Gene Expression 13\. Great Salmon Run 14\. Group Search Optimizer 15\. Human Inspired Algorithm 16\. Roach Infestation algorithm 17\. Queen-bee algorithm 18\. Shuffled frog leaping algorithm 19\. Forest Optimization Algorithm 20\. coral reefs optimization algorithm 21\. cultural evolution algorithm 22\. Grey Wolf Optimizer 23\. probabilistic pso 24\. omicron aco algorithm 25\. shark smell optimization 26\. social spider algorithm 27\. sosial insects behavior algorithm 28\. sperm whale algorithm Evolutionary Optimization ========================= 1\. Genetic Algorithm 2\. Genetic Programming 3\. Evolutionary Strategies 4\. Differential Evolution 5\. Paddy Field Algorithm 6\. Queen-bee Evolution 7\. Quantum Inspired Social Evolution Physic and Chemistry inspired algorithm ======================================= 1\. Big bang-Big Crunch 2\. Block hole algorithm 3\. Central force optimization 4\. Charged System search 5\. Electro-magnetism optimization 6\. Galaxy based search algorithm 7\. Gravitational search 8\. Harmony search algorithm 9\. Intelligent water drop algorithm 10\. River formation algorithm 11\. Self-propelled dynamics 12\. Simulated Annealing 13\. Stachastic diffusion search 14\. Spiral optimization 15\. Water Cycle algorithm 16\. Artificial Physics optimization 17\. Binary Gravitational search algorithm 18\. Continous quantum ant colony optimization 19\. Extended artificial physics optimization 20\. Extended Central force optimization 21\. Electromagnetism-like heuristic 22\. Gravitational Interaction optimization 23\. Hysteristetic Optimization algorithm 24\. Hybrid quantum-inspired GA 25\. Immune gravitational inspired algorithm 26\. Improved quantum evolutinary algorithm 27\. Linear programming 28\. Quantum-inspired bacterial swarming 29\. Quantum-inspired evolutionary algorithm 30\. Quantum-inspired genetic algorithm 31\. Quantum-behaved PSO 32\. Unified big bang-chaotic big crunch 33\. Vector model of artificial physics 34\. Versatile quantum-inspired evolutionary algorithm 35\. Space Gravitational Algorithm 36\. Ion Motion Algorithm 37\. Light Ray Optimization Algorithm 38\. Ray Optimization 39\. Photosynthetic Algorithms 40\. floorplanning algorithm 41\. Gases Brownian Motion Optimization 42\. gradient-type optimization 43\. mean-variance optimization 44\. Mine blast algorithm 45\. moth flame optimization 46\. multi battalion search algorithm 47\. music inspired optimization 48\. no free lunch theorems algorithm 49\. Optics inspired optimization 50\. runner-root algorithm 51\. sine cosine algorithm 52\. pitch tracking algorithm 53\. Stochastic Fractal Search algorithm 54\. stroke volume optimization 55\. Stud krill herd algorithm 56\. The Great Deluge Algorithm 57\. Water Evaporation Optimization 58\. water wave optimization algorithm 59\. Island model algorithm 60\. 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Let's partner up to take your project to the next level! pip install mlc-ai-nightly -f https://mlc.ai/wheels https://mlc.ai/ https://mlc.ai/summer22/ Day 1: Introduction to Unity: TVMScript Introduction to Unity: Relax and PyTorch TVM BYOC in Practice Get Started with TVM on Adreno GPU Introduction to Unity: Metaschedule How to Bring microTVM to a custom IDE Day 2: Community Keynote PyTorch 2.0: the journey to bringing compiler technologies to the core of PyTorch Support QNN Dialect for TVM with MediaTek Neuron and Devise the Scheduler for Acceleration On-Device Training Under 256KB Memory AMD Tutorial TVM at TI: Accelerating inference using the C7x/MMA Adreno GPU: 4x speed-up and upstreaming to TVM mainline Transfer-Tuning: Reusing Auto-Schedules for Efficient Tensor Program Code Generation Improvement in the TVM OpenCL codegen to autogenerate optimal convolution kernels for Adreno GPUs TVM Unity: Pass Infrastructure and BYOC Renesas Hardware accelerators with Apache TVM Introduction on 4th Gen Intel Xeon processor and BF16 support with TVM Hidet: Task Mapping Programming Paradigm for Deep Learning Tensor Programs Towards Building a Responsible Data Economy Optimizing SYCL Device Kernels with AKG Adreno GPU Performance Enhancements using TVM Improvements to CMSIS-NN integration in TVM UMA: Universal Modular Accelerator Interface Day 3: TVM Unity for Dynamic Models Empower Tensorflow serving with backend TVM Enabling Conditional Computing on Hexagon target Decoupled Model Schedule for Large Deep Learning Model Training Using TVM to bring Bayesian neural networks to embedded hardware Efficient Support of TVM Scan OP on RISC-V Vector Extension Improvements to Ethos-U55 support in TVM including CI on Alif Semiconductor boards Compiling Dynamic Shapes TVM Packaging in 2023: delivering TVM to end users Cross-Platform Training Using Automatic Differentiation on Relax IR AutoTVM: Reducing tuning space by cross axis filtering SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning Analytical Tensorization and Fusion for Compute-intensive Operators CUTLASS 3.0: Next Generation Composable and Reusable GPU Linear Algebra Library Enabling Data Movement and Computation Pipelining in Deep Learning Compiler Automating DL Compiler Bug Finding with NNSmith TVM at NIO TVM at Tencent Integrating the Andes RISC-V Processors into TVM Alpa: A Compiler for Distributed Deep Learning ACRoBat: Compiler and Runtime Techniques for Efficient Auto-Batching of Dynamic Deep Learning Computations Channel Folding: a Transform Pass for Optimizing Mobilenets ========================================================================Day 1: ************************ Introduction to Unity: TVMScript [https://github.com/cyx-6/TVM- Demo/blob/main/tvmscript.ipynb](https://github.com/cyx-6/TVM- Demo/blob/main/tvmscript.ipynb) Gan NN show us some hidden patter in history we can not see before. “I always have a slip of paper at hand, on which I note down the ideas of certain pages. On the backside I write down the bibliographic details. After finishing the book I go through my notes and think how these notes might be relevant for already written notes in the slip-box. It means that I always read with an eye towards possible connections in the slip-box.” (Luhmann et al., 1987, 150) Deep representation learning Model evaluation. Camera cheaper lidar Point cloud because of we need 3d Capturing reality 1\. 𝐀𝐝𝐝/𝐂𝐨𝐦𝐦𝐢𝐭 𝐀𝐥𝐥 Standard way: git add . git commit -m "Message" Another way: git commit -a -m "Message" 𝟐\. 𝐀𝐥𝐢𝐚𝐬𝐞𝐬 With aliases, you can write your own Git commands that do anything you want. Eg: git config --global alias.ac '!git add -A && git commit -m' (alias called ac, git add -A && git commit -m will do the full add and commit) 𝟑\. 𝐑𝐞𝐯𝐞𝐫𝐭 The revert command simply allows us to undo any commit on the current branch. Eg: git revert 486bdb2 Another way: git revert HEAD (for recent commits) 𝟒\. 𝐑𝐞𝐟𝐥𝐨𝐠 This command lets you easily see the recent commits, pulls, resets, pushes, etc on your local machine. Eg: git reflog 𝟓\. 𝐏𝐫𝐞𝐭𝐭𝐲 𝐋𝐨𝐠𝐬 Gives you the ability to print out a pretty log of your commits/branches. Eg: git log --graph --decorate --oneline 𝟔\. 𝐒𝐞𝐚𝐫𝐜𝐡𝐢𝐧𝐠 𝐋𝐨𝐠𝐬 One can also use the log command to search for specific changes in the code. Eg: git log -S "A promise in JavaScript is very similar" 𝟕\. 𝐒𝐭𝐚𝐬𝐡 This command will stash (store them locally) all your code changes but does not actually commit them. Eg: git stash 𝟖\. 𝐑𝐞𝐦𝐨𝐯𝐞 𝐃𝐞𝐚𝐝 𝐁𝐫𝐚𝐧𝐜𝐡𝐞𝐬 This command will delete all the tracking information for branches that are on your local machine that are not in the remote repository, but it does not delete your local branches. Eg: git remote update --prune 𝟗\. 𝐁𝐢𝐬𝐞𝐜𝐭 For finding which commits caused certain bugs Eg: git bisect start git bisect bad git bisect good 48c86d6 𝟏𝟎\. 𝐃𝐞𝐬𝐭𝐫𝐨𝐲 𝐋𝐨𝐜𝐚𝐥 𝐂𝐡𝐚𝐧𝐠𝐞𝐬 One can wipe out all changes on your local branch to exactly what is in the remote branch. Eg: git reset --hard origin/main Don’t trust your devices IoT. software and hardware are together for better business. Newsletter investing every 3 months 1\. Prototyping. New bie 2\. Patent. Website. ( list of investors) 3\. Pre seed. First founding 1M VC, inistution, anjel capital. 400 000 preseed. Quveribel. Equtible rund convertible non agreement Template. Convertabel lone 1\. Germ standar inistitude 2\. 4\. Equity. Venture builder. 20% 200 000 5\. 100 000 per year to become unocorn in less than 10 years 6\. Soniy corn 100k unicorn 1M 7\. 360 euro per years for database of investor 8\. Convertable loan: Pay interst rate 5% to 8% = 18 months later (2M found in 10M) convert on based . 9\. Invester Never act as co-founder = full time = 20% 10\. Project profit, 11\. Full time after foun rising Make a plan for your business; take your time to make calculations by creating a target audience. Your target audience determines how you approach your business plan. By studying your target audience, you are making empirical research and collecting information from them Then, secure a good partnership if need be, and get enough capital to start up. * * What the people need * Why people need it * When the people need it * It's affordability * It's ease of use * It's maintenance and revenue Pair programming The SB7 Framework harnesses the influence of stories. The structure describes the 7 most common story elements: • Character • Problem • Guide • Plan • Calls to action • Failure • Success Dear [Hiring Manager’s Name], I am writing to apply for the position of computer vision for IoT and cloud at [Company Name]. I am a highly skilled and experienced computer vision engineer with a strong background in IoT and cloud technologies. I believe that my skills and experience make me an ideal candidate for this position and I am excited about the opportunity to contribute to the success of your organization. I have a solid understanding of computer vision algorithms and techniques, as well as experience in developing and implementing computer vision systems. I am proficient in programming languages such as Python, C++, and Java, and have experience with popular computer vision libraries such as OpenCV, TensorFlow, and PyTorch. In addition, I have a strong background in IoT and cloud technologies, including experience with IoT platforms such as AWS IoT, Azure IoT, and Google Cloud IoT. I am familiar with cloud computing technologies such as AWS, Azure, and Google Cloud, and have experience with deploying and managing computer vision systems on these platforms. I am also a team player and have excellent communication skills. I am able to work with cross-functional teams and can effectively communicate with both technical and non-technical stakeholders. I am also highly motivated, and I am always looking for ways to improve my skills and stay up-to-date with the latest technologies. I am excited about the opportunity to join [Company Name] and to contribute to the development of cutting-edge computer vision systems for IoT and cloud. I am confident that my skills and experience make me a strong candidate for this position, and I look forward to discussing how I can contribute to your organization. Thank you for considering my application. I look forward to hearing from you soon. Sincerely, Title: "Unlocking the Power of Computer Vision for IoT and Cloud" Introduction: * Hi, and welcome to our video on the topic of computer vision for IoT and cloud. In this video, we're going to explore how computer vision technology can be used to enhance IoT and cloud-based systems, and how it can be used to unlock new possibilities for businesses and consumers alike. Body: * First, let's talk about what computer vision is and how it works. Essentially, computer vision is the technology that enables computers to understand and interpret visual information from the world around us. This can include things like images, videos, and even 3D models. * One of the key ways that computer vision can be used to enhance IoT and cloud-based systems is by enabling devices to better understand and interact with their environment. For example, a computer vision-enabled camera could be used to monitor a manufacturing facility and identify when a machine is in need of maintenance or when an employee is working in an unsafe manner. * Another way that computer vision can be used to enhance IoT and cloud-based systems is by enabling devices to better understand and interact with people. For example, a computer vision-enabled security camera could be used to identify individuals and track their movements, or a computer vision-enabled smart home system could be used to detect when someone is in the room and adjust the lighting or temperature accordingly. * Additionally, computer vision can also be used to enhance cloud-based systems by providing more accurate data and insights. For example, a computer vision-enabled drone could be used to collect data on crops and provide farmers with more accurate information about the health and growth of their crops. Conclusion: * Overall, computer vision technology has the potential to unlock new possibilities for businesses and consumers alike, by enabling IoT and cloud-based systems to better understand and interact with their environment and people. We hope this video has provided you with a better understanding of the potential of computer vision for IoT and cloud, and we look forward to seeing the new possibilities that will be created as this technology continues to evolve. Excited to share my latest project using computer vision and IoT to improve efficiency in manufacturing. I used a combination of machine learning algorithms and cloud computing to analyze data from cameras and sensors in real-time, resulting in a 20% increase in production speed. This was a challenging project but I enjoyed every step of it! I am always looking for new opportunities to apply my skills in computer vision and IoT to help companies improve their operations. Let's connect if you are working on a similar project or if you are looking for a developer with these skills. #computervision #IoT #cloudcomputing #manufacturingefficiency #machinelearning #developer" In this post, you briefly mention your experience and skills in computer vision and IoT, and you provide a specific example of a project you worked on that demonstrates your abilities. You also make it clear that you are open to new opportunities, and you invite others to connect with you. Using relevant hashtags such as #computervision #IoT #cloudcomputing can help your post reach a wider audience Exciting news! I just published a paper on a new object detection algorithm that I developed. The algorithm uses a combination of deep learning and computer vision techniques to improve accuracy and speed of object detection in real-world scenarios. This is a big step forward in the field of computer vision and I am proud to have contributed to it. I will be presenting my research at the Computer Vision Conference next month, if you're attending be sure to stop by and say hi! #computervision #objectdetection #deeplearning #research" In this post, you briefly explain the main findings and contributions of your research, and you express your excitement and pride in your work. You also mention the upcoming conference where you will be presenting your research, inviting your friends and colleagues to meet you in person. Also using relevant hashtags such as #computervision #objectdetection #deeplearning can help reach a wider audience interested in the field. Features stores 1\. Car parts detection 2\. Resize keep aspects ration 3\. 3.1 Perform damage detection 4\. 3.2Semantic segregation 5\. Transfer to original coordinates 1 class imbalance 2 class definition Maybe Class in between 3 inconstant annotations Color augmentation 1\. RGB shift 2\. Random brithness and contrast 3\. Sharpen 4\. Hue saturation value Why manually data augmented Becasu control of data. Not too rotate or change something Photogrammetry model Neural radiance fields (NeRF) NeRF in the wild \ [GitHub - google-research/tuning_playbook: A playbook for systematically maximizing the performance of deep learning models.](https://github.com/google-research/tuning_playbook) Yocto and Machine Learning + OpenCV: [https://www.yoctoproject.org](https://www.yoctoproject.org) [https://www.hackster.io/monica/running-machine-learning-on-maaxboard-s-yocto- image-part-1-6a4796](https://www.hackster.io/monica/running-machine-learning- on-maaxboard-s-yocto-image-part-1-6a4796) Bard Google: [https://blog.google/technology/ai/bard-google-ai-search- updates/](https://blog.google/technology/ai/bard-google-ai-search-updates/) [https://mustang.ir/questions/question/راه-اندازی-پروژه-های-گیت-هاب-با-git- pages](https://mustang.ir/questions/question/%D8%B1%D8%A7%D9%87-%D8%A7%D9%86%D8%AF%D8%A7%D8%B2%DB%8C-%D9%BE%D8%B1%D9%88%DA%98%D9%87-%D9%87%D8%A7%DB%8C-%DA%AF%DB%8C%D8%AA-%D9%87%D8%A7%D8%A8-%D8%A8%D8%A7-git- pages) Book: Project Management for Non-Project Managers [https://fa.wikipedia.org/wiki/علی_اکبرپور](https://fa.wikipedia.org/wiki/%D8%B9%D9%84%DB%8C_%D8%A7%DA%A9%D8%A8%D8%B1%D9%BE%D9%88%D8%B1) [https://www.kingorama.com](https://www.kingorama.com) شاهنامه سه بعدی [Accelerate deep learning model development with cloud custom environments - AWS Online Tech Talks - YouTube](https://m.youtube.com/watch?v=2Wt2zlkMtKI&noapp=1) [بخش هایی از کتاب Refactoring (نسخه رایگان)](https://www.developit.ir/refactoring/free.html#f7) [Performance Notes Of PyTorch Support for M1 and M2 GPUs - Lightning AI](https://lightning.ai/pages/community/community-discussions/performance- notes-of-pytorch-support-for-m1-and-m2-gpus/) [Investopedia Academy](https://academy.investopedia.com/) [HandBrake updated with AV1 and VP9 10-bit video encoding](https://9to5mac.com/2022/12/29/handbrake-support-av1-and- vp9-10-bit/) [How to Start Your Sole Proprietorship in 6 Simple Steps](https://qonto.com/en/blog/creators/administrative/sole-proprietorship- in-germany) [Duolingo English Test](https://englishtest.duolingo.com/applicants) [چالش‌های تولید محتوا برای مارکت اروپا و آمریکا - YouTube](https://m.youtube.com/watch?v=wW0HZdubuWQ) [PyTorch for Deep Learning & Machine Learning – Full Course - YouTube](https://m.youtube.com/watch?v=V_xro1bcAuA#dialog) [Why passive investing makes less sense in the current environment | Financial Times](https://archive.ph/0VucZ) [GitHub - google-research/tuning_playbook: A playbook for systematically maximizing the performance of deep learning models.](https://github.com/google-research/tuning_playbook) [GitHub - mgechev/google-interview-preparation-problems: leetcode problems I solved to prepare for my Google interview.](https://github.com/mgechev/google- interview-preparation-problems) [Bayesian Neural Networks and Variational Dropout](https://dmittov.github.io/variational_dropout/#/maximum-likelihood) [One machine learning question every day - bnomial](https://today.bnomial.com/?ref=email) Git remote add orgine Asynchronous Operation Anomaly detection Use experience. Personalizes. Prediction manage society mobility Personalization Covenant Platform. OpenMMLab Wordtune - AI-powered Writing Companion tree -v -I '*.png' -I '*.jpg' \--charset utf-8 >list2.txt 3D object using triangular mesh need vertices point cloud underlying surface of some 3D object, faster Definition of Done User Story complete Code\Implementation complete Code\Implementation Peer Reviews) approved Unit tests complete (if required) Testing Notes complete (if required) User Story Acceptance criteria defined and verified Backend: Python, Redis, Postgres, Celery Frontend: React, Redux, TypeScript DevOps: Terraform, Kubernetes, GitHub, Docker, AWS Data: Python (Data Science), Kafka, Fastapi, MLFlow, AWS SageMaker ML: Selcond core, Kubeflow, … [Sharpness](https://en.wikipedia.org/wiki/Sharpness_%28visual%29) ,[Noise](https://en.wikipedia.org/wiki/Image_noise), [Dynamic range](https://en.wikipedia.org/wiki/Dynamic_range), [Tone reproduction](https://en.wikipedia.org/wiki/Tone_reproduction) , [Contrast](https://en.wikipedia.org/wiki/Contrast_%28vision%29), [Color](https://en.wikipedia.org/wiki/Color), [Distortion](https://en.wikipedia.org/wiki/Distortion_%28optics%29) , [DSLR lenses](https://en.wikipedia.org/wiki/Lenses_for_SLR_and_DSLR_cameras), [Vignetting](https://en.wikipedia.org/wiki/Vignetting), [Exposure](https://en.wikipedia.org/wiki/Exposure_%28photography%29), Lateral [chromatic aberration](https://en.wikipedia.org/wiki/Chromatic_aberration) (LCA), [Lens flare](https://en.wikipedia.org/wiki/Lens_flare), Color, [Artifacts](https://en.wikipedia.org/wiki/Compression_artifact) ۱\. جهت انتخاب کلمه مورد نظرتان، دو بار روی آن تپ کنید. ۲\. برای انتخاب کل یک پاراگراف، کافیست چهار با روی آن تپ کنید. ۳\. یک انگشت را در ابتدا و انگشت دیگر را در آخر یک محدود گذاشته و کمی نگه دارید. متن میان دو انگشت انتخاب خواهد شد. ۴\. روی ابتدای محدوده ای دلخواه دو بار تپ کرده و بلافاصله با درگ کردن (کشیدن) پین محدوده ی انتخاب شده را گسترش دهید. (انگشت خود را پس از دومین تپ جدا نکنید) ۵\. برای انتخاب کل پاراگراف، به جز استفاده از مورد ۲، می توانید با دو انگشت، یک بار روی آن تپ کنید. namely motion estimation, motion smoothing, and image warping. Motion estimation algorithms often use a similarity transform to handle camera translations, rotations, and zooming. The tricky part is getting these algorithms to lock onto the background motion, 0\. video frames captured during fast motion are often blurry. Their appearance can be improved either using deblurring techniques (Section 10.3) or stealing sharper pixels from other frames with less motion or better focus (Matsushita, Ofek, Ge et al. 2006). Exercise 8.3 has you implement and test some of these ideas. 1\. Background subtraction 2\. Motion estimation 3\. Motion smoothing 4\. Image warping. image warping can result in missing borders around the image, which must be cropped, filled using information from other frames, or hallucinated using inpainting techniques (Section 10.5.1). Vision stabilization There is much recent work on Multi-view 3D reconstruction is a central research topic in computer vision that is driven in many different directions There are many available methods that can handle the noisy image completion problem In the case of surveillance using a fixed camera, there is no desired motion. In the case of most robotic applications, horizontal and vertical motions are desired, but rotation is not. In some cases of ground vehicles where the terrain is known to have many incline changes, or with aerial vehicles undergoing complicated maneuvers where the vehicle’s body is meant to be in varying orientations, rotation might be desired as the robot is meant to be at an angle at times. In robotics applications, computational complexity is extremely important due to the need for real-time operation. Also, it is likely that the center of rotation will not lie in the center of the image frame because the camera is rarely mounted at the robot’s center of mass. This first assumption is made in many video stabilization algorithms, and is a convenient way to seed the correct features with higher trust values. It is not an unreasonable assumption to make. Depending on the application, there is often a large portion of frames where local motion does not occur. In some situations, such as monitoring of steady traffic, there is no guarantee that local motion will not occur. This situation has not been tested, nor has our algorithm been designed to handle it. The second assumption comes from a combination of common sense, and the experience of many computer vision researchers. It makes sense that an object in the scene which does not move will be recognized more easily and more often. Being recognized consistently and consecutively is considered stable. On the other hand, objects which have local motion are less likely to be recognized as often. They might move through shadows, change orientation, or even move completely out of the scene. These possibilities all lead to a less stable class of features. It is likely that, more often than not, there are more background features than foreground features. Moving objects generally cover a small portion of the screen, which usually yields fewer features. Although uncommon, we did not want to make the assumption that this would occur in every frame. Certain scenes will consist of a large portion of local motion, or an object will move very close to the camera, consuming a much larger portion of the scene than the background. As long as some background features are discovered in each frame, our stabilization algorithm should succeed. # image processing tips: * the image size and kernel size need to depended. the best way is to use the one variable to define the size of the image and kernel together. * the coordinate of the image start at top left of the image/display * in order to change it to the normal coordinate you can use * grid of points; two matrix to X , Y coordinate * subtract half of W, H from X, Y in order to have normal coordinate system for our image * now we have cartesian coordinate * * cartesian coordinate to polar coordinate * تبدیل فضای کارتزین به پولار در خیلی از برنامه های پردازش تصویر کارایی دارد. برای پیدا کردن ترشلد ها هم می توان استفاده کرد * in MATLAB we can use ":"for example MatrixA(:) which means all entity of the matrix no mater how many dimensions we have but if we want to implemented in Python we can use numpy.flatten(). * in the MATLAB the round is different from python. if you want same result you need implement the rand function by yourself. * imge_mask=np.ones_like(image_source)*255 * imge_mask=imge_mask.astype(np.uint8) * imge_mask=imge_mask.flatten() ??? .ravel() * .asarray * np.logical_and( 1, 2) * indexes=[index for index in range(len(array1)) if array1[index] == True] * cv2.bitwise_not(yyy) * "olive" editor remove silence ![](https://lh5.googleusercontent.com/uz1tsz4Qy4dPzQzOtxekBVw0UwuYQ6BW31DaVXbLQTH- aJLInnaRUyrKqg4-- r_zsO5nj0pTm6oFMrFcyCwYUQfFNDHcgZIalLEc6l7_BABaoqRK7uGpRllFdVaf64L8_A=w1280) Questions: How to train model to add new classes? How to add a new class to an existing classifier in deep learning? Adding new Class to One Shot Learning trained model Is it possible to train a neural network as new classes are given? Merging all several models that detection system for all these tasks. Answer 1: There are several ways to add new classes to the trained model, which require just training for the new classes. * Incremental training ([GitHub](https://github.com/khurramjaved96/incremental-learning)) * continuously learn a stream of data ([GitHub](https://github.com/creme-ml/creme)) * online machine learning ([GitHub](https://github.com/GMvandeVen/continual-learning)) * Transfer Learning Twice * Continual learning approaches (Regularization, Expansion, Rehearsal) ([GitHub](https://github.com/facebookresearch/Adversarial-Continual-Learning)) Answer 2: Online learning is a term used to refer to a model which takes a continual or sequential stream of input data while training, in contrast to offline learning (also called batch learning), where the model is pre-trained on a static predefined dataset. Continual learning (also called incremental, continuous, lifelong learning) refers to a branch of ML working in an online learning context where models are designed to learn new tasks while maintaining performance on historic tasks. It can be applied to multiple problem paradigms (including Class- incremental learning, where each new task presents new class labels for an ever expanding super-classification problem). Do I need to train my whole model again on all four classes or is there any way I can just train my model on new class? Naively re-training the model on the updated dataset is indeed a solution. Continual learning seeks to address contexts where access to historic data (i.e. the original 3 classes) is not possible, or when retraining on an increasingly large dataset is impractical (for efficiency, space, privacy etc concerns). Multiple such models using different underlying architectures have been proposed, but almost all examples exclusively deal with image classification problems. Answer 3: You could use transfer learning (i.e. use a pre-trained model, then change its last layer to accommodate the new classes, and re-train this slightly modified model, maybe with a lower learning rate) to achieve that, but transfer learning does not necessarily attempt to retain any of the previously acquired information (especially if you don't use very small learning rates, you keep on training and you do not freeze the weights of the convolutional layers), but only to speed up training or when your new dataset is not big enough, by starting from a model that has already learned general features that are supposedly similar to the features needed for your specific task. There is also the related domain adaptation problem. There are more suitable approaches to perform incremental class learning (which is what you are asking for!), which directly address the [catastrophic forgetting problem](https://ai.stackexchange.com/a/13293/2444). For instance, you can take a look at this paper [Class-incremental Learning via Deep Model Consolidation](https://arxiv.org/pdf/1903.07864.pdf), which proposes the Deep Model Consolidation (DMC) approach. There are other continual/incremental learning approaches, many of them are described [here](https://ai.stackexchange.com/a/24529/2444) or in more detail [here](https://reader.elsevier.com/reader/sd/pii/S0893608019300231). Answer 4: by using Continual learning approaches to trained without losing the original classes. It has 3 categories: Regularization Expansion Rehearsal Answer 5: if you access to the dataset then you can download it and add all you new classes when you have " 'N' COCO Classes + 'M' New classes " after that you can fine tune model based on new dataset. you do not need all of the dataset just same number of image for all class enough. [https://learnopencv.com/stanford-mrnet-challenge-classifying-knee- mris/](https://learnopencv.com/stanford-mrnet-challenge-classifying-knee- mris/) Before start your machine learning project ask these questions and preparation: What is your inference hardware? specify the use case. specify model interface. how would we monitor performance after deployment? how can we approximate post-deployment monitoring before deployment? build a model and iteratively improve it. How to deploy the model at the end? monitor performance after deployment. what is your metric? How do you split your data (training and validation)? ### Preparation ML Project Workflow * [What is your hardware ?](/topics-and-projects/hardware) * specify the use case * specify model interface * how would we monitor performance after deployment? * how can we approximate post-deployment monitoring before deployment? * build a model and iteratively improve it * deploy the model * monitor performance * what is your are metric? * How do you split your data? ### Before Training deep learning model * using large model to train because * it is faster to train with lower overfit and faster converge due to best training * it is easier and higher compress in the final stage * model compression and acceleration: reducing parameters without significantly decreasing the model performance * Data: How to have good data for training deep learning models; How to Build and Enhance A Good Data Set For Your Deep Learning Project: using same config and data for training and inference, removing redundant (delete data which you don't need), get more data, Handle missing data, using data augmentation techniques or GAN to generate more data, re-scale/balance data, Transform your data (Change data types), Feature selection based on data-set and use case * * The data you don't need: removing redundant samples * get more data * Invent more data * data augmentation * Re-scale data * balance datasets * Transform your data * Feature selection based on dataset and use case * ML-Augmented Video Object Tracking: By applying and evaluating multiple algorithmic models, enhanced ability to scale object tracking in high-density video compositions. ### Training deep learning model * automated hyper-parameters * Using Hyperparameter tuning / Hyperparameter optimization tools * AutoML * genetic algorithm * population based training * bayesian optimization * You need to set some parameters and config for training * * Diagnostics * Weight Initialization * Learning rate * Activation function * Network Topology * Batches and Epochs * Regularization * Optimization and Loss * Early Stopping ### Continuous delivery * evolve with latest detection models * more data (no labels) * semi-supervised learning: big self-supervised models are strong semi-supervised learners ### After Training deep learning model * Parameter pruning * model pruning: reducing redundant parameters which are not sensitive to the performance. * aim: remove all connections with absolute weights below a threshold * Quantization * compresses by reducing the number of bits used to represent the weights * quantization effectively constraints the number of different weights we can use inside our kernels * per-channel quantization for weights, which improves performance by model compression and latency reduction. * Low rank matrix factorization (LRMF) * there exists latent structures in the data, by uncovering which we can obtain a compressed representation of the data * LRMF factorizes the original matrix into lower rank matrices while preserving latent structures and addressing the issue of sparseness * Compact convolutional filters (Video/CNN) * designing special structural convolutional filters to save parameters * replace over parametric filters with compact filters to achieve overall speedup while maintaining comparable accuracy * Knowledge distillation * training a compact neural network with distilled knowledge of a large model * distillation (knowledge transfer) from an ensemble of big networks into a much smaller network which learns directly from the cumbersome model's outputs, that is lighter to deploy * Binarized Neural Networks (BNNs) * Apache TVM (incubating) is a compiler stack for deep learning systems * Neural Networks Compression Framework (NNCF) ### Deep learning model in production * security: controls access to model(s) through secure packaging and execution * Test * auto training * using parallel processing and library such as GStreamer # Technology Docker AWS Flask Django # My Keynote (February 2021) 1. introduction 2. Machine Learning/ Deep Learning Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed 3. supervised Machine Learning 1. Deep Convolutional Neural Networks (DCNN) Architecture 2. Visualizing and Understanding Convolutional Networks 3. Object Detection by Deep Learning 4. [Video Tracking](/topics-and-projects/video-tracking) 5. Style Transfer 4. semi-supervised Machine Learning/ Deep Reinforcement learning (DRL) 1. Google 2. [Deep Reinforcement learning (DRL)](/topics-and-projects/drl) 5. unsupervised Machine Learning 1. Auto Encoder 6. Generative Adversarial Networks (GANs) 7. Tools 8. Pre trained model 9. Effect of Augmented Datasets to Train DCNNs 10. Training for more classes 11. Optimization 12. [Hardware](/topics-and-projects/hardware) 13. Production setup 14. post development 15. business , Gartner, Hype Cycle for emerging technologies, 2025 ### Advanced and practical 1. Inside CNN 1. Deep Convolutional Neural Networks Architecture 2. Convolution 3. Convolution Layer 4. Conv/FC Filters 5. Activation Functions 6. Layer Activations 7. Pooling Layer 8. Dropout ; L2 pooling 9. Why 1. Max-pooling is useful 2. How to see inside each layer and find important features * Visualizing and Understanding Convolutional Networks * [https://tensorspace.org/](https://tensorspace.org/) * [https://www.youtube.com/watch?v=AgkfIQ4IGaM](https://www.youtube.com/watch?v=AgkfIQ4IGaM) 2. Hands on python for deep learning 3. Fundamental deep learning 4. Installation: TensorFlow, PyTorch 5. [Using PC+eGPU for training video tracking](/topics-and-projects/source-code/compile) Summary of the summit * AI Hardware Europe Summit (July 2020) * [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit) * Apache TVM And Deep Learning Compilation Conference (December 2020) * [RISC-V Summit (December 2020) ](/workshops-and-events/risc-v) [https://www.inspectar.com/demo](https://www.inspectar.com/demo) for rasp # Face * Effective and precise face detection based on color and depth data * [https://www.sciencedirect.com/science/article/pii/S221083271400009X](https://www.sciencedirect.com/science/article/pii/S221083271400009X) * containing or not containing a face * Eigenface, Fisherface, waveletface, PCA (Principal Component Analysis), LDA (Linear Dis-criminant Analysis), Haar wavelet transform, and so on. * Viola–Jones detector * illumination changes and occlusion * depthinformation is used to filter the regions of the image where a candidate face regionis found by the Viola–Jones (VJ) detector * \- the first filtering rule is defined on the color of the region; since some false positiveshave colors not compatible with the face (e.g. shadows on jeans) a skin detector isapplied to remove the candidate face regions that do not contain skin pixels; * \- the second filtering rule is defined on the size of the face: using the depth mapit is quite easy to calculate the size of the candidate face region, which is use-ful to discard smallest and largest faces from the final result set; * \- the third filtering rule is defined on the depth map to discard flat objects (e.g.candidate faces found in a wall) or uneven objects (e.g. candidate face foundin the leaves of a tree). Combining color and depth data the candidate faceregion can be extracted from the background and measures of depth and reg-ularity are used for filtering out false positives. * The size criteria simply remove the candidate faces not included in a fixed rangesize ([12.5,30] cm). The size of a candidate face region is extracted from the depthmap according to the following approach. * image below * Gaussian mixture 3D morphable face model * [https://www.sciencedirect.com/science/article/pii/S0031320317303527](https://www.sciencedirect.com/science/article/pii/S0031320317303527) * * * Face Synthesis for Eyeglass-Robust Face Recognition * [https://paperswithcode.com/paper/face-synthesis-for-eyeglass-robust-face](https://paperswithcode.com/paper/face-synthesis-for-eyeglass-robust-face) * GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data * [https://paperswithcode.com/paper/genegan-learning-object-transfiguration-and](https://paperswithcode.com/paper/genegan-learning-object-transfiguration-and) * FacePoseNet: Making a Case for Landmark-Free Face Alignment * [https://paperswithcode.com/paper/faceposenet-making-a-case-for-landmark-free](https://paperswithcode.com/paper/faceposenet-making-a-case-for-landmark-free) * Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision * [https://paperswithcode.com/paper/learning-to-regress-3d-face-shape-and](https://paperswithcode.com/paper/learning-to-regress-3d-face-shape-and) * Unsupervised Eyeglasses Removal in the Wild * [https://paperswithcode.com/paper/unsupervised-eyeglasses-removal-in-the-wild](https://paperswithcode.com/paper/unsupervised-eyeglasses-removal-in-the-wild) * How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks) * [https://arxiv.org/pdf/1703.07332v3.pdf](https://arxiv.org/pdf/1703.07332v3.pdf) * (a) we construct, for the first time, a very strong baseline by combining a state-of-the-art architecture for landmark localization with a state-of-the-art residual block, train it on a very large yet synthetically expanded 2D facial landmark dataset and fi- nally evaluate it on all other 2D facial landmark datasets. * (b) We create a guided by 2D landmarks network which con- verts 2D landmark annotations to 3D and unifies all exist- ing datasets, leading to the creation of LS3D-W, the largest and most challenging 3D facial landmark dataset to date (~230,000 images). * (c) Following that, we train a neural network for 3D face alignment and evaluate it on the newly introduced LS3D-W. * (d) We further look into the effect of all “traditional” factors affecting face alignment performance like large pose, initialization and resolution, and introduce a “new” one, namely the size of the network. * (e) We show that both 2D and 3D face alignment networks achieve per- formance of remarkable accuracy which is probably close to saturating the datasets used. * Training and testing code as well as the dataset can be downloaded from https: //[www.adrianbulat.com/face-alignment/](http://www.adrianbulat.com/face-alignment/) ![](https://lh3.googleusercontent.com/9lvcVu- HI5oeKBlSMraQcnpp6MQ_gpnrRzOIbRJFnPhqa9SHXdiqGJdE2xf4P82zu_6Qx9Z4EgEk2l4djH0zQfpqMVsgVDOeANBbqrtXMZ72mIineYf- Kp4axCdz7PXp=w1280) 19.Sep.2021 [Medium](https://medium.com/p/626019137fa9/edit) [https://fi.co/madlibs](https://fi.co/madlibs) [https://orcid.org/0000-0001-8382-1389](https://orcid.org/0000-0001-8382-1389) Dreyer's English (learn write English) #book story Greek Mythology Explained: A Deeper Look at Classical Greek Lore and Myth **Papers:** CALTag: High Precision Fiducial Markers for Camera Diatom Autofocusing in Brightfield Microscopy: a Comparative Study :implementation variation of the laplacian Analysis of focus measure operators in shape-from-focus: why laplacian? Blure detection? Iqaf? Optical flow modeling and computation: A survey Toward general type 2 fuzzy logic systems based on zSlices \-------------------------------------------------------------------- Lost in space The OA Film:[ https://en.wikipedia.org/wiki/Shark_Tank](https://en.wikipedia.org/wiki/Shark_Tank) Movie Serial billons monk serial movies Python async Highly decoupled microservice Edex RIS-V , Self-car RISC-V Magazine Road map Game: over/under [https://www.sporcle.com/games/Hejman/underwhelmed](https://www.sporcle.com/games/Hejman/underwhelmed) \-------------------------------------------------------------------- \-------------------------------------------------------------------- GDPR in IoT The EU General Data Protection Regulation (GDPR) and Face Images in IoT The GDPR (General Data Protection Regulation), taking effect in May 2018, introduces strict requirements for personal data protection and the privacy rights of individuals. The EU regulations will set a new global standard for privacy rights and change the way organizations worldwide store and process personal data. The GDPR brings the importance of preserving the privacy of personal information to the forefront, yet the importance of face images within this context is often overlooked. The purpose of this paper is to introduce a solution that helps companies protect face images in IoT devices which record or process image by camera, to strengthen compliance with the GDPR. Our Face is our Identity Our face is the most fundamental and highly visible element of our identity. People recognize us when they see our face or a photo of our face. Recent years have seen exponential increase in the use, storage and dissemination of face images in both private and public sectors - in social networks, corporate databases, IoT, smart-city deployments, digital media, government applications, and nearly every organization’s databases. \--------------------- $(aws-okta env stage) aws s3 cp s3://dataset/archive.tar.gz /Users/a.zip aws s3 ls images | tail -n 100 aws s3 cp staging-images/test.jpg /Users/test.jpg \--------------------- screen -rD k get pods Docker RUN chmod +x /tmp/run.sh Can run docker in terminal and run code line by line docker run -it --rm debian:stable-slim bash apt-get update apt-get installl -y \-------------------------------- brew install awscli aws-okta kubectx kubernetes-cli tfenv touch ~/.aws/config \-------------------------------------------------------------------- docker image rm TETSTDFSAFDSADF docker image ls docker system prune docker run -p 5000:5000 nameDocker:latest docker build . -t nameDocker:latest docker container stop number-docker-name docker container ls * docker pull quay.io/test:v0.0.1 * docker run --rm -p 5000:5000 -it quay.io/test:v0.0.1 * curl --header "Content-Type: application/json" \--request POST --data '[{"fixed":7.4, "a":0, "b":0.56, "c":9.4}]'[ http://127.0.0.1:5000/predict](https://meet.google.com/linkredirect?authuser=0&dest=http%3A%2F%2F127.0.0.1%3A5000%2Fpredict) * docker run --rm -v /home/.aws/credentials:/root/.aws/credentials -it quay.io/test /bin/sh aws s3 ls --profile=test \-------------------------------- Cloud software engineer and consultant focusing on building highly available, scalable and fully automated infrastructure environments on top of Amazon Web Services and Microsoft Azure clouds. My goal is always to make my customers happy in the cloud. \---------------- Search google for 3d = tiger - iPhone show AR/VR \--------------- brew install youtube-dl \---------------------------- List: Collection bucket : 1 for week 2 for month 3 for future \-------------------------------------------------------------------- **• Per frame operation** – Detection – Classification – Segmentation – Feature extraction – Recognition **• Across frames ** – Tracking – Counting **• High level** – Intention – Relations – Analyzing ============================= Deep compression Pruning deep learning Hash table neural network Dl compression Deep compression =================================== Mini PCI-e slot * What have I learned so far: * Problem-based learning * real life scenarios * index card (answer , idea) * Think-Pair-Share * Leverage flip charts * Summarizing \-------------------------------------------------------------------- Self \\\ Advancing Self-Supervised and Semi-Supervised Learning with SimCLR \cite{Chen2020} %https://github.com/google-research/simclr first pretraining on a large unlabeled dataset and then fine-tuning on a smaller labeled dataset pretraining on large unlabeled image datasets, as demonstrated by Exemplar- CNN, Instance Discrimination, CPC, AMDIM, CMC, MoCo and others. “A Simple Framework for Contrastive Learning of Visual Representations”, 85.8\% top-5 accuracy using 1\% of labeled images on the ImageNet dataset contrastive learning algorithms linear evaluation protocol (Zhang et al., 2016; Oord et al.,2018; Bachman et al., 2019; Kolesnikov et al., 2019) unsupervised learning benefits more from bigger models than its supervised counterpart. \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- Some of optimization algorithms ======================== Swarm Algorithm =============== 1\. Ant Colony Optimization (ACO) was inspired by the research on the behavior of ant colonies 2\. Firefly Algorithm based on insects called fireflies 3\. Marriage in Honey Bees Optimization Algorithm (MBO algorithm) is inspired by the process of reproduction of Honey Bee 4\. Artificial Bee Colony Algorithm (ABC) is based on the recollection of the Honey Bees 5\. Wasp Swarm Algorithm was inspired on the Parasitic wasps 6\. Bee Collecting Pollen Algorithm (BCPA) 7\. Termite Algorithm 8\. Mosquito swarms Algorithm (MSA) 9\. zooplankton swarms Algorithm (ZSA) 10\. Bumblebees Swarms Algorithm (BSA) 11\. Fish Swarm Algorithm (FSA) 12\. Bacteria Foraging Algorithm (BFA) 13\. Particle Swarm Optimization (PSO) 14\. Cuckoo Search 15\. Bat Algorithm (BA) 16\. Accelerated PSO 17\. Bee System 18\. Beehive Algorithm 19\. Cat Swarm 20\. Consultant-guided search 21\. Eagle Strategy 22\. Fast Backterial swarming algorithm 23\. Good lattice swarm optimization 24\. Glowworm swarm optimization 25\. Hierarchical swarm model 26\. Krill Herd 27\. Monkey Search 28\. Virtual ant algorithm 29\. Virtual bees 30\. Weighted Swarm Algorithm 31\. Wisdom of Artificial Crowd algorithm 32\. Prey-predator algorithm 33\. Memetic algorithm 34\. Lion Optimization Algorithm 35\. Chicken Swarm Optimization 36\. Ant Lion Optimizer 37\. Compact Particle Swarm Optimization 38\. Fruit Fly Optimization Algorithm 39\. marine propeller optimization algorithm 40\. The Whale Optimization Algorithm 41\. virus colony search algorithm 42\. Slime mould optimization algorithm Ecology Inspired Algorithm ========================== 1\. Biogeography-based Optimization 2\. Invasive Weed Optimization 3\. Symbiosis-Inspired Optimization - PS2O 4\. Atmosphere Clouds Model 5\. Brain Storm Optimization 6\. Dolphin echolocation 7\. Japanese Tree Frog Calling algorithm 8\. Eco-inspired evolutionary algorithm 9\. Egyptian Vulture 10\. Fish School search 11\. Flower Pollination algorithm 12\. Gene Expression 13\. Great Salmon Run 14\. Group Search Optimizer 15\. Human Inspired Algorithm 16\. Roach Infestation algorithm 17\. Queen-bee algorithm 18\. Shuffled frog leaping algorithm 19\. Forest Optimization Algorithm 20\. coral reefs optimization algorithm 21\. cultural evolution algorithm 22\. Grey Wolf Optimizer 23\. probabilistic pso 24\. omicron aco algorithm 25\. shark smell optimization 26\. social spider algorithm 27\. sosial insects behavior algorithm 28\. sperm whale algorithm Evolutionary Optimization ========================= 1\. Genetic Algorithm 2\. Genetic Programming 3\. Evolutionary Strategies 4\. Differential Evolution 5\. Paddy Field Algorithm 6\. Queen-bee Evolution 7\. Quantum Inspired Social Evolution Physic and Chemistry inspired algorithm ======================================= 1\. Big bang-Big Crunch 2\. Block hole algorithm 3\. Central force optimization 4\. Charged System search 5\. Electro-magnetism optimization 6\. Galaxy based search algorithm 7\. Gravitational search 8\. Harmony search algorithm 9\. Intelligent water drop algorithm 10\. River formation algorithm 11\. Self-propelled dynamics 12\. Simulated Annealing 13\. Stachastic diffusion search 14\. Spiral optimization 15\. Water Cycle algorithm 16\. Artificial Physics optimization 17\. Binary Gravitational search algorithm 18\. Continous quantum ant colony optimization 19\. Extended artificial physics optimization 20\. Extended Central force optimization 21\. Electromagnetism-like heuristic 22\. Gravitational Interaction optimization 23\. Hysteristetic Optimization algorithm 24\. Hybrid quantum-inspired GA 25\. Immune gravitational inspired algorithm 26\. Improved quantum evolutinary algorithm 27\. Linear programming 28\. Quantum-inspired bacterial swarming 29\. Quantum-inspired evolutionary algorithm 30\. Quantum-inspired genetic algorithm 31\. Quantum-behaved PSO 32\. Unified big bang-chaotic big crunch 33\. Vector model of artificial physics 34\. Versatile quantum-inspired evolutionary algorithm 35\. Space Gravitational Algorithm 36\. Ion Motion Algorithm 37\. Light Ray Optimization Algorithm 38\. Ray Optimization 39\. Photosynthetic Algorithms 40\. floorplanning algorithm 41\. Gases Brownian Motion Optimization 42\. gradient-type optimization 43\. mean-variance optimization 44\. Mine blast algorithm 45\. moth flame optimization 46\. multi battalion search algorithm 47\. music inspired optimization 48\. no free lunch theorems algorithm 49\. Optics inspired optimization 50\. runner-root algorithm 51\. sine cosine algorithm 52\. pitch tracking algorithm 53\. Stochastic Fractal Search algorithm 54\. stroke volume optimization 55\. Stud krill herd algorithm 56\. The Great Deluge Algorithm 57\. Water Evaporation Optimization 58\. water wave optimization algorithm 59\. Island model algorithm 60\. 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Let's partner up to take your project to the next level! pip install mlc-ai-nightly -f https://mlc.ai/wheels https://mlc.ai/ https://mlc.ai/summer22/ Day 1: Introduction to Unity: TVMScript Introduction to Unity: Relax and PyTorch TVM BYOC in Practice Get Started with TVM on Adreno GPU Introduction to Unity: Metaschedule How to Bring microTVM to a custom IDE Day 2: Community Keynote PyTorch 2.0: the journey to bringing compiler technologies to the core of PyTorch Support QNN Dialect for TVM with MediaTek Neuron and Devise the Scheduler for Acceleration On-Device Training Under 256KB Memory AMD Tutorial TVM at TI: Accelerating inference using the C7x/MMA Adreno GPU: 4x speed-up and upstreaming to TVM mainline Transfer-Tuning: Reusing Auto-Schedules for Efficient Tensor Program Code Generation Improvement in the TVM OpenCL codegen to autogenerate optimal convolution kernels for Adreno GPUs TVM Unity: Pass Infrastructure and BYOC Renesas Hardware accelerators with Apache TVM Introduction on 4th Gen Intel Xeon processor and BF16 support with TVM Hidet: Task Mapping Programming Paradigm for Deep Learning Tensor Programs Towards Building a Responsible Data Economy Optimizing SYCL Device Kernels with AKG Adreno GPU Performance Enhancements using TVM Improvements to CMSIS-NN integration in TVM UMA: Universal Modular Accelerator Interface Day 3: TVM Unity for Dynamic Models Empower Tensorflow serving with backend TVM Enabling Conditional Computing on Hexagon target Decoupled Model Schedule for Large Deep Learning Model Training Using TVM to bring Bayesian neural networks to embedded hardware Efficient Support of TVM Scan OP on RISC-V Vector Extension Improvements to Ethos-U55 support in TVM including CI on Alif Semiconductor boards Compiling Dynamic Shapes TVM Packaging in 2023: delivering TVM to end users Cross-Platform Training Using Automatic Differentiation on Relax IR AutoTVM: Reducing tuning space by cross axis filtering SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning Analytical Tensorization and Fusion for Compute-intensive Operators CUTLASS 3.0: Next Generation Composable and Reusable GPU Linear Algebra Library Enabling Data Movement and Computation Pipelining in Deep Learning Compiler Automating DL Compiler Bug Finding with NNSmith TVM at NIO TVM at Tencent Integrating the Andes RISC-V Processors into TVM Alpa: A Compiler for Distributed Deep Learning ACRoBat: Compiler and Runtime Techniques for Efficient Auto-Batching of Dynamic Deep Learning Computations Channel Folding: a Transform Pass for Optimizing Mobilenets ========================================================================Day 1: ************************ Introduction to Unity: TVMScript [https://github.com/cyx-6/TVM- Demo/blob/main/tvmscript.ipynb](https://github.com/cyx-6/TVM- Demo/blob/main/tvmscript.ipynb) Gan NN show us some hidden patter in history we can not see before. “I always have a slip of paper at hand, on which I note down the ideas of certain pages. On the backside I write down the bibliographic details. After finishing the book I go through my notes and think how these notes might be relevant for already written notes in the slip-box. It means that I always read with an eye towards possible connections in the slip-box.” (Luhmann et al., 1987, 150) Deep representation learning Model evaluation. Camera cheaper lidar Point cloud because of we need 3d Capturing reality 1\. 𝐀𝐝𝐝/𝐂𝐨𝐦𝐦𝐢𝐭 𝐀𝐥𝐥 Standard way: git add . git commit -m "Message" Another way: git commit -a -m "Message" 𝟐\. 𝐀𝐥𝐢𝐚𝐬𝐞𝐬 With aliases, you can write your own Git commands that do anything you want. Eg: git config --global alias.ac '!git add -A && git commit -m' (alias called ac, git add -A && git commit -m will do the full add and commit) 𝟑\. 𝐑𝐞𝐯𝐞𝐫𝐭 The revert command simply allows us to undo any commit on the current branch. Eg: git revert 486bdb2 Another way: git revert HEAD (for recent commits) 𝟒\. 𝐑𝐞𝐟𝐥𝐨𝐠 This command lets you easily see the recent commits, pulls, resets, pushes, etc on your local machine. Eg: git reflog 𝟓\. 𝐏𝐫𝐞𝐭𝐭𝐲 𝐋𝐨𝐠𝐬 Gives you the ability to print out a pretty log of your commits/branches. Eg: git log --graph --decorate --oneline 𝟔\. 𝐒𝐞𝐚𝐫𝐜𝐡𝐢𝐧𝐠 𝐋𝐨𝐠𝐬 One can also use the log command to search for specific changes in the code. Eg: git log -S "A promise in JavaScript is very similar" 𝟕\. 𝐒𝐭𝐚𝐬𝐡 This command will stash (store them locally) all your code changes but does not actually commit them. Eg: git stash 𝟖\. 𝐑𝐞𝐦𝐨𝐯𝐞 𝐃𝐞𝐚𝐝 𝐁𝐫𝐚𝐧𝐜𝐡𝐞𝐬 This command will delete all the tracking information for branches that are on your local machine that are not in the remote repository, but it does not delete your local branches. Eg: git remote update --prune 𝟗\. 𝐁𝐢𝐬𝐞𝐜𝐭 For finding which commits caused certain bugs Eg: git bisect start git bisect bad git bisect good 48c86d6 𝟏𝟎\. 𝐃𝐞𝐬𝐭𝐫𝐨𝐲 𝐋𝐨𝐜𝐚𝐥 𝐂𝐡𝐚𝐧𝐠𝐞𝐬 One can wipe out all changes on your local branch to exactly what is in the remote branch. Eg: git reset --hard origin/main Don’t trust your devices IoT. software and hardware are together for better business. Newsletter investing every 3 months 1\. Prototyping. New bie 2\. Patent. Website. ( list of investors) 3\. Pre seed. First founding 1M VC, inistution, anjel capital. 400 000 preseed. Quveribel. Equtible rund convertible non agreement Template. Convertabel lone 1\. Germ standar inistitude 2\. 4\. Equity. Venture builder. 20% 200 000 5\. 100 000 per year to become unocorn in less than 10 years 6\. Soniy corn 100k unicorn 1M 7\. 360 euro per years for database of investor 8\. Convertable loan: Pay interst rate 5% to 8% = 18 months later (2M found in 10M) convert on based . 9\. Invester Never act as co-founder = full time = 20% 10\. Project profit, 11\. Full time after foun rising Make a plan for your business; take your time to make calculations by creating a target audience. Your target audience determines how you approach your business plan. By studying your target audience, you are making empirical research and collecting information from them Then, secure a good partnership if need be, and get enough capital to start up. * * What the people need * Why people need it * When the people need it * It's affordability * It's ease of use * It's maintenance and revenue Pair programming The SB7 Framework harnesses the influence of stories. The structure describes the 7 most common story elements: • Character • Problem • Guide • Plan • Calls to action • Failure • Success Dear [Hiring Manager’s Name], I am writing to apply for the position of computer vision for IoT and cloud at [Company Name]. I am a highly skilled and experienced computer vision engineer with a strong background in IoT and cloud technologies. I believe that my skills and experience make me an ideal candidate for this position and I am excited about the opportunity to contribute to the success of your organization. I have a solid understanding of computer vision algorithms and techniques, as well as experience in developing and implementing computer vision systems. I am proficient in programming languages such as Python, C++, and Java, and have experience with popular computer vision libraries such as OpenCV, TensorFlow, and PyTorch. In addition, I have a strong background in IoT and cloud technologies, including experience with IoT platforms such as AWS IoT, Azure IoT, and Google Cloud IoT. I am familiar with cloud computing technologies such as AWS, Azure, and Google Cloud, and have experience with deploying and managing computer vision systems on these platforms. I am also a team player and have excellent communication skills. I am able to work with cross-functional teams and can effectively communicate with both technical and non-technical stakeholders. I am also highly motivated, and I am always looking for ways to improve my skills and stay up-to-date with the latest technologies. I am excited about the opportunity to join [Company Name] and to contribute to the development of cutting-edge computer vision systems for IoT and cloud. I am confident that my skills and experience make me a strong candidate for this position, and I look forward to discussing how I can contribute to your organization. Thank you for considering my application. I look forward to hearing from you soon. Sincerely, Title: "Unlocking the Power of Computer Vision for IoT and Cloud" Introduction: * Hi, and welcome to our video on the topic of computer vision for IoT and cloud. In this video, we're going to explore how computer vision technology can be used to enhance IoT and cloud-based systems, and how it can be used to unlock new possibilities for businesses and consumers alike. Body: * First, let's talk about what computer vision is and how it works. Essentially, computer vision is the technology that enables computers to understand and interpret visual information from the world around us. This can include things like images, videos, and even 3D models. * One of the key ways that computer vision can be used to enhance IoT and cloud-based systems is by enabling devices to better understand and interact with their environment. For example, a computer vision-enabled camera could be used to monitor a manufacturing facility and identify when a machine is in need of maintenance or when an employee is working in an unsafe manner. * Another way that computer vision can be used to enhance IoT and cloud-based systems is by enabling devices to better understand and interact with people. For example, a computer vision-enabled security camera could be used to identify individuals and track their movements, or a computer vision-enabled smart home system could be used to detect when someone is in the room and adjust the lighting or temperature accordingly. * Additionally, computer vision can also be used to enhance cloud-based systems by providing more accurate data and insights. For example, a computer vision-enabled drone could be used to collect data on crops and provide farmers with more accurate information about the health and growth of their crops. Conclusion: * Overall, computer vision technology has the potential to unlock new possibilities for businesses and consumers alike, by enabling IoT and cloud-based systems to better understand and interact with their environment and people. We hope this video has provided you with a better understanding of the potential of computer vision for IoT and cloud, and we look forward to seeing the new possibilities that will be created as this technology continues to evolve. Excited to share my latest project using computer vision and IoT to improve efficiency in manufacturing. I used a combination of machine learning algorithms and cloud computing to analyze data from cameras and sensors in real-time, resulting in a 20% increase in production speed. This was a challenging project but I enjoyed every step of it! I am always looking for new opportunities to apply my skills in computer vision and IoT to help companies improve their operations. Let's connect if you are working on a similar project or if you are looking for a developer with these skills. #computervision #IoT #cloudcomputing #manufacturingefficiency #machinelearning #developer" In this post, you briefly mention your experience and skills in computer vision and IoT, and you provide a specific example of a project you worked on that demonstrates your abilities. You also make it clear that you are open to new opportunities, and you invite others to connect with you. Using relevant hashtags such as #computervision #IoT #cloudcomputing can help your post reach a wider audience Exciting news! I just published a paper on a new object detection algorithm that I developed. The algorithm uses a combination of deep learning and computer vision techniques to improve accuracy and speed of object detection in real-world scenarios. This is a big step forward in the field of computer vision and I am proud to have contributed to it. I will be presenting my research at the Computer Vision Conference next month, if you're attending be sure to stop by and say hi! #computervision #objectdetection #deeplearning #research" In this post, you briefly explain the main findings and contributions of your research, and you express your excitement and pride in your work. You also mention the upcoming conference where you will be presenting your research, inviting your friends and colleagues to meet you in person. Also using relevant hashtags such as #computervision #objectdetection #deeplearning can help reach a wider audience interested in the field. Features stores 1\. Car parts detection 2\. Resize keep aspects ration 3\. 3.1 Perform damage detection 4\. 3.2Semantic segregation 5\. Transfer to original coordinates 1 class imbalance 2 class definition Maybe Class in between 3 inconstant annotations Color augmentation 1\. RGB shift 2\. Random brithness and contrast 3\. Sharpen 4\. Hue saturation value Why manually data augmented Becasu control of data. Not too rotate or change something Photogrammetry model Neural radiance fields (NeRF) NeRF in the wild \ [GitHub - google-research/tuning_playbook: A playbook for systematically maximizing the performance of deep learning models.](https://github.com/google-research/tuning_playbook) Yocto and Machine Learning + OpenCV: [https://www.yoctoproject.org](https://www.yoctoproject.org) [https://www.hackster.io/monica/running-machine-learning-on-maaxboard-s-yocto- image-part-1-6a4796](https://www.hackster.io/monica/running-machine-learning- on-maaxboard-s-yocto-image-part-1-6a4796) Bard Google: [https://blog.google/technology/ai/bard-google-ai-search- updates/](https://blog.google/technology/ai/bard-google-ai-search-updates/) [https://mustang.ir/questions/question/راه-اندازی-پروژه-های-گیت-هاب-با-git- pages](https://mustang.ir/questions/question/%D8%B1%D8%A7%D9%87-%D8%A7%D9%86%D8%AF%D8%A7%D8%B2%DB%8C-%D9%BE%D8%B1%D9%88%DA%98%D9%87-%D9%87%D8%A7%DB%8C-%DA%AF%DB%8C%D8%AA-%D9%87%D8%A7%D8%A8-%D8%A8%D8%A7-git- pages) Book: Project Management for Non-Project Managers [https://fa.wikipedia.org/wiki/علی_اکبرپور](https://fa.wikipedia.org/wiki/%D8%B9%D9%84%DB%8C_%D8%A7%DA%A9%D8%A8%D8%B1%D9%BE%D9%88%D8%B1) [https://www.kingorama.com](https://www.kingorama.com) شاهنامه سه بعدی [Accelerate deep learning model development with cloud custom environments - AWS Online Tech Talks - YouTube](https://m.youtube.com/watch?v=2Wt2zlkMtKI&noapp=1) [بخش هایی از کتاب Refactoring (نسخه رایگان)](https://www.developit.ir/refactoring/free.html#f7) [Performance Notes Of PyTorch Support for M1 and M2 GPUs - Lightning AI](https://lightning.ai/pages/community/community-discussions/performance- notes-of-pytorch-support-for-m1-and-m2-gpus/) [Investopedia Academy](https://academy.investopedia.com/) [HandBrake updated with AV1 and VP9 10-bit video encoding](https://9to5mac.com/2022/12/29/handbrake-support-av1-and- vp9-10-bit/) [How to Start Your Sole Proprietorship in 6 Simple Steps](https://qonto.com/en/blog/creators/administrative/sole-proprietorship- in-germany) [Duolingo English Test](https://englishtest.duolingo.com/applicants) [چالش‌های تولید محتوا برای مارکت اروپا و آمریکا - YouTube](https://m.youtube.com/watch?v=wW0HZdubuWQ) [PyTorch for Deep Learning & Machine Learning – Full Course - YouTube](https://m.youtube.com/watch?v=V_xro1bcAuA#dialog) [Why passive investing makes less sense in the current environment | Financial Times](https://archive.ph/0VucZ) [GitHub - google-research/tuning_playbook: A playbook for systematically maximizing the performance of deep learning models.](https://github.com/google-research/tuning_playbook) [GitHub - mgechev/google-interview-preparation-problems: leetcode problems I solved to prepare for my Google interview.](https://github.com/mgechev/google- interview-preparation-problems) [Bayesian Neural Networks and Variational Dropout](https://dmittov.github.io/variational_dropout/#/maximum-likelihood) [One machine learning question every day - bnomial](https://today.bnomial.com/?ref=email) Git remote add orgine Asynchronous Operation Anomaly detection Use experience. Personalizes. Prediction manage society mobility Personalization Covenant Platform. OpenMMLab Wordtune - AI-powered Writing Companion tree -v -I '*.png' -I '*.jpg' \--charset utf-8 >list2.txt 3D object using triangular mesh need vertices point cloud underlying surface of some 3D object, faster Definition of Done User Story complete Code\Implementation complete Code\Implementation Peer Reviews) approved Unit tests complete (if required) Testing Notes complete (if required) User Story Acceptance criteria defined and verified Backend: Python, Redis, Postgres, Celery Frontend: React, Redux, TypeScript DevOps: Terraform, Kubernetes, GitHub, Docker, AWS Data: Python (Data Science), Kafka, Fastapi, MLFlow, AWS SageMaker ML: Selcond core, Kubeflow, … [Sharpness](https://en.wikipedia.org/wiki/Sharpness_%28visual%29) ,[Noise](https://en.wikipedia.org/wiki/Image_noise), [Dynamic range](https://en.wikipedia.org/wiki/Dynamic_range), [Tone reproduction](https://en.wikipedia.org/wiki/Tone_reproduction) , [Contrast](https://en.wikipedia.org/wiki/Contrast_%28vision%29), [Color](https://en.wikipedia.org/wiki/Color), [Distortion](https://en.wikipedia.org/wiki/Distortion_%28optics%29) , [DSLR lenses](https://en.wikipedia.org/wiki/Lenses_for_SLR_and_DSLR_cameras), [Vignetting](https://en.wikipedia.org/wiki/Vignetting), [Exposure](https://en.wikipedia.org/wiki/Exposure_%28photography%29), Lateral [chromatic aberration](https://en.wikipedia.org/wiki/Chromatic_aberration) (LCA), [Lens flare](https://en.wikipedia.org/wiki/Lens_flare), Color, [Artifacts](https://en.wikipedia.org/wiki/Compression_artifact) ۱\. جهت انتخاب کلمه مورد نظرتان، دو بار روی آن تپ کنید. ۲\. برای انتخاب کل یک پاراگراف، کافیست چهار با روی آن تپ کنید. ۳\. یک انگشت را در ابتدا و انگشت دیگر را در آخر یک محدود گذاشته و کمی نگه دارید. متن میان دو انگشت انتخاب خواهد شد. ۴\. روی ابتدای محدوده ای دلخواه دو بار تپ کرده و بلافاصله با درگ کردن (کشیدن) پین محدوده ی انتخاب شده را گسترش دهید. (انگشت خود را پس از دومین تپ جدا نکنید) ۵\. برای انتخاب کل پاراگراف، به جز استفاده از مورد ۲، می توانید با دو انگشت، یک بار روی آن تپ کنید. namely motion estimation, motion smoothing, and image warping. Motion estimation algorithms often use a similarity transform to handle camera translations, rotations, and zooming. The tricky part is getting these algorithms to lock onto the background motion, 0\. video frames captured during fast motion are often blurry. Their appearance can be improved either using deblurring techniques (Section 10.3) or stealing sharper pixels from other frames with less motion or better focus (Matsushita, Ofek, Ge et al. 2006). Exercise 8.3 has you implement and test some of these ideas. 1\. Background subtraction 2\. Motion estimation 3\. Motion smoothing 4\. Image warping. image warping can result in missing borders around the image, which must be cropped, filled using information from other frames, or hallucinated using inpainting techniques (Section 10.5.1). Vision stabilization There is much recent work on Multi-view 3D reconstruction is a central research topic in computer vision that is driven in many different directions There are many available methods that can handle the noisy image completion problem In the case of surveillance using a fixed camera, there is no desired motion. In the case of most robotic applications, horizontal and vertical motions are desired, but rotation is not. In some cases of ground vehicles where the terrain is known to have many incline changes, or with aerial vehicles undergoing complicated maneuvers where the vehicle’s body is meant to be in varying orientations, rotation might be desired as the robot is meant to be at an angle at times. In robotics applications, computational complexity is extremely important due to the need for real-time operation. Also, it is likely that the center of rotation will not lie in the center of the image frame because the camera is rarely mounted at the robot’s center of mass. This first assumption is made in many video stabilization algorithms, and is a convenient way to seed the correct features with higher trust values. It is not an unreasonable assumption to make. Depending on the application, there is often a large portion of frames where local motion does not occur. In some situations, such as monitoring of steady traffic, there is no guarantee that local motion will not occur. This situation has not been tested, nor has our algorithm been designed to handle it. The second assumption comes from a combination of common sense, and the experience of many computer vision researchers. It makes sense that an object in the scene which does not move will be recognized more easily and more often. Being recognized consistently and consecutively is considered stable. On the other hand, objects which have local motion are less likely to be recognized as often. They might move through shadows, change orientation, or even move completely out of the scene. These possibilities all lead to a less stable class of features. It is likely that, more often than not, there are more background features than foreground features. Moving objects generally cover a small portion of the screen, which usually yields fewer features. Although uncommon, we did not want to make the assumption that this would occur in every frame. Certain scenes will consist of a large portion of local motion, or an object will move very close to the camera, consuming a much larger portion of the scene than the background. As long as some background features are discovered in each frame, our stabilization algorithm should succeed. # image processing tips: * the image size and kernel size need to depended. the best way is to use the one variable to define the size of the image and kernel together. * the coordinate of the image start at top left of the image/display * in order to change it to the normal coordinate you can use * grid of points; two matrix to X , Y coordinate * subtract half of W, H from X, Y in order to have normal coordinate system for our image * now we have cartesian coordinate * * cartesian coordinate to polar coordinate * تبدیل فضای کارتزین به پولار در خیلی از برنامه های پردازش تصویر کارایی دارد. برای پیدا کردن ترشلد ها هم می توان استفاده کرد * in MATLAB we can use ":"for example MatrixA(:) which means all entity of the matrix no mater how many dimensions we have but if we want to implemented in Python we can use numpy.flatten(). * in the MATLAB the round is different from python. if you want same result you need implement the rand function by yourself. * imge_mask=np.ones_like(image_source)*255 * imge_mask=imge_mask.astype(np.uint8) * imge_mask=imge_mask.flatten() ??? .ravel() * .asarray * np.logical_and( 1, 2) * indexes=[index for index in range(len(array1)) if array1[index] == True] * cv2.bitwise_not(yyy) * "olive" editor remove silence ![](https://lh5.googleusercontent.com/uz1tsz4Qy4dPzQzOtxekBVw0UwuYQ6BW31DaVXbLQTH- aJLInnaRUyrKqg4-- r_zsO5nj0pTm6oFMrFcyCwYUQfFNDHcgZIalLEc6l7_BABaoqRK7uGpRllFdVaf64L8_A=w1280) Questions: How to train model to add new classes? How to add a new class to an existing classifier in deep learning? Adding new Class to One Shot Learning trained model Is it possible to train a neural network as new classes are given? Merging all several models that detection system for all these tasks. Answer 1: There are several ways to add new classes to the trained model, which require just training for the new classes. * Incremental training ([GitHub](https://github.com/khurramjaved96/incremental-learning)) * continuously learn a stream of data ([GitHub](https://github.com/creme-ml/creme)) * online machine learning ([GitHub](https://github.com/GMvandeVen/continual-learning)) * Transfer Learning Twice * Continual learning approaches (Regularization, Expansion, Rehearsal) ([GitHub](https://github.com/facebookresearch/Adversarial-Continual-Learning)) Answer 2: Online learning is a term used to refer to a model which takes a continual or sequential stream of input data while training, in contrast to offline learning (also called batch learning), where the model is pre-trained on a static predefined dataset. Continual learning (also called incremental, continuous, lifelong learning) refers to a branch of ML working in an online learning context where models are designed to learn new tasks while maintaining performance on historic tasks. It can be applied to multiple problem paradigms (including Class- incremental learning, where each new task presents new class labels for an ever expanding super-classification problem). Do I need to train my whole model again on all four classes or is there any way I can just train my model on new class? Naively re-training the model on the updated dataset is indeed a solution. Continual learning seeks to address contexts where access to historic data (i.e. the original 3 classes) is not possible, or when retraining on an increasingly large dataset is impractical (for efficiency, space, privacy etc concerns). Multiple such models using different underlying architectures have been proposed, but almost all examples exclusively deal with image classification problems. Answer 3: You could use transfer learning (i.e. use a pre-trained model, then change its last layer to accommodate the new classes, and re-train this slightly modified model, maybe with a lower learning rate) to achieve that, but transfer learning does not necessarily attempt to retain any of the previously acquired information (especially if you don't use very small learning rates, you keep on training and you do not freeze the weights of the convolutional layers), but only to speed up training or when your new dataset is not big enough, by starting from a model that has already learned general features that are supposedly similar to the features needed for your specific task. There is also the related domain adaptation problem. There are more suitable approaches to perform incremental class learning (which is what you are asking for!), which directly address the [catastrophic forgetting problem](https://ai.stackexchange.com/a/13293/2444). For instance, you can take a look at this paper [Class-incremental Learning via Deep Model Consolidation](https://arxiv.org/pdf/1903.07864.pdf), which proposes the Deep Model Consolidation (DMC) approach. There are other continual/incremental learning approaches, many of them are described [here](https://ai.stackexchange.com/a/24529/2444) or in more detail [here](https://reader.elsevier.com/reader/sd/pii/S0893608019300231). Answer 4: by using Continual learning approaches to trained without losing the original classes. It has 3 categories: Regularization Expansion Rehearsal Answer 5: if you access to the dataset then you can download it and add all you new classes when you have " 'N' COCO Classes + 'M' New classes " after that you can fine tune model based on new dataset. you do not need all of the dataset just same number of image for all class enough. [https://learnopencv.com/stanford-mrnet-challenge-classifying-knee- mris/](https://learnopencv.com/stanford-mrnet-challenge-classifying-knee- mris/) Before start your machine learning project ask these questions and preparation: What is your inference hardware? specify the use case. specify model interface. how would we monitor performance after deployment? how can we approximate post-deployment monitoring before deployment? build a model and iteratively improve it. How to deploy the model at the end? monitor performance after deployment. what is your metric? How do you split your data (training and validation)? ### Preparation ML Project Workflow * [What is your hardware ?](/topics-and-projects/hardware) * specify the use case * specify model interface * how would we monitor performance after deployment? * how can we approximate post-deployment monitoring before deployment? * build a model and iteratively improve it * deploy the model * monitor performance * what is your are metric? * How do you split your data? ### Before Training deep learning model * using large model to train because * it is faster to train with lower overfit and faster converge due to best training * it is easier and higher compress in the final stage * model compression and acceleration: reducing parameters without significantly decreasing the model performance * Data: How to have good data for training deep learning models; How to Build and Enhance A Good Data Set For Your Deep Learning Project: using same config and data for training and inference, removing redundant (delete data which you don't need), get more data, Handle missing data, using data augmentation techniques or GAN to generate more data, re-scale/balance data, Transform your data (Change data types), Feature selection based on data-set and use case * * The data you don't need: removing redundant samples * get more data * Invent more data * data augmentation * Re-scale data * balance datasets * Transform your data * Feature selection based on dataset and use case * ML-Augmented Video Object Tracking: By applying and evaluating multiple algorithmic models, enhanced ability to scale object tracking in high-density video compositions. ### Training deep learning model * automated hyper-parameters * Using Hyperparameter tuning / Hyperparameter optimization tools * AutoML * genetic algorithm * population based training * bayesian optimization * You need to set some parameters and config for training * * Diagnostics * Weight Initialization * Learning rate * Activation function * Network Topology * Batches and Epochs * Regularization * Optimization and Loss * Early Stopping ### Continuous delivery * evolve with latest detection models * more data (no labels) * semi-supervised learning: big self-supervised models are strong semi-supervised learners ### After Training deep learning model * Parameter pruning * model pruning: reducing redundant parameters which are not sensitive to the performance. * aim: remove all connections with absolute weights below a threshold * Quantization * compresses by reducing the number of bits used to represent the weights * quantization effectively constraints the number of different weights we can use inside our kernels * per-channel quantization for weights, which improves performance by model compression and latency reduction. * Low rank matrix factorization (LRMF) * there exists latent structures in the data, by uncovering which we can obtain a compressed representation of the data * LRMF factorizes the original matrix into lower rank matrices while preserving latent structures and addressing the issue of sparseness * Compact convolutional filters (Video/CNN) * designing special structural convolutional filters to save parameters * replace over parametric filters with compact filters to achieve overall speedup while maintaining comparable accuracy * Knowledge distillation * training a compact neural network with distilled knowledge of a large model * distillation (knowledge transfer) from an ensemble of big networks into a much smaller network which learns directly from the cumbersome model's outputs, that is lighter to deploy * Binarized Neural Networks (BNNs) * Apache TVM (incubating) is a compiler stack for deep learning systems * Neural Networks Compression Framework (NNCF) ### Deep learning model in production * security: controls access to model(s) through secure packaging and execution * Test * auto training * using parallel processing and library such as GStreamer # Technology Docker AWS Flask Django # My Keynote (February 2021) 1. introduction 2. Machine Learning/ Deep Learning Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed 3. supervised Machine Learning 1. Deep Convolutional Neural Networks (DCNN) Architecture 2. Visualizing and Understanding Convolutional Networks 3. Object Detection by Deep Learning 4. [Video Tracking](/topics-and-projects/video-tracking) 5. Style Transfer 4. semi-supervised Machine Learning/ Deep Reinforcement learning (DRL) 1. Google 2. [Deep Reinforcement learning (DRL)](/topics-and-projects/drl) 5. unsupervised Machine Learning 1. Auto Encoder 6. Generative Adversarial Networks (GANs) 7. Tools 8. Pre trained model 9. Effect of Augmented Datasets to Train DCNNs 10. Training for more classes 11. Optimization 12. [Hardware](/topics-and-projects/hardware) 13. Production setup 14. post development 15. business , Gartner, Hype Cycle for emerging technologies, 2025 ### Advanced and practical 1. Inside CNN 1. Deep Convolutional Neural Networks Architecture 2. Convolution 3. Convolution Layer 4. Conv/FC Filters 5. Activation Functions 6. Layer Activations 7. Pooling Layer 8. Dropout ; L2 pooling 9. Why 1. Max-pooling is useful 2. How to see inside each layer and find important features * Visualizing and Understanding Convolutional Networks * [https://tensorspace.org/](https://tensorspace.org/) * [https://www.youtube.com/watch?v=AgkfIQ4IGaM](https://www.youtube.com/watch?v=AgkfIQ4IGaM) 2. Hands on python for deep learning 3. Fundamental deep learning 4. Installation: TensorFlow, PyTorch 5. [Using PC+eGPU for training video tracking](/topics-and-projects/source-code/compile) Summary of the summit * AI Hardware Europe Summit (July 2020) * [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit) * Apache TVM And Deep Learning Compilation Conference (December 2020) * [RISC-V Summit (December 2020) ](/workshops-and-events/risc-v) [https://www.inspectar.com/demo](https://www.inspectar.com/demo) for rasp # Face * Effective and precise face detection based on color and depth data * [https://www.sciencedirect.com/science/article/pii/S221083271400009X](https://www.sciencedirect.com/science/article/pii/S221083271400009X) * containing or not containing a face * Eigenface, Fisherface, waveletface, PCA (Principal Component Analysis), LDA (Linear Dis-criminant Analysis), Haar wavelet transform, and so on. * Viola–Jones detector * illumination changes and occlusion * depthinformation is used to filter the regions of the image where a candidate face regionis found by the Viola–Jones (VJ) detector * \- the first filtering rule is defined on the color of the region; since some false positiveshave colors not compatible with the face (e.g. shadows on jeans) a skin detector isapplied to remove the candidate face regions that do not contain skin pixels; * \- the second filtering rule is defined on the size of the face: using the depth mapit is quite easy to calculate the size of the candidate face region, which is use-ful to discard smallest and largest faces from the final result set; * \- the third filtering rule is defined on the depth map to discard flat objects (e.g.candidate faces found in a wall) or uneven objects (e.g. candidate face foundin the leaves of a tree). Combining color and depth data the candidate faceregion can be extracted from the background and measures of depth and reg-ularity are used for filtering out false positives. * The size criteria simply remove the candidate faces not included in a fixed rangesize ([12.5,30] cm). The size of a candidate face region is extracted from the depthmap according to the following approach. * image below * Gaussian mixture 3D morphable face model * [https://www.sciencedirect.com/science/article/pii/S0031320317303527](https://www.sciencedirect.com/science/article/pii/S0031320317303527) * * * Face Synthesis for Eyeglass-Robust Face Recognition * [https://paperswithcode.com/paper/face-synthesis-for-eyeglass-robust-face](https://paperswithcode.com/paper/face-synthesis-for-eyeglass-robust-face) * GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data * [https://paperswithcode.com/paper/genegan-learning-object-transfiguration-and](https://paperswithcode.com/paper/genegan-learning-object-transfiguration-and) * FacePoseNet: Making a Case for Landmark-Free Face Alignment * [https://paperswithcode.com/paper/faceposenet-making-a-case-for-landmark-free](https://paperswithcode.com/paper/faceposenet-making-a-case-for-landmark-free) * Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision * [https://paperswithcode.com/paper/learning-to-regress-3d-face-shape-and](https://paperswithcode.com/paper/learning-to-regress-3d-face-shape-and) * Unsupervised Eyeglasses Removal in the Wild * [https://paperswithcode.com/paper/unsupervised-eyeglasses-removal-in-the-wild](https://paperswithcode.com/paper/unsupervised-eyeglasses-removal-in-the-wild) * How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks) * [https://arxiv.org/pdf/1703.07332v3.pdf](https://arxiv.org/pdf/1703.07332v3.pdf) * (a) we construct, for the first time, a very strong baseline by combining a state-of-the-art architecture for landmark localization with a state-of-the-art residual block, train it on a very large yet synthetically expanded 2D facial landmark dataset and fi- nally evaluate it on all other 2D facial landmark datasets. * (b) We create a guided by 2D landmarks network which con- verts 2D landmark annotations to 3D and unifies all exist- ing datasets, leading to the creation of LS3D-W, the largest and most challenging 3D facial landmark dataset to date (~230,000 images). * (c) Following that, we train a neural network for 3D face alignment and evaluate it on the newly introduced LS3D-W. * (d) We further look into the effect of all “traditional” factors affecting face alignment performance like large pose, initialization and resolution, and introduce a “new” one, namely the size of the network. * (e) We show that both 2D and 3D face alignment networks achieve per- formance of remarkable accuracy which is probably close to saturating the datasets used. * Training and testing code as well as the dataset can be downloaded from https: //[www.adrianbulat.com/face-alignment/](http://www.adrianbulat.com/face-alignment/) ![](https://lh3.googleusercontent.com/9lvcVu- HI5oeKBlSMraQcnpp6MQ_gpnrRzOIbRJFnPhqa9SHXdiqGJdE2xf4P82zu_6Qx9Z4EgEk2l4djH0zQfpqMVsgVDOeANBbqrtXMZ72mIineYf- Kp4axCdz7PXp=w1280) 19.Sep.2021 [Medium](https://medium.com/p/626019137fa9/edit) [https://fi.co/madlibs](https://fi.co/madlibs) [https://orcid.org/0000-0001-8382-1389](https://orcid.org/0000-0001-8382-1389) Dreyer's English (learn write English) #book story Greek Mythology Explained: A Deeper Look at Classical Greek Lore and Myth **Papers:** CALTag: High Precision Fiducial Markers for Camera Diatom Autofocusing in Brightfield Microscopy: a Comparative Study :implementation variation of the laplacian Analysis of focus measure operators in shape-from-focus: why laplacian? Blure detection? Iqaf? Optical flow modeling and computation: A survey Toward general type 2 fuzzy logic systems based on zSlices \-------------------------------------------------------------------- Lost in space The OA Film:[ https://en.wikipedia.org/wiki/Shark_Tank](https://en.wikipedia.org/wiki/Shark_Tank) Movie Serial billons monk serial movies Python async Highly decoupled microservice Edex RIS-V , Self-car RISC-V Magazine Road map Game: over/under [https://www.sporcle.com/games/Hejman/underwhelmed](https://www.sporcle.com/games/Hejman/underwhelmed) \-------------------------------------------------------------------- \-------------------------------------------------------------------- GDPR in IoT The EU General Data Protection Regulation (GDPR) and Face Images in IoT The GDPR (General Data Protection Regulation), taking effect in May 2018, introduces strict requirements for personal data protection and the privacy rights of individuals. The EU regulations will set a new global standard for privacy rights and change the way organizations worldwide store and process personal data. The GDPR brings the importance of preserving the privacy of personal information to the forefront, yet the importance of face images within this context is often overlooked. The purpose of this paper is to introduce a solution that helps companies protect face images in IoT devices which record or process image by camera, to strengthen compliance with the GDPR. Our Face is our Identity Our face is the most fundamental and highly visible element of our identity. People recognize us when they see our face or a photo of our face. Recent years have seen exponential increase in the use, storage and dissemination of face images in both private and public sectors - in social networks, corporate databases, IoT, smart-city deployments, digital media, government applications, and nearly every organization’s databases. \--------------------- $(aws-okta env stage) aws s3 cp s3://dataset/archive.tar.gz /Users/a.zip aws s3 ls images | tail -n 100 aws s3 cp staging-images/test.jpg /Users/test.jpg \--------------------- screen -rD k get pods Docker RUN chmod +x /tmp/run.sh Can run docker in terminal and run code line by line docker run -it --rm debian:stable-slim bash apt-get update apt-get installl -y \-------------------------------- brew install awscli aws-okta kubectx kubernetes-cli tfenv touch ~/.aws/config \-------------------------------------------------------------------- docker image rm TETSTDFSAFDSADF docker image ls docker system prune docker run -p 5000:5000 nameDocker:latest docker build . -t nameDocker:latest docker container stop number-docker-name docker container ls * docker pull quay.io/test:v0.0.1 * docker run --rm -p 5000:5000 -it quay.io/test:v0.0.1 * curl --header "Content-Type: application/json" \--request POST --data '[{"fixed":7.4, "a":0, "b":0.56, "c":9.4}]'[ http://127.0.0.1:5000/predict](https://meet.google.com/linkredirect?authuser=0&dest=http%3A%2F%2F127.0.0.1%3A5000%2Fpredict) * docker run --rm -v /home/.aws/credentials:/root/.aws/credentials -it quay.io/test /bin/sh aws s3 ls --profile=test \-------------------------------- Cloud software engineer and consultant focusing on building highly available, scalable and fully automated infrastructure environments on top of Amazon Web Services and Microsoft Azure clouds. My goal is always to make my customers happy in the cloud. \---------------- Search google for 3d = tiger - iPhone show AR/VR \--------------- brew install youtube-dl \---------------------------- List: Collection bucket : 1 for week 2 for month 3 for future \-------------------------------------------------------------------- **• Per frame operation** – Detection – Classification – Segmentation – Feature extraction – Recognition **• Across frames ** – Tracking – Counting **• High level** – Intention – Relations – Analyzing ============================= Deep compression Pruning deep learning Hash table neural network Dl compression Deep compression =================================== Mini PCI-e slot * What have I learned so far: * Problem-based learning * real life scenarios * index card (answer , idea) * Think-Pair-Share * Leverage flip charts * Summarizing \-------------------------------------------------------------------- Self \\\ Advancing Self-Supervised and Semi-Supervised Learning with SimCLR \cite{Chen2020} %https://github.com/google-research/simclr first pretraining on a large unlabeled dataset and then fine-tuning on a smaller labeled dataset pretraining on large unlabeled image datasets, as demonstrated by Exemplar- CNN, Instance Discrimination, CPC, AMDIM, CMC, MoCo and others. “A Simple Framework for Contrastive Learning of Visual Representations”, 85.8\% top-5 accuracy using 1\% of labeled images on the ImageNet dataset contrastive learning algorithms linear evaluation protocol (Zhang et al., 2016; Oord et al.,2018; Bachman et al., 2019; Kolesnikov et al., 2019) unsupervised learning benefits more from bigger models than its supervised counterpart. \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- Some of optimization algorithms ======================== Swarm Algorithm =============== 1\. Ant Colony Optimization (ACO) was inspired by the research on the behavior of ant colonies 2\. Firefly Algorithm based on insects called fireflies 3\. Marriage in Honey Bees Optimization Algorithm (MBO algorithm) is inspired by the process of reproduction of Honey Bee 4\. Artificial Bee Colony Algorithm (ABC) is based on the recollection of the Honey Bees 5\. Wasp Swarm Algorithm was inspired on the Parasitic wasps 6\. Bee Collecting Pollen Algorithm (BCPA) 7\. Termite Algorithm 8\. Mosquito swarms Algorithm (MSA) 9\. zooplankton swarms Algorithm (ZSA) 10\. Bumblebees Swarms Algorithm (BSA) 11\. Fish Swarm Algorithm (FSA) 12\. Bacteria Foraging Algorithm (BFA) 13\. Particle Swarm Optimization (PSO) 14\. Cuckoo Search 15\. Bat Algorithm (BA) 16\. Accelerated PSO 17\. Bee System 18\. Beehive Algorithm 19\. Cat Swarm 20\. Consultant-guided search 21\. Eagle Strategy 22\. Fast Backterial swarming algorithm 23\. Good lattice swarm optimization 24\. Glowworm swarm optimization 25\. Hierarchical swarm model 26\. Krill Herd 27\. Monkey Search 28\. Virtual ant algorithm 29\. Virtual bees 30\. Weighted Swarm Algorithm 31\. Wisdom of Artificial Crowd algorithm 32\. Prey-predator algorithm 33\. Memetic algorithm 34\. Lion Optimization Algorithm 35\. Chicken Swarm Optimization 36\. Ant Lion Optimizer 37\. Compact Particle Swarm Optimization 38\. Fruit Fly Optimization Algorithm 39\. marine propeller optimization algorithm 40\. The Whale Optimization Algorithm 41\. virus colony search algorithm 42\. Slime mould optimization algorithm Ecology Inspired Algorithm ========================== 1\. Biogeography-based Optimization 2\. Invasive Weed Optimization 3\. Symbiosis-Inspired Optimization - PS2O 4\. Atmosphere Clouds Model 5\. Brain Storm Optimization 6\. Dolphin echolocation 7\. Japanese Tree Frog Calling algorithm 8\. Eco-inspired evolutionary algorithm 9\. Egyptian Vulture 10\. Fish School search 11\. Flower Pollination algorithm 12\. Gene Expression 13\. Great Salmon Run 14\. Group Search Optimizer 15\. Human Inspired Algorithm 16\. Roach Infestation algorithm 17\. Queen-bee algorithm 18\. Shuffled frog leaping algorithm 19\. Forest Optimization Algorithm 20\. coral reefs optimization algorithm 21\. cultural evolution algorithm 22\. Grey Wolf Optimizer 23\. probabilistic pso 24\. omicron aco algorithm 25\. shark smell optimization 26\. social spider algorithm 27\. sosial insects behavior algorithm 28\. sperm whale algorithm Evolutionary Optimization ========================= 1\. Genetic Algorithm 2\. Genetic Programming 3\. Evolutionary Strategies 4\. Differential Evolution 5\. Paddy Field Algorithm 6\. Queen-bee Evolution 7\. Quantum Inspired Social Evolution Physic and Chemistry inspired algorithm ======================================= 1\. Big bang-Big Crunch 2\. Block hole algorithm 3\. Central force optimization 4\. Charged System search 5\. Electro-magnetism optimization 6\. Galaxy based search algorithm 7\. Gravitational search 8\. Harmony search algorithm 9\. Intelligent water drop algorithm 10\. River formation algorithm 11\. Self-propelled dynamics 12\. Simulated Annealing 13\. Stachastic diffusion search 14\. Spiral optimization 15\. Water Cycle algorithm 16\. Artificial Physics optimization 17\. Binary Gravitational search algorithm 18\. Continous quantum ant colony optimization 19\. Extended artificial physics optimization 20\. Extended Central force optimization 21\. Electromagnetism-like heuristic 22\. Gravitational Interaction optimization 23\. Hysteristetic Optimization algorithm 24\. Hybrid quantum-inspired GA 25\. Immune gravitational inspired algorithm 26\. Improved quantum evolutinary algorithm 27\. Linear programming 28\. Quantum-inspired bacterial swarming 29\. Quantum-inspired evolutionary algorithm 30\. Quantum-inspired genetic algorithm 31\. Quantum-behaved PSO 32\. Unified big bang-chaotic big crunch 33\. Vector model of artificial physics 34\. Versatile quantum-inspired evolutionary algorithm 35\. Space Gravitational Algorithm 36\. Ion Motion Algorithm 37\. Light Ray Optimization Algorithm 38\. Ray Optimization 39\. Photosynthetic Algorithms 40\. floorplanning algorithm 41\. Gases Brownian Motion Optimization 42\. gradient-type optimization 43\. mean-variance optimization 44\. Mine blast algorithm 45\. moth flame optimization 46\. multi battalion search algorithm 47\. music inspired optimization 48\. no free lunch theorems algorithm 49\. Optics inspired optimization 50\. runner-root algorithm 51\. sine cosine algorithm 52\. pitch tracking algorithm 53\. Stochastic Fractal Search algorithm 54\. stroke volume optimization 55\. Stud krill herd algorithm 56\. The Great Deluge Algorithm 57\. Water Evaporation Optimization 58\. water wave optimization algorithm 59\. Island model algorithm 60\. 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Let's partner up to take your project to the next level! pip install mlc-ai-nightly -f https://mlc.ai/wheels https://mlc.ai/ https://mlc.ai/summer22/ Day 1: Introduction to Unity: TVMScript Introduction to Unity: Relax and PyTorch TVM BYOC in Practice Get Started with TVM on Adreno GPU Introduction to Unity: Metaschedule How to Bring microTVM to a custom IDE Day 2: Community Keynote PyTorch 2.0: the journey to bringing compiler technologies to the core of PyTorch Support QNN Dialect for TVM with MediaTek Neuron and Devise the Scheduler for Acceleration On-Device Training Under 256KB Memory AMD Tutorial TVM at TI: Accelerating inference using the C7x/MMA Adreno GPU: 4x speed-up and upstreaming to TVM mainline Transfer-Tuning: Reusing Auto-Schedules for Efficient Tensor Program Code Generation Improvement in the TVM OpenCL codegen to autogenerate optimal convolution kernels for Adreno GPUs TVM Unity: Pass Infrastructure and BYOC Renesas Hardware accelerators with Apache TVM Introduction on 4th Gen Intel Xeon processor and BF16 support with TVM Hidet: Task Mapping Programming Paradigm for Deep Learning Tensor Programs Towards Building a Responsible Data Economy Optimizing SYCL Device Kernels with AKG Adreno GPU Performance Enhancements using TVM Improvements to CMSIS-NN integration in TVM UMA: Universal Modular Accelerator Interface Day 3: TVM Unity for Dynamic Models Empower Tensorflow serving with backend TVM Enabling Conditional Computing on Hexagon target Decoupled Model Schedule for Large Deep Learning Model Training Using TVM to bring Bayesian neural networks to embedded hardware Efficient Support of TVM Scan OP on RISC-V Vector Extension Improvements to Ethos-U55 support in TVM including CI on Alif Semiconductor boards Compiling Dynamic Shapes TVM Packaging in 2023: delivering TVM to end users Cross-Platform Training Using Automatic Differentiation on Relax IR AutoTVM: Reducing tuning space by cross axis filtering SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning Analytical Tensorization and Fusion for Compute-intensive Operators CUTLASS 3.0: Next Generation Composable and Reusable GPU Linear Algebra Library Enabling Data Movement and Computation Pipelining in Deep Learning Compiler Automating DL Compiler Bug Finding with NNSmith TVM at NIO TVM at Tencent Integrating the Andes RISC-V Processors into TVM Alpa: A Compiler for Distributed Deep Learning ACRoBat: Compiler and Runtime Techniques for Efficient Auto-Batching of Dynamic Deep Learning Computations Channel Folding: a Transform Pass for Optimizing Mobilenets ========================================================================Day 1: ************************ Introduction to Unity: TVMScript [https://github.com/cyx-6/TVM- Demo/blob/main/tvmscript.ipynb](https://github.com/cyx-6/TVM- Demo/blob/main/tvmscript.ipynb) Gan NN show us some hidden patter in history we can not see before. “I always have a slip of paper at hand, on which I note down the ideas of certain pages. On the backside I write down the bibliographic details. After finishing the book I go through my notes and think how these notes might be relevant for already written notes in the slip-box. It means that I always read with an eye towards possible connections in the slip-box.” (Luhmann et al., 1987, 150) Deep representation learning Model evaluation. Camera cheaper lidar Point cloud because of we need 3d Capturing reality 1\. 𝐀𝐝𝐝/𝐂𝐨𝐦𝐦𝐢𝐭 𝐀𝐥𝐥 Standard way: git add . git commit -m "Message" Another way: git commit -a -m "Message" 𝟐\. 𝐀𝐥𝐢𝐚𝐬𝐞𝐬 With aliases, you can write your own Git commands that do anything you want. Eg: git config --global alias.ac '!git add -A && git commit -m' (alias called ac, git add -A && git commit -m will do the full add and commit) 𝟑\. 𝐑𝐞𝐯𝐞𝐫𝐭 The revert command simply allows us to undo any commit on the current branch. Eg: git revert 486bdb2 Another way: git revert HEAD (for recent commits) 𝟒\. 𝐑𝐞𝐟𝐥𝐨𝐠 This command lets you easily see the recent commits, pulls, resets, pushes, etc on your local machine. Eg: git reflog 𝟓\. 𝐏𝐫𝐞𝐭𝐭𝐲 𝐋𝐨𝐠𝐬 Gives you the ability to print out a pretty log of your commits/branches. Eg: git log --graph --decorate --oneline 𝟔\. 𝐒𝐞𝐚𝐫𝐜𝐡𝐢𝐧𝐠 𝐋𝐨𝐠𝐬 One can also use the log command to search for specific changes in the code. Eg: git log -S "A promise in JavaScript is very similar" 𝟕\. 𝐒𝐭𝐚𝐬𝐡 This command will stash (store them locally) all your code changes but does not actually commit them. Eg: git stash 𝟖\. 𝐑𝐞𝐦𝐨𝐯𝐞 𝐃𝐞𝐚𝐝 𝐁𝐫𝐚𝐧𝐜𝐡𝐞𝐬 This command will delete all the tracking information for branches that are on your local machine that are not in the remote repository, but it does not delete your local branches. Eg: git remote update --prune 𝟗\. 𝐁𝐢𝐬𝐞𝐜𝐭 For finding which commits caused certain bugs Eg: git bisect start git bisect bad git bisect good 48c86d6 𝟏𝟎\. 𝐃𝐞𝐬𝐭𝐫𝐨𝐲 𝐋𝐨𝐜𝐚𝐥 𝐂𝐡𝐚𝐧𝐠𝐞𝐬 One can wipe out all changes on your local branch to exactly what is in the remote branch. Eg: git reset --hard origin/main Don’t trust your devices IoT. software and hardware are together for better business. Newsletter investing every 3 months 1\. Prototyping. New bie 2\. Patent. Website. ( list of investors) 3\. Pre seed. First founding 1M VC, inistution, anjel capital. 400 000 preseed. Quveribel. Equtible rund convertible non agreement Template. Convertabel lone 1\. Germ standar inistitude 2\. 4\. Equity. Venture builder. 20% 200 000 5\. 100 000 per year to become unocorn in less than 10 years 6\. Soniy corn 100k unicorn 1M 7\. 360 euro per years for database of investor 8\. Convertable loan: Pay interst rate 5% to 8% = 18 months later (2M found in 10M) convert on based . 9\. Invester Never act as co-founder = full time = 20% 10\. Project profit, 11\. Full time after foun rising Make a plan for your business; take your time to make calculations by creating a target audience. Your target audience determines how you approach your business plan. By studying your target audience, you are making empirical research and collecting information from them Then, secure a good partnership if need be, and get enough capital to start up. * * What the people need * Why people need it * When the people need it * It's affordability * It's ease of use * It's maintenance and revenue Pair programming The SB7 Framework harnesses the influence of stories. The structure describes the 7 most common story elements: • Character • Problem • Guide • Plan • Calls to action • Failure • Success Dear [Hiring Manager’s Name], I am writing to apply for the position of computer vision for IoT and cloud at [Company Name]. I am a highly skilled and experienced computer vision engineer with a strong background in IoT and cloud technologies. I believe that my skills and experience make me an ideal candidate for this position and I am excited about the opportunity to contribute to the success of your organization. I have a solid understanding of computer vision algorithms and techniques, as well as experience in developing and implementing computer vision systems. I am proficient in programming languages such as Python, C++, and Java, and have experience with popular computer vision libraries such as OpenCV, TensorFlow, and PyTorch. In addition, I have a strong background in IoT and cloud technologies, including experience with IoT platforms such as AWS IoT, Azure IoT, and Google Cloud IoT. I am familiar with cloud computing technologies such as AWS, Azure, and Google Cloud, and have experience with deploying and managing computer vision systems on these platforms. I am also a team player and have excellent communication skills. I am able to work with cross-functional teams and can effectively communicate with both technical and non-technical stakeholders. I am also highly motivated, and I am always looking for ways to improve my skills and stay up-to-date with the latest technologies. I am excited about the opportunity to join [Company Name] and to contribute to the development of cutting-edge computer vision systems for IoT and cloud. I am confident that my skills and experience make me a strong candidate for this position, and I look forward to discussing how I can contribute to your organization. Thank you for considering my application. I look forward to hearing from you soon. Sincerely, Title: "Unlocking the Power of Computer Vision for IoT and Cloud" Introduction: * Hi, and welcome to our video on the topic of computer vision for IoT and cloud. In this video, we're going to explore how computer vision technology can be used to enhance IoT and cloud-based systems, and how it can be used to unlock new possibilities for businesses and consumers alike. Body: * First, let's talk about what computer vision is and how it works. Essentially, computer vision is the technology that enables computers to understand and interpret visual information from the world around us. This can include things like images, videos, and even 3D models. * One of the key ways that computer vision can be used to enhance IoT and cloud-based systems is by enabling devices to better understand and interact with their environment. For example, a computer vision-enabled camera could be used to monitor a manufacturing facility and identify when a machine is in need of maintenance or when an employee is working in an unsafe manner. * Another way that computer vision can be used to enhance IoT and cloud-based systems is by enabling devices to better understand and interact with people. For example, a computer vision-enabled security camera could be used to identify individuals and track their movements, or a computer vision-enabled smart home system could be used to detect when someone is in the room and adjust the lighting or temperature accordingly. * Additionally, computer vision can also be used to enhance cloud-based systems by providing more accurate data and insights. For example, a computer vision-enabled drone could be used to collect data on crops and provide farmers with more accurate information about the health and growth of their crops. Conclusion: * Overall, computer vision technology has the potential to unlock new possibilities for businesses and consumers alike, by enabling IoT and cloud-based systems to better understand and interact with their environment and people. We hope this video has provided you with a better understanding of the potential of computer vision for IoT and cloud, and we look forward to seeing the new possibilities that will be created as this technology continues to evolve. Excited to share my latest project using computer vision and IoT to improve efficiency in manufacturing. I used a combination of machine learning algorithms and cloud computing to analyze data from cameras and sensors in real-time, resulting in a 20% increase in production speed. This was a challenging project but I enjoyed every step of it! I am always looking for new opportunities to apply my skills in computer vision and IoT to help companies improve their operations. Let's connect if you are working on a similar project or if you are looking for a developer with these skills. #computervision #IoT #cloudcomputing #manufacturingefficiency #machinelearning #developer" In this post, you briefly mention your experience and skills in computer vision and IoT, and you provide a specific example of a project you worked on that demonstrates your abilities. You also make it clear that you are open to new opportunities, and you invite others to connect with you. Using relevant hashtags such as #computervision #IoT #cloudcomputing can help your post reach a wider audience Exciting news! I just published a paper on a new object detection algorithm that I developed. The algorithm uses a combination of deep learning and computer vision techniques to improve accuracy and speed of object detection in real-world scenarios. This is a big step forward in the field of computer vision and I am proud to have contributed to it. I will be presenting my research at the Computer Vision Conference next month, if you're attending be sure to stop by and say hi! #computervision #objectdetection #deeplearning #research" In this post, you briefly explain the main findings and contributions of your research, and you express your excitement and pride in your work. You also mention the upcoming conference where you will be presenting your research, inviting your friends and colleagues to meet you in person. Also using relevant hashtags such as #computervision #objectdetection #deeplearning can help reach a wider audience interested in the field. Features stores 1\. Car parts detection 2\. Resize keep aspects ration 3\. 3.1 Perform damage detection 4\. 3.2Semantic segregation 5\. Transfer to original coordinates 1 class imbalance 2 class definition Maybe Class in between 3 inconstant annotations Color augmentation 1\. RGB shift 2\. Random brithness and contrast 3\. Sharpen 4\. Hue saturation value Why manually data augmented Becasu control of data. Not too rotate or change something Photogrammetry model Neural radiance fields (NeRF) NeRF in the wild \ [GitHub - google-research/tuning_playbook: A playbook for systematically maximizing the performance of deep learning models.](https://github.com/google-research/tuning_playbook) Yocto and Machine Learning + OpenCV: [https://www.yoctoproject.org](https://www.yoctoproject.org) [https://www.hackster.io/monica/running-machine-learning-on-maaxboard-s-yocto- image-part-1-6a4796](https://www.hackster.io/monica/running-machine-learning- on-maaxboard-s-yocto-image-part-1-6a4796) Bard Google: [https://blog.google/technology/ai/bard-google-ai-search- updates/](https://blog.google/technology/ai/bard-google-ai-search-updates/) [https://mustang.ir/questions/question/راه-اندازی-پروژه-های-گیت-هاب-با-git- pages](https://mustang.ir/questions/question/%D8%B1%D8%A7%D9%87-%D8%A7%D9%86%D8%AF%D8%A7%D8%B2%DB%8C-%D9%BE%D8%B1%D9%88%DA%98%D9%87-%D9%87%D8%A7%DB%8C-%DA%AF%DB%8C%D8%AA-%D9%87%D8%A7%D8%A8-%D8%A8%D8%A7-git- pages) Book: Project Management for Non-Project Managers [https://fa.wikipedia.org/wiki/علی_اکبرپور](https://fa.wikipedia.org/wiki/%D8%B9%D9%84%DB%8C_%D8%A7%DA%A9%D8%A8%D8%B1%D9%BE%D9%88%D8%B1) [https://www.kingorama.com](https://www.kingorama.com) شاهنامه سه بعدی [Accelerate deep learning model development with cloud custom environments - AWS Online Tech Talks - YouTube](https://m.youtube.com/watch?v=2Wt2zlkMtKI&noapp=1) [بخش هایی از کتاب Refactoring (نسخه رایگان)](https://www.developit.ir/refactoring/free.html#f7) [Performance Notes Of PyTorch Support for M1 and M2 GPUs - Lightning AI](https://lightning.ai/pages/community/community-discussions/performance- notes-of-pytorch-support-for-m1-and-m2-gpus/) [Investopedia Academy](https://academy.investopedia.com/) [HandBrake updated with AV1 and VP9 10-bit video encoding](https://9to5mac.com/2022/12/29/handbrake-support-av1-and- vp9-10-bit/) [How to Start Your Sole Proprietorship in 6 Simple Steps](https://qonto.com/en/blog/creators/administrative/sole-proprietorship- in-germany) [Duolingo English Test](https://englishtest.duolingo.com/applicants) [چالش‌های تولید محتوا برای مارکت اروپا و آمریکا - YouTube](https://m.youtube.com/watch?v=wW0HZdubuWQ) [PyTorch for Deep Learning & Machine Learning – Full Course - YouTube](https://m.youtube.com/watch?v=V_xro1bcAuA#dialog) [Why passive investing makes less sense in the current environment | Financial Times](https://archive.ph/0VucZ) [GitHub - google-research/tuning_playbook: A playbook for systematically maximizing the performance of deep learning models.](https://github.com/google-research/tuning_playbook) [GitHub - mgechev/google-interview-preparation-problems: leetcode problems I solved to prepare for my Google interview.](https://github.com/mgechev/google- interview-preparation-problems) [Bayesian Neural Networks and Variational Dropout](https://dmittov.github.io/variational_dropout/#/maximum-likelihood) [One machine learning question every day - bnomial](https://today.bnomial.com/?ref=email) Git remote add orgine Asynchronous Operation Anomaly detection Use experience. Personalizes. Prediction manage society mobility Personalization Covenant Platform. OpenMMLab Wordtune - AI-powered Writing Companion tree -v -I '*.png' -I '*.jpg' \--charset utf-8 >list2.txt 3D object using triangular mesh need vertices point cloud underlying surface of some 3D object, faster Definition of Done User Story complete Code\Implementation complete Code\Implementation Peer Reviews) approved Unit tests complete (if required) Testing Notes complete (if required) User Story Acceptance criteria defined and verified Backend: Python, Redis, Postgres, Celery Frontend: React, Redux, TypeScript DevOps: Terraform, Kubernetes, GitHub, Docker, AWS Data: Python (Data Science), Kafka, Fastapi, MLFlow, AWS SageMaker ML: Selcond core, Kubeflow, … [Sharpness](https://en.wikipedia.org/wiki/Sharpness_%28visual%29) ,[Noise](https://en.wikipedia.org/wiki/Image_noise), [Dynamic range](https://en.wikipedia.org/wiki/Dynamic_range), [Tone reproduction](https://en.wikipedia.org/wiki/Tone_reproduction) , [Contrast](https://en.wikipedia.org/wiki/Contrast_%28vision%29), [Color](https://en.wikipedia.org/wiki/Color), [Distortion](https://en.wikipedia.org/wiki/Distortion_%28optics%29) , [DSLR lenses](https://en.wikipedia.org/wiki/Lenses_for_SLR_and_DSLR_cameras), [Vignetting](https://en.wikipedia.org/wiki/Vignetting), [Exposure](https://en.wikipedia.org/wiki/Exposure_%28photography%29), Lateral [chromatic aberration](https://en.wikipedia.org/wiki/Chromatic_aberration) (LCA), [Lens flare](https://en.wikipedia.org/wiki/Lens_flare), Color, [Artifacts](https://en.wikipedia.org/wiki/Compression_artifact) ۱\. جهت انتخاب کلمه مورد نظرتان، دو بار روی آن تپ کنید. ۲\. برای انتخاب کل یک پاراگراف، کافیست چهار با روی آن تپ کنید. ۳\. یک انگشت را در ابتدا و انگشت دیگر را در آخر یک محدود گذاشته و کمی نگه دارید. متن میان دو انگشت انتخاب خواهد شد. ۴\. روی ابتدای محدوده ای دلخواه دو بار تپ کرده و بلافاصله با درگ کردن (کشیدن) پین محدوده ی انتخاب شده را گسترش دهید. (انگشت خود را پس از دومین تپ جدا نکنید) ۵\. برای انتخاب کل پاراگراف، به جز استفاده از مورد ۲، می توانید با دو انگشت، یک بار روی آن تپ کنید. namely motion estimation, motion smoothing, and image warping. Motion estimation algorithms often use a similarity transform to handle camera translations, rotations, and zooming. The tricky part is getting these algorithms to lock onto the background motion, 0\. video frames captured during fast motion are often blurry. Their appearance can be improved either using deblurring techniques (Section 10.3) or stealing sharper pixels from other frames with less motion or better focus (Matsushita, Ofek, Ge et al. 2006). Exercise 8.3 has you implement and test some of these ideas. 1\. Background subtraction 2\. Motion estimation 3\. Motion smoothing 4\. Image warping. image warping can result in missing borders around the image, which must be cropped, filled using information from other frames, or hallucinated using inpainting techniques (Section 10.5.1). Vision stabilization There is much recent work on Multi-view 3D reconstruction is a central research topic in computer vision that is driven in many different directions There are many available methods that can handle the noisy image completion problem In the case of surveillance using a fixed camera, there is no desired motion. In the case of most robotic applications, horizontal and vertical motions are desired, but rotation is not. In some cases of ground vehicles where the terrain is known to have many incline changes, or with aerial vehicles undergoing complicated maneuvers where the vehicle’s body is meant to be in varying orientations, rotation might be desired as the robot is meant to be at an angle at times. In robotics applications, computational complexity is extremely important due to the need for real-time operation. Also, it is likely that the center of rotation will not lie in the center of the image frame because the camera is rarely mounted at the robot’s center of mass. This first assumption is made in many video stabilization algorithms, and is a convenient way to seed the correct features with higher trust values. It is not an unreasonable assumption to make. Depending on the application, there is often a large portion of frames where local motion does not occur. In some situations, such as monitoring of steady traffic, there is no guarantee that local motion will not occur. This situation has not been tested, nor has our algorithm been designed to handle it. The second assumption comes from a combination of common sense, and the experience of many computer vision researchers. It makes sense that an object in the scene which does not move will be recognized more easily and more often. Being recognized consistently and consecutively is considered stable. On the other hand, objects which have local motion are less likely to be recognized as often. They might move through shadows, change orientation, or even move completely out of the scene. These possibilities all lead to a less stable class of features. It is likely that, more often than not, there are more background features than foreground features. Moving objects generally cover a small portion of the screen, which usually yields fewer features. Although uncommon, we did not want to make the assumption that this would occur in every frame. Certain scenes will consist of a large portion of local motion, or an object will move very close to the camera, consuming a much larger portion of the scene than the background. As long as some background features are discovered in each frame, our stabilization algorithm should succeed. # image processing tips: * the image size and kernel size need to depended. the best way is to use the one variable to define the size of the image and kernel together. * the coordinate of the image start at top left of the image/display * in order to change it to the normal coordinate you can use * grid of points; two matrix to X , Y coordinate * subtract half of W, H from X, Y in order to have normal coordinate system for our image * now we have cartesian coordinate * * cartesian coordinate to polar coordinate * تبدیل فضای کارتزین به پولار در خیلی از برنامه های پردازش تصویر کارایی دارد. برای پیدا کردن ترشلد ها هم می توان استفاده کرد * in MATLAB we can use ":"for example MatrixA(:) which means all entity of the matrix no mater how many dimensions we have but if we want to implemented in Python we can use numpy.flatten(). * in the MATLAB the round is different from python. if you want same result you need implement the rand function by yourself. * imge_mask=np.ones_like(image_source)*255 * imge_mask=imge_mask.astype(np.uint8) * imge_mask=imge_mask.flatten() ??? .ravel() * .asarray * np.logical_and( 1, 2) * indexes=[index for index in range(len(array1)) if array1[index] == True] * cv2.bitwise_not(yyy) * "olive" editor remove silence ![](https://lh5.googleusercontent.com/uz1tsz4Qy4dPzQzOtxekBVw0UwuYQ6BW31DaVXbLQTH- aJLInnaRUyrKqg4-- r_zsO5nj0pTm6oFMrFcyCwYUQfFNDHcgZIalLEc6l7_BABaoqRK7uGpRllFdVaf64L8_A=w1280) Questions: How to train model to add new classes? How to add a new class to an existing classifier in deep learning? Adding new Class to One Shot Learning trained model Is it possible to train a neural network as new classes are given? Merging all several models that detection system for all these tasks. Answer 1: There are several ways to add new classes to the trained model, which require just training for the new classes. * Incremental training ([GitHub](https://github.com/khurramjaved96/incremental-learning)) * continuously learn a stream of data ([GitHub](https://github.com/creme-ml/creme)) * online machine learning ([GitHub](https://github.com/GMvandeVen/continual-learning)) * Transfer Learning Twice * Continual learning approaches (Regularization, Expansion, Rehearsal) ([GitHub](https://github.com/facebookresearch/Adversarial-Continual-Learning)) Answer 2: Online learning is a term used to refer to a model which takes a continual or sequential stream of input data while training, in contrast to offline learning (also called batch learning), where the model is pre-trained on a static predefined dataset. Continual learning (also called incremental, continuous, lifelong learning) refers to a branch of ML working in an online learning context where models are designed to learn new tasks while maintaining performance on historic tasks. It can be applied to multiple problem paradigms (including Class- incremental learning, where each new task presents new class labels for an ever expanding super-classification problem). Do I need to train my whole model again on all four classes or is there any way I can just train my model on new class? Naively re-training the model on the updated dataset is indeed a solution. Continual learning seeks to address contexts where access to historic data (i.e. the original 3 classes) is not possible, or when retraining on an increasingly large dataset is impractical (for efficiency, space, privacy etc concerns). Multiple such models using different underlying architectures have been proposed, but almost all examples exclusively deal with image classification problems. Answer 3: You could use transfer learning (i.e. use a pre-trained model, then change its last layer to accommodate the new classes, and re-train this slightly modified model, maybe with a lower learning rate) to achieve that, but transfer learning does not necessarily attempt to retain any of the previously acquired information (especially if you don't use very small learning rates, you keep on training and you do not freeze the weights of the convolutional layers), but only to speed up training or when your new dataset is not big enough, by starting from a model that has already learned general features that are supposedly similar to the features needed for your specific task. There is also the related domain adaptation problem. There are more suitable approaches to perform incremental class learning (which is what you are asking for!), which directly address the [catastrophic forgetting problem](https://ai.stackexchange.com/a/13293/2444). For instance, you can take a look at this paper [Class-incremental Learning via Deep Model Consolidation](https://arxiv.org/pdf/1903.07864.pdf), which proposes the Deep Model Consolidation (DMC) approach. There are other continual/incremental learning approaches, many of them are described [here](https://ai.stackexchange.com/a/24529/2444) or in more detail [here](https://reader.elsevier.com/reader/sd/pii/S0893608019300231). Answer 4: by using Continual learning approaches to trained without losing the original classes. It has 3 categories: Regularization Expansion Rehearsal Answer 5: if you access to the dataset then you can download it and add all you new classes when you have " 'N' COCO Classes + 'M' New classes " after that you can fine tune model based on new dataset. you do not need all of the dataset just same number of image for all class enough. [https://learnopencv.com/stanford-mrnet-challenge-classifying-knee- mris/](https://learnopencv.com/stanford-mrnet-challenge-classifying-knee- mris/) Before start your machine learning project ask these questions and preparation: What is your inference hardware? specify the use case. specify model interface. how would we monitor performance after deployment? how can we approximate post-deployment monitoring before deployment? build a model and iteratively improve it. How to deploy the model at the end? monitor performance after deployment. what is your metric? How do you split your data (training and validation)? ### Preparation ML Project Workflow * [What is your hardware ?](/topics-and-projects/hardware) * specify the use case * specify model interface * how would we monitor performance after deployment? * how can we approximate post-deployment monitoring before deployment? * build a model and iteratively improve it * deploy the model * monitor performance * what is your are metric? * How do you split your data? ### Before Training deep learning model * using large model to train because * it is faster to train with lower overfit and faster converge due to best training * it is easier and higher compress in the final stage * model compression and acceleration: reducing parameters without significantly decreasing the model performance * Data: How to have good data for training deep learning models; How to Build and Enhance A Good Data Set For Your Deep Learning Project: using same config and data for training and inference, removing redundant (delete data which you don't need), get more data, Handle missing data, using data augmentation techniques or GAN to generate more data, re-scale/balance data, Transform your data (Change data types), Feature selection based on data-set and use case * * The data you don't need: removing redundant samples * get more data * Invent more data * data augmentation * Re-scale data * balance datasets * Transform your data * Feature selection based on dataset and use case * ML-Augmented Video Object Tracking: By applying and evaluating multiple algorithmic models, enhanced ability to scale object tracking in high-density video compositions. ### Training deep learning model * automated hyper-parameters * Using Hyperparameter tuning / Hyperparameter optimization tools * AutoML * genetic algorithm * population based training * bayesian optimization * You need to set some parameters and config for training * * Diagnostics * Weight Initialization * Learning rate * Activation function * Network Topology * Batches and Epochs * Regularization * Optimization and Loss * Early Stopping ### Continuous delivery * evolve with latest detection models * more data (no labels) * semi-supervised learning: big self-supervised models are strong semi-supervised learners ### After Training deep learning model * Parameter pruning * model pruning: reducing redundant parameters which are not sensitive to the performance. * aim: remove all connections with absolute weights below a threshold * Quantization * compresses by reducing the number of bits used to represent the weights * quantization effectively constraints the number of different weights we can use inside our kernels * per-channel quantization for weights, which improves performance by model compression and latency reduction. * Low rank matrix factorization (LRMF) * there exists latent structures in the data, by uncovering which we can obtain a compressed representation of the data * LRMF factorizes the original matrix into lower rank matrices while preserving latent structures and addressing the issue of sparseness * Compact convolutional filters (Video/CNN) * designing special structural convolutional filters to save parameters * replace over parametric filters with compact filters to achieve overall speedup while maintaining comparable accuracy * Knowledge distillation * training a compact neural network with distilled knowledge of a large model * distillation (knowledge transfer) from an ensemble of big networks into a much smaller network which learns directly from the cumbersome model's outputs, that is lighter to deploy * Binarized Neural Networks (BNNs) * Apache TVM (incubating) is a compiler stack for deep learning systems * Neural Networks Compression Framework (NNCF) ### Deep learning model in production * security: controls access to model(s) through secure packaging and execution * Test * auto training * using parallel processing and library such as GStreamer # Technology Docker AWS Flask Django # My Keynote (February 2021) 1. introduction 2. Machine Learning/ Deep Learning Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed 3. supervised Machine Learning 1. Deep Convolutional Neural Networks (DCNN) Architecture 2. Visualizing and Understanding Convolutional Networks 3. Object Detection by Deep Learning 4. [Video Tracking](/topics-and-projects/video-tracking) 5. Style Transfer 4. semi-supervised Machine Learning/ Deep Reinforcement learning (DRL) 1. Google 2. [Deep Reinforcement learning (DRL)](/topics-and-projects/drl) 5. unsupervised Machine Learning 1. Auto Encoder 6. Generative Adversarial Networks (GANs) 7. Tools 8. Pre trained model 9. Effect of Augmented Datasets to Train DCNNs 10. Training for more classes 11. Optimization 12. [Hardware](/topics-and-projects/hardware) 13. Production setup 14. post development 15. business , Gartner, Hype Cycle for emerging technologies, 2025 ### Advanced and practical 1. Inside CNN 1. Deep Convolutional Neural Networks Architecture 2. Convolution 3. Convolution Layer 4. Conv/FC Filters 5. Activation Functions 6. Layer Activations 7. Pooling Layer 8. Dropout ; L2 pooling 9. Why 1. Max-pooling is useful 2. How to see inside each layer and find important features * Visualizing and Understanding Convolutional Networks * [https://tensorspace.org/](https://tensorspace.org/) * [https://www.youtube.com/watch?v=AgkfIQ4IGaM](https://www.youtube.com/watch?v=AgkfIQ4IGaM) 2. Hands on python for deep learning 3. Fundamental deep learning 4. Installation: TensorFlow, PyTorch 5. [Using PC+eGPU for training video tracking](/topics-and-projects/source-code/compile) Summary of the summit * AI Hardware Europe Summit (July 2020) * [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit) * Apache TVM And Deep Learning Compilation Conference (December 2020) * [RISC-V Summit (December 2020) ](/workshops-and-events/risc-v) [https://www.inspectar.com/demo](https://www.inspectar.com/demo) for rasp # Face * Effective and precise face detection based on color and depth data * [https://www.sciencedirect.com/science/article/pii/S221083271400009X](https://www.sciencedirect.com/science/article/pii/S221083271400009X) * containing or not containing a face * Eigenface, Fisherface, waveletface, PCA (Principal Component Analysis), LDA (Linear Dis-criminant Analysis), Haar wavelet transform, and so on. * Viola–Jones detector * illumination changes and occlusion * depthinformation is used to filter the regions of the image where a candidate face regionis found by the Viola–Jones (VJ) detector * \- the first filtering rule is defined on the color of the region; since some false positiveshave colors not compatible with the face (e.g. shadows on jeans) a skin detector isapplied to remove the candidate face regions that do not contain skin pixels; * \- the second filtering rule is defined on the size of the face: using the depth mapit is quite easy to calculate the size of the candidate face region, which is use-ful to discard smallest and largest faces from the final result set; * \- the third filtering rule is defined on the depth map to discard flat objects (e.g.candidate faces found in a wall) or uneven objects (e.g. candidate face foundin the leaves of a tree). Combining color and depth data the candidate faceregion can be extracted from the background and measures of depth and reg-ularity are used for filtering out false positives. * The size criteria simply remove the candidate faces not included in a fixed rangesize ([12.5,30] cm). The size of a candidate face region is extracted from the depthmap according to the following approach. * image below * Gaussian mixture 3D morphable face model * [https://www.sciencedirect.com/science/article/pii/S0031320317303527](https://www.sciencedirect.com/science/article/pii/S0031320317303527) * * * Face Synthesis for Eyeglass-Robust Face Recognition * [https://paperswithcode.com/paper/face-synthesis-for-eyeglass-robust-face](https://paperswithcode.com/paper/face-synthesis-for-eyeglass-robust-face) * GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data * [https://paperswithcode.com/paper/genegan-learning-object-transfiguration-and](https://paperswithcode.com/paper/genegan-learning-object-transfiguration-and) * FacePoseNet: Making a Case for Landmark-Free Face Alignment * [https://paperswithcode.com/paper/faceposenet-making-a-case-for-landmark-free](https://paperswithcode.com/paper/faceposenet-making-a-case-for-landmark-free) * Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision * [https://paperswithcode.com/paper/learning-to-regress-3d-face-shape-and](https://paperswithcode.com/paper/learning-to-regress-3d-face-shape-and) * Unsupervised Eyeglasses Removal in the Wild * [https://paperswithcode.com/paper/unsupervised-eyeglasses-removal-in-the-wild](https://paperswithcode.com/paper/unsupervised-eyeglasses-removal-in-the-wild) * How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks) * [https://arxiv.org/pdf/1703.07332v3.pdf](https://arxiv.org/pdf/1703.07332v3.pdf) * (a) we construct, for the first time, a very strong baseline by combining a state-of-the-art architecture for landmark localization with a state-of-the-art residual block, train it on a very large yet synthetically expanded 2D facial landmark dataset and fi- nally evaluate it on all other 2D facial landmark datasets. * (b) We create a guided by 2D landmarks network which con- verts 2D landmark annotations to 3D and unifies all exist- ing datasets, leading to the creation of LS3D-W, the largest and most challenging 3D facial landmark dataset to date (~230,000 images). * (c) Following that, we train a neural network for 3D face alignment and evaluate it on the newly introduced LS3D-W. * (d) We further look into the effect of all “traditional” factors affecting face alignment performance like large pose, initialization and resolution, and introduce a “new” one, namely the size of the network. * (e) We show that both 2D and 3D face alignment networks achieve per- formance of remarkable accuracy which is probably close to saturating the datasets used. * Training and testing code as well as the dataset can be downloaded from https: //[www.adrianbulat.com/face-alignment/](http://www.adrianbulat.com/face-alignment/) ![](https://lh3.googleusercontent.com/9lvcVu- HI5oeKBlSMraQcnpp6MQ_gpnrRzOIbRJFnPhqa9SHXdiqGJdE2xf4P82zu_6Qx9Z4EgEk2l4djH0zQfpqMVsgVDOeANBbqrtXMZ72mIineYf- Kp4axCdz7PXp=w1280) 19.Sep.2021 [Medium](https://medium.com/p/626019137fa9/edit) [https://fi.co/madlibs](https://fi.co/madlibs) [https://orcid.org/0000-0001-8382-1389](https://orcid.org/0000-0001-8382-1389) Dreyer's English (learn write English) #book story Greek Mythology Explained: A Deeper Look at Classical Greek Lore and Myth **Papers:** CALTag: High Precision Fiducial Markers for Camera Diatom Autofocusing in Brightfield Microscopy: a Comparative Study :implementation variation of the laplacian Analysis of focus measure operators in shape-from-focus: why laplacian? Blure detection? Iqaf? Optical flow modeling and computation: A survey Toward general type 2 fuzzy logic systems based on zSlices \-------------------------------------------------------------------- Lost in space The OA Film:[ https://en.wikipedia.org/wiki/Shark_Tank](https://en.wikipedia.org/wiki/Shark_Tank) Movie Serial billons monk serial movies Python async Highly decoupled microservice Edex RIS-V , Self-car RISC-V Magazine Road map Game: over/under [https://www.sporcle.com/games/Hejman/underwhelmed](https://www.sporcle.com/games/Hejman/underwhelmed) \-------------------------------------------------------------------- \-------------------------------------------------------------------- GDPR in IoT The EU General Data Protection Regulation (GDPR) and Face Images in IoT The GDPR (General Data Protection Regulation), taking effect in May 2018, introduces strict requirements for personal data protection and the privacy rights of individuals. The EU regulations will set a new global standard for privacy rights and change the way organizations worldwide store and process personal data. The GDPR brings the importance of preserving the privacy of personal information to the forefront, yet the importance of face images within this context is often overlooked. The purpose of this paper is to introduce a solution that helps companies protect face images in IoT devices which record or process image by camera, to strengthen compliance with the GDPR. Our Face is our Identity Our face is the most fundamental and highly visible element of our identity. People recognize us when they see our face or a photo of our face. Recent years have seen exponential increase in the use, storage and dissemination of face images in both private and public sectors - in social networks, corporate databases, IoT, smart-city deployments, digital media, government applications, and nearly every organization’s databases. \--------------------- $(aws-okta env stage) aws s3 cp s3://dataset/archive.tar.gz /Users/a.zip aws s3 ls images | tail -n 100 aws s3 cp staging-images/test.jpg /Users/test.jpg \--------------------- screen -rD k get pods Docker RUN chmod +x /tmp/run.sh Can run docker in terminal and run code line by line docker run -it --rm debian:stable-slim bash apt-get update apt-get installl -y \-------------------------------- brew install awscli aws-okta kubectx kubernetes-cli tfenv touch ~/.aws/config \-------------------------------------------------------------------- docker image rm TETSTDFSAFDSADF docker image ls docker system prune docker run -p 5000:5000 nameDocker:latest docker build . -t nameDocker:latest docker container stop number-docker-name docker container ls * docker pull quay.io/test:v0.0.1 * docker run --rm -p 5000:5000 -it quay.io/test:v0.0.1 * curl --header "Content-Type: application/json" \--request POST --data '[{"fixed":7.4, "a":0, "b":0.56, "c":9.4}]'[ http://127.0.0.1:5000/predict](https://meet.google.com/linkredirect?authuser=0&dest=http%3A%2F%2F127.0.0.1%3A5000%2Fpredict) * docker run --rm -v /home/.aws/credentials:/root/.aws/credentials -it quay.io/test /bin/sh aws s3 ls --profile=test \-------------------------------- Cloud software engineer and consultant focusing on building highly available, scalable and fully automated infrastructure environments on top of Amazon Web Services and Microsoft Azure clouds. My goal is always to make my customers happy in the cloud. \---------------- Search google for 3d = tiger - iPhone show AR/VR \--------------- brew install youtube-dl \---------------------------- List: Collection bucket : 1 for week 2 for month 3 for future \-------------------------------------------------------------------- **• Per frame operation** – Detection – Classification – Segmentation – Feature extraction – Recognition **• Across frames ** – Tracking – Counting **• High level** – Intention – Relations – Analyzing ============================= Deep compression Pruning deep learning Hash table neural network Dl compression Deep compression =================================== Mini PCI-e slot * What have I learned so far: * Problem-based learning * real life scenarios * index card (answer , idea) * Think-Pair-Share * Leverage flip charts * Summarizing \-------------------------------------------------------------------- Self \\\ Advancing Self-Supervised and Semi-Supervised Learning with SimCLR \cite{Chen2020} %https://github.com/google-research/simclr first pretraining on a large unlabeled dataset and then fine-tuning on a smaller labeled dataset pretraining on large unlabeled image datasets, as demonstrated by Exemplar- CNN, Instance Discrimination, CPC, AMDIM, CMC, MoCo and others. “A Simple Framework for Contrastive Learning of Visual Representations”, 85.8\% top-5 accuracy using 1\% of labeled images on the ImageNet dataset contrastive learning algorithms linear evaluation protocol (Zhang et al., 2016; Oord et al.,2018; Bachman et al., 2019; Kolesnikov et al., 2019) unsupervised learning benefits more from bigger models than its supervised counterpart. \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- Some of optimization algorithms ======================== Swarm Algorithm =============== 1\. Ant Colony Optimization (ACO) was inspired by the research on the behavior of ant colonies 2\. Firefly Algorithm based on insects called fireflies 3\. Marriage in Honey Bees Optimization Algorithm (MBO algorithm) is inspired by the process of reproduction of Honey Bee 4\. Artificial Bee Colony Algorithm (ABC) is based on the recollection of the Honey Bees 5\. Wasp Swarm Algorithm was inspired on the Parasitic wasps 6\. Bee Collecting Pollen Algorithm (BCPA) 7\. Termite Algorithm 8\. Mosquito swarms Algorithm (MSA) 9\. zooplankton swarms Algorithm (ZSA) 10\. Bumblebees Swarms Algorithm (BSA) 11\. Fish Swarm Algorithm (FSA) 12\. Bacteria Foraging Algorithm (BFA) 13\. Particle Swarm Optimization (PSO) 14\. Cuckoo Search 15\. Bat Algorithm (BA) 16\. Accelerated PSO 17\. Bee System 18\. Beehive Algorithm 19\. Cat Swarm 20\. Consultant-guided search 21\. Eagle Strategy 22\. Fast Backterial swarming algorithm 23\. Good lattice swarm optimization 24\. Glowworm swarm optimization 25\. Hierarchical swarm model 26\. Krill Herd 27\. Monkey Search 28\. Virtual ant algorithm 29\. Virtual bees 30\. Weighted Swarm Algorithm 31\. Wisdom of Artificial Crowd algorithm 32\. Prey-predator algorithm 33\. Memetic algorithm 34\. Lion Optimization Algorithm 35\. Chicken Swarm Optimization 36\. Ant Lion Optimizer 37\. Compact Particle Swarm Optimization 38\. Fruit Fly Optimization Algorithm 39\. marine propeller optimization algorithm 40\. The Whale Optimization Algorithm 41\. virus colony search algorithm 42\. Slime mould optimization algorithm Ecology Inspired Algorithm ========================== 1\. Biogeography-based Optimization 2\. Invasive Weed Optimization 3\. Symbiosis-Inspired Optimization - PS2O 4\. Atmosphere Clouds Model 5\. Brain Storm Optimization 6\. Dolphin echolocation 7\. Japanese Tree Frog Calling algorithm 8\. Eco-inspired evolutionary algorithm 9\. Egyptian Vulture 10\. Fish School search 11\. Flower Pollination algorithm 12\. Gene Expression 13\. Great Salmon Run 14\. Group Search Optimizer 15\. Human Inspired Algorithm 16\. Roach Infestation algorithm 17\. Queen-bee algorithm 18\. Shuffled frog leaping algorithm 19\. Forest Optimization Algorithm 20\. coral reefs optimization algorithm 21\. cultural evolution algorithm 22\. Grey Wolf Optimizer 23\. probabilistic pso 24\. omicron aco algorithm 25\. shark smell optimization 26\. social spider algorithm 27\. sosial insects behavior algorithm 28\. sperm whale algorithm Evolutionary Optimization ========================= 1\. Genetic Algorithm 2\. Genetic Programming 3\. Evolutionary Strategies 4\. Differential Evolution 5\. Paddy Field Algorithm 6\. Queen-bee Evolution 7\. Quantum Inspired Social Evolution Physic and Chemistry inspired algorithm ======================================= 1\. Big bang-Big Crunch 2\. Block hole algorithm 3\. Central force optimization 4\. Charged System search 5\. Electro-magnetism optimization 6\. Galaxy based search algorithm 7\. Gravitational search 8\. Harmony search algorithm 9\. Intelligent water drop algorithm 10\. River formation algorithm 11\. Self-propelled dynamics 12\. Simulated Annealing 13\. Stachastic diffusion search 14\. Spiral optimization 15\. Water Cycle algorithm 16\. Artificial Physics optimization 17\. Binary Gravitational search algorithm 18\. Continous quantum ant colony optimization 19\. Extended artificial physics optimization 20\. Extended Central force optimization 21\. Electromagnetism-like heuristic 22\. Gravitational Interaction optimization 23\. Hysteristetic Optimization algorithm 24\. Hybrid quantum-inspired GA 25\. Immune gravitational inspired algorithm 26\. Improved quantum evolutinary algorithm 27\. Linear programming 28\. Quantum-inspired bacterial swarming 29\. Quantum-inspired evolutionary algorithm 30\. Quantum-inspired genetic algorithm 31\. Quantum-behaved PSO 32\. Unified big bang-chaotic big crunch 33\. Vector model of artificial physics 34\. Versatile quantum-inspired evolutionary algorithm 35\. Space Gravitational Algorithm 36\. Ion Motion Algorithm 37\. Light Ray Optimization Algorithm 38\. Ray Optimization 39\. Photosynthetic Algorithms 40\. floorplanning algorithm 41\. Gases Brownian Motion Optimization 42\. gradient-type optimization 43\. mean-variance optimization 44\. Mine blast algorithm 45\. moth flame optimization 46\. multi battalion search algorithm 47\. music inspired optimization 48\. no free lunch theorems algorithm 49\. Optics inspired optimization 50\. runner-root algorithm 51\. sine cosine algorithm 52\. pitch tracking algorithm 53\. Stochastic Fractal Search algorithm 54\. stroke volume optimization 55\. Stud krill herd algorithm 56\. The Great Deluge Algorithm 57\. Water Evaporation Optimization 58\. water wave optimization algorithm 59\. Island model algorithm 60\. 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Let's partner up to take your project to the next level! pip install mlc-ai-nightly -f https://mlc.ai/wheels https://mlc.ai/ https://mlc.ai/summer22/ Day 1: Introduction to Unity: TVMScript Introduction to Unity: Relax and PyTorch TVM BYOC in Practice Get Started with TVM on Adreno GPU Introduction to Unity: Metaschedule How to Bring microTVM to a custom IDE Day 2: Community Keynote PyTorch 2.0: the journey to bringing compiler technologies to the core of PyTorch Support QNN Dialect for TVM with MediaTek Neuron and Devise the Scheduler for Acceleration On-Device Training Under 256KB Memory AMD Tutorial TVM at TI: Accelerating inference using the C7x/MMA Adreno GPU: 4x speed-up and upstreaming to TVM mainline Transfer-Tuning: Reusing Auto-Schedules for Efficient Tensor Program Code Generation Improvement in the TVM OpenCL codegen to autogenerate optimal convolution kernels for Adreno GPUs TVM Unity: Pass Infrastructure and BYOC Renesas Hardware accelerators with Apache TVM Introduction on 4th Gen Intel Xeon processor and BF16 support with TVM Hidet: Task Mapping Programming Paradigm for Deep Learning Tensor Programs Towards Building a Responsible Data Economy Optimizing SYCL Device Kernels with AKG Adreno GPU Performance Enhancements using TVM Improvements to CMSIS-NN integration in TVM UMA: Universal Modular Accelerator Interface Day 3: TVM Unity for Dynamic Models Empower Tensorflow serving with backend TVM Enabling Conditional Computing on Hexagon target Decoupled Model Schedule for Large Deep Learning Model Training Using TVM to bring Bayesian neural networks to embedded hardware Efficient Support of TVM Scan OP on RISC-V Vector Extension Improvements to Ethos-U55 support in TVM including CI on Alif Semiconductor boards Compiling Dynamic Shapes TVM Packaging in 2023: delivering TVM to end users Cross-Platform Training Using Automatic Differentiation on Relax IR AutoTVM: Reducing tuning space by cross axis filtering SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning Analytical Tensorization and Fusion for Compute-intensive Operators CUTLASS 3.0: Next Generation Composable and Reusable GPU Linear Algebra Library Enabling Data Movement and Computation Pipelining in Deep Learning Compiler Automating DL Compiler Bug Finding with NNSmith TVM at NIO TVM at Tencent Integrating the Andes RISC-V Processors into TVM Alpa: A Compiler for Distributed Deep Learning ACRoBat: Compiler and Runtime Techniques for Efficient Auto-Batching of Dynamic Deep Learning Computations Channel Folding: a Transform Pass for Optimizing Mobilenets ========================================================================Day 1: ************************ Introduction to Unity: TVMScript [https://github.com/cyx-6/TVM- Demo/blob/main/tvmscript.ipynb](https://github.com/cyx-6/TVM- Demo/blob/main/tvmscript.ipynb) Gan NN show us some hidden patter in history we can not see before. “I always have a slip of paper at hand, on which I note down the ideas of certain pages. On the backside I write down the bibliographic details. After finishing the book I go through my notes and think how these notes might be relevant for already written notes in the slip-box. It means that I always read with an eye towards possible connections in the slip-box.” (Luhmann et al., 1987, 150) Deep representation learning Model evaluation. Camera cheaper lidar Point cloud because of we need 3d Capturing reality 1\. 𝐀𝐝𝐝/𝐂𝐨𝐦𝐦𝐢𝐭 𝐀𝐥𝐥 Standard way: git add . git commit -m "Message" Another way: git commit -a -m "Message" 𝟐\. 𝐀𝐥𝐢𝐚𝐬𝐞𝐬 With aliases, you can write your own Git commands that do anything you want. Eg: git config --global alias.ac '!git add -A && git commit -m' (alias called ac, git add -A && git commit -m will do the full add and commit) 𝟑\. 𝐑𝐞𝐯𝐞𝐫𝐭 The revert command simply allows us to undo any commit on the current branch. Eg: git revert 486bdb2 Another way: git revert HEAD (for recent commits) 𝟒\. 𝐑𝐞𝐟𝐥𝐨𝐠 This command lets you easily see the recent commits, pulls, resets, pushes, etc on your local machine. Eg: git reflog 𝟓\. 𝐏𝐫𝐞𝐭𝐭𝐲 𝐋𝐨𝐠𝐬 Gives you the ability to print out a pretty log of your commits/branches. Eg: git log --graph --decorate --oneline 𝟔\. 𝐒𝐞𝐚𝐫𝐜𝐡𝐢𝐧𝐠 𝐋𝐨𝐠𝐬 One can also use the log command to search for specific changes in the code. Eg: git log -S "A promise in JavaScript is very similar" 𝟕\. 𝐒𝐭𝐚𝐬𝐡 This command will stash (store them locally) all your code changes but does not actually commit them. Eg: git stash 𝟖\. 𝐑𝐞𝐦𝐨𝐯𝐞 𝐃𝐞𝐚𝐝 𝐁𝐫𝐚𝐧𝐜𝐡𝐞𝐬 This command will delete all the tracking information for branches that are on your local machine that are not in the remote repository, but it does not delete your local branches. Eg: git remote update --prune 𝟗\. 𝐁𝐢𝐬𝐞𝐜𝐭 For finding which commits caused certain bugs Eg: git bisect start git bisect bad git bisect good 48c86d6 𝟏𝟎\. 𝐃𝐞𝐬𝐭𝐫𝐨𝐲 𝐋𝐨𝐜𝐚𝐥 𝐂𝐡𝐚𝐧𝐠𝐞𝐬 One can wipe out all changes on your local branch to exactly what is in the remote branch. Eg: git reset --hard origin/main Don’t trust your devices IoT. software and hardware are together for better business. Newsletter investing every 3 months 1\. Prototyping. New bie 2\. Patent. Website. ( list of investors) 3\. Pre seed. First founding 1M VC, inistution, anjel capital. 400 000 preseed. Quveribel. Equtible rund convertible non agreement Template. Convertabel lone 1\. Germ standar inistitude 2\. 4\. Equity. Venture builder. 20% 200 000 5\. 100 000 per year to become unocorn in less than 10 years 6\. Soniy corn 100k unicorn 1M 7\. 360 euro per years for database of investor 8\. Convertable loan: Pay interst rate 5% to 8% = 18 months later (2M found in 10M) convert on based . 9\. Invester Never act as co-founder = full time = 20% 10\. Project profit, 11\. Full time after foun rising Make a plan for your business; take your time to make calculations by creating a target audience. Your target audience determines how you approach your business plan. By studying your target audience, you are making empirical research and collecting information from them Then, secure a good partnership if need be, and get enough capital to start up. * * What the people need * Why people need it * When the people need it * It's affordability * It's ease of use * It's maintenance and revenue Pair programming The SB7 Framework harnesses the influence of stories. The structure describes the 7 most common story elements: • Character • Problem • Guide • Plan • Calls to action • Failure • Success Dear [Hiring Manager’s Name], I am writing to apply for the position of computer vision for IoT and cloud at [Company Name]. I am a highly skilled and experienced computer vision engineer with a strong background in IoT and cloud technologies. I believe that my skills and experience make me an ideal candidate for this position and I am excited about the opportunity to contribute to the success of your organization. I have a solid understanding of computer vision algorithms and techniques, as well as experience in developing and implementing computer vision systems. I am proficient in programming languages such as Python, C++, and Java, and have experience with popular computer vision libraries such as OpenCV, TensorFlow, and PyTorch. In addition, I have a strong background in IoT and cloud technologies, including experience with IoT platforms such as AWS IoT, Azure IoT, and Google Cloud IoT. I am familiar with cloud computing technologies such as AWS, Azure, and Google Cloud, and have experience with deploying and managing computer vision systems on these platforms. I am also a team player and have excellent communication skills. I am able to work with cross-functional teams and can effectively communicate with both technical and non-technical stakeholders. I am also highly motivated, and I am always looking for ways to improve my skills and stay up-to-date with the latest technologies. I am excited about the opportunity to join [Company Name] and to contribute to the development of cutting-edge computer vision systems for IoT and cloud. I am confident that my skills and experience make me a strong candidate for this position, and I look forward to discussing how I can contribute to your organization. Thank you for considering my application. I look forward to hearing from you soon. Sincerely, Title: "Unlocking the Power of Computer Vision for IoT and Cloud" Introduction: * Hi, and welcome to our video on the topic of computer vision for IoT and cloud. In this video, we're going to explore how computer vision technology can be used to enhance IoT and cloud-based systems, and how it can be used to unlock new possibilities for businesses and consumers alike. Body: * First, let's talk about what computer vision is and how it works. Essentially, computer vision is the technology that enables computers to understand and interpret visual information from the world around us. This can include things like images, videos, and even 3D models. * One of the key ways that computer vision can be used to enhance IoT and cloud-based systems is by enabling devices to better understand and interact with their environment. For example, a computer vision-enabled camera could be used to monitor a manufacturing facility and identify when a machine is in need of maintenance or when an employee is working in an unsafe manner. * Another way that computer vision can be used to enhance IoT and cloud-based systems is by enabling devices to better understand and interact with people. For example, a computer vision-enabled security camera could be used to identify individuals and track their movements, or a computer vision-enabled smart home system could be used to detect when someone is in the room and adjust the lighting or temperature accordingly. * Additionally, computer vision can also be used to enhance cloud-based systems by providing more accurate data and insights. For example, a computer vision-enabled drone could be used to collect data on crops and provide farmers with more accurate information about the health and growth of their crops. Conclusion: * Overall, computer vision technology has the potential to unlock new possibilities for businesses and consumers alike, by enabling IoT and cloud-based systems to better understand and interact with their environment and people. We hope this video has provided you with a better understanding of the potential of computer vision for IoT and cloud, and we look forward to seeing the new possibilities that will be created as this technology continues to evolve. Excited to share my latest project using computer vision and IoT to improve efficiency in manufacturing. I used a combination of machine learning algorithms and cloud computing to analyze data from cameras and sensors in real-time, resulting in a 20% increase in production speed. This was a challenging project but I enjoyed every step of it! I am always looking for new opportunities to apply my skills in computer vision and IoT to help companies improve their operations. Let's connect if you are working on a similar project or if you are looking for a developer with these skills. #computervision #IoT #cloudcomputing #manufacturingefficiency #machinelearning #developer" In this post, you briefly mention your experience and skills in computer vision and IoT, and you provide a specific example of a project you worked on that demonstrates your abilities. You also make it clear that you are open to new opportunities, and you invite others to connect with you. Using relevant hashtags such as #computervision #IoT #cloudcomputing can help your post reach a wider audience Exciting news! I just published a paper on a new object detection algorithm that I developed. The algorithm uses a combination of deep learning and computer vision techniques to improve accuracy and speed of object detection in real-world scenarios. This is a big step forward in the field of computer vision and I am proud to have contributed to it. I will be presenting my research at the Computer Vision Conference next month, if you're attending be sure to stop by and say hi! #computervision #objectdetection #deeplearning #research" In this post, you briefly explain the main findings and contributions of your research, and you express your excitement and pride in your work. You also mention the upcoming conference where you will be presenting your research, inviting your friends and colleagues to meet you in person. Also using relevant hashtags such as #computervision #objectdetection #deeplearning can help reach a wider audience interested in the field. Features stores 1\. Car parts detection 2\. Resize keep aspects ration 3\. 3.1 Perform damage detection 4\. 3.2Semantic segregation 5\. Transfer to original coordinates 1 class imbalance 2 class definition Maybe Class in between 3 inconstant annotations Color augmentation 1\. RGB shift 2\. Random brithness and contrast 3\. Sharpen 4\. Hue saturation value Why manually data augmented Becasu control of data. Not too rotate or change something Photogrammetry model Neural radiance fields (NeRF) NeRF in the wild \ [GitHub - google-research/tuning_playbook: A playbook for systematically maximizing the performance of deep learning models.](https://github.com/google-research/tuning_playbook) Yocto and Machine Learning + OpenCV: [https://www.yoctoproject.org](https://www.yoctoproject.org) [https://www.hackster.io/monica/running-machine-learning-on-maaxboard-s-yocto- image-part-1-6a4796](https://www.hackster.io/monica/running-machine-learning- on-maaxboard-s-yocto-image-part-1-6a4796) Bard Google: [https://blog.google/technology/ai/bard-google-ai-search- updates/](https://blog.google/technology/ai/bard-google-ai-search-updates/) [https://mustang.ir/questions/question/راه-اندازی-پروژه-های-گیت-هاب-با-git- pages](https://mustang.ir/questions/question/%D8%B1%D8%A7%D9%87-%D8%A7%D9%86%D8%AF%D8%A7%D8%B2%DB%8C-%D9%BE%D8%B1%D9%88%DA%98%D9%87-%D9%87%D8%A7%DB%8C-%DA%AF%DB%8C%D8%AA-%D9%87%D8%A7%D8%A8-%D8%A8%D8%A7-git- pages) Book: Project Management for Non-Project Managers [https://fa.wikipedia.org/wiki/علی_اکبرپور](https://fa.wikipedia.org/wiki/%D8%B9%D9%84%DB%8C_%D8%A7%DA%A9%D8%A8%D8%B1%D9%BE%D9%88%D8%B1) [https://www.kingorama.com](https://www.kingorama.com) شاهنامه سه بعدی [Accelerate deep learning model development with cloud custom environments - AWS Online Tech Talks - YouTube](https://m.youtube.com/watch?v=2Wt2zlkMtKI&noapp=1) [بخش هایی از کتاب Refactoring (نسخه رایگان)](https://www.developit.ir/refactoring/free.html#f7) [Performance Notes Of PyTorch Support for M1 and M2 GPUs - Lightning AI](https://lightning.ai/pages/community/community-discussions/performance- notes-of-pytorch-support-for-m1-and-m2-gpus/) [Investopedia Academy](https://academy.investopedia.com/) [HandBrake updated with AV1 and VP9 10-bit video encoding](https://9to5mac.com/2022/12/29/handbrake-support-av1-and- vp9-10-bit/) [How to Start Your Sole Proprietorship in 6 Simple Steps](https://qonto.com/en/blog/creators/administrative/sole-proprietorship- in-germany) [Duolingo English Test](https://englishtest.duolingo.com/applicants) [چالش‌های تولید محتوا برای مارکت اروپا و آمریکا - YouTube](https://m.youtube.com/watch?v=wW0HZdubuWQ) [PyTorch for Deep Learning & Machine Learning – Full Course - YouTube](https://m.youtube.com/watch?v=V_xro1bcAuA#dialog) [Why passive investing makes less sense in the current environment | Financial Times](https://archive.ph/0VucZ) [GitHub - google-research/tuning_playbook: A playbook for systematically maximizing the performance of deep learning models.](https://github.com/google-research/tuning_playbook) [GitHub - mgechev/google-interview-preparation-problems: leetcode problems I solved to prepare for my Google interview.](https://github.com/mgechev/google- interview-preparation-problems) [Bayesian Neural Networks and Variational Dropout](https://dmittov.github.io/variational_dropout/#/maximum-likelihood) [One machine learning question every day - bnomial](https://today.bnomial.com/?ref=email) Git remote add orgine Asynchronous Operation Anomaly detection Use experience. Personalizes. Prediction manage society mobility Personalization Covenant Platform. OpenMMLab Wordtune - AI-powered Writing Companion tree -v -I '*.png' -I '*.jpg' \--charset utf-8 >list2.txt 3D object using triangular mesh need vertices point cloud underlying surface of some 3D object, faster Definition of Done User Story complete Code\Implementation complete Code\Implementation Peer Reviews) approved Unit tests complete (if required) Testing Notes complete (if required) User Story Acceptance criteria defined and verified Backend: Python, Redis, Postgres, Celery Frontend: React, Redux, TypeScript DevOps: Terraform, Kubernetes, GitHub, Docker, AWS Data: Python (Data Science), Kafka, Fastapi, MLFlow, AWS SageMaker ML: Selcond core, Kubeflow, … [Sharpness](https://en.wikipedia.org/wiki/Sharpness_%28visual%29) ,[Noise](https://en.wikipedia.org/wiki/Image_noise), [Dynamic range](https://en.wikipedia.org/wiki/Dynamic_range), [Tone reproduction](https://en.wikipedia.org/wiki/Tone_reproduction) , [Contrast](https://en.wikipedia.org/wiki/Contrast_%28vision%29), [Color](https://en.wikipedia.org/wiki/Color), [Distortion](https://en.wikipedia.org/wiki/Distortion_%28optics%29) , [DSLR lenses](https://en.wikipedia.org/wiki/Lenses_for_SLR_and_DSLR_cameras), [Vignetting](https://en.wikipedia.org/wiki/Vignetting), [Exposure](https://en.wikipedia.org/wiki/Exposure_%28photography%29), Lateral [chromatic aberration](https://en.wikipedia.org/wiki/Chromatic_aberration) (LCA), [Lens flare](https://en.wikipedia.org/wiki/Lens_flare), Color, [Artifacts](https://en.wikipedia.org/wiki/Compression_artifact) ۱\. جهت انتخاب کلمه مورد نظرتان، دو بار روی آن تپ کنید. ۲\. برای انتخاب کل یک پاراگراف، کافیست چهار با روی آن تپ کنید. ۳\. یک انگشت را در ابتدا و انگشت دیگر را در آخر یک محدود گذاشته و کمی نگه دارید. متن میان دو انگشت انتخاب خواهد شد. ۴\. روی ابتدای محدوده ای دلخواه دو بار تپ کرده و بلافاصله با درگ کردن (کشیدن) پین محدوده ی انتخاب شده را گسترش دهید. (انگشت خود را پس از دومین تپ جدا نکنید) ۵\. برای انتخاب کل پاراگراف، به جز استفاده از مورد ۲، می توانید با دو انگشت، یک بار روی آن تپ کنید. namely motion estimation, motion smoothing, and image warping. Motion estimation algorithms often use a similarity transform to handle camera translations, rotations, and zooming. The tricky part is getting these algorithms to lock onto the background motion, 0\. video frames captured during fast motion are often blurry. Their appearance can be improved either using deblurring techniques (Section 10.3) or stealing sharper pixels from other frames with less motion or better focus (Matsushita, Ofek, Ge et al. 2006). Exercise 8.3 has you implement and test some of these ideas. 1\. Background subtraction 2\. Motion estimation 3\. Motion smoothing 4\. Image warping. image warping can result in missing borders around the image, which must be cropped, filled using information from other frames, or hallucinated using inpainting techniques (Section 10.5.1). Vision stabilization There is much recent work on Multi-view 3D reconstruction is a central research topic in computer vision that is driven in many different directions There are many available methods that can handle the noisy image completion problem In the case of surveillance using a fixed camera, there is no desired motion. In the case of most robotic applications, horizontal and vertical motions are desired, but rotation is not. In some cases of ground vehicles where the terrain is known to have many incline changes, or with aerial vehicles undergoing complicated maneuvers where the vehicle’s body is meant to be in varying orientations, rotation might be desired as the robot is meant to be at an angle at times. In robotics applications, computational complexity is extremely important due to the need for real-time operation. Also, it is likely that the center of rotation will not lie in the center of the image frame because the camera is rarely mounted at the robot’s center of mass. This first assumption is made in many video stabilization algorithms, and is a convenient way to seed the correct features with higher trust values. It is not an unreasonable assumption to make. Depending on the application, there is often a large portion of frames where local motion does not occur. In some situations, such as monitoring of steady traffic, there is no guarantee that local motion will not occur. This situation has not been tested, nor has our algorithm been designed to handle it. The second assumption comes from a combination of common sense, and the experience of many computer vision researchers. It makes sense that an object in the scene which does not move will be recognized more easily and more often. Being recognized consistently and consecutively is considered stable. On the other hand, objects which have local motion are less likely to be recognized as often. They might move through shadows, change orientation, or even move completely out of the scene. These possibilities all lead to a less stable class of features. It is likely that, more often than not, there are more background features than foreground features. Moving objects generally cover a small portion of the screen, which usually yields fewer features. Although uncommon, we did not want to make the assumption that this would occur in every frame. Certain scenes will consist of a large portion of local motion, or an object will move very close to the camera, consuming a much larger portion of the scene than the background. As long as some background features are discovered in each frame, our stabilization algorithm should succeed. # image processing tips: * the image size and kernel size need to depended. the best way is to use the one variable to define the size of the image and kernel together. * the coordinate of the image start at top left of the image/display * in order to change it to the normal coordinate you can use * grid of points; two matrix to X , Y coordinate * subtract half of W, H from X, Y in order to have normal coordinate system for our image * now we have cartesian coordinate * * cartesian coordinate to polar coordinate * تبدیل فضای کارتزین به پولار در خیلی از برنامه های پردازش تصویر کارایی دارد. برای پیدا کردن ترشلد ها هم می توان استفاده کرد * in MATLAB we can use ":"for example MatrixA(:) which means all entity of the matrix no mater how many dimensions we have but if we want to implemented in Python we can use numpy.flatten(). * in the MATLAB the round is different from python. if you want same result you need implement the rand function by yourself. * imge_mask=np.ones_like(image_source)*255 * imge_mask=imge_mask.astype(np.uint8) * imge_mask=imge_mask.flatten() ??? .ravel() * .asarray * np.logical_and( 1, 2) * indexes=[index for index in range(len(array1)) if array1[index] == True] * cv2.bitwise_not(yyy) * "olive" editor remove silence ![](https://lh5.googleusercontent.com/uz1tsz4Qy4dPzQzOtxekBVw0UwuYQ6BW31DaVXbLQTH- aJLInnaRUyrKqg4-- r_zsO5nj0pTm6oFMrFcyCwYUQfFNDHcgZIalLEc6l7_BABaoqRK7uGpRllFdVaf64L8_A=w1280) Questions: How to train model to add new classes? How to add a new class to an existing classifier in deep learning? Adding new Class to One Shot Learning trained model Is it possible to train a neural network as new classes are given? Merging all several models that detection system for all these tasks. Answer 1: There are several ways to add new classes to the trained model, which require just training for the new classes. * Incremental training ([GitHub](https://github.com/khurramjaved96/incremental-learning)) * continuously learn a stream of data ([GitHub](https://github.com/creme-ml/creme)) * online machine learning ([GitHub](https://github.com/GMvandeVen/continual-learning)) * Transfer Learning Twice * Continual learning approaches (Regularization, Expansion, Rehearsal) ([GitHub](https://github.com/facebookresearch/Adversarial-Continual-Learning)) Answer 2: Online learning is a term used to refer to a model which takes a continual or sequential stream of input data while training, in contrast to offline learning (also called batch learning), where the model is pre-trained on a static predefined dataset. Continual learning (also called incremental, continuous, lifelong learning) refers to a branch of ML working in an online learning context where models are designed to learn new tasks while maintaining performance on historic tasks. It can be applied to multiple problem paradigms (including Class- incremental learning, where each new task presents new class labels for an ever expanding super-classification problem). Do I need to train my whole model again on all four classes or is there any way I can just train my model on new class? Naively re-training the model on the updated dataset is indeed a solution. Continual learning seeks to address contexts where access to historic data (i.e. the original 3 classes) is not possible, or when retraining on an increasingly large dataset is impractical (for efficiency, space, privacy etc concerns). Multiple such models using different underlying architectures have been proposed, but almost all examples exclusively deal with image classification problems. Answer 3: You could use transfer learning (i.e. use a pre-trained model, then change its last layer to accommodate the new classes, and re-train this slightly modified model, maybe with a lower learning rate) to achieve that, but transfer learning does not necessarily attempt to retain any of the previously acquired information (especially if you don't use very small learning rates, you keep on training and you do not freeze the weights of the convolutional layers), but only to speed up training or when your new dataset is not big enough, by starting from a model that has already learned general features that are supposedly similar to the features needed for your specific task. There is also the related domain adaptation problem. There are more suitable approaches to perform incremental class learning (which is what you are asking for!), which directly address the [catastrophic forgetting problem](https://ai.stackexchange.com/a/13293/2444). For instance, you can take a look at this paper [Class-incremental Learning via Deep Model Consolidation](https://arxiv.org/pdf/1903.07864.pdf), which proposes the Deep Model Consolidation (DMC) approach. There are other continual/incremental learning approaches, many of them are described [here](https://ai.stackexchange.com/a/24529/2444) or in more detail [here](https://reader.elsevier.com/reader/sd/pii/S0893608019300231). Answer 4: by using Continual learning approaches to trained without losing the original classes. It has 3 categories: Regularization Expansion Rehearsal Answer 5: if you access to the dataset then you can download it and add all you new classes when you have " 'N' COCO Classes + 'M' New classes " after that you can fine tune model based on new dataset. you do not need all of the dataset just same number of image for all class enough. [https://learnopencv.com/stanford-mrnet-challenge-classifying-knee- mris/](https://learnopencv.com/stanford-mrnet-challenge-classifying-knee- mris/) Before start your machine learning project ask these questions and preparation: What is your inference hardware? specify the use case. specify model interface. how would we monitor performance after deployment? how can we approximate post-deployment monitoring before deployment? build a model and iteratively improve it. How to deploy the model at the end? monitor performance after deployment. what is your metric? How do you split your data (training and validation)? ### Preparation ML Project Workflow * [What is your hardware ?](/topics-and-projects/hardware) * specify the use case * specify model interface * how would we monitor performance after deployment? * how can we approximate post-deployment monitoring before deployment? * build a model and iteratively improve it * deploy the model * monitor performance * what is your are metric? * How do you split your data? ### Before Training deep learning model * using large model to train because * it is faster to train with lower overfit and faster converge due to best training * it is easier and higher compress in the final stage * model compression and acceleration: reducing parameters without significantly decreasing the model performance * Data: How to have good data for training deep learning models; How to Build and Enhance A Good Data Set For Your Deep Learning Project: using same config and data for training and inference, removing redundant (delete data which you don't need), get more data, Handle missing data, using data augmentation techniques or GAN to generate more data, re-scale/balance data, Transform your data (Change data types), Feature selection based on data-set and use case * * The data you don't need: removing redundant samples * get more data * Invent more data * data augmentation * Re-scale data * balance datasets * Transform your data * Feature selection based on dataset and use case * ML-Augmented Video Object Tracking: By applying and evaluating multiple algorithmic models, enhanced ability to scale object tracking in high-density video compositions. ### Training deep learning model * automated hyper-parameters * Using Hyperparameter tuning / Hyperparameter optimization tools * AutoML * genetic algorithm * population based training * bayesian optimization * You need to set some parameters and config for training * * Diagnostics * Weight Initialization * Learning rate * Activation function * Network Topology * Batches and Epochs * Regularization * Optimization and Loss * Early Stopping ### Continuous delivery * evolve with latest detection models * more data (no labels) * semi-supervised learning: big self-supervised models are strong semi-supervised learners ### After Training deep learning model * Parameter pruning * model pruning: reducing redundant parameters which are not sensitive to the performance. * aim: remove all connections with absolute weights below a threshold * Quantization * compresses by reducing the number of bits used to represent the weights * quantization effectively constraints the number of different weights we can use inside our kernels * per-channel quantization for weights, which improves performance by model compression and latency reduction. * Low rank matrix factorization (LRMF) * there exists latent structures in the data, by uncovering which we can obtain a compressed representation of the data * LRMF factorizes the original matrix into lower rank matrices while preserving latent structures and addressing the issue of sparseness * Compact convolutional filters (Video/CNN) * designing special structural convolutional filters to save parameters * replace over parametric filters with compact filters to achieve overall speedup while maintaining comparable accuracy * Knowledge distillation * training a compact neural network with distilled knowledge of a large model * distillation (knowledge transfer) from an ensemble of big networks into a much smaller network which learns directly from the cumbersome model's outputs, that is lighter to deploy * Binarized Neural Networks (BNNs) * Apache TVM (incubating) is a compiler stack for deep learning systems * Neural Networks Compression Framework (NNCF) ### Deep learning model in production * security: controls access to model(s) through secure packaging and execution * Test * auto training * using parallel processing and library such as GStreamer # Technology Docker AWS Flask Django # My Keynote (February 2021) 1. introduction 2. Machine Learning/ Deep Learning Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed 3. supervised Machine Learning 1. Deep Convolutional Neural Networks (DCNN) Architecture 2. Visualizing and Understanding Convolutional Networks 3. Object Detection by Deep Learning 4. [Video Tracking](/topics-and-projects/video-tracking) 5. Style Transfer 4. semi-supervised Machine Learning/ Deep Reinforcement learning (DRL) 1. Google 2. [Deep Reinforcement learning (DRL)](/topics-and-projects/drl) 5. unsupervised Machine Learning 1. Auto Encoder 6. Generative Adversarial Networks (GANs) 7. Tools 8. Pre trained model 9. Effect of Augmented Datasets to Train DCNNs 10. Training for more classes 11. Optimization 12. [Hardware](/topics-and-projects/hardware) 13. Production setup 14. post development 15. business , Gartner, Hype Cycle for emerging technologies, 2025 ### Advanced and practical 1. Inside CNN 1. Deep Convolutional Neural Networks Architecture 2. Convolution 3. Convolution Layer 4. Conv/FC Filters 5. Activation Functions 6. Layer Activations 7. Pooling Layer 8. Dropout ; L2 pooling 9. Why 1. Max-pooling is useful 2. How to see inside each layer and find important features * Visualizing and Understanding Convolutional Networks * [https://tensorspace.org/](https://tensorspace.org/) * [https://www.youtube.com/watch?v=AgkfIQ4IGaM](https://www.youtube.com/watch?v=AgkfIQ4IGaM) 2. Hands on python for deep learning 3. Fundamental deep learning 4. Installation: TensorFlow, PyTorch 5. [Using PC+eGPU for training video tracking](/topics-and-projects/source-code/compile) Summary of the summit * AI Hardware Europe Summit (July 2020) * [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit) * Apache TVM And Deep Learning Compilation Conference (December 2020) * [RISC-V Summit (December 2020) ](/workshops-and-events/risc-v) [https://www.inspectar.com/demo](https://www.inspectar.com/demo) for rasp # Face * Effective and precise face detection based on color and depth data * [https://www.sciencedirect.com/science/article/pii/S221083271400009X](https://www.sciencedirect.com/science/article/pii/S221083271400009X) * containing or not containing a face * Eigenface, Fisherface, waveletface, PCA (Principal Component Analysis), LDA (Linear Dis-criminant Analysis), Haar wavelet transform, and so on. * Viola–Jones detector * illumination changes and occlusion * depthinformation is used to filter the regions of the image where a candidate face regionis found by the Viola–Jones (VJ) detector * \- the first filtering rule is defined on the color of the region; since some false positiveshave colors not compatible with the face (e.g. shadows on jeans) a skin detector isapplied to remove the candidate face regions that do not contain skin pixels; * \- the second filtering rule is defined on the size of the face: using the depth mapit is quite easy to calculate the size of the candidate face region, which is use-ful to discard smallest and largest faces from the final result set; * \- the third filtering rule is defined on the depth map to discard flat objects (e.g.candidate faces found in a wall) or uneven objects (e.g. candidate face foundin the leaves of a tree). Combining color and depth data the candidate faceregion can be extracted from the background and measures of depth and reg-ularity are used for filtering out false positives. * The size criteria simply remove the candidate faces not included in a fixed rangesize ([12.5,30] cm). The size of a candidate face region is extracted from the depthmap according to the following approach. * image below * Gaussian mixture 3D morphable face model * [https://www.sciencedirect.com/science/article/pii/S0031320317303527](https://www.sciencedirect.com/science/article/pii/S0031320317303527) * * * Face Synthesis for Eyeglass-Robust Face Recognition * [https://paperswithcode.com/paper/face-synthesis-for-eyeglass-robust-face](https://paperswithcode.com/paper/face-synthesis-for-eyeglass-robust-face) * GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data * [https://paperswithcode.com/paper/genegan-learning-object-transfiguration-and](https://paperswithcode.com/paper/genegan-learning-object-transfiguration-and) * FacePoseNet: Making a Case for Landmark-Free Face Alignment * [https://paperswithcode.com/paper/faceposenet-making-a-case-for-landmark-free](https://paperswithcode.com/paper/faceposenet-making-a-case-for-landmark-free) * Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision * [https://paperswithcode.com/paper/learning-to-regress-3d-face-shape-and](https://paperswithcode.com/paper/learning-to-regress-3d-face-shape-and) * Unsupervised Eyeglasses Removal in the Wild * [https://paperswithcode.com/paper/unsupervised-eyeglasses-removal-in-the-wild](https://paperswithcode.com/paper/unsupervised-eyeglasses-removal-in-the-wild) * How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks) * [https://arxiv.org/pdf/1703.07332v3.pdf](https://arxiv.org/pdf/1703.07332v3.pdf) * (a) we construct, for the first time, a very strong baseline by combining a state-of-the-art architecture for landmark localization with a state-of-the-art residual block, train it on a very large yet synthetically expanded 2D facial landmark dataset and fi- nally evaluate it on all other 2D facial landmark datasets. * (b) We create a guided by 2D landmarks network which con- verts 2D landmark annotations to 3D and unifies all exist- ing datasets, leading to the creation of LS3D-W, the largest and most challenging 3D facial landmark dataset to date (~230,000 images). * (c) Following that, we train a neural network for 3D face alignment and evaluate it on the newly introduced LS3D-W. * (d) We further look into the effect of all “traditional” factors affecting face alignment performance like large pose, initialization and resolution, and introduce a “new” one, namely the size of the network. * (e) We show that both 2D and 3D face alignment networks achieve per- formance of remarkable accuracy which is probably close to saturating the datasets used. * Training and testing code as well as the dataset can be downloaded from https: //[www.adrianbulat.com/face-alignment/](http://www.adrianbulat.com/face-alignment/) ![](https://lh3.googleusercontent.com/9lvcVu- HI5oeKBlSMraQcnpp6MQ_gpnrRzOIbRJFnPhqa9SHXdiqGJdE2xf4P82zu_6Qx9Z4EgEk2l4djH0zQfpqMVsgVDOeANBbqrtXMZ72mIineYf- Kp4axCdz7PXp=w1280) 19.Sep.2021 [Medium](https://medium.com/p/626019137fa9/edit) [https://fi.co/madlibs](https://fi.co/madlibs) [https://orcid.org/0000-0001-8382-1389](https://orcid.org/0000-0001-8382-1389) Dreyer's English (learn write English) #book story Greek Mythology Explained: A Deeper Look at Classical Greek Lore and Myth **Papers:** CALTag: High Precision Fiducial Markers for Camera Diatom Autofocusing in Brightfield Microscopy: a Comparative Study :implementation variation of the laplacian Analysis of focus measure operators in shape-from-focus: why laplacian? Blure detection? Iqaf? Optical flow modeling and computation: A survey Toward general type 2 fuzzy logic systems based on zSlices \-------------------------------------------------------------------- Lost in space The OA Film:[ https://en.wikipedia.org/wiki/Shark_Tank](https://en.wikipedia.org/wiki/Shark_Tank) Movie Serial billons monk serial movies Python async Highly decoupled microservice Edex RIS-V , Self-car RISC-V Magazine Road map Game: over/under [https://www.sporcle.com/games/Hejman/underwhelmed](https://www.sporcle.com/games/Hejman/underwhelmed) \-------------------------------------------------------------------- \-------------------------------------------------------------------- GDPR in IoT The EU General Data Protection Regulation (GDPR) and Face Images in IoT The GDPR (General Data Protection Regulation), taking effect in May 2018, introduces strict requirements for personal data protection and the privacy rights of individuals. The EU regulations will set a new global standard for privacy rights and change the way organizations worldwide store and process personal data. The GDPR brings the importance of preserving the privacy of personal information to the forefront, yet the importance of face images within this context is often overlooked. The purpose of this paper is to introduce a solution that helps companies protect face images in IoT devices which record or process image by camera, to strengthen compliance with the GDPR. Our Face is our Identity Our face is the most fundamental and highly visible element of our identity. People recognize us when they see our face or a photo of our face. Recent years have seen exponential increase in the use, storage and dissemination of face images in both private and public sectors - in social networks, corporate databases, IoT, smart-city deployments, digital media, government applications, and nearly every organization’s databases. \--------------------- $(aws-okta env stage) aws s3 cp s3://dataset/archive.tar.gz /Users/a.zip aws s3 ls images | tail -n 100 aws s3 cp staging-images/test.jpg /Users/test.jpg \--------------------- screen -rD k get pods Docker RUN chmod +x /tmp/run.sh Can run docker in terminal and run code line by line docker run -it --rm debian:stable-slim bash apt-get update apt-get installl -y \-------------------------------- brew install awscli aws-okta kubectx kubernetes-cli tfenv touch ~/.aws/config \-------------------------------------------------------------------- docker image rm TETSTDFSAFDSADF docker image ls docker system prune docker run -p 5000:5000 nameDocker:latest docker build . -t nameDocker:latest docker container stop number-docker-name docker container ls * docker pull quay.io/test:v0.0.1 * docker run --rm -p 5000:5000 -it quay.io/test:v0.0.1 * curl --header "Content-Type: application/json" \--request POST --data '[{"fixed":7.4, "a":0, "b":0.56, "c":9.4}]'[ http://127.0.0.1:5000/predict](https://meet.google.com/linkredirect?authuser=0&dest=http%3A%2F%2F127.0.0.1%3A5000%2Fpredict) * docker run --rm -v /home/.aws/credentials:/root/.aws/credentials -it quay.io/test /bin/sh aws s3 ls --profile=test \-------------------------------- Cloud software engineer and consultant focusing on building highly available, scalable and fully automated infrastructure environments on top of Amazon Web Services and Microsoft Azure clouds. My goal is always to make my customers happy in the cloud. \---------------- Search google for 3d = tiger - iPhone show AR/VR \--------------- brew install youtube-dl \---------------------------- List: Collection bucket : 1 for week 2 for month 3 for future \-------------------------------------------------------------------- **• Per frame operation** – Detection – Classification – Segmentation – Feature extraction – Recognition **• Across frames ** – Tracking – Counting **• High level** – Intention – Relations – Analyzing ============================= Deep compression Pruning deep learning Hash table neural network Dl compression Deep compression =================================== Mini PCI-e slot * What have I learned so far: * Problem-based learning * real life scenarios * index card (answer , idea) * Think-Pair-Share * Leverage flip charts * Summarizing \-------------------------------------------------------------------- Self \\\ Advancing Self-Supervised and Semi-Supervised Learning with SimCLR \cite{Chen2020} %https://github.com/google-research/simclr first pretraining on a large unlabeled dataset and then fine-tuning on a smaller labeled dataset pretraining on large unlabeled image datasets, as demonstrated by Exemplar- CNN, Instance Discrimination, CPC, AMDIM, CMC, MoCo and others. “A Simple Framework for Contrastive Learning of Visual Representations”, 85.8\% top-5 accuracy using 1\% of labeled images on the ImageNet dataset contrastive learning algorithms linear evaluation protocol (Zhang et al., 2016; Oord et al.,2018; Bachman et al., 2019; Kolesnikov et al., 2019) unsupervised learning benefits more from bigger models than its supervised counterpart. \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- Some of optimization algorithms ======================== Swarm Algorithm =============== 1\. Ant Colony Optimization (ACO) was inspired by the research on the behavior of ant colonies 2\. Firefly Algorithm based on insects called fireflies 3\. Marriage in Honey Bees Optimization Algorithm (MBO algorithm) is inspired by the process of reproduction of Honey Bee 4\. Artificial Bee Colony Algorithm (ABC) is based on the recollection of the Honey Bees 5\. Wasp Swarm Algorithm was inspired on the Parasitic wasps 6\. Bee Collecting Pollen Algorithm (BCPA) 7\. Termite Algorithm 8\. Mosquito swarms Algorithm (MSA) 9\. zooplankton swarms Algorithm (ZSA) 10\. Bumblebees Swarms Algorithm (BSA) 11\. Fish Swarm Algorithm (FSA) 12\. Bacteria Foraging Algorithm (BFA) 13\. Particle Swarm Optimization (PSO) 14\. Cuckoo Search 15\. Bat Algorithm (BA) 16\. Accelerated PSO 17\. Bee System 18\. Beehive Algorithm 19\. Cat Swarm 20\. Consultant-guided search 21\. Eagle Strategy 22\. Fast Backterial swarming algorithm 23\. Good lattice swarm optimization 24\. Glowworm swarm optimization 25\. Hierarchical swarm model 26\. Krill Herd 27\. Monkey Search 28\. Virtual ant algorithm 29\. Virtual bees 30\. Weighted Swarm Algorithm 31\. Wisdom of Artificial Crowd algorithm 32\. Prey-predator algorithm 33\. Memetic algorithm 34\. Lion Optimization Algorithm 35\. Chicken Swarm Optimization 36\. Ant Lion Optimizer 37\. Compact Particle Swarm Optimization 38\. Fruit Fly Optimization Algorithm 39\. marine propeller optimization algorithm 40\. The Whale Optimization Algorithm 41\. virus colony search algorithm 42\. Slime mould optimization algorithm Ecology Inspired Algorithm ========================== 1\. Biogeography-based Optimization 2\. Invasive Weed Optimization 3\. Symbiosis-Inspired Optimization - PS2O 4\. Atmosphere Clouds Model 5\. Brain Storm Optimization 6\. Dolphin echolocation 7\. Japanese Tree Frog Calling algorithm 8\. Eco-inspired evolutionary algorithm 9\. Egyptian Vulture 10\. Fish School search 11\. Flower Pollination algorithm 12\. Gene Expression 13\. Great Salmon Run 14\. Group Search Optimizer 15\. Human Inspired Algorithm 16\. Roach Infestation algorithm 17\. Queen-bee algorithm 18\. Shuffled frog leaping algorithm 19\. Forest Optimization Algorithm 20\. coral reefs optimization algorithm 21\. cultural evolution algorithm 22\. Grey Wolf Optimizer 23\. probabilistic pso 24\. omicron aco algorithm 25\. shark smell optimization 26\. social spider algorithm 27\. sosial insects behavior algorithm 28\. sperm whale algorithm Evolutionary Optimization ========================= 1\. Genetic Algorithm 2\. Genetic Programming 3\. Evolutionary Strategies 4\. Differential Evolution 5\. Paddy Field Algorithm 6\. Queen-bee Evolution 7\. Quantum Inspired Social Evolution Physic and Chemistry inspired algorithm ======================================= 1\. Big bang-Big Crunch 2\. Block hole algorithm 3\. Central force optimization 4\. Charged System search 5\. Electro-magnetism optimization 6\. Galaxy based search algorithm 7\. Gravitational search 8\. Harmony search algorithm 9\. Intelligent water drop algorithm 10\. River formation algorithm 11\. Self-propelled dynamics 12\. Simulated Annealing 13\. Stachastic diffusion search 14\. Spiral optimization 15\. Water Cycle algorithm 16\. Artificial Physics optimization 17\. Binary Gravitational search algorithm 18\. Continous quantum ant colony optimization 19\. Extended artificial physics optimization 20\. Extended Central force optimization 21\. Electromagnetism-like heuristic 22\. Gravitational Interaction optimization 23\. Hysteristetic Optimization algorithm 24\. Hybrid quantum-inspired GA 25\. Immune gravitational inspired algorithm 26\. Improved quantum evolutinary algorithm 27\. Linear programming 28\. Quantum-inspired bacterial swarming 29\. Quantum-inspired evolutionary algorithm 30\. Quantum-inspired genetic algorithm 31\. Quantum-behaved PSO 32\. Unified big bang-chaotic big crunch 33\. Vector model of artificial physics 34\. Versatile quantum-inspired evolutionary algorithm 35\. Space Gravitational Algorithm 36\. Ion Motion Algorithm 37\. Light Ray Optimization Algorithm 38\. Ray Optimization 39\. Photosynthetic Algorithms 40\. floorplanning algorithm 41\. Gases Brownian Motion Optimization 42\. gradient-type optimization 43\. mean-variance optimization 44\. Mine blast algorithm 45\. moth flame optimization 46\. multi battalion search algorithm 47\. music inspired optimization 48\. no free lunch theorems algorithm 49\. Optics inspired optimization 50\. runner-root algorithm 51\. sine cosine algorithm 52\. pitch tracking algorithm 53\. Stochastic Fractal Search algorithm 54\. stroke volume optimization 55\. Stud krill herd algorithm 56\. The Great Deluge Algorithm 57\. Water Evaporation Optimization 58\. water wave optimization algorithm 59\. Island model algorithm 60\. 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Let's partner up to take your project to the next level! pip install mlc-ai-nightly -f https://mlc.ai/wheels https://mlc.ai/ https://mlc.ai/summer22/ Day 1: Introduction to Unity: TVMScript Introduction to Unity: Relax and PyTorch TVM BYOC in Practice Get Started with TVM on Adreno GPU Introduction to Unity: Metaschedule How to Bring microTVM to a custom IDE Day 2: Community Keynote PyTorch 2.0: the journey to bringing compiler technologies to the core of PyTorch Support QNN Dialect for TVM with MediaTek Neuron and Devise the Scheduler for Acceleration On-Device Training Under 256KB Memory AMD Tutorial TVM at TI: Accelerating inference using the C7x/MMA Adreno GPU: 4x speed-up and upstreaming to TVM mainline Transfer-Tuning: Reusing Auto-Schedules for Efficient Tensor Program Code Generation Improvement in the TVM OpenCL codegen to autogenerate optimal convolution kernels for Adreno GPUs TVM Unity: Pass Infrastructure and BYOC Renesas Hardware accelerators with Apache TVM Introduction on 4th Gen Intel Xeon processor and BF16 support with TVM Hidet: Task Mapping Programming Paradigm for Deep Learning Tensor Programs Towards Building a Responsible Data Economy Optimizing SYCL Device Kernels with AKG Adreno GPU Performance Enhancements using TVM Improvements to CMSIS-NN integration in TVM UMA: Universal Modular Accelerator Interface Day 3: TVM Unity for Dynamic Models Empower Tensorflow serving with backend TVM Enabling Conditional Computing on Hexagon target Decoupled Model Schedule for Large Deep Learning Model Training Using TVM to bring Bayesian neural networks to embedded hardware Efficient Support of TVM Scan OP on RISC-V Vector Extension Improvements to Ethos-U55 support in TVM including CI on Alif Semiconductor boards Compiling Dynamic Shapes TVM Packaging in 2023: delivering TVM to end users Cross-Platform Training Using Automatic Differentiation on Relax IR AutoTVM: Reducing tuning space by cross axis filtering SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning Analytical Tensorization and Fusion for Compute-intensive Operators CUTLASS 3.0: Next Generation Composable and Reusable GPU Linear Algebra Library Enabling Data Movement and Computation Pipelining in Deep Learning Compiler Automating DL Compiler Bug Finding with NNSmith TVM at NIO TVM at Tencent Integrating the Andes RISC-V Processors into TVM Alpa: A Compiler for Distributed Deep Learning ACRoBat: Compiler and Runtime Techniques for Efficient Auto-Batching of Dynamic Deep Learning Computations Channel Folding: a Transform Pass for Optimizing Mobilenets ========================================================================Day 1: ************************ Introduction to Unity: TVMScript [https://github.com/cyx-6/TVM- Demo/blob/main/tvmscript.ipynb](https://github.com/cyx-6/TVM- Demo/blob/main/tvmscript.ipynb) Gan NN show us some hidden patter in history we can not see before. “I always have a slip of paper at hand, on which I note down the ideas of certain pages. On the backside I write down the bibliographic details. After finishing the book I go through my notes and think how these notes might be relevant for already written notes in the slip-box. It means that I always read with an eye towards possible connections in the slip-box.” (Luhmann et al., 1987, 150) Deep representation learning Model evaluation. Camera cheaper lidar Point cloud because of we need 3d Capturing reality 1\. 𝐀𝐝𝐝/𝐂𝐨𝐦𝐦𝐢𝐭 𝐀𝐥𝐥 Standard way: git add . git commit -m "Message" Another way: git commit -a -m "Message" 𝟐\. 𝐀𝐥𝐢𝐚𝐬𝐞𝐬 With aliases, you can write your own Git commands that do anything you want. Eg: git config --global alias.ac '!git add -A && git commit -m' (alias called ac, git add -A && git commit -m will do the full add and commit) 𝟑\. 𝐑𝐞𝐯𝐞𝐫𝐭 The revert command simply allows us to undo any commit on the current branch. Eg: git revert 486bdb2 Another way: git revert HEAD (for recent commits) 𝟒\. 𝐑𝐞𝐟𝐥𝐨𝐠 This command lets you easily see the recent commits, pulls, resets, pushes, etc on your local machine. Eg: git reflog 𝟓\. 𝐏𝐫𝐞𝐭𝐭𝐲 𝐋𝐨𝐠𝐬 Gives you the ability to print out a pretty log of your commits/branches. Eg: git log --graph --decorate --oneline 𝟔\. 𝐒𝐞𝐚𝐫𝐜𝐡𝐢𝐧𝐠 𝐋𝐨𝐠𝐬 One can also use the log command to search for specific changes in the code. Eg: git log -S "A promise in JavaScript is very similar" 𝟕\. 𝐒𝐭𝐚𝐬𝐡 This command will stash (store them locally) all your code changes but does not actually commit them. Eg: git stash 𝟖\. 𝐑𝐞𝐦𝐨𝐯𝐞 𝐃𝐞𝐚𝐝 𝐁𝐫𝐚𝐧𝐜𝐡𝐞𝐬 This command will delete all the tracking information for branches that are on your local machine that are not in the remote repository, but it does not delete your local branches. Eg: git remote update --prune 𝟗\. 𝐁𝐢𝐬𝐞𝐜𝐭 For finding which commits caused certain bugs Eg: git bisect start git bisect bad git bisect good 48c86d6 𝟏𝟎\. 𝐃𝐞𝐬𝐭𝐫𝐨𝐲 𝐋𝐨𝐜𝐚𝐥 𝐂𝐡𝐚𝐧𝐠𝐞𝐬 One can wipe out all changes on your local branch to exactly what is in the remote branch. Eg: git reset --hard origin/main Don’t trust your devices IoT. software and hardware are together for better business. Newsletter investing every 3 months 1\. Prototyping. New bie 2\. Patent. Website. ( list of investors) 3\. Pre seed. First founding 1M VC, inistution, anjel capital. 400 000 preseed. Quveribel. Equtible rund convertible non agreement Template. Convertabel lone 1\. Germ standar inistitude 2\. 4\. Equity. Venture builder. 20% 200 000 5\. 100 000 per year to become unocorn in less than 10 years 6\. Soniy corn 100k unicorn 1M 7\. 360 euro per years for database of investor 8\. Convertable loan: Pay interst rate 5% to 8% = 18 months later (2M found in 10M) convert on based . 9\. Invester Never act as co-founder = full time = 20% 10\. Project profit, 11\. Full time after foun rising Make a plan for your business; take your time to make calculations by creating a target audience. Your target audience determines how you approach your business plan. By studying your target audience, you are making empirical research and collecting information from them Then, secure a good partnership if need be, and get enough capital to start up. * * What the people need * Why people need it * When the people need it * It's affordability * It's ease of use * It's maintenance and revenue Pair programming The SB7 Framework harnesses the influence of stories. The structure describes the 7 most common story elements: • Character • Problem • Guide • Plan • Calls to action • Failure • Success Dear [Hiring Manager’s Name], I am writing to apply for the position of computer vision for IoT and cloud at [Company Name]. I am a highly skilled and experienced computer vision engineer with a strong background in IoT and cloud technologies. I believe that my skills and experience make me an ideal candidate for this position and I am excited about the opportunity to contribute to the success of your organization. I have a solid understanding of computer vision algorithms and techniques, as well as experience in developing and implementing computer vision systems. I am proficient in programming languages such as Python, C++, and Java, and have experience with popular computer vision libraries such as OpenCV, TensorFlow, and PyTorch. In addition, I have a strong background in IoT and cloud technologies, including experience with IoT platforms such as AWS IoT, Azure IoT, and Google Cloud IoT. I am familiar with cloud computing technologies such as AWS, Azure, and Google Cloud, and have experience with deploying and managing computer vision systems on these platforms. I am also a team player and have excellent communication skills. I am able to work with cross-functional teams and can effectively communicate with both technical and non-technical stakeholders. I am also highly motivated, and I am always looking for ways to improve my skills and stay up-to-date with the latest technologies. I am excited about the opportunity to join [Company Name] and to contribute to the development of cutting-edge computer vision systems for IoT and cloud. I am confident that my skills and experience make me a strong candidate for this position, and I look forward to discussing how I can contribute to your organization. Thank you for considering my application. I look forward to hearing from you soon. Sincerely, Title: "Unlocking the Power of Computer Vision for IoT and Cloud" Introduction: * Hi, and welcome to our video on the topic of computer vision for IoT and cloud. In this video, we're going to explore how computer vision technology can be used to enhance IoT and cloud-based systems, and how it can be used to unlock new possibilities for businesses and consumers alike. Body: * First, let's talk about what computer vision is and how it works. Essentially, computer vision is the technology that enables computers to understand and interpret visual information from the world around us. This can include things like images, videos, and even 3D models. * One of the key ways that computer vision can be used to enhance IoT and cloud-based systems is by enabling devices to better understand and interact with their environment. For example, a computer vision-enabled camera could be used to monitor a manufacturing facility and identify when a machine is in need of maintenance or when an employee is working in an unsafe manner. * Another way that computer vision can be used to enhance IoT and cloud-based systems is by enabling devices to better understand and interact with people. For example, a computer vision-enabled security camera could be used to identify individuals and track their movements, or a computer vision-enabled smart home system could be used to detect when someone is in the room and adjust the lighting or temperature accordingly. * Additionally, computer vision can also be used to enhance cloud-based systems by providing more accurate data and insights. For example, a computer vision-enabled drone could be used to collect data on crops and provide farmers with more accurate information about the health and growth of their crops. Conclusion: * Overall, computer vision technology has the potential to unlock new possibilities for businesses and consumers alike, by enabling IoT and cloud-based systems to better understand and interact with their environment and people. We hope this video has provided you with a better understanding of the potential of computer vision for IoT and cloud, and we look forward to seeing the new possibilities that will be created as this technology continues to evolve. Excited to share my latest project using computer vision and IoT to improve efficiency in manufacturing. I used a combination of machine learning algorithms and cloud computing to analyze data from cameras and sensors in real-time, resulting in a 20% increase in production speed. This was a challenging project but I enjoyed every step of it! I am always looking for new opportunities to apply my skills in computer vision and IoT to help companies improve their operations. Let's connect if you are working on a similar project or if you are looking for a developer with these skills. #computervision #IoT #cloudcomputing #manufacturingefficiency #machinelearning #developer" In this post, you briefly mention your experience and skills in computer vision and IoT, and you provide a specific example of a project you worked on that demonstrates your abilities. You also make it clear that you are open to new opportunities, and you invite others to connect with you. Using relevant hashtags such as #computervision #IoT #cloudcomputing can help your post reach a wider audience Exciting news! I just published a paper on a new object detection algorithm that I developed. The algorithm uses a combination of deep learning and computer vision techniques to improve accuracy and speed of object detection in real-world scenarios. This is a big step forward in the field of computer vision and I am proud to have contributed to it. I will be presenting my research at the Computer Vision Conference next month, if you're attending be sure to stop by and say hi! #computervision #objectdetection #deeplearning #research" In this post, you briefly explain the main findings and contributions of your research, and you express your excitement and pride in your work. You also mention the upcoming conference where you will be presenting your research, inviting your friends and colleagues to meet you in person. Also using relevant hashtags such as #computervision #objectdetection #deeplearning can help reach a wider audience interested in the field. Features stores 1\. Car parts detection 2\. Resize keep aspects ration 3\. 3.1 Perform damage detection 4\. 3.2Semantic segregation 5\. Transfer to original coordinates 1 class imbalance 2 class definition Maybe Class in between 3 inconstant annotations Color augmentation 1\. RGB shift 2\. Random brithness and contrast 3\. Sharpen 4\. Hue saturation value Why manually data augmented Becasu control of data. Not too rotate or change something Photogrammetry model Neural radiance fields (NeRF) NeRF in the wild \ [GitHub - google-research/tuning_playbook: A playbook for systematically maximizing the performance of deep learning models.](https://github.com/google-research/tuning_playbook) Yocto and Machine Learning + OpenCV: [https://www.yoctoproject.org](https://www.yoctoproject.org) [https://www.hackster.io/monica/running-machine-learning-on-maaxboard-s-yocto- image-part-1-6a4796](https://www.hackster.io/monica/running-machine-learning- on-maaxboard-s-yocto-image-part-1-6a4796) Bard Google: [https://blog.google/technology/ai/bard-google-ai-search- updates/](https://blog.google/technology/ai/bard-google-ai-search-updates/) [https://mustang.ir/questions/question/راه-اندازی-پروژه-های-گیت-هاب-با-git- pages](https://mustang.ir/questions/question/%D8%B1%D8%A7%D9%87-%D8%A7%D9%86%D8%AF%D8%A7%D8%B2%DB%8C-%D9%BE%D8%B1%D9%88%DA%98%D9%87-%D9%87%D8%A7%DB%8C-%DA%AF%DB%8C%D8%AA-%D9%87%D8%A7%D8%A8-%D8%A8%D8%A7-git- pages) Book: Project Management for Non-Project Managers [https://fa.wikipedia.org/wiki/علی_اکبرپور](https://fa.wikipedia.org/wiki/%D8%B9%D9%84%DB%8C_%D8%A7%DA%A9%D8%A8%D8%B1%D9%BE%D9%88%D8%B1) [https://www.kingorama.com](https://www.kingorama.com) شاهنامه سه بعدی [Accelerate deep learning model development with cloud custom environments - AWS Online Tech Talks - YouTube](https://m.youtube.com/watch?v=2Wt2zlkMtKI&noapp=1) [بخش هایی از کتاب Refactoring (نسخه رایگان)](https://www.developit.ir/refactoring/free.html#f7) [Performance Notes Of PyTorch Support for M1 and M2 GPUs - Lightning AI](https://lightning.ai/pages/community/community-discussions/performance- notes-of-pytorch-support-for-m1-and-m2-gpus/) [Investopedia Academy](https://academy.investopedia.com/) [HandBrake updated with AV1 and VP9 10-bit video encoding](https://9to5mac.com/2022/12/29/handbrake-support-av1-and- vp9-10-bit/) [How to Start Your Sole Proprietorship in 6 Simple Steps](https://qonto.com/en/blog/creators/administrative/sole-proprietorship- in-germany) [Duolingo English Test](https://englishtest.duolingo.com/applicants) [چالش‌های تولید محتوا برای مارکت اروپا و آمریکا - YouTube](https://m.youtube.com/watch?v=wW0HZdubuWQ) [PyTorch for Deep Learning & Machine Learning – Full Course - YouTube](https://m.youtube.com/watch?v=V_xro1bcAuA#dialog) [Why passive investing makes less sense in the current environment | Financial Times](https://archive.ph/0VucZ) [GitHub - google-research/tuning_playbook: A playbook for systematically maximizing the performance of deep learning models.](https://github.com/google-research/tuning_playbook) [GitHub - mgechev/google-interview-preparation-problems: leetcode problems I solved to prepare for my Google interview.](https://github.com/mgechev/google- interview-preparation-problems) [Bayesian Neural Networks and Variational Dropout](https://dmittov.github.io/variational_dropout/#/maximum-likelihood) [One machine learning question every day - bnomial](https://today.bnomial.com/?ref=email) Git remote add orgine Asynchronous Operation Anomaly detection Use experience. Personalizes. Prediction manage society mobility Personalization Covenant Platform. OpenMMLab Wordtune - AI-powered Writing Companion tree -v -I '*.png' -I '*.jpg' \--charset utf-8 >list2.txt 3D object using triangular mesh need vertices point cloud underlying surface of some 3D object, faster Definition of Done User Story complete Code\Implementation complete Code\Implementation Peer Reviews) approved Unit tests complete (if required) Testing Notes complete (if required) User Story Acceptance criteria defined and verified Backend: Python, Redis, Postgres, Celery Frontend: React, Redux, TypeScript DevOps: Terraform, Kubernetes, GitHub, Docker, AWS Data: Python (Data Science), Kafka, Fastapi, MLFlow, AWS SageMaker ML: Selcond core, Kubeflow, … [Sharpness](https://en.wikipedia.org/wiki/Sharpness_%28visual%29) ,[Noise](https://en.wikipedia.org/wiki/Image_noise), [Dynamic range](https://en.wikipedia.org/wiki/Dynamic_range), [Tone reproduction](https://en.wikipedia.org/wiki/Tone_reproduction) , [Contrast](https://en.wikipedia.org/wiki/Contrast_%28vision%29), [Color](https://en.wikipedia.org/wiki/Color), [Distortion](https://en.wikipedia.org/wiki/Distortion_%28optics%29) , [DSLR lenses](https://en.wikipedia.org/wiki/Lenses_for_SLR_and_DSLR_cameras), [Vignetting](https://en.wikipedia.org/wiki/Vignetting), [Exposure](https://en.wikipedia.org/wiki/Exposure_%28photography%29), Lateral [chromatic aberration](https://en.wikipedia.org/wiki/Chromatic_aberration) (LCA), [Lens flare](https://en.wikipedia.org/wiki/Lens_flare), Color, [Artifacts](https://en.wikipedia.org/wiki/Compression_artifact) ۱\. جهت انتخاب کلمه مورد نظرتان، دو بار روی آن تپ کنید. ۲\. برای انتخاب کل یک پاراگراف، کافیست چهار با روی آن تپ کنید. ۳\. یک انگشت را در ابتدا و انگشت دیگر را در آخر یک محدود گذاشته و کمی نگه دارید. متن میان دو انگشت انتخاب خواهد شد. ۴\. روی ابتدای محدوده ای دلخواه دو بار تپ کرده و بلافاصله با درگ کردن (کشیدن) پین محدوده ی انتخاب شده را گسترش دهید. (انگشت خود را پس از دومین تپ جدا نکنید) ۵\. برای انتخاب کل پاراگراف، به جز استفاده از مورد ۲، می توانید با دو انگشت، یک بار روی آن تپ کنید. namely motion estimation, motion smoothing, and image warping. Motion estimation algorithms often use a similarity transform to handle camera translations, rotations, and zooming. The tricky part is getting these algorithms to lock onto the background motion, 0\. video frames captured during fast motion are often blurry. Their appearance can be improved either using deblurring techniques (Section 10.3) or stealing sharper pixels from other frames with less motion or better focus (Matsushita, Ofek, Ge et al. 2006). Exercise 8.3 has you implement and test some of these ideas. 1\. Background subtraction 2\. Motion estimation 3\. Motion smoothing 4\. Image warping. image warping can result in missing borders around the image, which must be cropped, filled using information from other frames, or hallucinated using inpainting techniques (Section 10.5.1). Vision stabilization There is much recent work on Multi-view 3D reconstruction is a central research topic in computer vision that is driven in many different directions There are many available methods that can handle the noisy image completion problem In the case of surveillance using a fixed camera, there is no desired motion. In the case of most robotic applications, horizontal and vertical motions are desired, but rotation is not. In some cases of ground vehicles where the terrain is known to have many incline changes, or with aerial vehicles undergoing complicated maneuvers where the vehicle’s body is meant to be in varying orientations, rotation might be desired as the robot is meant to be at an angle at times. In robotics applications, computational complexity is extremely important due to the need for real-time operation. Also, it is likely that the center of rotation will not lie in the center of the image frame because the camera is rarely mounted at the robot’s center of mass. This first assumption is made in many video stabilization algorithms, and is a convenient way to seed the correct features with higher trust values. It is not an unreasonable assumption to make. Depending on the application, there is often a large portion of frames where local motion does not occur. In some situations, such as monitoring of steady traffic, there is no guarantee that local motion will not occur. This situation has not been tested, nor has our algorithm been designed to handle it. The second assumption comes from a combination of common sense, and the experience of many computer vision researchers. It makes sense that an object in the scene which does not move will be recognized more easily and more often. Being recognized consistently and consecutively is considered stable. On the other hand, objects which have local motion are less likely to be recognized as often. They might move through shadows, change orientation, or even move completely out of the scene. These possibilities all lead to a less stable class of features. It is likely that, more often than not, there are more background features than foreground features. Moving objects generally cover a small portion of the screen, which usually yields fewer features. Although uncommon, we did not want to make the assumption that this would occur in every frame. Certain scenes will consist of a large portion of local motion, or an object will move very close to the camera, consuming a much larger portion of the scene than the background. As long as some background features are discovered in each frame, our stabilization algorithm should succeed. # image processing tips: * the image size and kernel size need to depended. the best way is to use the one variable to define the size of the image and kernel together. * the coordinate of the image start at top left of the image/display * in order to change it to the normal coordinate you can use * grid of points; two matrix to X , Y coordinate * subtract half of W, H from X, Y in order to have normal coordinate system for our image * now we have cartesian coordinate * * cartesian coordinate to polar coordinate * تبدیل فضای کارتزین به پولار در خیلی از برنامه های پردازش تصویر کارایی دارد. برای پیدا کردن ترشلد ها هم می توان استفاده کرد * in MATLAB we can use ":"for example MatrixA(:) which means all entity of the matrix no mater how many dimensions we have but if we want to implemented in Python we can use numpy.flatten(). * in the MATLAB the round is different from python. if you want same result you need implement the rand function by yourself. * imge_mask=np.ones_like(image_source)*255 * imge_mask=imge_mask.astype(np.uint8) * imge_mask=imge_mask.flatten() ??? .ravel() * .asarray * np.logical_and( 1, 2) * indexes=[index for index in range(len(array1)) if array1[index] == True] * cv2.bitwise_not(yyy) * "olive" editor remove silence ![](https://lh5.googleusercontent.com/uz1tsz4Qy4dPzQzOtxekBVw0UwuYQ6BW31DaVXbLQTH- aJLInnaRUyrKqg4-- r_zsO5nj0pTm6oFMrFcyCwYUQfFNDHcgZIalLEc6l7_BABaoqRK7uGpRllFdVaf64L8_A=w1280) Questions: How to train model to add new classes? How to add a new class to an existing classifier in deep learning? Adding new Class to One Shot Learning trained model Is it possible to train a neural network as new classes are given? Merging all several models that detection system for all these tasks. Answer 1: There are several ways to add new classes to the trained model, which require just training for the new classes. * Incremental training ([GitHub](https://github.com/khurramjaved96/incremental-learning)) * continuously learn a stream of data ([GitHub](https://github.com/creme-ml/creme)) * online machine learning ([GitHub](https://github.com/GMvandeVen/continual-learning)) * Transfer Learning Twice * Continual learning approaches (Regularization, Expansion, Rehearsal) ([GitHub](https://github.com/facebookresearch/Adversarial-Continual-Learning)) Answer 2: Online learning is a term used to refer to a model which takes a continual or sequential stream of input data while training, in contrast to offline learning (also called batch learning), where the model is pre-trained on a static predefined dataset. Continual learning (also called incremental, continuous, lifelong learning) refers to a branch of ML working in an online learning context where models are designed to learn new tasks while maintaining performance on historic tasks. It can be applied to multiple problem paradigms (including Class- incremental learning, where each new task presents new class labels for an ever expanding super-classification problem). Do I need to train my whole model again on all four classes or is there any way I can just train my model on new class? Naively re-training the model on the updated dataset is indeed a solution. Continual learning seeks to address contexts where access to historic data (i.e. the original 3 classes) is not possible, or when retraining on an increasingly large dataset is impractical (for efficiency, space, privacy etc concerns). Multiple such models using different underlying architectures have been proposed, but almost all examples exclusively deal with image classification problems. Answer 3: You could use transfer learning (i.e. use a pre-trained model, then change its last layer to accommodate the new classes, and re-train this slightly modified model, maybe with a lower learning rate) to achieve that, but transfer learning does not necessarily attempt to retain any of the previously acquired information (especially if you don't use very small learning rates, you keep on training and you do not freeze the weights of the convolutional layers), but only to speed up training or when your new dataset is not big enough, by starting from a model that has already learned general features that are supposedly similar to the features needed for your specific task. There is also the related domain adaptation problem. There are more suitable approaches to perform incremental class learning (which is what you are asking for!), which directly address the [catastrophic forgetting problem](https://ai.stackexchange.com/a/13293/2444). For instance, you can take a look at this paper [Class-incremental Learning via Deep Model Consolidation](https://arxiv.org/pdf/1903.07864.pdf), which proposes the Deep Model Consolidation (DMC) approach. There are other continual/incremental learning approaches, many of them are described [here](https://ai.stackexchange.com/a/24529/2444) or in more detail [here](https://reader.elsevier.com/reader/sd/pii/S0893608019300231). Answer 4: by using Continual learning approaches to trained without losing the original classes. It has 3 categories: Regularization Expansion Rehearsal Answer 5: if you access to the dataset then you can download it and add all you new classes when you have " 'N' COCO Classes + 'M' New classes " after that you can fine tune model based on new dataset. you do not need all of the dataset just same number of image for all class enough. [https://learnopencv.com/stanford-mrnet-challenge-classifying-knee- mris/](https://learnopencv.com/stanford-mrnet-challenge-classifying-knee- mris/) Before start your machine learning project ask these questions and preparation: What is your inference hardware? specify the use case. specify model interface. how would we monitor performance after deployment? how can we approximate post-deployment monitoring before deployment? build a model and iteratively improve it. How to deploy the model at the end? monitor performance after deployment. what is your metric? How do you split your data (training and validation)? ### Preparation ML Project Workflow * [What is your hardware ?](/topics-and-projects/hardware) * specify the use case * specify model interface * how would we monitor performance after deployment? * how can we approximate post-deployment monitoring before deployment? * build a model and iteratively improve it * deploy the model * monitor performance * what is your are metric? * How do you split your data? ### Before Training deep learning model * using large model to train because * it is faster to train with lower overfit and faster converge due to best training * it is easier and higher compress in the final stage * model compression and acceleration: reducing parameters without significantly decreasing the model performance * Data: How to have good data for training deep learning models; How to Build and Enhance A Good Data Set For Your Deep Learning Project: using same config and data for training and inference, removing redundant (delete data which you don't need), get more data, Handle missing data, using data augmentation techniques or GAN to generate more data, re-scale/balance data, Transform your data (Change data types), Feature selection based on data-set and use case * * The data you don't need: removing redundant samples * get more data * Invent more data * data augmentation * Re-scale data * balance datasets * Transform your data * Feature selection based on dataset and use case * ML-Augmented Video Object Tracking: By applying and evaluating multiple algorithmic models, enhanced ability to scale object tracking in high-density video compositions. ### Training deep learning model * automated hyper-parameters * Using Hyperparameter tuning / Hyperparameter optimization tools * AutoML * genetic algorithm * population based training * bayesian optimization * You need to set some parameters and config for training * * Diagnostics * Weight Initialization * Learning rate * Activation function * Network Topology * Batches and Epochs * Regularization * Optimization and Loss * Early Stopping ### Continuous delivery * evolve with latest detection models * more data (no labels) * semi-supervised learning: big self-supervised models are strong semi-supervised learners ### After Training deep learning model * Parameter pruning * model pruning: reducing redundant parameters which are not sensitive to the performance. * aim: remove all connections with absolute weights below a threshold * Quantization * compresses by reducing the number of bits used to represent the weights * quantization effectively constraints the number of different weights we can use inside our kernels * per-channel quantization for weights, which improves performance by model compression and latency reduction. * Low rank matrix factorization (LRMF) * there exists latent structures in the data, by uncovering which we can obtain a compressed representation of the data * LRMF factorizes the original matrix into lower rank matrices while preserving latent structures and addressing the issue of sparseness * Compact convolutional filters (Video/CNN) * designing special structural convolutional filters to save parameters * replace over parametric filters with compact filters to achieve overall speedup while maintaining comparable accuracy * Knowledge distillation * training a compact neural network with distilled knowledge of a large model * distillation (knowledge transfer) from an ensemble of big networks into a much smaller network which learns directly from the cumbersome model's outputs, that is lighter to deploy * Binarized Neural Networks (BNNs) * Apache TVM (incubating) is a compiler stack for deep learning systems * Neural Networks Compression Framework (NNCF) ### Deep learning model in production * security: controls access to model(s) through secure packaging and execution * Test * auto training * using parallel processing and library such as GStreamer # Technology Docker AWS Flask Django # My Keynote (February 2021) 1. introduction 2. Machine Learning/ Deep Learning Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed 3. supervised Machine Learning 1. Deep Convolutional Neural Networks (DCNN) Architecture 2. Visualizing and Understanding Convolutional Networks 3. Object Detection by Deep Learning 4. [Video Tracking](/topics-and-projects/video-tracking) 5. Style Transfer 4. semi-supervised Machine Learning/ Deep Reinforcement learning (DRL) 1. Google 2. [Deep Reinforcement learning (DRL)](/topics-and-projects/drl) 5. unsupervised Machine Learning 1. Auto Encoder 6. Generative Adversarial Networks (GANs) 7. Tools 8. Pre trained model 9. Effect of Augmented Datasets to Train DCNNs 10. Training for more classes 11. Optimization 12. [Hardware](/topics-and-projects/hardware) 13. Production setup 14. post development 15. business , Gartner, Hype Cycle for emerging technologies, 2025 ### Advanced and practical 1. Inside CNN 1. Deep Convolutional Neural Networks Architecture 2. Convolution 3. Convolution Layer 4. Conv/FC Filters 5. Activation Functions 6. Layer Activations 7. Pooling Layer 8. Dropout ; L2 pooling 9. Why 1. Max-pooling is useful 2. How to see inside each layer and find important features * Visualizing and Understanding Convolutional Networks * [https://tensorspace.org/](https://tensorspace.org/) * [https://www.youtube.com/watch?v=AgkfIQ4IGaM](https://www.youtube.com/watch?v=AgkfIQ4IGaM) 2. Hands on python for deep learning 3. Fundamental deep learning 4. Installation: TensorFlow, PyTorch 5. [Using PC+eGPU for training video tracking](/topics-and-projects/source-code/compile) Summary of the summit * AI Hardware Europe Summit (July 2020) * [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit) * Apache TVM And Deep Learning Compilation Conference (December 2020) * [RISC-V Summit (December 2020) ](/workshops-and-events/risc-v) [https://www.inspectar.com/demo](https://www.inspectar.com/demo) for rasp # Face * Effective and precise face detection based on color and depth data * [https://www.sciencedirect.com/science/article/pii/S221083271400009X](https://www.sciencedirect.com/science/article/pii/S221083271400009X) * containing or not containing a face * Eigenface, Fisherface, waveletface, PCA (Principal Component Analysis), LDA (Linear Dis-criminant Analysis), Haar wavelet transform, and so on. * Viola–Jones detector * illumination changes and occlusion * depthinformation is used to filter the regions of the image where a candidate face regionis found by the Viola–Jones (VJ) detector * \- the first filtering rule is defined on the color of the region; since some false positiveshave colors not compatible with the face (e.g. shadows on jeans) a skin detector isapplied to remove the candidate face regions that do not contain skin pixels; * \- the second filtering rule is defined on the size of the face: using the depth mapit is quite easy to calculate the size of the candidate face region, which is use-ful to discard smallest and largest faces from the final result set; * \- the third filtering rule is defined on the depth map to discard flat objects (e.g.candidate faces found in a wall) or uneven objects (e.g. candidate face foundin the leaves of a tree). Combining color and depth data the candidate faceregion can be extracted from the background and measures of depth and reg-ularity are used for filtering out false positives. * The size criteria simply remove the candidate faces not included in a fixed rangesize ([12.5,30] cm). The size of a candidate face region is extracted from the depthmap according to the following approach. * image below * Gaussian mixture 3D morphable face model * [https://www.sciencedirect.com/science/article/pii/S0031320317303527](https://www.sciencedirect.com/science/article/pii/S0031320317303527) * * * Face Synthesis for Eyeglass-Robust Face Recognition * [https://paperswithcode.com/paper/face-synthesis-for-eyeglass-robust-face](https://paperswithcode.com/paper/face-synthesis-for-eyeglass-robust-face) * GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data * [https://paperswithcode.com/paper/genegan-learning-object-transfiguration-and](https://paperswithcode.com/paper/genegan-learning-object-transfiguration-and) * FacePoseNet: Making a Case for Landmark-Free Face Alignment * [https://paperswithcode.com/paper/faceposenet-making-a-case-for-landmark-free](https://paperswithcode.com/paper/faceposenet-making-a-case-for-landmark-free) * Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision * [https://paperswithcode.com/paper/learning-to-regress-3d-face-shape-and](https://paperswithcode.com/paper/learning-to-regress-3d-face-shape-and) * Unsupervised Eyeglasses Removal in the Wild * [https://paperswithcode.com/paper/unsupervised-eyeglasses-removal-in-the-wild](https://paperswithcode.com/paper/unsupervised-eyeglasses-removal-in-the-wild) * How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks) * [https://arxiv.org/pdf/1703.07332v3.pdf](https://arxiv.org/pdf/1703.07332v3.pdf) * (a) we construct, for the first time, a very strong baseline by combining a state-of-the-art architecture for landmark localization with a state-of-the-art residual block, train it on a very large yet synthetically expanded 2D facial landmark dataset and fi- nally evaluate it on all other 2D facial landmark datasets. * (b) We create a guided by 2D landmarks network which con- verts 2D landmark annotations to 3D and unifies all exist- ing datasets, leading to the creation of LS3D-W, the largest and most challenging 3D facial landmark dataset to date (~230,000 images). * (c) Following that, we train a neural network for 3D face alignment and evaluate it on the newly introduced LS3D-W. * (d) We further look into the effect of all “traditional” factors affecting face alignment performance like large pose, initialization and resolution, and introduce a “new” one, namely the size of the network. * (e) We show that both 2D and 3D face alignment networks achieve per- formance of remarkable accuracy which is probably close to saturating the datasets used. * Training and testing code as well as the dataset can be downloaded from https: //[www.adrianbulat.com/face-alignment/](http://www.adrianbulat.com/face-alignment/) ![](https://lh3.googleusercontent.com/9lvcVu- HI5oeKBlSMraQcnpp6MQ_gpnrRzOIbRJFnPhqa9SHXdiqGJdE2xf4P82zu_6Qx9Z4EgEk2l4djH0zQfpqMVsgVDOeANBbqrtXMZ72mIineYf- Kp4axCdz7PXp=w1280) 19.Sep.2021 [Medium](https://medium.com/p/626019137fa9/edit) [https://fi.co/madlibs](https://fi.co/madlibs) [https://orcid.org/0000-0001-8382-1389](https://orcid.org/0000-0001-8382-1389) Dreyer's English (learn write English) #book story Greek Mythology Explained: A Deeper Look at Classical Greek Lore and Myth **Papers:** CALTag: High Precision Fiducial Markers for Camera Diatom Autofocusing in Brightfield Microscopy: a Comparative Study :implementation variation of the laplacian Analysis of focus measure operators in shape-from-focus: why laplacian? Blure detection? Iqaf? Optical flow modeling and computation: A survey Toward general type 2 fuzzy logic systems based on zSlices \-------------------------------------------------------------------- Lost in space The OA Film:[ https://en.wikipedia.org/wiki/Shark_Tank](https://en.wikipedia.org/wiki/Shark_Tank) Movie Serial billons monk serial movies Python async Highly decoupled microservice Edex RIS-V , Self-car RISC-V Magazine Road map Game: over/under [https://www.sporcle.com/games/Hejman/underwhelmed](https://www.sporcle.com/games/Hejman/underwhelmed) \-------------------------------------------------------------------- \-------------------------------------------------------------------- GDPR in IoT The EU General Data Protection Regulation (GDPR) and Face Images in IoT The GDPR (General Data Protection Regulation), taking effect in May 2018, introduces strict requirements for personal data protection and the privacy rights of individuals. The EU regulations will set a new global standard for privacy rights and change the way organizations worldwide store and process personal data. The GDPR brings the importance of preserving the privacy of personal information to the forefront, yet the importance of face images within this context is often overlooked. The purpose of this paper is to introduce a solution that helps companies protect face images in IoT devices which record or process image by camera, to strengthen compliance with the GDPR. Our Face is our Identity Our face is the most fundamental and highly visible element of our identity. People recognize us when they see our face or a photo of our face. Recent years have seen exponential increase in the use, storage and dissemination of face images in both private and public sectors - in social networks, corporate databases, IoT, smart-city deployments, digital media, government applications, and nearly every organization’s databases. \--------------------- $(aws-okta env stage) aws s3 cp s3://dataset/archive.tar.gz /Users/a.zip aws s3 ls images | tail -n 100 aws s3 cp staging-images/test.jpg /Users/test.jpg \--------------------- screen -rD k get pods Docker RUN chmod +x /tmp/run.sh Can run docker in terminal and run code line by line docker run -it --rm debian:stable-slim bash apt-get update apt-get installl -y \-------------------------------- brew install awscli aws-okta kubectx kubernetes-cli tfenv touch ~/.aws/config \-------------------------------------------------------------------- docker image rm TETSTDFSAFDSADF docker image ls docker system prune docker run -p 5000:5000 nameDocker:latest docker build . -t nameDocker:latest docker container stop number-docker-name docker container ls * docker pull quay.io/test:v0.0.1 * docker run --rm -p 5000:5000 -it quay.io/test:v0.0.1 * curl --header "Content-Type: application/json" \--request POST --data '[{"fixed":7.4, "a":0, "b":0.56, "c":9.4}]'[ http://127.0.0.1:5000/predict](https://meet.google.com/linkredirect?authuser=0&dest=http%3A%2F%2F127.0.0.1%3A5000%2Fpredict) * docker run --rm -v /home/.aws/credentials:/root/.aws/credentials -it quay.io/test /bin/sh aws s3 ls --profile=test \-------------------------------- Cloud software engineer and consultant focusing on building highly available, scalable and fully automated infrastructure environments on top of Amazon Web Services and Microsoft Azure clouds. My goal is always to make my customers happy in the cloud. \---------------- Search google for 3d = tiger - iPhone show AR/VR \--------------- brew install youtube-dl \---------------------------- List: Collection bucket : 1 for week 2 for month 3 for future \-------------------------------------------------------------------- **• Per frame operation** – Detection – Classification – Segmentation – Feature extraction – Recognition **• Across frames ** – Tracking – Counting **• High level** – Intention – Relations – Analyzing ============================= Deep compression Pruning deep learning Hash table neural network Dl compression Deep compression =================================== Mini PCI-e slot * What have I learned so far: * Problem-based learning * real life scenarios * index card (answer , idea) * Think-Pair-Share * Leverage flip charts * Summarizing \-------------------------------------------------------------------- Self \\\ Advancing Self-Supervised and Semi-Supervised Learning with SimCLR \cite{Chen2020} %https://github.com/google-research/simclr first pretraining on a large unlabeled dataset and then fine-tuning on a smaller labeled dataset pretraining on large unlabeled image datasets, as demonstrated by Exemplar- CNN, Instance Discrimination, CPC, AMDIM, CMC, MoCo and others. “A Simple Framework for Contrastive Learning of Visual Representations”, 85.8\% top-5 accuracy using 1\% of labeled images on the ImageNet dataset contrastive learning algorithms linear evaluation protocol (Zhang et al., 2016; Oord et al.,2018; Bachman et al., 2019; Kolesnikov et al., 2019) unsupervised learning benefits more from bigger models than its supervised counterpart. \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- Some of optimization algorithms ======================== Swarm Algorithm =============== 1\. Ant Colony Optimization (ACO) was inspired by the research on the behavior of ant colonies 2\. Firefly Algorithm based on insects called fireflies 3\. Marriage in Honey Bees Optimization Algorithm (MBO algorithm) is inspired by the process of reproduction of Honey Bee 4\. Artificial Bee Colony Algorithm (ABC) is based on the recollection of the Honey Bees 5\. Wasp Swarm Algorithm was inspired on the Parasitic wasps 6\. Bee Collecting Pollen Algorithm (BCPA) 7\. Termite Algorithm 8\. Mosquito swarms Algorithm (MSA) 9\. zooplankton swarms Algorithm (ZSA) 10\. Bumblebees Swarms Algorithm (BSA) 11\. Fish Swarm Algorithm (FSA) 12\. Bacteria Foraging Algorithm (BFA) 13\. Particle Swarm Optimization (PSO) 14\. Cuckoo Search 15\. Bat Algorithm (BA) 16\. Accelerated PSO 17\. Bee System 18\. Beehive Algorithm 19\. Cat Swarm 20\. Consultant-guided search 21\. Eagle Strategy 22\. Fast Backterial swarming algorithm 23\. Good lattice swarm optimization 24\. Glowworm swarm optimization 25\. Hierarchical swarm model 26\. Krill Herd 27\. Monkey Search 28\. Virtual ant algorithm 29\. Virtual bees 30\. Weighted Swarm Algorithm 31\. Wisdom of Artificial Crowd algorithm 32\. Prey-predator algorithm 33\. Memetic algorithm 34\. Lion Optimization Algorithm 35\. Chicken Swarm Optimization 36\. Ant Lion Optimizer 37\. Compact Particle Swarm Optimization 38\. Fruit Fly Optimization Algorithm 39\. marine propeller optimization algorithm 40\. The Whale Optimization Algorithm 41\. virus colony search algorithm 42\. Slime mould optimization algorithm Ecology Inspired Algorithm ========================== 1\. Biogeography-based Optimization 2\. Invasive Weed Optimization 3\. Symbiosis-Inspired Optimization - PS2O 4\. Atmosphere Clouds Model 5\. Brain Storm Optimization 6\. Dolphin echolocation 7\. Japanese Tree Frog Calling algorithm 8\. Eco-inspired evolutionary algorithm 9\. Egyptian Vulture 10\. Fish School search 11\. Flower Pollination algorithm 12\. Gene Expression 13\. Great Salmon Run 14\. Group Search Optimizer 15\. Human Inspired Algorithm 16\. Roach Infestation algorithm 17\. Queen-bee algorithm 18\. Shuffled frog leaping algorithm 19\. Forest Optimization Algorithm 20\. coral reefs optimization algorithm 21\. cultural evolution algorithm 22\. Grey Wolf Optimizer 23\. probabilistic pso 24\. omicron aco algorithm 25\. shark smell optimization 26\. social spider algorithm 27\. sosial insects behavior algorithm 28\. sperm whale algorithm Evolutionary Optimization ========================= 1\. Genetic Algorithm 2\. Genetic Programming 3\. Evolutionary Strategies 4\. Differential Evolution 5\. Paddy Field Algorithm 6\. Queen-bee Evolution 7\. Quantum Inspired Social Evolution Physic and Chemistry inspired algorithm ======================================= 1\. Big bang-Big Crunch 2\. Block hole algorithm 3\. Central force optimization 4\. Charged System search 5\. Electro-magnetism optimization 6\. Galaxy based search algorithm 7\. Gravitational search 8\. Harmony search algorithm 9\. Intelligent water drop algorithm 10\. River formation algorithm 11\. Self-propelled dynamics 12\. Simulated Annealing 13\. Stachastic diffusion search 14\. Spiral optimization 15\. Water Cycle algorithm 16\. Artificial Physics optimization 17\. Binary Gravitational search algorithm 18\. Continous quantum ant colony optimization 19\. Extended artificial physics optimization 20\. Extended Central force optimization 21\. Electromagnetism-like heuristic 22\. Gravitational Interaction optimization 23\. Hysteristetic Optimization algorithm 24\. Hybrid quantum-inspired GA 25\. Immune gravitational inspired algorithm 26\. Improved quantum evolutinary algorithm 27\. Linear programming 28\. Quantum-inspired bacterial swarming 29\. Quantum-inspired evolutionary algorithm 30\. Quantum-inspired genetic algorithm 31\. Quantum-behaved PSO 32\. Unified big bang-chaotic big crunch 33\. Vector model of artificial physics 34\. Versatile quantum-inspired evolutionary algorithm 35\. Space Gravitational Algorithm 36\. Ion Motion Algorithm 37\. Light Ray Optimization Algorithm 38\. Ray Optimization 39\. Photosynthetic Algorithms 40\. floorplanning algorithm 41\. Gases Brownian Motion Optimization 42\. gradient-type optimization 43\. mean-variance optimization 44\. Mine blast algorithm 45\. moth flame optimization 46\. multi battalion search algorithm 47\. music inspired optimization 48\. no free lunch theorems algorithm 49\. Optics inspired optimization 50\. runner-root algorithm 51\. sine cosine algorithm 52\. pitch tracking algorithm 53\. Stochastic Fractal Search algorithm 54\. stroke volume optimization 55\. Stud krill herd algorithm 56\. The Great Deluge Algorithm 57\. Water Evaporation Optimization 58\. water wave optimization algorithm 59\. Island model algorithm 60\. 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Let's partner up to take your project to the next level! pip install mlc-ai-nightly -f https://mlc.ai/wheels https://mlc.ai/ https://mlc.ai/summer22/ Day 1: Introduction to Unity: TVMScript Introduction to Unity: Relax and PyTorch TVM BYOC in Practice Get Started with TVM on Adreno GPU Introduction to Unity: Metaschedule How to Bring microTVM to a custom IDE Day 2: Community Keynote PyTorch 2.0: the journey to bringing compiler technologies to the core of PyTorch Support QNN Dialect for TVM with MediaTek Neuron and Devise the Scheduler for Acceleration On-Device Training Under 256KB Memory AMD Tutorial TVM at TI: Accelerating inference using the C7x/MMA Adreno GPU: 4x speed-up and upstreaming to TVM mainline Transfer-Tuning: Reusing Auto-Schedules for Efficient Tensor Program Code Generation Improvement in the TVM OpenCL codegen to autogenerate optimal convolution kernels for Adreno GPUs TVM Unity: Pass Infrastructure and BYOC Renesas Hardware accelerators with Apache TVM Introduction on 4th Gen Intel Xeon processor and BF16 support with TVM Hidet: Task Mapping Programming Paradigm for Deep Learning Tensor Programs Towards Building a Responsible Data Economy Optimizing SYCL Device Kernels with AKG Adreno GPU Performance Enhancements using TVM Improvements to CMSIS-NN integration in TVM UMA: Universal Modular Accelerator Interface Day 3: TVM Unity for Dynamic Models Empower Tensorflow serving with backend TVM Enabling Conditional Computing on Hexagon target Decoupled Model Schedule for Large Deep Learning Model Training Using TVM to bring Bayesian neural networks to embedded hardware Efficient Support of TVM Scan OP on RISC-V Vector Extension Improvements to Ethos-U55 support in TVM including CI on Alif Semiconductor boards Compiling Dynamic Shapes TVM Packaging in 2023: delivering TVM to end users Cross-Platform Training Using Automatic Differentiation on Relax IR AutoTVM: Reducing tuning space by cross axis filtering SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning Analytical Tensorization and Fusion for Compute-intensive Operators CUTLASS 3.0: Next Generation Composable and Reusable GPU Linear Algebra Library Enabling Data Movement and Computation Pipelining in Deep Learning Compiler Automating DL Compiler Bug Finding with NNSmith TVM at NIO TVM at Tencent Integrating the Andes RISC-V Processors into TVM Alpa: A Compiler for Distributed Deep Learning ACRoBat: Compiler and Runtime Techniques for Efficient Auto-Batching of Dynamic Deep Learning Computations Channel Folding: a Transform Pass for Optimizing Mobilenets ========================================================================Day 1: ************************ Introduction to Unity: TVMScript [https://github.com/cyx-6/TVM- Demo/blob/main/tvmscript.ipynb](https://github.com/cyx-6/TVM- Demo/blob/main/tvmscript.ipynb) Gan NN show us some hidden patter in history we can not see before. “I always have a slip of paper at hand, on which I note down the ideas of certain pages. On the backside I write down the bibliographic details. After finishing the book I go through my notes and think how these notes might be relevant for already written notes in the slip-box. It means that I always read with an eye towards possible connections in the slip-box.” (Luhmann et al., 1987, 150) Deep representation learning Model evaluation. Camera cheaper lidar Point cloud because of we need 3d Capturing reality 1\. 𝐀𝐝𝐝/𝐂𝐨𝐦𝐦𝐢𝐭 𝐀𝐥𝐥 Standard way: git add . git commit -m "Message" Another way: git commit -a -m "Message" 𝟐\. 𝐀𝐥𝐢𝐚𝐬𝐞𝐬 With aliases, you can write your own Git commands that do anything you want. Eg: git config --global alias.ac '!git add -A && git commit -m' (alias called ac, git add -A && git commit -m will do the full add and commit) 𝟑\. 𝐑𝐞𝐯𝐞𝐫𝐭 The revert command simply allows us to undo any commit on the current branch. Eg: git revert 486bdb2 Another way: git revert HEAD (for recent commits) 𝟒\. 𝐑𝐞𝐟𝐥𝐨𝐠 This command lets you easily see the recent commits, pulls, resets, pushes, etc on your local machine. Eg: git reflog 𝟓\. 𝐏𝐫𝐞𝐭𝐭𝐲 𝐋𝐨𝐠𝐬 Gives you the ability to print out a pretty log of your commits/branches. Eg: git log --graph --decorate --oneline 𝟔\. 𝐒𝐞𝐚𝐫𝐜𝐡𝐢𝐧𝐠 𝐋𝐨𝐠𝐬 One can also use the log command to search for specific changes in the code. Eg: git log -S "A promise in JavaScript is very similar" 𝟕\. 𝐒𝐭𝐚𝐬𝐡 This command will stash (store them locally) all your code changes but does not actually commit them. Eg: git stash 𝟖\. 𝐑𝐞𝐦𝐨𝐯𝐞 𝐃𝐞𝐚𝐝 𝐁𝐫𝐚𝐧𝐜𝐡𝐞𝐬 This command will delete all the tracking information for branches that are on your local machine that are not in the remote repository, but it does not delete your local branches. Eg: git remote update --prune 𝟗\. 𝐁𝐢𝐬𝐞𝐜𝐭 For finding which commits caused certain bugs Eg: git bisect start git bisect bad git bisect good 48c86d6 𝟏𝟎\. 𝐃𝐞𝐬𝐭𝐫𝐨𝐲 𝐋𝐨𝐜𝐚𝐥 𝐂𝐡𝐚𝐧𝐠𝐞𝐬 One can wipe out all changes on your local branch to exactly what is in the remote branch. Eg: git reset --hard origin/main Don’t trust your devices IoT. software and hardware are together for better business. Newsletter investing every 3 months 1\. Prototyping. New bie 2\. Patent. Website. ( list of investors) 3\. Pre seed. First founding 1M VC, inistution, anjel capital. 400 000 preseed. Quveribel. Equtible rund convertible non agreement Template. Convertabel lone 1\. Germ standar inistitude 2\. 4\. Equity. Venture builder. 20% 200 000 5\. 100 000 per year to become unocorn in less than 10 years 6\. Soniy corn 100k unicorn 1M 7\. 360 euro per years for database of investor 8\. Convertable loan: Pay interst rate 5% to 8% = 18 months later (2M found in 10M) convert on based . 9\. Invester Never act as co-founder = full time = 20% 10\. Project profit, 11\. Full time after foun rising Make a plan for your business; take your time to make calculations by creating a target audience. Your target audience determines how you approach your business plan. By studying your target audience, you are making empirical research and collecting information from them Then, secure a good partnership if need be, and get enough capital to start up. * * What the people need * Why people need it * When the people need it * It's affordability * It's ease of use * It's maintenance and revenue Pair programming The SB7 Framework harnesses the influence of stories. The structure describes the 7 most common story elements: • Character • Problem • Guide • Plan • Calls to action • Failure • Success Dear [Hiring Manager’s Name], I am writing to apply for the position of computer vision for IoT and cloud at [Company Name]. I am a highly skilled and experienced computer vision engineer with a strong background in IoT and cloud technologies. I believe that my skills and experience make me an ideal candidate for this position and I am excited about the opportunity to contribute to the success of your organization. I have a solid understanding of computer vision algorithms and techniques, as well as experience in developing and implementing computer vision systems. I am proficient in programming languages such as Python, C++, and Java, and have experience with popular computer vision libraries such as OpenCV, TensorFlow, and PyTorch. In addition, I have a strong background in IoT and cloud technologies, including experience with IoT platforms such as AWS IoT, Azure IoT, and Google Cloud IoT. I am familiar with cloud computing technologies such as AWS, Azure, and Google Cloud, and have experience with deploying and managing computer vision systems on these platforms. I am also a team player and have excellent communication skills. I am able to work with cross-functional teams and can effectively communicate with both technical and non-technical stakeholders. I am also highly motivated, and I am always looking for ways to improve my skills and stay up-to-date with the latest technologies. I am excited about the opportunity to join [Company Name] and to contribute to the development of cutting-edge computer vision systems for IoT and cloud. I am confident that my skills and experience make me a strong candidate for this position, and I look forward to discussing how I can contribute to your organization. Thank you for considering my application. I look forward to hearing from you soon. Sincerely, Title: "Unlocking the Power of Computer Vision for IoT and Cloud" Introduction: * Hi, and welcome to our video on the topic of computer vision for IoT and cloud. In this video, we're going to explore how computer vision technology can be used to enhance IoT and cloud-based systems, and how it can be used to unlock new possibilities for businesses and consumers alike. Body: * First, let's talk about what computer vision is and how it works. Essentially, computer vision is the technology that enables computers to understand and interpret visual information from the world around us. This can include things like images, videos, and even 3D models. * One of the key ways that computer vision can be used to enhance IoT and cloud-based systems is by enabling devices to better understand and interact with their environment. For example, a computer vision-enabled camera could be used to monitor a manufacturing facility and identify when a machine is in need of maintenance or when an employee is working in an unsafe manner. * Another way that computer vision can be used to enhance IoT and cloud-based systems is by enabling devices to better understand and interact with people. For example, a computer vision-enabled security camera could be used to identify individuals and track their movements, or a computer vision-enabled smart home system could be used to detect when someone is in the room and adjust the lighting or temperature accordingly. * Additionally, computer vision can also be used to enhance cloud-based systems by providing more accurate data and insights. For example, a computer vision-enabled drone could be used to collect data on crops and provide farmers with more accurate information about the health and growth of their crops. Conclusion: * Overall, computer vision technology has the potential to unlock new possibilities for businesses and consumers alike, by enabling IoT and cloud-based systems to better understand and interact with their environment and people. We hope this video has provided you with a better understanding of the potential of computer vision for IoT and cloud, and we look forward to seeing the new possibilities that will be created as this technology continues to evolve. Excited to share my latest project using computer vision and IoT to improve efficiency in manufacturing. I used a combination of machine learning algorithms and cloud computing to analyze data from cameras and sensors in real-time, resulting in a 20% increase in production speed. This was a challenging project but I enjoyed every step of it! I am always looking for new opportunities to apply my skills in computer vision and IoT to help companies improve their operations. Let's connect if you are working on a similar project or if you are looking for a developer with these skills. #computervision #IoT #cloudcomputing #manufacturingefficiency #machinelearning #developer" In this post, you briefly mention your experience and skills in computer vision and IoT, and you provide a specific example of a project you worked on that demonstrates your abilities. You also make it clear that you are open to new opportunities, and you invite others to connect with you. Using relevant hashtags such as #computervision #IoT #cloudcomputing can help your post reach a wider audience Exciting news! I just published a paper on a new object detection algorithm that I developed. The algorithm uses a combination of deep learning and computer vision techniques to improve accuracy and speed of object detection in real-world scenarios. This is a big step forward in the field of computer vision and I am proud to have contributed to it. I will be presenting my research at the Computer Vision Conference next month, if you're attending be sure to stop by and say hi! #computervision #objectdetection #deeplearning #research" In this post, you briefly explain the main findings and contributions of your research, and you express your excitement and pride in your work. You also mention the upcoming conference where you will be presenting your research, inviting your friends and colleagues to meet you in person. Also using relevant hashtags such as #computervision #objectdetection #deeplearning can help reach a wider audience interested in the field. Features stores 1\. Car parts detection 2\. Resize keep aspects ration 3\. 3.1 Perform damage detection 4\. 3.2Semantic segregation 5\. Transfer to original coordinates 1 class imbalance 2 class definition Maybe Class in between 3 inconstant annotations Color augmentation 1\. RGB shift 2\. Random brithness and contrast 3\. Sharpen 4\. Hue saturation value Why manually data augmented Becasu control of data. Not too rotate or change something Photogrammetry model Neural radiance fields (NeRF) NeRF in the wild \ [GitHub - google-research/tuning_playbook: A playbook for systematically maximizing the performance of deep learning models.](https://github.com/google-research/tuning_playbook) Yocto and Machine Learning + OpenCV: [https://www.yoctoproject.org](https://www.yoctoproject.org) [https://www.hackster.io/monica/running-machine-learning-on-maaxboard-s-yocto- image-part-1-6a4796](https://www.hackster.io/monica/running-machine-learning- on-maaxboard-s-yocto-image-part-1-6a4796) Bard Google: [https://blog.google/technology/ai/bard-google-ai-search- updates/](https://blog.google/technology/ai/bard-google-ai-search-updates/) [https://mustang.ir/questions/question/راه-اندازی-پروژه-های-گیت-هاب-با-git- pages](https://mustang.ir/questions/question/%D8%B1%D8%A7%D9%87-%D8%A7%D9%86%D8%AF%D8%A7%D8%B2%DB%8C-%D9%BE%D8%B1%D9%88%DA%98%D9%87-%D9%87%D8%A7%DB%8C-%DA%AF%DB%8C%D8%AA-%D9%87%D8%A7%D8%A8-%D8%A8%D8%A7-git- pages) Book: Project Management for Non-Project Managers [https://fa.wikipedia.org/wiki/علی_اکبرپور](https://fa.wikipedia.org/wiki/%D8%B9%D9%84%DB%8C_%D8%A7%DA%A9%D8%A8%D8%B1%D9%BE%D9%88%D8%B1) [https://www.kingorama.com](https://www.kingorama.com) شاهنامه سه بعدی [Accelerate deep learning model development with cloud custom environments - AWS Online Tech Talks - YouTube](https://m.youtube.com/watch?v=2Wt2zlkMtKI&noapp=1) [بخش هایی از کتاب Refactoring (نسخه رایگان)](https://www.developit.ir/refactoring/free.html#f7) [Performance Notes Of PyTorch Support for M1 and M2 GPUs - Lightning AI](https://lightning.ai/pages/community/community-discussions/performance- notes-of-pytorch-support-for-m1-and-m2-gpus/) [Investopedia Academy](https://academy.investopedia.com/) [HandBrake updated with AV1 and VP9 10-bit video encoding](https://9to5mac.com/2022/12/29/handbrake-support-av1-and- vp9-10-bit/) [How to Start Your Sole Proprietorship in 6 Simple Steps](https://qonto.com/en/blog/creators/administrative/sole-proprietorship- in-germany) [Duolingo English Test](https://englishtest.duolingo.com/applicants) [چالش‌های تولید محتوا برای مارکت اروپا و آمریکا - YouTube](https://m.youtube.com/watch?v=wW0HZdubuWQ) [PyTorch for Deep Learning & Machine Learning – Full Course - YouTube](https://m.youtube.com/watch?v=V_xro1bcAuA#dialog) [Why passive investing makes less sense in the current environment | Financial Times](https://archive.ph/0VucZ) [GitHub - google-research/tuning_playbook: A playbook for systematically maximizing the performance of deep learning models.](https://github.com/google-research/tuning_playbook) [GitHub - mgechev/google-interview-preparation-problems: leetcode problems I solved to prepare for my Google interview.](https://github.com/mgechev/google- interview-preparation-problems) [Bayesian Neural Networks and Variational Dropout](https://dmittov.github.io/variational_dropout/#/maximum-likelihood) [One machine learning question every day - bnomial](https://today.bnomial.com/?ref=email) Git remote add orgine Asynchronous Operation Anomaly detection Use experience. Personalizes. Prediction manage society mobility Personalization Covenant Platform. OpenMMLab Wordtune - AI-powered Writing Companion tree -v -I '*.png' -I '*.jpg' \--charset utf-8 >list2.txt 3D object using triangular mesh need vertices point cloud underlying surface of some 3D object, faster Definition of Done User Story complete Code\Implementation complete Code\Implementation Peer Reviews) approved Unit tests complete (if required) Testing Notes complete (if required) User Story Acceptance criteria defined and verified Backend: Python, Redis, Postgres, Celery Frontend: React, Redux, TypeScript DevOps: Terraform, Kubernetes, GitHub, Docker, AWS Data: Python (Data Science), Kafka, Fastapi, MLFlow, AWS SageMaker ML: Selcond core, Kubeflow, … [Sharpness](https://en.wikipedia.org/wiki/Sharpness_%28visual%29) ,[Noise](https://en.wikipedia.org/wiki/Image_noise), [Dynamic range](https://en.wikipedia.org/wiki/Dynamic_range), [Tone reproduction](https://en.wikipedia.org/wiki/Tone_reproduction) , [Contrast](https://en.wikipedia.org/wiki/Contrast_%28vision%29), [Color](https://en.wikipedia.org/wiki/Color), [Distortion](https://en.wikipedia.org/wiki/Distortion_%28optics%29) , [DSLR lenses](https://en.wikipedia.org/wiki/Lenses_for_SLR_and_DSLR_cameras), [Vignetting](https://en.wikipedia.org/wiki/Vignetting), [Exposure](https://en.wikipedia.org/wiki/Exposure_%28photography%29), Lateral [chromatic aberration](https://en.wikipedia.org/wiki/Chromatic_aberration) (LCA), [Lens flare](https://en.wikipedia.org/wiki/Lens_flare), Color, [Artifacts](https://en.wikipedia.org/wiki/Compression_artifact) ۱\. جهت انتخاب کلمه مورد نظرتان، دو بار روی آن تپ کنید. ۲\. برای انتخاب کل یک پاراگراف، کافیست چهار با روی آن تپ کنید. ۳\. یک انگشت را در ابتدا و انگشت دیگر را در آخر یک محدود گذاشته و کمی نگه دارید. متن میان دو انگشت انتخاب خواهد شد. ۴\. روی ابتدای محدوده ای دلخواه دو بار تپ کرده و بلافاصله با درگ کردن (کشیدن) پین محدوده ی انتخاب شده را گسترش دهید. (انگشت خود را پس از دومین تپ جدا نکنید) ۵\. برای انتخاب کل پاراگراف، به جز استفاده از مورد ۲، می توانید با دو انگشت، یک بار روی آن تپ کنید. namely motion estimation, motion smoothing, and image warping. Motion estimation algorithms often use a similarity transform to handle camera translations, rotations, and zooming. The tricky part is getting these algorithms to lock onto the background motion, 0\. video frames captured during fast motion are often blurry. Their appearance can be improved either using deblurring techniques (Section 10.3) or stealing sharper pixels from other frames with less motion or better focus (Matsushita, Ofek, Ge et al. 2006). Exercise 8.3 has you implement and test some of these ideas. 1\. Background subtraction 2\. Motion estimation 3\. Motion smoothing 4\. Image warping. image warping can result in missing borders around the image, which must be cropped, filled using information from other frames, or hallucinated using inpainting techniques (Section 10.5.1). Vision stabilization There is much recent work on Multi-view 3D reconstruction is a central research topic in computer vision that is driven in many different directions There are many available methods that can handle the noisy image completion problem In the case of surveillance using a fixed camera, there is no desired motion. In the case of most robotic applications, horizontal and vertical motions are desired, but rotation is not. In some cases of ground vehicles where the terrain is known to have many incline changes, or with aerial vehicles undergoing complicated maneuvers where the vehicle’s body is meant to be in varying orientations, rotation might be desired as the robot is meant to be at an angle at times. In robotics applications, computational complexity is extremely important due to the need for real-time operation. Also, it is likely that the center of rotation will not lie in the center of the image frame because the camera is rarely mounted at the robot’s center of mass. This first assumption is made in many video stabilization algorithms, and is a convenient way to seed the correct features with higher trust values. It is not an unreasonable assumption to make. Depending on the application, there is often a large portion of frames where local motion does not occur. In some situations, such as monitoring of steady traffic, there is no guarantee that local motion will not occur. This situation has not been tested, nor has our algorithm been designed to handle it. The second assumption comes from a combination of common sense, and the experience of many computer vision researchers. It makes sense that an object in the scene which does not move will be recognized more easily and more often. Being recognized consistently and consecutively is considered stable. On the other hand, objects which have local motion are less likely to be recognized as often. They might move through shadows, change orientation, or even move completely out of the scene. These possibilities all lead to a less stable class of features. It is likely that, more often than not, there are more background features than foreground features. Moving objects generally cover a small portion of the screen, which usually yields fewer features. Although uncommon, we did not want to make the assumption that this would occur in every frame. Certain scenes will consist of a large portion of local motion, or an object will move very close to the camera, consuming a much larger portion of the scene than the background. As long as some background features are discovered in each frame, our stabilization algorithm should succeed. # image processing tips: * the image size and kernel size need to depended. the best way is to use the one variable to define the size of the image and kernel together. * the coordinate of the image start at top left of the image/display * in order to change it to the normal coordinate you can use * grid of points; two matrix to X , Y coordinate * subtract half of W, H from X, Y in order to have normal coordinate system for our image * now we have cartesian coordinate * * cartesian coordinate to polar coordinate * تبدیل فضای کارتزین به پولار در خیلی از برنامه های پردازش تصویر کارایی دارد. برای پیدا کردن ترشلد ها هم می توان استفاده کرد * in MATLAB we can use ":"for example MatrixA(:) which means all entity of the matrix no mater how many dimensions we have but if we want to implemented in Python we can use numpy.flatten(). * in the MATLAB the round is different from python. if you want same result you need implement the rand function by yourself. * imge_mask=np.ones_like(image_source)*255 * imge_mask=imge_mask.astype(np.uint8) * imge_mask=imge_mask.flatten() ??? .ravel() * .asarray * np.logical_and( 1, 2) * indexes=[index for index in range(len(array1)) if array1[index] == True] * cv2.bitwise_not(yyy) * "olive" editor remove silence ![](https://lh4.googleusercontent.com/xQhg0wTRjVMTgd80Wp5yUfIUhCLsnv0yrnarGLh2oFljJOK6lbAUJ2noIioZidtG_3NGmbQpKcdEmJnKNLD0ZwWzcCsLXrV4BNdtVKhQYpCVGGBWkY9gE3j8qcUo_Dk0_w=w1280) Questions: How to train model to add new classes? How to add a new class to an existing classifier in deep learning? Adding new Class to One Shot Learning trained model Is it possible to train a neural network as new classes are given? Merging all several models that detection system for all these tasks. Answer 1: There are several ways to add new classes to the trained model, which require just training for the new classes. * Incremental training ([GitHub](https://github.com/khurramjaved96/incremental-learning)) * continuously learn a stream of data ([GitHub](https://github.com/creme-ml/creme)) * online machine learning ([GitHub](https://github.com/GMvandeVen/continual-learning)) * Transfer Learning Twice * Continual learning approaches (Regularization, Expansion, Rehearsal) ([GitHub](https://github.com/facebookresearch/Adversarial-Continual-Learning)) Answer 2: Online learning is a term used to refer to a model which takes a continual or sequential stream of input data while training, in contrast to offline learning (also called batch learning), where the model is pre-trained on a static predefined dataset. Continual learning (also called incremental, continuous, lifelong learning) refers to a branch of ML working in an online learning context where models are designed to learn new tasks while maintaining performance on historic tasks. It can be applied to multiple problem paradigms (including Class- incremental learning, where each new task presents new class labels for an ever expanding super-classification problem). Do I need to train my whole model again on all four classes or is there any way I can just train my model on new class? Naively re-training the model on the updated dataset is indeed a solution. Continual learning seeks to address contexts where access to historic data (i.e. the original 3 classes) is not possible, or when retraining on an increasingly large dataset is impractical (for efficiency, space, privacy etc concerns). Multiple such models using different underlying architectures have been proposed, but almost all examples exclusively deal with image classification problems. Answer 3: You could use transfer learning (i.e. use a pre-trained model, then change its last layer to accommodate the new classes, and re-train this slightly modified model, maybe with a lower learning rate) to achieve that, but transfer learning does not necessarily attempt to retain any of the previously acquired information (especially if you don't use very small learning rates, you keep on training and you do not freeze the weights of the convolutional layers), but only to speed up training or when your new dataset is not big enough, by starting from a model that has already learned general features that are supposedly similar to the features needed for your specific task. There is also the related domain adaptation problem. There are more suitable approaches to perform incremental class learning (which is what you are asking for!), which directly address the [catastrophic forgetting problem](https://ai.stackexchange.com/a/13293/2444). For instance, you can take a look at this paper [Class-incremental Learning via Deep Model Consolidation](https://arxiv.org/pdf/1903.07864.pdf), which proposes the Deep Model Consolidation (DMC) approach. There are other continual/incremental learning approaches, many of them are described [here](https://ai.stackexchange.com/a/24529/2444) or in more detail [here](https://reader.elsevier.com/reader/sd/pii/S0893608019300231). Answer 4: by using Continual learning approaches to trained without losing the original classes. It has 3 categories: Regularization Expansion Rehearsal Answer 5: if you access to the dataset then you can download it and add all you new classes when you have " 'N' COCO Classes + 'M' New classes " after that you can fine tune model based on new dataset. you do not need all of the dataset just same number of image for all class enough. [https://learnopencv.com/stanford-mrnet-challenge-classifying-knee- mris/](https://learnopencv.com/stanford-mrnet-challenge-classifying-knee- mris/) Before start your machine learning project ask these questions and preparation: What is your inference hardware? specify the use case. specify model interface. how would we monitor performance after deployment? how can we approximate post-deployment monitoring before deployment? build a model and iteratively improve it. How to deploy the model at the end? monitor performance after deployment. what is your metric? How do you split your data (training and validation)? ### Preparation ML Project Workflow * [What is your hardware ?](/topics-and-projects/hardware) * specify the use case * specify model interface * how would we monitor performance after deployment? * how can we approximate post-deployment monitoring before deployment? * build a model and iteratively improve it * deploy the model * monitor performance * what is your are metric? * How do you split your data? ### Before Training deep learning model * using large model to train because * it is faster to train with lower overfit and faster converge due to best training * it is easier and higher compress in the final stage * model compression and acceleration: reducing parameters without significantly decreasing the model performance * Data: How to have good data for training deep learning models; How to Build and Enhance A Good Data Set For Your Deep Learning Project: using same config and data for training and inference, removing redundant (delete data which you don't need), get more data, Handle missing data, using data augmentation techniques or GAN to generate more data, re-scale/balance data, Transform your data (Change data types), Feature selection based on data-set and use case * * The data you don't need: removing redundant samples * get more data * Invent more data * data augmentation * Re-scale data * balance datasets * Transform your data * Feature selection based on dataset and use case * ML-Augmented Video Object Tracking: By applying and evaluating multiple algorithmic models, enhanced ability to scale object tracking in high-density video compositions. ### Training deep learning model * automated hyper-parameters * Using Hyperparameter tuning / Hyperparameter optimization tools * AutoML * genetic algorithm * population based training * bayesian optimization * You need to set some parameters and config for training * * Diagnostics * Weight Initialization * Learning rate * Activation function * Network Topology * Batches and Epochs * Regularization * Optimization and Loss * Early Stopping ### Continuous delivery * evolve with latest detection models * more data (no labels) * semi-supervised learning: big self-supervised models are strong semi-supervised learners ### After Training deep learning model * Parameter pruning * model pruning: reducing redundant parameters which are not sensitive to the performance. * aim: remove all connections with absolute weights below a threshold * Quantization * compresses by reducing the number of bits used to represent the weights * quantization effectively constraints the number of different weights we can use inside our kernels * per-channel quantization for weights, which improves performance by model compression and latency reduction. * Low rank matrix factorization (LRMF) * there exists latent structures in the data, by uncovering which we can obtain a compressed representation of the data * LRMF factorizes the original matrix into lower rank matrices while preserving latent structures and addressing the issue of sparseness * Compact convolutional filters (Video/CNN) * designing special structural convolutional filters to save parameters * replace over parametric filters with compact filters to achieve overall speedup while maintaining comparable accuracy * Knowledge distillation * training a compact neural network with distilled knowledge of a large model * distillation (knowledge transfer) from an ensemble of big networks into a much smaller network which learns directly from the cumbersome model's outputs, that is lighter to deploy * Binarized Neural Networks (BNNs) * Apache TVM (incubating) is a compiler stack for deep learning systems * Neural Networks Compression Framework (NNCF) ### Deep learning model in production * security: controls access to model(s) through secure packaging and execution * Test * auto training * using parallel processing and library such as GStreamer # Technology Docker AWS Flask Django # My Keynote (February 2021) 1. introduction 2. Machine Learning/ Deep Learning Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed 3. supervised Machine Learning 1. Deep Convolutional Neural Networks (DCNN) Architecture 2. Visualizing and Understanding Convolutional Networks 3. Object Detection by Deep Learning 4. [Video Tracking](/topics-and-projects/video-tracking) 5. Style Transfer 4. semi-supervised Machine Learning/ Deep Reinforcement learning (DRL) 1. Google 2. [Deep Reinforcement learning (DRL)](/topics-and-projects/drl) 5. unsupervised Machine Learning 1. Auto Encoder 6. Generative Adversarial Networks (GANs) 7. Tools 8. Pre trained model 9. Effect of Augmented Datasets to Train DCNNs 10. Training for more classes 11. Optimization 12. [Hardware](/topics-and-projects/hardware) 13. Production setup 14. post development 15. business , Gartner, Hype Cycle for emerging technologies, 2025 ### Advanced and practical 1. Inside CNN 1. Deep Convolutional Neural Networks Architecture 2. Convolution 3. Convolution Layer 4. Conv/FC Filters 5. Activation Functions 6. Layer Activations 7. Pooling Layer 8. Dropout ; L2 pooling 9. Why 1. Max-pooling is useful 2. How to see inside each layer and find important features * Visualizing and Understanding Convolutional Networks * [https://tensorspace.org/](https://tensorspace.org/) * [https://www.youtube.com/watch?v=AgkfIQ4IGaM](https://www.youtube.com/watch?v=AgkfIQ4IGaM) 2. Hands on python for deep learning 3. Fundamental deep learning 4. Installation: TensorFlow, PyTorch 5. [Using PC+eGPU for training video tracking](/topics-and-projects/source-code/compile) Summary of the summit * AI Hardware Europe Summit (July 2020) * [The Edge AI & Brain Inspired Computing (November 2020) ](/workshops-and-events/edge-ai-summit) * Apache TVM And Deep Learning Compilation Conference (December 2020) * [RISC-V Summit (December 2020) ](/workshops-and-events/risc-v) [https://www.inspectar.com/demo](https://www.inspectar.com/demo) for rasp # Face * Effective and precise face detection based on color and depth data * [https://www.sciencedirect.com/science/article/pii/S221083271400009X](https://www.sciencedirect.com/science/article/pii/S221083271400009X) * containing or not containing a face * Eigenface, Fisherface, waveletface, PCA (Principal Component Analysis), LDA (Linear Dis-criminant Analysis), Haar wavelet transform, and so on. * Viola–Jones detector * illumination changes and occlusion * depthinformation is used to filter the regions of the image where a candidate face regionis found by the Viola–Jones (VJ) detector * \- the first filtering rule is defined on the color of the region; since some false positiveshave colors not compatible with the face (e.g. shadows on jeans) a skin detector isapplied to remove the candidate face regions that do not contain skin pixels; * \- the second filtering rule is defined on the size of the face: using the depth mapit is quite easy to calculate the size of the candidate face region, which is use-ful to discard smallest and largest faces from the final result set; * \- the third filtering rule is defined on the depth map to discard flat objects (e.g.candidate faces found in a wall) or uneven objects (e.g. candidate face foundin the leaves of a tree). Combining color and depth data the candidate faceregion can be extracted from the background and measures of depth and reg-ularity are used for filtering out false positives. * The size criteria simply remove the candidate faces not included in a fixed rangesize ([12.5,30] cm). The size of a candidate face region is extracted from the depthmap according to the following approach. * image below * Gaussian mixture 3D morphable face model * [https://www.sciencedirect.com/science/article/pii/S0031320317303527](https://www.sciencedirect.com/science/article/pii/S0031320317303527) * * * Face Synthesis for Eyeglass-Robust Face Recognition * [https://paperswithcode.com/paper/face-synthesis-for-eyeglass-robust-face](https://paperswithcode.com/paper/face-synthesis-for-eyeglass-robust-face) * GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data * [https://paperswithcode.com/paper/genegan-learning-object-transfiguration-and](https://paperswithcode.com/paper/genegan-learning-object-transfiguration-and) * FacePoseNet: Making a Case for Landmark-Free Face Alignment * [https://paperswithcode.com/paper/faceposenet-making-a-case-for-landmark-free](https://paperswithcode.com/paper/faceposenet-making-a-case-for-landmark-free) * Learning to Regress 3D Face Shape and Expression from an Image without 3D Supervision * [https://paperswithcode.com/paper/learning-to-regress-3d-face-shape-and](https://paperswithcode.com/paper/learning-to-regress-3d-face-shape-and) * Unsupervised Eyeglasses Removal in the Wild * [https://paperswithcode.com/paper/unsupervised-eyeglasses-removal-in-the-wild](https://paperswithcode.com/paper/unsupervised-eyeglasses-removal-in-the-wild) * How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks) * [https://arxiv.org/pdf/1703.07332v3.pdf](https://arxiv.org/pdf/1703.07332v3.pdf) * (a) we construct, for the first time, a very strong baseline by combining a state-of-the-art architecture for landmark localization with a state-of-the-art residual block, train it on a very large yet synthetically expanded 2D facial landmark dataset and fi- nally evaluate it on all other 2D facial landmark datasets. * (b) We create a guided by 2D landmarks network which con- verts 2D landmark annotations to 3D and unifies all exist- ing datasets, leading to the creation of LS3D-W, the largest and most challenging 3D facial landmark dataset to date (~230,000 images). * (c) Following that, we train a neural network for 3D face alignment and evaluate it on the newly introduced LS3D-W. * (d) We further look into the effect of all “traditional” factors affecting face alignment performance like large pose, initialization and resolution, and introduce a “new” one, namely the size of the network. * (e) We show that both 2D and 3D face alignment networks achieve per- formance of remarkable accuracy which is probably close to saturating the datasets used. * Training and testing code as well as the dataset can be downloaded from https: //[www.adrianbulat.com/face-alignment/](http://www.adrianbulat.com/face-alignment/) ![](https://lh3.googleusercontent.com/2SmFaXBTdS9THqCBo8sLkr9sJDjO1PQiQNAxF14YpFCE3sAJ5rtNCNamDWRCZYEMukbD1q8BUE4VA9dFvx0Lb3V-4aymzq1w64fpZybAB3mn3Da4wvmSAmaL746MvgcG=w1280) 19.Sep.2021 [Medium](https://medium.com/p/626019137fa9/edit) [https://fi.co/madlibs](https://fi.co/madlibs) [https://orcid.org/0000-0001-8382-1389](https://orcid.org/0000-0001-8382-1389) Dreyer's English (learn write English) #book story Greek Mythology Explained: A Deeper Look at Classical Greek Lore and Myth **Papers:** CALTag: High Precision Fiducial Markers for Camera Diatom Autofocusing in Brightfield Microscopy: a Comparative Study :implementation variation of the laplacian Analysis of focus measure operators in shape-from-focus: why laplacian? Blure detection? Iqaf? Optical flow modeling and computation: A survey Toward general type 2 fuzzy logic systems based on zSlices \-------------------------------------------------------------------- Lost in space The OA Film:[ https://en.wikipedia.org/wiki/Shark_Tank](https://en.wikipedia.org/wiki/Shark_Tank) Movie Serial billons monk serial movies Python async Highly decoupled microservice Edex RIS-V , Self-car RISC-V Magazine Road map Game: over/under [https://www.sporcle.com/games/Hejman/underwhelmed](https://www.sporcle.com/games/Hejman/underwhelmed) \-------------------------------------------------------------------- \-------------------------------------------------------------------- GDPR in IoT The EU General Data Protection Regulation (GDPR) and Face Images in IoT The GDPR (General Data Protection Regulation), taking effect in May 2018, introduces strict requirements for personal data protection and the privacy rights of individuals. The EU regulations will set a new global standard for privacy rights and change the way organizations worldwide store and process personal data. The GDPR brings the importance of preserving the privacy of personal information to the forefront, yet the importance of face images within this context is often overlooked. The purpose of this paper is to introduce a solution that helps companies protect face images in IoT devices which record or process image by camera, to strengthen compliance with the GDPR. Our Face is our Identity Our face is the most fundamental and highly visible element of our identity. People recognize us when they see our face or a photo of our face. Recent years have seen exponential increase in the use, storage and dissemination of face images in both private and public sectors - in social networks, corporate databases, IoT, smart-city deployments, digital media, government applications, and nearly every organization’s databases. \--------------------- $(aws-okta env stage) aws s3 cp s3://dataset/archive.tar.gz /Users/a.zip aws s3 ls images | tail -n 100 aws s3 cp staging-images/test.jpg /Users/test.jpg \--------------------- screen -rD k get pods Docker RUN chmod +x /tmp/run.sh Can run docker in terminal and run code line by line docker run -it --rm debian:stable-slim bash apt-get update apt-get installl -y \-------------------------------- brew install awscli aws-okta kubectx kubernetes-cli tfenv touch ~/.aws/config \-------------------------------------------------------------------- docker image rm TETSTDFSAFDSADF docker image ls docker system prune docker run -p 5000:5000 nameDocker:latest docker build . -t nameDocker:latest docker container stop number-docker-name docker container ls * docker pull quay.io/test:v0.0.1 * docker run --rm -p 5000:5000 -it quay.io/test:v0.0.1 * curl --header "Content-Type: application/json" \--request POST --data '[{"fixed":7.4, "a":0, "b":0.56, "c":9.4}]'[ http://127.0.0.1:5000/predict](https://meet.google.com/linkredirect?authuser=0&dest=http%3A%2F%2F127.0.0.1%3A5000%2Fpredict) * docker run --rm -v /home/.aws/credentials:/root/.aws/credentials -it quay.io/test /bin/sh aws s3 ls --profile=test \-------------------------------- Cloud software engineer and consultant focusing on building highly available, scalable and fully automated infrastructure environments on top of Amazon Web Services and Microsoft Azure clouds. My goal is always to make my customers happy in the cloud. \---------------- Search google for 3d = tiger - iPhone show AR/VR \--------------- brew install youtube-dl \---------------------------- List: Collection bucket : 1 for week 2 for month 3 for future \-------------------------------------------------------------------- **• Per frame operation** – Detection – Classification – Segmentation – Feature extraction – Recognition **• Across frames ** – Tracking – Counting **• High level** – Intention – Relations – Analyzing ============================= Deep compression Pruning deep learning Hash table neural network Dl compression Deep compression =================================== Mini PCI-e slot * What have I learned so far: * Problem-based learning * real life scenarios * index card (answer , idea) * Think-Pair-Share * Leverage flip charts * Summarizing \-------------------------------------------------------------------- Self \\\ Advancing Self-Supervised and Semi-Supervised Learning with SimCLR \cite{Chen2020} %https://github.com/google-research/simclr first pretraining on a large unlabeled dataset and then fine-tuning on a smaller labeled dataset pretraining on large unlabeled image datasets, as demonstrated by Exemplar- CNN, Instance Discrimination, CPC, AMDIM, CMC, MoCo and others. “A Simple Framework for Contrastive Learning of Visual Representations”, 85.8\% top-5 accuracy using 1\% of labeled images on the ImageNet dataset contrastive learning algorithms linear evaluation protocol (Zhang et al., 2016; Oord et al.,2018; Bachman et al., 2019; Kolesnikov et al., 2019) unsupervised learning benefits more from bigger models than its supervised counterpart. \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- \-------------------------------------------------------------------- Some of optimization algorithms ======================== Swarm Algorithm =============== 1\. Ant Colony Optimization (ACO) was inspired by the research on the behavior of ant colonies 2\. Firefly Algorithm based on insects called fireflies 3\. Marriage in Honey Bees Optimization Algorithm (MBO algorithm) is inspired by the process of reproduction of Honey Bee 4\. Artificial Bee Colony Algorithm (ABC) is based on the recollection of the Honey Bees 5\. Wasp Swarm Algorithm was inspired on the Parasitic wasps 6\. Bee Collecting Pollen Algorithm (BCPA) 7\. Termite Algorithm 8\. Mosquito swarms Algorithm (MSA) 9\. zooplankton swarms Algorithm (ZSA) 10\. Bumblebees Swarms Algorithm (BSA) 11\. Fish Swarm Algorithm (FSA) 12\. Bacteria Foraging Algorithm (BFA) 13\. Particle Swarm Optimization (PSO) 14\. Cuckoo Search 15\. Bat Algorithm (BA) 16\. Accelerated PSO 17\. Bee System 18\. Beehive Algorithm 19\. Cat Swarm 20\. Consultant-guided search 21\. Eagle Strategy 22\. Fast Backterial swarming algorithm 23\. Good lattice swarm optimization 24\. Glowworm swarm optimization 25\. Hierarchical swarm model 26\. Krill Herd 27\. Monkey Search 28\. Virtual ant algorithm 29\. Virtual bees 30\. Weighted Swarm Algorithm 31\. Wisdom of Artificial Crowd algorithm 32\. Prey-predator algorithm 33\. Memetic algorithm 34\. Lion Optimization Algorithm 35\. Chicken Swarm Optimization 36\. Ant Lion Optimizer 37\. Compact Particle Swarm Optimization 38\. Fruit Fly Optimization Algorithm 39\. marine propeller optimization algorithm 40\. The Whale Optimization Algorithm 41\. virus colony search algorithm 42\. Slime mould optimization algorithm Ecology Inspired Algorithm ========================== 1\. Biogeography-based Optimization 2\. Invasive Weed Optimization 3\. Symbiosis-Inspired Optimization - PS2O 4\. Atmosphere Clouds Model 5\. Brain Storm Optimization 6\. Dolphin echolocation 7\. Japanese Tree Frog Calling algorithm 8\. Eco-inspired evolutionary algorithm 9\. Egyptian Vulture 10\. Fish School search 11\. Flower Pollination algorithm 12\. Gene Expression 13\. Great Salmon Run 14\. Group Search Optimizer 15\. Human Inspired Algorithm 16\. Roach Infestation algorithm 17\. Queen-bee algorithm 18\. Shuffled frog leaping algorithm 19\. Forest Optimization Algorithm 20\. coral reefs optimization algorithm 21\. cultural evolution algorithm 22\. Grey Wolf Optimizer 23\. probabilistic pso 24\. omicron aco algorithm 25\. shark smell optimization 26\. social spider algorithm 27\. sosial insects behavior algorithm 28\. sperm whale algorithm Evolutionary Optimization ========================= 1\. Genetic Algorithm 2\. Genetic Programming 3\. Evolutionary Strategies 4\. Differential Evolution 5\. Paddy Field Algorithm 6\. Queen-bee Evolution 7\. Quantum Inspired Social Evolution Physic and Chemistry inspired algorithm ======================================= 1\. Big bang-Big Crunch 2\. Block hole algorithm 3\. Central force optimization 4\. Charged System search 5\. Electro-magnetism optimization 6\. Galaxy based search algorithm 7\. Gravitational search 8\. Harmony search algorithm 9\. Intelligent water drop algorithm 10\. River formation algorithm 11\. Self-propelled dynamics 12\. Simulated Annealing 13\. Stachastic diffusion search 14\. Spiral optimization 15\. Water Cycle algorithm 16\. Artificial Physics optimization 17\. Binary Gravitational search algorithm 18\. Continous quantum ant colony optimization 19\. Extended artificial physics optimization 20\. Extended Central force optimization 21\. Electromagnetism-like heuristic 22\. Gravitational Interaction optimization 23\. Hysteristetic Optimization algorithm 24\. Hybrid quantum-inspired GA 25\. Immune gravitational inspired algorithm 26\. Improved quantum evolutinary algorithm 27\. Linear programming 28\. Quantum-inspired bacterial swarming 29\. Quantum-inspired evolutionary algorithm 30\. Quantum-inspired genetic algorithm 31\. Quantum-behaved PSO 32\. Unified big bang-chaotic big crunch 33\. Vector model of artificial physics 34\. Versatile quantum-inspired evolutionary algorithm 35\. Space Gravitational Algorithm 36\. Ion Motion Algorithm 37\. Light Ray Optimization Algorithm 38\. Ray Optimization 39\. Photosynthetic Algorithms 40\. floorplanning algorithm 41\. Gases Brownian Motion Optimization 42\. gradient-type optimization 43\. mean-variance optimization 44\. Mine blast algorithm 45\. moth flame optimization 46\. multi battalion search algorithm 47\. music inspired optimization 48\. no free lunch theorems algorithm 49\. Optics inspired optimization 50\. runner-root algorithm 51\. sine cosine algorithm 52\. pitch tracking algorithm 53\. Stochastic Fractal Search algorithm 54\. stroke volume optimization 55\. Stud krill herd algorithm 56\. The Great Deluge Algorithm 57\. Water Evaporation Optimization 58\. water wave optimization algorithm 59\. Island model algorithm 60\. Steady State model Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. 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Full Setup Guide with Jetson Nano [https://www.pirahansiah.com/topics-and-projects/source- code/iot](https://www.pirahansiah.com/topics-and-projects/source-code/iot) #iPad #VSCode #IoT #ssh #Jetson #CUDA #C++ www.pirahansiah.com March 2023 https://www.pirahansiah.com/topics-and-projects/source-code/iot VSCode on iPad Pro - Full Setup Guide with Jetson Nano // Install VSCode server sudo apt-get install curl curl -fsSL https://deb.nodesource.com/setup_16.x | sudo -E bash - sudo apt-get update && sudo apt-get install yarn Y sudo apt-get install -y nodejs sudo npm --force install -g yarn yarn global add code-server ~/.yarn/bin/code-server // config VSCode server to use by other device like iPad, ... in local network ~/.yarn/bin/code-server gedit ~/.config/code-server/config.yaml bind-addr: 0.0.0.0:8080 auth: none password: pirahansiah cert: false sudo gedit /etc/systemd/system/code-server.service [Unit] Description=code-server After=network.target [Service] User=pi Group=pi WorkingDirectory=/home/pi Environment="PATH=/usr/bin" ExecStart=/home/pirahansiah/.yarn/bin/code-server [Install] WantedBy=multi-user.target sudo systemctl stop code-server ~/.yarn/bin/code-server // just run this command after restart device ~/.yarn/bin/code-server http://pirahansiah.local:8080/ => name of your device // CUDA config for VSCode // https://www.pirahansiah.com/topics-and-projects/source-code/iot open foldr in VSCode code-server . // web python testing sudo apt-get install chromium-chromedriver from selenium import webdriver from selenium.webdriver.common.by import By from selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support import expected_conditions as EC import time # Launch the browser and open the website driver = webdriver.Chrome() driver.get("https://www.pirahansiah.com/") # Save a screenshot of the page driver.save_screenshot("pirahansiah.png") # Close the browser driver.quit() sudo apt install virtualenv virtualenv --python=python3.8 pirahansiah source pirahansiah/bin/activate sudo apt-get install python3.8 sudo rm /usr/bin/python3 sudo ln -s /usr/bin/python3.8 /usr/bin/python3 sudo apt-get --reinstall install python3-minimal tasks.json { "version": "2.0.0", "tasks": [ { "label": "build", "type": "shell", "command": "/usr/local/cuda/bin/nvcc", "args": [ "-gencode", "arch=compute_53,code=sm_53", "-I${workspaceFolder}", "-o", "${workspaceFolder}/${fileBasenameNoExtension}", "${file}", "-g" ], "group": { "kind": "build", "isDefault": true }, "presentation": { "echo": true, "reveal": "always", "focus": false, "panel": "shared" }, "problemMatcher": { "owner": "cpp", "fileLocation": ["absolute"], "pattern": { "regexp": "^(.*):(\\\d+):(\\\d+):\\\s+(warning|error):\\\s+(.*)$", "file": 1, "line": 2, "column": 3, "severity": 4, "message": 5 } } } ] } launch.json { "version": "0.2.0", "configurations": [ { "name": "(gdb) Launch", "type": "node", "request": "launch", "cwd": "${workspaceFolder}", "program": "/home/farshid/code/vscode-test1/testCUDA", "args": [], "stopOnEntry": false, "runtimeExecutable": "/usr/bin/gdb", "runtimeArgs": [ "\--interpreter=mi2", "-ex", "set confirm off", "-ex", "tui enable", "-ex", "set startup-with-shell off", "-ex", "set substitute-path /usr/share/gdb /usr/local/cuda/bin", "-ex", "file ${workspaceFolder}/${fileBasenameNoExtension}", "-ex", "run", "\--quiet" ], "env": {}, "console": "integratedTerminal", "preLaunchTask": "build" } ] } c_cpp_properties.json { "configurations": [ { "name": "Jetson Nano - Debug", "includePath": [ "${workspaceFolder}/**" ], "defines": [], "compilerPath": "/usr/local/cuda/bin/nvcc", "cStandard": "c11", "cppStandard": "c++17", "intelliSenseMode": "gcc-x64", "compilerArgs": [ "-g", "-O0", "\--compiler-options", "-Wall", "-Wextra", "-Wpedantic", "-Wno-deprecated-gpu-targets" ] } ], "version": 4 } [https://www.youtube.com/watch?v=11YfaGi0Fpk](https://www.youtube.com/watch?v=11YfaGi0Fpk) [https://techcraft.co/videos/2022/2/vscode-on-ipad-pro-full-setup-guide-with- raspberry-pi/](https://techcraft.co/videos/2022/2/vscode-on-ipad-pro-full- setup-guide-with-raspberry-pi/) 00:00 VSCode on iPad Pro 00:44 Installing NodeJS 01:30 Install code-server 02:32 Default configuration 04:08 Connecting from Blink 05:53 Full screen Safari 07:10 Re-enable password authentication 07:34 Auto start code-server 09:38 Installing extensions 12:10 Secure mode [https://canyouseeme.org](https://canyouseeme.org) [https://www.youtube.com/watch?v=2tIUts0fyFk](https://www.youtube.com/watch?v=2tIUts0fyFk) [https://whatismyipaddress.com/i](https://whatismyipaddress.com/i) [https://www.youtube.com/watch?v=ZKfnGqMrnug](https://www.youtube.com/watch?v=ZKfnGqMrnug) [https://github.com/facebookresearch/llama](https://github.com/facebookresearch/llama) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil) check S3 bucket in AWS for image and video files and versioning Check Docker load balancer, memory usage, ... GPU Video Tracking on Mac 1. create conda based on python 3.6 * conda create env_full -y \--name farshid python=3.6 * conda activate farshid 2. install OpenVino from Intel for converting deep learning model based on intel chips * conda install -y openvino-ie4py -c intel 3. install video library * conda install -y -c conda-forge ffmpeg 4. install pytorch and torchvision * conda install -y pytorch torchvision -c pytorch 5. conda install -y -c conda-forge matplotlib 6. conda install -y pandas scikit-learn plotly 7. conda install -y -c conda-forge opencv seaborn 8. conda install -y -c conda-forge tensorflow pip install torch torchvision torchaudio pip install matplotlib pandas scikit-learn plotly opencv seaborn tensorflow # Test for 2021 **3D Multi-Object Tracking: A Baseline and New Evaluation Metrics (IROS 2020, ECCVW 2020)[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv)**[**https://github.com/xinshuoweng/AB3DMOT**](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv) **** Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD- ts8sZsK3X32Hb2FD)[https://github.com/elliottwu/unsup3d](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD- ts8sZsK3X32Hb2FD) This repository contains the public release of the Python implementation of our Aggregate View Object Detection (AVOD) network for 3D object detection.[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl)[https://github.com/kujason/avod](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl) 𝚙𝚒𝚙 𝚒𝚗𝚜𝚝𝚊𝚕𝚕 𝚔3𝚍 # Run on Ubuntu PC + eGPU apt search nvidia-driver apt-cache search nvidia-driver sudo apt update sudo apt upgrade sudo apt install nvidia-driver-455 sudo reboot nvidia-smi Download cuDNN v7.6.5 (November 5th, 2019), for CUDA 10.0 * tar -xzvf cudnn-10.0-linux-x64-v7.6.5.32.tgz * sudo cp cuda/include/cudnn*.h /usr/local/cuda/include * sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64 * sudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn* * sudo dpkg -i libcudnn7_7.6.5.32-1+cuda10.0_amd64.deb * sudo dpkg -i libcudnn7-dev_7.6.5.32-1+cuda10.0_amd64.deb * sudo dpkg -i libcudnn7-doc_7.6.5.32-1+cuda10.0_amd64.deb * sudo apt-get install \ apt-transport-https \ ca-certificates \ curl \ gnupg-agent \ software-properties-common curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add - sudo apt-key fingerprint 0EBFCD88 sudo add-apt-repository \ "deb [arch=amd64] https://download.docker.com/linux/ubuntu \ $(lsb_release -cs) \ stable" sudo apt-get update sudo apt-get install docker-ce docker-ce-cli containerd.io Make sure you have installed the NVIDIA driver and Docker engine for your Linux distribution Note that you do not need to install the CUDA Toolkit on the host system, but the NVIDIA driver needs to be installed distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \ && curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - \ && curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia- docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list curl -s -L https://nvidia.github.io/nvidia-container- runtime/experimental/$distribution/nvidia-container-runtime.list | sudo tee /etc/apt/sources.list.d/nvidia-container-runtime.list sudo apt-get install -y nvidia-docker2 sudo systemctl restart docker sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi [Installing on CentOS 8 (AWS)](https://www.google.com/url?q=https%3A%2F%2Fdocs.nvidia.com%2Fdatacenter%2Fcloud- native%2Fcontainer-toolkit%2Finstall- guide.html%23docker&sa=D&sntz=1&usg=AOvVaw38cOFoMlAZS6R9Z4bKrU5Q) pip install cython; pip install -U 'git+[https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fcocodataset%2Fcocoapi.git%23subdirectory%3DPythonAPI&sa=D&sntz=1&usg=AOvVaw2XHIv2zk5mbXQ5VpooNoT4)' CenterTrack_ROOT=/home/farshid/code/CenterTrack git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT cd CenterTrack_ROOT pip install -r requirements.txt cd $CenterTrack_ROOT/src/lib/model/networks/ git clone https://github.com/CharlesShang/DCNv2/ cd DCNv2 ./make.sh [https://github.com/xingyizhou/CenterTrack/blob/master/readme/MODEL_ZOO.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb) # cvat sudo groupadd docker sudo usermod -aG docker $USER sudo apt-get --no-install-recommends install -y python3-pip python3-setuptools sudo python3 -m pip install setuptools docker-compose sudo apt-get --no-install-recommends install -y git git clone https://github.com/opencv/cvat cd cvat sudo docker-compose build sudo docker-compose up -d sudo docker exec -it cvat bash -ic 'python3 ~/manage.py createsuperuser' [http://localhost:8080/](http://www.google.com/url?q=http%3A%2F%2Flocalhost%3A8080%2F&sa=D&sntz=1&usg=AOvVaw3oeouV3qFXFcGyLGuDDpKa) # Towards-Realtime-MOT * conda activate cuda100 * pip install motmetrics * pip install cython_bbox * conda install -c conda-forge ffmpeg * [https://openaccess.thecvf.com/content_CVPR_2020/papers/Xu_How_to_Train_Your_Deep_Multi- Object_Tracker_CVPR_2020_paper.pdf](https://www.google.com/url?q=https%3A%2F%2Fopenaccess.thecvf.com%2Fcontent_CVPR_2020%2Fpapers%2FXu_How_to_Train_Your_Deep_Multi- Object_Tracker_CVPR_2020_paper.pdf&sa=D&sntz=1&usg=AOvVaw0GwXtXPI4_xmM- qU7ZrLmr) [https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki) git clone https://gitlab.inria.fr/yixu/deepmot.git sudo apt-get install libpng-dev sudo apt install libfreetype6-dev pip install -r requirements.txt ImportError: torch.utils.ffi is deprecated. Please use cpp extensions instead. conda create -y --name cuda92 python=3.6 conda activate cuda92 source activate cuda92 conda install pytorch==0.4.1 torchvision==0.2.0 cudatoolkit=9.2 -c pytorch conda install -c conda-forge ffmpeg * conda create -n cuda100 * conda activate cuda100 conda install pytorch torchvision cudatoolkit=10.0 -c pytorch # [https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki) AWS # AWS ## [Towards-Realtime-MOT ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fifzhang%2FFairMOT&sa=D&sntz=1&usg=AOvVaw3KRCUNb1LrlLLYIG2wTulN) * conda create -n FairMOT * conda activate FairMOT * conda install pytorch==1.2.0 torchvision==0.4.0 cudatoolkit=10.0 -c pytorch * cd ${FAIRMOT_ROOT} * pip install -r requirements.txt * conda install -c conda-forge ffmpeg * [MOTS: Multi-Object Tracking and Segmentation](http://www.google.com/url?q=http%3A%2F%2Farxiv.org%2Fabs%2F1902.03604&sa=D&sntz=1&usg=AOvVaw08PSI8pfXxFnpxsLjY3pix) * Paper: [https://arxiv.org/pdf/1902.03604](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fpdf%2F1902.03604&sa=D&sntz=1&usg=AOvVaw2JyGSgtPJlejKIa5-k3N3t) * Dataset: [https://motchallenge.net/data/MOTS/](https://www.google.com/url?q=https%3A%2F%2Fmotchallenge.net%2Fdata%2FMOTS%2F&sa=D&sntz=1&usg=AOvVaw0YWYn2oQZiewTAxCzJH_Fo) * This benchmark extends the traditional Multi-Object Tracking benchmark to a new benchmark defined on a pixel-level with precise segmentation masks. We annotated 8 challenging video sequences (4 training, 4 test) in unconstrained environments filmed with both static and moving cameras. Tracking, segmentation and evaluation are done in image coordinates. All sequences have been annotated with high accuracy on a pixel level, strictly following a well-defined protocol. [https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD) Setup: * cd /media/farshid/exfat128/code * Conda * conda create --name CenterTrack36cuda10 python=3.6 * conda activate CenterTrack * conda install pytorch torchvision -c pytorch * pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI' * CenterTrack_ROOT=/media/farshid/exfat128/code/CenterTrack * git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT * pip install -r requirements.txt * cd $CenterTrack_ROOT/src/lib/model/networks/ * git clone https://github.com/CharlesShang/DCNv2/ * cd DCNv2 * ./make.sh * Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb). * # AWS (11 December 2020) [https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD) * Conda * conda create --name CenterTrack36cuda10 python=3.6 * conda activate CenterTrack36cuda10 * conda install pytorch torchvision cudatoolkit=10.0 -c pytorch * conda install -c conda-forge ffmpeg * pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI' * CenterTrack_ROOT=/ * git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT * pip install -r requirements.txt * cd $CenterTrack_ROOT/src/lib/model/networks/ * git clone https://github.com/CharlesShang/DCNv2/ * cd DCNv2 * ./make.sh * Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb). * ### Training * cd $CenterTrack_ROOT/src/tools/ * bash get_mot_17.sh * Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. 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I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil) check S3 bucket in AWS for image and video files and versioning Check Docker load balancer, memory usage, ... GPU Video Tracking on Mac 1. create conda based on python 3.6 * conda create env_full -y \--name farshid python=3.6 * conda activate farshid 2. install OpenVino from Intel for converting deep learning model based on intel chips * conda install -y openvino-ie4py -c intel 3. install video library * conda install -y -c conda-forge ffmpeg 4. install pytorch and torchvision * conda install -y pytorch torchvision -c pytorch 5. conda install -y -c conda-forge matplotlib 6. conda install -y pandas scikit-learn plotly 7. conda install -y -c conda-forge opencv seaborn 8. conda install -y -c conda-forge tensorflow pip install torch torchvision torchaudio pip install matplotlib pandas scikit-learn plotly opencv seaborn tensorflow # Test for 2021 **3D Multi-Object Tracking: A Baseline and New Evaluation Metrics (IROS 2020, ECCVW 2020)[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv)**[**https://github.com/xinshuoweng/AB3DMOT**](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv) **** Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD- ts8sZsK3X32Hb2FD)[https://github.com/elliottwu/unsup3d](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD- ts8sZsK3X32Hb2FD) This repository contains the public release of the Python implementation of our Aggregate View Object Detection (AVOD) network for 3D object detection.[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl)[https://github.com/kujason/avod](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl) 𝚙𝚒𝚙 𝚒𝚗𝚜𝚝𝚊𝚕𝚕 𝚔3𝚍 # Run on Ubuntu PC + eGPU apt search nvidia-driver apt-cache search nvidia-driver sudo apt update sudo apt upgrade sudo apt install nvidia-driver-455 sudo reboot nvidia-smi Download cuDNN v7.6.5 (November 5th, 2019), for CUDA 10.0 * tar -xzvf cudnn-10.0-linux-x64-v7.6.5.32.tgz * sudo cp cuda/include/cudnn*.h /usr/local/cuda/include * sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64 * sudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn* * sudo dpkg -i libcudnn7_7.6.5.32-1+cuda10.0_amd64.deb * sudo dpkg -i libcudnn7-dev_7.6.5.32-1+cuda10.0_amd64.deb * sudo dpkg -i libcudnn7-doc_7.6.5.32-1+cuda10.0_amd64.deb * sudo apt-get install \ apt-transport-https \ ca-certificates \ curl \ gnupg-agent \ software-properties-common curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add - sudo apt-key fingerprint 0EBFCD88 sudo add-apt-repository \ "deb [arch=amd64] https://download.docker.com/linux/ubuntu \ $(lsb_release -cs) \ stable" sudo apt-get update sudo apt-get install docker-ce docker-ce-cli containerd.io Make sure you have installed the NVIDIA driver and Docker engine for your Linux distribution Note that you do not need to install the CUDA Toolkit on the host system, but the NVIDIA driver needs to be installed distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \ && curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - \ && curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia- docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list curl -s -L https://nvidia.github.io/nvidia-container- runtime/experimental/$distribution/nvidia-container-runtime.list | sudo tee /etc/apt/sources.list.d/nvidia-container-runtime.list sudo apt-get install -y nvidia-docker2 sudo systemctl restart docker sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi [Installing on CentOS 8 (AWS)](https://www.google.com/url?q=https%3A%2F%2Fdocs.nvidia.com%2Fdatacenter%2Fcloud- native%2Fcontainer-toolkit%2Finstall- guide.html%23docker&sa=D&sntz=1&usg=AOvVaw38cOFoMlAZS6R9Z4bKrU5Q) pip install cython; pip install -U 'git+[https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fcocodataset%2Fcocoapi.git%23subdirectory%3DPythonAPI&sa=D&sntz=1&usg=AOvVaw2XHIv2zk5mbXQ5VpooNoT4)' CenterTrack_ROOT=/home/farshid/code/CenterTrack git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT cd CenterTrack_ROOT pip install -r requirements.txt cd $CenterTrack_ROOT/src/lib/model/networks/ git clone https://github.com/CharlesShang/DCNv2/ cd DCNv2 ./make.sh [https://github.com/xingyizhou/CenterTrack/blob/master/readme/MODEL_ZOO.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb) # cvat sudo groupadd docker sudo usermod -aG docker $USER sudo apt-get --no-install-recommends install -y python3-pip python3-setuptools sudo python3 -m pip install setuptools docker-compose sudo apt-get --no-install-recommends install -y git git clone https://github.com/opencv/cvat cd cvat sudo docker-compose build sudo docker-compose up -d sudo docker exec -it cvat bash -ic 'python3 ~/manage.py createsuperuser' [http://localhost:8080/](http://www.google.com/url?q=http%3A%2F%2Flocalhost%3A8080%2F&sa=D&sntz=1&usg=AOvVaw3oeouV3qFXFcGyLGuDDpKa) # Towards-Realtime-MOT * conda activate cuda100 * pip install motmetrics * pip install cython_bbox * conda install -c conda-forge ffmpeg * [https://openaccess.thecvf.com/content_CVPR_2020/papers/Xu_How_to_Train_Your_Deep_Multi- Object_Tracker_CVPR_2020_paper.pdf](https://www.google.com/url?q=https%3A%2F%2Fopenaccess.thecvf.com%2Fcontent_CVPR_2020%2Fpapers%2FXu_How_to_Train_Your_Deep_Multi- Object_Tracker_CVPR_2020_paper.pdf&sa=D&sntz=1&usg=AOvVaw0GwXtXPI4_xmM- qU7ZrLmr) [https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki) git clone https://gitlab.inria.fr/yixu/deepmot.git sudo apt-get install libpng-dev sudo apt install libfreetype6-dev pip install -r requirements.txt ImportError: torch.utils.ffi is deprecated. Please use cpp extensions instead. conda create -y --name cuda92 python=3.6 conda activate cuda92 source activate cuda92 conda install pytorch==0.4.1 torchvision==0.2.0 cudatoolkit=9.2 -c pytorch conda install -c conda-forge ffmpeg * conda create -n cuda100 * conda activate cuda100 conda install pytorch torchvision cudatoolkit=10.0 -c pytorch # [https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki) AWS # AWS ## [Towards-Realtime-MOT ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fifzhang%2FFairMOT&sa=D&sntz=1&usg=AOvVaw3KRCUNb1LrlLLYIG2wTulN) * conda create -n FairMOT * conda activate FairMOT * conda install pytorch==1.2.0 torchvision==0.4.0 cudatoolkit=10.0 -c pytorch * cd ${FAIRMOT_ROOT} * pip install -r requirements.txt * conda install -c conda-forge ffmpeg * [MOTS: Multi-Object Tracking and Segmentation](http://www.google.com/url?q=http%3A%2F%2Farxiv.org%2Fabs%2F1902.03604&sa=D&sntz=1&usg=AOvVaw08PSI8pfXxFnpxsLjY3pix) * Paper: [https://arxiv.org/pdf/1902.03604](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fpdf%2F1902.03604&sa=D&sntz=1&usg=AOvVaw2JyGSgtPJlejKIa5-k3N3t) * Dataset: [https://motchallenge.net/data/MOTS/](https://www.google.com/url?q=https%3A%2F%2Fmotchallenge.net%2Fdata%2FMOTS%2F&sa=D&sntz=1&usg=AOvVaw0YWYn2oQZiewTAxCzJH_Fo) * This benchmark extends the traditional Multi-Object Tracking benchmark to a new benchmark defined on a pixel-level with precise segmentation masks. We annotated 8 challenging video sequences (4 training, 4 test) in unconstrained environments filmed with both static and moving cameras. Tracking, segmentation and evaluation are done in image coordinates. All sequences have been annotated with high accuracy on a pixel level, strictly following a well-defined protocol. [https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD) Setup: * cd /media/farshid/exfat128/code * Conda * conda create --name CenterTrack36cuda10 python=3.6 * conda activate CenterTrack * conda install pytorch torchvision -c pytorch * pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI' * CenterTrack_ROOT=/media/farshid/exfat128/code/CenterTrack * git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT * pip install -r requirements.txt * cd $CenterTrack_ROOT/src/lib/model/networks/ * git clone https://github.com/CharlesShang/DCNv2/ * cd DCNv2 * ./make.sh * Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb). * # AWS (11 December 2020) [https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD) * Conda * conda create --name CenterTrack36cuda10 python=3.6 * conda activate CenterTrack36cuda10 * conda install pytorch torchvision cudatoolkit=10.0 -c pytorch * conda install -c conda-forge ffmpeg * pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI' * CenterTrack_ROOT=/ * git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT * pip install -r requirements.txt * cd $CenterTrack_ROOT/src/lib/model/networks/ * git clone https://github.com/CharlesShang/DCNv2/ * cd DCNv2 * ./make.sh * Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb). * ### Training * cd $CenterTrack_ROOT/src/tools/ * bash get_mot_17.sh * Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/qY1sh7akX3HXB9hYYu2OsIVsGoUlGaEsm_n- RYYv92ytQ2eJUxQxXfwWzOtvEQIYYKpMG7mSME0CWWY4Yu9QpRc=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/qY1sh7akX3HXB9hYYu2OsIVsGoUlGaEsm_n- RYYv92ytQ2eJUxQxXfwWzOtvEQIYYKpMG7mSME0CWWY4Yu9QpRc=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # compile and setup source code sudo apt-get -o Dpkg::Options::="\--force-overwrite" install --fix-broken ======================== sudo nano ~/.bashsrc export PATH=/usr/local/cuda/bin${PATH:+:${PATH}} export LD_LIBRARY_PATH=/usr/local/cuda/lib64\ ${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}} =============== nvcc -std=c++17 -arch=sm_60 test.cu # cvtest: Computer Vision Test ## Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Do you want to test your output of computer vision application which is video or images? ## Standard test for computer vision application There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil) check S3 bucket in AWS for image and video files and versioning Check Docker load balancer, memory usage, ... GPU Video Tracking on Mac 1. create conda based on python 3.6 * conda create env_full -y \--name farshid python=3.6 * conda activate farshid 2. install OpenVino from Intel for converting deep learning model based on intel chips * conda install -y openvino-ie4py -c intel 3. install video library * conda install -y -c conda-forge ffmpeg 4. install pytorch and torchvision * conda install -y pytorch torchvision -c pytorch 5. conda install -y -c conda-forge matplotlib 6. conda install -y pandas scikit-learn plotly 7. conda install -y -c conda-forge opencv seaborn 8. conda install -y -c conda-forge tensorflow pip install torch torchvision torchaudio pip install matplotlib pandas scikit-learn plotly opencv seaborn tensorflow # Test for 2021 **3D Multi-Object Tracking: A Baseline and New Evaluation Metrics (IROS 2020, ECCVW 2020)[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv)**[**https://github.com/xinshuoweng/AB3DMOT**](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv) **** Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD- ts8sZsK3X32Hb2FD)[https://github.com/elliottwu/unsup3d](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD- ts8sZsK3X32Hb2FD) This repository contains the public release of the Python implementation of our Aggregate View Object Detection (AVOD) network for 3D object detection.[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl)[https://github.com/kujason/avod](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl) 𝚙𝚒𝚙 𝚒𝚗𝚜𝚝𝚊𝚕𝚕 𝚔3𝚍 # Run on Ubuntu PC + eGPU apt search nvidia-driver apt-cache search nvidia-driver sudo apt update sudo apt upgrade sudo apt install nvidia-driver-455 sudo reboot nvidia-smi Download cuDNN v7.6.5 (November 5th, 2019), for CUDA 10.0 * tar -xzvf cudnn-10.0-linux-x64-v7.6.5.32.tgz * sudo cp cuda/include/cudnn*.h /usr/local/cuda/include * sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64 * sudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn* * sudo dpkg -i libcudnn7_7.6.5.32-1+cuda10.0_amd64.deb * sudo dpkg -i libcudnn7-dev_7.6.5.32-1+cuda10.0_amd64.deb * sudo dpkg -i libcudnn7-doc_7.6.5.32-1+cuda10.0_amd64.deb * sudo apt-get install \ apt-transport-https \ ca-certificates \ curl \ gnupg-agent \ software-properties-common curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add - sudo apt-key fingerprint 0EBFCD88 sudo add-apt-repository \ "deb [arch=amd64] https://download.docker.com/linux/ubuntu \ $(lsb_release -cs) \ stable" sudo apt-get update sudo apt-get install docker-ce docker-ce-cli containerd.io Make sure you have installed the NVIDIA driver and Docker engine for your Linux distribution Note that you do not need to install the CUDA Toolkit on the host system, but the NVIDIA driver needs to be installed distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \ && curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - \ && curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia- docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list curl -s -L https://nvidia.github.io/nvidia-container- runtime/experimental/$distribution/nvidia-container-runtime.list | sudo tee /etc/apt/sources.list.d/nvidia-container-runtime.list sudo apt-get install -y nvidia-docker2 sudo systemctl restart docker sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi [Installing on CentOS 8 (AWS)](https://www.google.com/url?q=https%3A%2F%2Fdocs.nvidia.com%2Fdatacenter%2Fcloud- native%2Fcontainer-toolkit%2Finstall- guide.html%23docker&sa=D&sntz=1&usg=AOvVaw38cOFoMlAZS6R9Z4bKrU5Q) pip install cython; pip install -U 'git+[https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fcocodataset%2Fcocoapi.git%23subdirectory%3DPythonAPI&sa=D&sntz=1&usg=AOvVaw2XHIv2zk5mbXQ5VpooNoT4)' CenterTrack_ROOT=/home/farshid/code/CenterTrack git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT cd CenterTrack_ROOT pip install -r requirements.txt cd $CenterTrack_ROOT/src/lib/model/networks/ git clone https://github.com/CharlesShang/DCNv2/ cd DCNv2 ./make.sh [https://github.com/xingyizhou/CenterTrack/blob/master/readme/MODEL_ZOO.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb) # cvat sudo groupadd docker sudo usermod -aG docker $USER sudo apt-get --no-install-recommends install -y python3-pip python3-setuptools sudo python3 -m pip install setuptools docker-compose sudo apt-get --no-install-recommends install -y git git clone https://github.com/opencv/cvat cd cvat sudo docker-compose build sudo docker-compose up -d sudo docker exec -it cvat bash -ic 'python3 ~/manage.py createsuperuser' [http://localhost:8080/](http://www.google.com/url?q=http%3A%2F%2Flocalhost%3A8080%2F&sa=D&sntz=1&usg=AOvVaw3oeouV3qFXFcGyLGuDDpKa) # Towards-Realtime-MOT * conda activate cuda100 * pip install motmetrics * pip install cython_bbox * conda install -c conda-forge ffmpeg * [https://openaccess.thecvf.com/content_CVPR_2020/papers/Xu_How_to_Train_Your_Deep_Multi- Object_Tracker_CVPR_2020_paper.pdf](https://www.google.com/url?q=https%3A%2F%2Fopenaccess.thecvf.com%2Fcontent_CVPR_2020%2Fpapers%2FXu_How_to_Train_Your_Deep_Multi- Object_Tracker_CVPR_2020_paper.pdf&sa=D&sntz=1&usg=AOvVaw0GwXtXPI4_xmM- qU7ZrLmr) [https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki) git clone https://gitlab.inria.fr/yixu/deepmot.git sudo apt-get install libpng-dev sudo apt install libfreetype6-dev pip install -r requirements.txt ImportError: torch.utils.ffi is deprecated. Please use cpp extensions instead. conda create -y --name cuda92 python=3.6 conda activate cuda92 source activate cuda92 conda install pytorch==0.4.1 torchvision==0.2.0 cudatoolkit=9.2 -c pytorch conda install -c conda-forge ffmpeg * conda create -n cuda100 * conda activate cuda100 conda install pytorch torchvision cudatoolkit=10.0 -c pytorch # [https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki) AWS # AWS ## [Towards-Realtime-MOT ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fifzhang%2FFairMOT&sa=D&sntz=1&usg=AOvVaw3KRCUNb1LrlLLYIG2wTulN) * conda create -n FairMOT * conda activate FairMOT * conda install pytorch==1.2.0 torchvision==0.4.0 cudatoolkit=10.0 -c pytorch * cd ${FAIRMOT_ROOT} * pip install -r requirements.txt * conda install -c conda-forge ffmpeg * [MOTS: Multi-Object Tracking and Segmentation](http://www.google.com/url?q=http%3A%2F%2Farxiv.org%2Fabs%2F1902.03604&sa=D&sntz=1&usg=AOvVaw08PSI8pfXxFnpxsLjY3pix) * Paper: [https://arxiv.org/pdf/1902.03604](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fpdf%2F1902.03604&sa=D&sntz=1&usg=AOvVaw2JyGSgtPJlejKIa5-k3N3t) * Dataset: [https://motchallenge.net/data/MOTS/](https://www.google.com/url?q=https%3A%2F%2Fmotchallenge.net%2Fdata%2FMOTS%2F&sa=D&sntz=1&usg=AOvVaw0YWYn2oQZiewTAxCzJH_Fo) * This benchmark extends the traditional Multi-Object Tracking benchmark to a new benchmark defined on a pixel-level with precise segmentation masks. We annotated 8 challenging video sequences (4 training, 4 test) in unconstrained environments filmed with both static and moving cameras. Tracking, segmentation and evaluation are done in image coordinates. All sequences have been annotated with high accuracy on a pixel level, strictly following a well-defined protocol. [https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD) Setup: * cd /media/farshid/exfat128/code * Conda * conda create --name CenterTrack36cuda10 python=3.6 * conda activate CenterTrack * conda install pytorch torchvision -c pytorch * pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI' * CenterTrack_ROOT=/media/farshid/exfat128/code/CenterTrack * git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT * pip install -r requirements.txt * cd $CenterTrack_ROOT/src/lib/model/networks/ * git clone https://github.com/CharlesShang/DCNv2/ * cd DCNv2 * ./make.sh * Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb). * # AWS (11 December 2020) [https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD) * Conda * conda create --name CenterTrack36cuda10 python=3.6 * conda activate CenterTrack36cuda10 * conda install pytorch torchvision cudatoolkit=10.0 -c pytorch * conda install -c conda-forge ffmpeg * pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI' * CenterTrack_ROOT=/ * git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT * pip install -r requirements.txt * cd $CenterTrack_ROOT/src/lib/model/networks/ * git clone https://github.com/CharlesShang/DCNv2/ * cd DCNv2 * ./make.sh * Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb). * ### Training * cd $CenterTrack_ROOT/src/tools/ * bash get_mot_17.sh * Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/qY1sh7akX3HXB9hYYu2OsIVsGoUlGaEsm_n- RYYv92ytQ2eJUxQxXfwWzOtvEQIYYKpMG7mSME0CWWY4Yu9QpRc=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/qY1sh7akX3HXB9hYYu2OsIVsGoUlGaEsm_n- RYYv92ytQ2eJUxQxXfwWzOtvEQIYYKpMG7mSME0CWWY4Yu9QpRc=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # compile and setup source code sudo apt-get -o Dpkg::Options::="\--force-overwrite" install --fix-broken ======================== sudo nano ~/.bashsrc export PATH=/usr/local/cuda/bin${PATH:+:${PATH}} export LD_LIBRARY_PATH=/usr/local/cuda/lib64\ ${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}} =============== nvcc -std=c++17 -arch=sm_60 test.cu # cvtest: Computer Vision Test ## Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Do you want to test your output of computer vision application which is video or images? ## Standard test for computer vision application There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil) check S3 bucket in AWS for image and video files and versioning Check Docker load balancer, memory usage, ... GPU Video Tracking on Mac 1. create conda based on python 3.6 * conda create env_full -y \--name farshid python=3.6 * conda activate farshid 2. install OpenVino from Intel for converting deep learning model based on intel chips * conda install -y openvino-ie4py -c intel 3. install video library * conda install -y -c conda-forge ffmpeg 4. install pytorch and torchvision * conda install -y pytorch torchvision -c pytorch 5. conda install -y -c conda-forge matplotlib 6. conda install -y pandas scikit-learn plotly 7. conda install -y -c conda-forge opencv seaborn 8. conda install -y -c conda-forge tensorflow pip install torch torchvision torchaudio pip install matplotlib pandas scikit-learn plotly opencv seaborn tensorflow # Test for 2021 **3D Multi-Object Tracking: A Baseline and New Evaluation Metrics (IROS 2020, ECCVW 2020)[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv)**[**https://github.com/xinshuoweng/AB3DMOT**](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv) **** Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD- ts8sZsK3X32Hb2FD)[https://github.com/elliottwu/unsup3d](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD- ts8sZsK3X32Hb2FD) This repository contains the public release of the Python implementation of our Aggregate View Object Detection (AVOD) network for 3D object detection.[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl)[https://github.com/kujason/avod](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl) 𝚙𝚒𝚙 𝚒𝚗𝚜𝚝𝚊𝚕𝚕 𝚔3𝚍 # Run on Ubuntu PC + eGPU apt search nvidia-driver apt-cache search nvidia-driver sudo apt update sudo apt upgrade sudo apt install nvidia-driver-455 sudo reboot nvidia-smi Download cuDNN v7.6.5 (November 5th, 2019), for CUDA 10.0 * tar -xzvf cudnn-10.0-linux-x64-v7.6.5.32.tgz * sudo cp cuda/include/cudnn*.h /usr/local/cuda/include * sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64 * sudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn* * sudo dpkg -i libcudnn7_7.6.5.32-1+cuda10.0_amd64.deb * sudo dpkg -i libcudnn7-dev_7.6.5.32-1+cuda10.0_amd64.deb * sudo dpkg -i libcudnn7-doc_7.6.5.32-1+cuda10.0_amd64.deb * sudo apt-get install \ apt-transport-https \ ca-certificates \ curl \ gnupg-agent \ software-properties-common curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add - sudo apt-key fingerprint 0EBFCD88 sudo add-apt-repository \ "deb [arch=amd64] https://download.docker.com/linux/ubuntu \ $(lsb_release -cs) \ stable" sudo apt-get update sudo apt-get install docker-ce docker-ce-cli containerd.io Make sure you have installed the NVIDIA driver and Docker engine for your Linux distribution Note that you do not need to install the CUDA Toolkit on the host system, but the NVIDIA driver needs to be installed distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \ && curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - \ && curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia- docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list curl -s -L https://nvidia.github.io/nvidia-container- runtime/experimental/$distribution/nvidia-container-runtime.list | sudo tee /etc/apt/sources.list.d/nvidia-container-runtime.list sudo apt-get install -y nvidia-docker2 sudo systemctl restart docker sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi [Installing on CentOS 8 (AWS)](https://www.google.com/url?q=https%3A%2F%2Fdocs.nvidia.com%2Fdatacenter%2Fcloud- native%2Fcontainer-toolkit%2Finstall- guide.html%23docker&sa=D&sntz=1&usg=AOvVaw38cOFoMlAZS6R9Z4bKrU5Q) pip install cython; pip install -U 'git+[https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fcocodataset%2Fcocoapi.git%23subdirectory%3DPythonAPI&sa=D&sntz=1&usg=AOvVaw2XHIv2zk5mbXQ5VpooNoT4)' CenterTrack_ROOT=/home/farshid/code/CenterTrack git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT cd CenterTrack_ROOT pip install -r requirements.txt cd $CenterTrack_ROOT/src/lib/model/networks/ git clone https://github.com/CharlesShang/DCNv2/ cd DCNv2 ./make.sh [https://github.com/xingyizhou/CenterTrack/blob/master/readme/MODEL_ZOO.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb) # cvat sudo groupadd docker sudo usermod -aG docker $USER sudo apt-get --no-install-recommends install -y python3-pip python3-setuptools sudo python3 -m pip install setuptools docker-compose sudo apt-get --no-install-recommends install -y git git clone https://github.com/opencv/cvat cd cvat sudo docker-compose build sudo docker-compose up -d sudo docker exec -it cvat bash -ic 'python3 ~/manage.py createsuperuser' [http://localhost:8080/](http://www.google.com/url?q=http%3A%2F%2Flocalhost%3A8080%2F&sa=D&sntz=1&usg=AOvVaw3oeouV3qFXFcGyLGuDDpKa) # Towards-Realtime-MOT * conda activate cuda100 * pip install motmetrics * pip install cython_bbox * conda install -c conda-forge ffmpeg * [https://openaccess.thecvf.com/content_CVPR_2020/papers/Xu_How_to_Train_Your_Deep_Multi- Object_Tracker_CVPR_2020_paper.pdf](https://www.google.com/url?q=https%3A%2F%2Fopenaccess.thecvf.com%2Fcontent_CVPR_2020%2Fpapers%2FXu_How_to_Train_Your_Deep_Multi- Object_Tracker_CVPR_2020_paper.pdf&sa=D&sntz=1&usg=AOvVaw0GwXtXPI4_xmM- qU7ZrLmr) [https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki) git clone https://gitlab.inria.fr/yixu/deepmot.git sudo apt-get install libpng-dev sudo apt install libfreetype6-dev pip install -r requirements.txt ImportError: torch.utils.ffi is deprecated. Please use cpp extensions instead. conda create -y --name cuda92 python=3.6 conda activate cuda92 source activate cuda92 conda install pytorch==0.4.1 torchvision==0.2.0 cudatoolkit=9.2 -c pytorch conda install -c conda-forge ffmpeg * conda create -n cuda100 * conda activate cuda100 conda install pytorch torchvision cudatoolkit=10.0 -c pytorch # [https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki) AWS # AWS ## [Towards-Realtime-MOT ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fifzhang%2FFairMOT&sa=D&sntz=1&usg=AOvVaw3KRCUNb1LrlLLYIG2wTulN) * conda create -n FairMOT * conda activate FairMOT * conda install pytorch==1.2.0 torchvision==0.4.0 cudatoolkit=10.0 -c pytorch * cd ${FAIRMOT_ROOT} * pip install -r requirements.txt * conda install -c conda-forge ffmpeg * [MOTS: Multi-Object Tracking and Segmentation](http://www.google.com/url?q=http%3A%2F%2Farxiv.org%2Fabs%2F1902.03604&sa=D&sntz=1&usg=AOvVaw08PSI8pfXxFnpxsLjY3pix) * Paper: [https://arxiv.org/pdf/1902.03604](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fpdf%2F1902.03604&sa=D&sntz=1&usg=AOvVaw2JyGSgtPJlejKIa5-k3N3t) * Dataset: [https://motchallenge.net/data/MOTS/](https://www.google.com/url?q=https%3A%2F%2Fmotchallenge.net%2Fdata%2FMOTS%2F&sa=D&sntz=1&usg=AOvVaw0YWYn2oQZiewTAxCzJH_Fo) * This benchmark extends the traditional Multi-Object Tracking benchmark to a new benchmark defined on a pixel-level with precise segmentation masks. We annotated 8 challenging video sequences (4 training, 4 test) in unconstrained environments filmed with both static and moving cameras. Tracking, segmentation and evaluation are done in image coordinates. All sequences have been annotated with high accuracy on a pixel level, strictly following a well-defined protocol. [https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD) Setup: * cd /media/farshid/exfat128/code * Conda * conda create --name CenterTrack36cuda10 python=3.6 * conda activate CenterTrack * conda install pytorch torchvision -c pytorch * pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI' * CenterTrack_ROOT=/media/farshid/exfat128/code/CenterTrack * git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT * pip install -r requirements.txt * cd $CenterTrack_ROOT/src/lib/model/networks/ * git clone https://github.com/CharlesShang/DCNv2/ * cd DCNv2 * ./make.sh * Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb). * # AWS (11 December 2020) [https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD) * Conda * conda create --name CenterTrack36cuda10 python=3.6 * conda activate CenterTrack36cuda10 * conda install pytorch torchvision cudatoolkit=10.0 -c pytorch * conda install -c conda-forge ffmpeg * pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI' * CenterTrack_ROOT=/ * git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT * pip install -r requirements.txt * cd $CenterTrack_ROOT/src/lib/model/networks/ * git clone https://github.com/CharlesShang/DCNv2/ * cd DCNv2 * ./make.sh * Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb). * ### Training * cd $CenterTrack_ROOT/src/tools/ * bash get_mot_17.sh * Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/qY1sh7akX3HXB9hYYu2OsIVsGoUlGaEsm_n- RYYv92ytQ2eJUxQxXfwWzOtvEQIYYKpMG7mSME0CWWY4Yu9QpRc=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/qY1sh7akX3HXB9hYYu2OsIVsGoUlGaEsm_n- RYYv92ytQ2eJUxQxXfwWzOtvEQIYYKpMG7mSME0CWWY4Yu9QpRc=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # compile and setup source code sudo apt-get -o Dpkg::Options::="\--force-overwrite" install --fix-broken ======================== sudo nano ~/.bashsrc export PATH=/usr/local/cuda/bin${PATH:+:${PATH}} export LD_LIBRARY_PATH=/usr/local/cuda/lib64\ ${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}} =============== nvcc -std=c++17 -arch=sm_60 test.cu # cvtest: Computer Vision Test ## Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Do you want to test your output of computer vision application which is video or images? ## Standard test for computer vision application There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil) check S3 bucket in AWS for image and video files and versioning Check Docker load balancer, memory usage, ... GPU Video Tracking on Mac 1. create conda based on python 3.6 * conda create env_full -y \--name farshid python=3.6 * conda activate farshid 2. install OpenVino from Intel for converting deep learning model based on intel chips * conda install -y openvino-ie4py -c intel 3. install video library * conda install -y -c conda-forge ffmpeg 4. install pytorch and torchvision * conda install -y pytorch torchvision -c pytorch 5. conda install -y -c conda-forge matplotlib 6. conda install -y pandas scikit-learn plotly 7. conda install -y -c conda-forge opencv seaborn 8. conda install -y -c conda-forge tensorflow pip install torch torchvision torchaudio pip install matplotlib pandas scikit-learn plotly opencv seaborn tensorflow # Test for 2021 **3D Multi-Object Tracking: A Baseline and New Evaluation Metrics (IROS 2020, ECCVW 2020)[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv)**[**https://github.com/xinshuoweng/AB3DMOT**](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv) **** Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD- ts8sZsK3X32Hb2FD)[https://github.com/elliottwu/unsup3d](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD- ts8sZsK3X32Hb2FD) This repository contains the public release of the Python implementation of our Aggregate View Object Detection (AVOD) network for 3D object detection.[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl)[https://github.com/kujason/avod](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl) 𝚙𝚒𝚙 𝚒𝚗𝚜𝚝𝚊𝚕𝚕 𝚔3𝚍 # Run on Ubuntu PC + eGPU apt search nvidia-driver apt-cache search nvidia-driver sudo apt update sudo apt upgrade sudo apt install nvidia-driver-455 sudo reboot nvidia-smi Download cuDNN v7.6.5 (November 5th, 2019), for CUDA 10.0 * tar -xzvf cudnn-10.0-linux-x64-v7.6.5.32.tgz * sudo cp cuda/include/cudnn*.h /usr/local/cuda/include * sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64 * sudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn* * sudo dpkg -i libcudnn7_7.6.5.32-1+cuda10.0_amd64.deb * sudo dpkg -i libcudnn7-dev_7.6.5.32-1+cuda10.0_amd64.deb * sudo dpkg -i libcudnn7-doc_7.6.5.32-1+cuda10.0_amd64.deb * sudo apt-get install \ apt-transport-https \ ca-certificates \ curl \ gnupg-agent \ software-properties-common curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add - sudo apt-key fingerprint 0EBFCD88 sudo add-apt-repository \ "deb [arch=amd64] https://download.docker.com/linux/ubuntu \ $(lsb_release -cs) \ stable" sudo apt-get update sudo apt-get install docker-ce docker-ce-cli containerd.io Make sure you have installed the NVIDIA driver and Docker engine for your Linux distribution Note that you do not need to install the CUDA Toolkit on the host system, but the NVIDIA driver needs to be installed distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \ && curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - \ && curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia- docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list curl -s -L https://nvidia.github.io/nvidia-container- runtime/experimental/$distribution/nvidia-container-runtime.list | sudo tee /etc/apt/sources.list.d/nvidia-container-runtime.list sudo apt-get install -y nvidia-docker2 sudo systemctl restart docker sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi [Installing on CentOS 8 (AWS)](https://www.google.com/url?q=https%3A%2F%2Fdocs.nvidia.com%2Fdatacenter%2Fcloud- native%2Fcontainer-toolkit%2Finstall- guide.html%23docker&sa=D&sntz=1&usg=AOvVaw38cOFoMlAZS6R9Z4bKrU5Q) pip install cython; pip install -U 'git+[https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fcocodataset%2Fcocoapi.git%23subdirectory%3DPythonAPI&sa=D&sntz=1&usg=AOvVaw2XHIv2zk5mbXQ5VpooNoT4)' CenterTrack_ROOT=/home/farshid/code/CenterTrack git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT cd CenterTrack_ROOT pip install -r requirements.txt cd $CenterTrack_ROOT/src/lib/model/networks/ git clone https://github.com/CharlesShang/DCNv2/ cd DCNv2 ./make.sh [https://github.com/xingyizhou/CenterTrack/blob/master/readme/MODEL_ZOO.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb) # cvat sudo groupadd docker sudo usermod -aG docker $USER sudo apt-get --no-install-recommends install -y python3-pip python3-setuptools sudo python3 -m pip install setuptools docker-compose sudo apt-get --no-install-recommends install -y git git clone https://github.com/opencv/cvat cd cvat sudo docker-compose build sudo docker-compose up -d sudo docker exec -it cvat bash -ic 'python3 ~/manage.py createsuperuser' [http://localhost:8080/](http://www.google.com/url?q=http%3A%2F%2Flocalhost%3A8080%2F&sa=D&sntz=1&usg=AOvVaw3oeouV3qFXFcGyLGuDDpKa) # Towards-Realtime-MOT * conda activate cuda100 * pip install motmetrics * pip install cython_bbox * conda install -c conda-forge ffmpeg * [https://openaccess.thecvf.com/content_CVPR_2020/papers/Xu_How_to_Train_Your_Deep_Multi- Object_Tracker_CVPR_2020_paper.pdf](https://www.google.com/url?q=https%3A%2F%2Fopenaccess.thecvf.com%2Fcontent_CVPR_2020%2Fpapers%2FXu_How_to_Train_Your_Deep_Multi- Object_Tracker_CVPR_2020_paper.pdf&sa=D&sntz=1&usg=AOvVaw0GwXtXPI4_xmM- qU7ZrLmr) [https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki) git clone https://gitlab.inria.fr/yixu/deepmot.git sudo apt-get install libpng-dev sudo apt install libfreetype6-dev pip install -r requirements.txt ImportError: torch.utils.ffi is deprecated. Please use cpp extensions instead. conda create -y --name cuda92 python=3.6 conda activate cuda92 source activate cuda92 conda install pytorch==0.4.1 torchvision==0.2.0 cudatoolkit=9.2 -c pytorch conda install -c conda-forge ffmpeg * conda create -n cuda100 * conda activate cuda100 conda install pytorch torchvision cudatoolkit=10.0 -c pytorch # [https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki) AWS # AWS ## [Towards-Realtime-MOT ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fifzhang%2FFairMOT&sa=D&sntz=1&usg=AOvVaw3KRCUNb1LrlLLYIG2wTulN) * conda create -n FairMOT * conda activate FairMOT * conda install pytorch==1.2.0 torchvision==0.4.0 cudatoolkit=10.0 -c pytorch * cd ${FAIRMOT_ROOT} * pip install -r requirements.txt * conda install -c conda-forge ffmpeg * [MOTS: Multi-Object Tracking and Segmentation](http://www.google.com/url?q=http%3A%2F%2Farxiv.org%2Fabs%2F1902.03604&sa=D&sntz=1&usg=AOvVaw08PSI8pfXxFnpxsLjY3pix) * Paper: [https://arxiv.org/pdf/1902.03604](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fpdf%2F1902.03604&sa=D&sntz=1&usg=AOvVaw2JyGSgtPJlejKIa5-k3N3t) * Dataset: [https://motchallenge.net/data/MOTS/](https://www.google.com/url?q=https%3A%2F%2Fmotchallenge.net%2Fdata%2FMOTS%2F&sa=D&sntz=1&usg=AOvVaw0YWYn2oQZiewTAxCzJH_Fo) * This benchmark extends the traditional Multi-Object Tracking benchmark to a new benchmark defined on a pixel-level with precise segmentation masks. We annotated 8 challenging video sequences (4 training, 4 test) in unconstrained environments filmed with both static and moving cameras. Tracking, segmentation and evaluation are done in image coordinates. All sequences have been annotated with high accuracy on a pixel level, strictly following a well-defined protocol. [https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD) Setup: * cd /media/farshid/exfat128/code * Conda * conda create --name CenterTrack36cuda10 python=3.6 * conda activate CenterTrack * conda install pytorch torchvision -c pytorch * pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI' * CenterTrack_ROOT=/media/farshid/exfat128/code/CenterTrack * git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT * pip install -r requirements.txt * cd $CenterTrack_ROOT/src/lib/model/networks/ * git clone https://github.com/CharlesShang/DCNv2/ * cd DCNv2 * ./make.sh * Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb). * # AWS (11 December 2020) [https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD) * Conda * conda create --name CenterTrack36cuda10 python=3.6 * conda activate CenterTrack36cuda10 * conda install pytorch torchvision cudatoolkit=10.0 -c pytorch * conda install -c conda-forge ffmpeg * pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI' * CenterTrack_ROOT=/ * git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT * pip install -r requirements.txt * cd $CenterTrack_ROOT/src/lib/model/networks/ * git clone https://github.com/CharlesShang/DCNv2/ * cd DCNv2 * ./make.sh * Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb). * ### Training * cd $CenterTrack_ROOT/src/tools/ * bash get_mot_17.sh * Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/qY1sh7akX3HXB9hYYu2OsIVsGoUlGaEsm_n- RYYv92ytQ2eJUxQxXfwWzOtvEQIYYKpMG7mSME0CWWY4Yu9QpRc=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/qY1sh7akX3HXB9hYYu2OsIVsGoUlGaEsm_n- RYYv92ytQ2eJUxQxXfwWzOtvEQIYYKpMG7mSME0CWWY4Yu9QpRc=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # compile and setup source code sudo apt-get -o Dpkg::Options::="\--force-overwrite" install --fix-broken ======================== sudo nano ~/.bashsrc export PATH=/usr/local/cuda/bin${PATH:+:${PATH}} export LD_LIBRARY_PATH=/usr/local/cuda/lib64\ ${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}} =============== nvcc -std=c++17 -arch=sm_60 test.cu # cvtest: Computer Vision Test ## Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Do you want to test your output of computer vision application which is video or images? ## Standard test for computer vision application There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil) check S3 bucket in AWS for image and video files and versioning Check Docker load balancer, memory usage, ... GPU Video Tracking on Mac 1. create conda based on python 3.6 * conda create env_full -y \--name farshid python=3.6 * conda activate farshid 2. install OpenVino from Intel for converting deep learning model based on intel chips * conda install -y openvino-ie4py -c intel 3. install video library * conda install -y -c conda-forge ffmpeg 4. install pytorch and torchvision * conda install -y pytorch torchvision -c pytorch 5. conda install -y -c conda-forge matplotlib 6. conda install -y pandas scikit-learn plotly 7. conda install -y -c conda-forge opencv seaborn 8. conda install -y -c conda-forge tensorflow pip install torch torchvision torchaudio pip install matplotlib pandas scikit-learn plotly opencv seaborn tensorflow # Test for 2021 **3D Multi-Object Tracking: A Baseline and New Evaluation Metrics (IROS 2020, ECCVW 2020)[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv)**[**https://github.com/xinshuoweng/AB3DMOT**](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv) **** Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD- ts8sZsK3X32Hb2FD)[https://github.com/elliottwu/unsup3d](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD- ts8sZsK3X32Hb2FD) This repository contains the public release of the Python implementation of our Aggregate View Object Detection (AVOD) network for 3D object detection.[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl)[https://github.com/kujason/avod](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl) 𝚙𝚒𝚙 𝚒𝚗𝚜𝚝𝚊𝚕𝚕 𝚔3𝚍 # Run on Ubuntu PC + eGPU apt search nvidia-driver apt-cache search nvidia-driver sudo apt update sudo apt upgrade sudo apt install nvidia-driver-455 sudo reboot nvidia-smi Download cuDNN v7.6.5 (November 5th, 2019), for CUDA 10.0 * tar -xzvf cudnn-10.0-linux-x64-v7.6.5.32.tgz * sudo cp cuda/include/cudnn*.h /usr/local/cuda/include * sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64 * sudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn* * sudo dpkg -i libcudnn7_7.6.5.32-1+cuda10.0_amd64.deb * sudo dpkg -i libcudnn7-dev_7.6.5.32-1+cuda10.0_amd64.deb * sudo dpkg -i libcudnn7-doc_7.6.5.32-1+cuda10.0_amd64.deb * sudo apt-get install \ apt-transport-https \ ca-certificates \ curl \ gnupg-agent \ software-properties-common curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add - sudo apt-key fingerprint 0EBFCD88 sudo add-apt-repository \ "deb [arch=amd64] https://download.docker.com/linux/ubuntu \ $(lsb_release -cs) \ stable" sudo apt-get update sudo apt-get install docker-ce docker-ce-cli containerd.io Make sure you have installed the NVIDIA driver and Docker engine for your Linux distribution Note that you do not need to install the CUDA Toolkit on the host system, but the NVIDIA driver needs to be installed distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \ && curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - \ && curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia- docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list curl -s -L https://nvidia.github.io/nvidia-container- runtime/experimental/$distribution/nvidia-container-runtime.list | sudo tee /etc/apt/sources.list.d/nvidia-container-runtime.list sudo apt-get install -y nvidia-docker2 sudo systemctl restart docker sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi [Installing on CentOS 8 (AWS)](https://www.google.com/url?q=https%3A%2F%2Fdocs.nvidia.com%2Fdatacenter%2Fcloud- native%2Fcontainer-toolkit%2Finstall- guide.html%23docker&sa=D&sntz=1&usg=AOvVaw38cOFoMlAZS6R9Z4bKrU5Q) pip install cython; pip install -U 'git+[https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fcocodataset%2Fcocoapi.git%23subdirectory%3DPythonAPI&sa=D&sntz=1&usg=AOvVaw2XHIv2zk5mbXQ5VpooNoT4)' CenterTrack_ROOT=/home/farshid/code/CenterTrack git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT cd CenterTrack_ROOT pip install -r requirements.txt cd $CenterTrack_ROOT/src/lib/model/networks/ git clone https://github.com/CharlesShang/DCNv2/ cd DCNv2 ./make.sh [https://github.com/xingyizhou/CenterTrack/blob/master/readme/MODEL_ZOO.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb) # cvat sudo groupadd docker sudo usermod -aG docker $USER sudo apt-get --no-install-recommends install -y python3-pip python3-setuptools sudo python3 -m pip install setuptools docker-compose sudo apt-get --no-install-recommends install -y git git clone https://github.com/opencv/cvat cd cvat sudo docker-compose build sudo docker-compose up -d sudo docker exec -it cvat bash -ic 'python3 ~/manage.py createsuperuser' [http://localhost:8080/](http://www.google.com/url?q=http%3A%2F%2Flocalhost%3A8080%2F&sa=D&sntz=1&usg=AOvVaw3oeouV3qFXFcGyLGuDDpKa) # Towards-Realtime-MOT * conda activate cuda100 * pip install motmetrics * pip install cython_bbox * conda install -c conda-forge ffmpeg * [https://openaccess.thecvf.com/content_CVPR_2020/papers/Xu_How_to_Train_Your_Deep_Multi- Object_Tracker_CVPR_2020_paper.pdf](https://www.google.com/url?q=https%3A%2F%2Fopenaccess.thecvf.com%2Fcontent_CVPR_2020%2Fpapers%2FXu_How_to_Train_Your_Deep_Multi- Object_Tracker_CVPR_2020_paper.pdf&sa=D&sntz=1&usg=AOvVaw0GwXtXPI4_xmM- qU7ZrLmr) [https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki) git clone https://gitlab.inria.fr/yixu/deepmot.git sudo apt-get install libpng-dev sudo apt install libfreetype6-dev pip install -r requirements.txt ImportError: torch.utils.ffi is deprecated. Please use cpp extensions instead. conda create -y --name cuda92 python=3.6 conda activate cuda92 source activate cuda92 conda install pytorch==0.4.1 torchvision==0.2.0 cudatoolkit=9.2 -c pytorch conda install -c conda-forge ffmpeg * conda create -n cuda100 * conda activate cuda100 conda install pytorch torchvision cudatoolkit=10.0 -c pytorch # [https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki) AWS # AWS ## [Towards-Realtime-MOT ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fifzhang%2FFairMOT&sa=D&sntz=1&usg=AOvVaw3KRCUNb1LrlLLYIG2wTulN) * conda create -n FairMOT * conda activate FairMOT * conda install pytorch==1.2.0 torchvision==0.4.0 cudatoolkit=10.0 -c pytorch * cd ${FAIRMOT_ROOT} * pip install -r requirements.txt * conda install -c conda-forge ffmpeg * [MOTS: Multi-Object Tracking and Segmentation](http://www.google.com/url?q=http%3A%2F%2Farxiv.org%2Fabs%2F1902.03604&sa=D&sntz=1&usg=AOvVaw08PSI8pfXxFnpxsLjY3pix) * Paper: [https://arxiv.org/pdf/1902.03604](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fpdf%2F1902.03604&sa=D&sntz=1&usg=AOvVaw2JyGSgtPJlejKIa5-k3N3t) * Dataset: [https://motchallenge.net/data/MOTS/](https://www.google.com/url?q=https%3A%2F%2Fmotchallenge.net%2Fdata%2FMOTS%2F&sa=D&sntz=1&usg=AOvVaw0YWYn2oQZiewTAxCzJH_Fo) * This benchmark extends the traditional Multi-Object Tracking benchmark to a new benchmark defined on a pixel-level with precise segmentation masks. We annotated 8 challenging video sequences (4 training, 4 test) in unconstrained environments filmed with both static and moving cameras. Tracking, segmentation and evaluation are done in image coordinates. All sequences have been annotated with high accuracy on a pixel level, strictly following a well-defined protocol. [https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD) Setup: * cd /media/farshid/exfat128/code * Conda * conda create --name CenterTrack36cuda10 python=3.6 * conda activate CenterTrack * conda install pytorch torchvision -c pytorch * pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI' * CenterTrack_ROOT=/media/farshid/exfat128/code/CenterTrack * git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT * pip install -r requirements.txt * cd $CenterTrack_ROOT/src/lib/model/networks/ * git clone https://github.com/CharlesShang/DCNv2/ * cd DCNv2 * ./make.sh * Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb). * # AWS (11 December 2020) [https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD) * Conda * conda create --name CenterTrack36cuda10 python=3.6 * conda activate CenterTrack36cuda10 * conda install pytorch torchvision cudatoolkit=10.0 -c pytorch * conda install -c conda-forge ffmpeg * pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI' * CenterTrack_ROOT=/ * git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT * pip install -r requirements.txt * cd $CenterTrack_ROOT/src/lib/model/networks/ * git clone https://github.com/CharlesShang/DCNv2/ * cd DCNv2 * ./make.sh * Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb). * ### Training * cd $CenterTrack_ROOT/src/tools/ * bash get_mot_17.sh * Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/qY1sh7akX3HXB9hYYu2OsIVsGoUlGaEsm_n- RYYv92ytQ2eJUxQxXfwWzOtvEQIYYKpMG7mSME0CWWY4Yu9QpRc=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/qY1sh7akX3HXB9hYYu2OsIVsGoUlGaEsm_n- RYYv92ytQ2eJUxQxXfwWzOtvEQIYYKpMG7mSME0CWWY4Yu9QpRc=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # compile and setup source code sudo apt-get -o Dpkg::Options::="\--force-overwrite" install --fix-broken ======================== sudo nano ~/.bashsrc export PATH=/usr/local/cuda/bin${PATH:+:${PATH}} export LD_LIBRARY_PATH=/usr/local/cuda/lib64\ ${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}} =============== nvcc -std=c++17 -arch=sm_60 test.cu # cvtest: Computer Vision Test ## Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Do you want to test your output of computer vision application which is video or images? ## Standard test for computer vision application There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil) check S3 bucket in AWS for image and video files and versioning Check Docker load balancer, memory usage, ... GPU Video Tracking on Mac 1. create conda based on python 3.6 * conda create env_full -y \--name farshid python=3.6 * conda activate farshid 2. install OpenVino from Intel for converting deep learning model based on intel chips * conda install -y openvino-ie4py -c intel 3. install video library * conda install -y -c conda-forge ffmpeg 4. install pytorch and torchvision * conda install -y pytorch torchvision -c pytorch 5. conda install -y -c conda-forge matplotlib 6. conda install -y pandas scikit-learn plotly 7. conda install -y -c conda-forge opencv seaborn 8. conda install -y -c conda-forge tensorflow pip install torch torchvision torchaudio pip install matplotlib pandas scikit-learn plotly opencv seaborn tensorflow # Test for 2021 **3D Multi-Object Tracking: A Baseline and New Evaluation Metrics (IROS 2020, ECCVW 2020)[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv)**[**https://github.com/xinshuoweng/AB3DMOT**](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv) **** Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD- ts8sZsK3X32Hb2FD)[https://github.com/elliottwu/unsup3d](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD- ts8sZsK3X32Hb2FD) This repository contains the public release of the Python implementation of our Aggregate View Object Detection (AVOD) network for 3D object detection.[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl)[https://github.com/kujason/avod](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl) 𝚙𝚒𝚙 𝚒𝚗𝚜𝚝𝚊𝚕𝚕 𝚔3𝚍 # Run on Ubuntu PC + eGPU apt search nvidia-driver apt-cache search nvidia-driver sudo apt update sudo apt upgrade sudo apt install nvidia-driver-455 sudo reboot nvidia-smi Download cuDNN v7.6.5 (November 5th, 2019), for CUDA 10.0 * tar -xzvf cudnn-10.0-linux-x64-v7.6.5.32.tgz * sudo cp cuda/include/cudnn*.h /usr/local/cuda/include * sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64 * sudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn* * sudo dpkg -i libcudnn7_7.6.5.32-1+cuda10.0_amd64.deb * sudo dpkg -i libcudnn7-dev_7.6.5.32-1+cuda10.0_amd64.deb * sudo dpkg -i libcudnn7-doc_7.6.5.32-1+cuda10.0_amd64.deb * sudo apt-get install \ apt-transport-https \ ca-certificates \ curl \ gnupg-agent \ software-properties-common curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add - sudo apt-key fingerprint 0EBFCD88 sudo add-apt-repository \ "deb [arch=amd64] https://download.docker.com/linux/ubuntu \ $(lsb_release -cs) \ stable" sudo apt-get update sudo apt-get install docker-ce docker-ce-cli containerd.io Make sure you have installed the NVIDIA driver and Docker engine for your Linux distribution Note that you do not need to install the CUDA Toolkit on the host system, but the NVIDIA driver needs to be installed distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \ && curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - \ && curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia- docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list curl -s -L https://nvidia.github.io/nvidia-container- runtime/experimental/$distribution/nvidia-container-runtime.list | sudo tee /etc/apt/sources.list.d/nvidia-container-runtime.list sudo apt-get install -y nvidia-docker2 sudo systemctl restart docker sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi [Installing on CentOS 8 (AWS)](https://www.google.com/url?q=https%3A%2F%2Fdocs.nvidia.com%2Fdatacenter%2Fcloud- native%2Fcontainer-toolkit%2Finstall- guide.html%23docker&sa=D&sntz=1&usg=AOvVaw38cOFoMlAZS6R9Z4bKrU5Q) pip install cython; pip install -U 'git+[https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fcocodataset%2Fcocoapi.git%23subdirectory%3DPythonAPI&sa=D&sntz=1&usg=AOvVaw2XHIv2zk5mbXQ5VpooNoT4)' CenterTrack_ROOT=/home/farshid/code/CenterTrack git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT cd CenterTrack_ROOT pip install -r requirements.txt cd $CenterTrack_ROOT/src/lib/model/networks/ git clone https://github.com/CharlesShang/DCNv2/ cd DCNv2 ./make.sh [https://github.com/xingyizhou/CenterTrack/blob/master/readme/MODEL_ZOO.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb) # cvat sudo groupadd docker sudo usermod -aG docker $USER sudo apt-get --no-install-recommends install -y python3-pip python3-setuptools sudo python3 -m pip install setuptools docker-compose sudo apt-get --no-install-recommends install -y git git clone https://github.com/opencv/cvat cd cvat sudo docker-compose build sudo docker-compose up -d sudo docker exec -it cvat bash -ic 'python3 ~/manage.py createsuperuser' [http://localhost:8080/](http://www.google.com/url?q=http%3A%2F%2Flocalhost%3A8080%2F&sa=D&sntz=1&usg=AOvVaw3oeouV3qFXFcGyLGuDDpKa) # Towards-Realtime-MOT * conda activate cuda100 * pip install motmetrics * pip install cython_bbox * conda install -c conda-forge ffmpeg * [https://openaccess.thecvf.com/content_CVPR_2020/papers/Xu_How_to_Train_Your_Deep_Multi- Object_Tracker_CVPR_2020_paper.pdf](https://www.google.com/url?q=https%3A%2F%2Fopenaccess.thecvf.com%2Fcontent_CVPR_2020%2Fpapers%2FXu_How_to_Train_Your_Deep_Multi- Object_Tracker_CVPR_2020_paper.pdf&sa=D&sntz=1&usg=AOvVaw0GwXtXPI4_xmM- qU7ZrLmr) [https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki) git clone https://gitlab.inria.fr/yixu/deepmot.git sudo apt-get install libpng-dev sudo apt install libfreetype6-dev pip install -r requirements.txt ImportError: torch.utils.ffi is deprecated. Please use cpp extensions instead. conda create -y --name cuda92 python=3.6 conda activate cuda92 source activate cuda92 conda install pytorch==0.4.1 torchvision==0.2.0 cudatoolkit=9.2 -c pytorch conda install -c conda-forge ffmpeg * conda create -n cuda100 * conda activate cuda100 conda install pytorch torchvision cudatoolkit=10.0 -c pytorch # [https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki) AWS # AWS ## [Towards-Realtime-MOT ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fifzhang%2FFairMOT&sa=D&sntz=1&usg=AOvVaw3KRCUNb1LrlLLYIG2wTulN) * conda create -n FairMOT * conda activate FairMOT * conda install pytorch==1.2.0 torchvision==0.4.0 cudatoolkit=10.0 -c pytorch * cd ${FAIRMOT_ROOT} * pip install -r requirements.txt * conda install -c conda-forge ffmpeg * [MOTS: Multi-Object Tracking and Segmentation](http://www.google.com/url?q=http%3A%2F%2Farxiv.org%2Fabs%2F1902.03604&sa=D&sntz=1&usg=AOvVaw08PSI8pfXxFnpxsLjY3pix) * Paper: [https://arxiv.org/pdf/1902.03604](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fpdf%2F1902.03604&sa=D&sntz=1&usg=AOvVaw2JyGSgtPJlejKIa5-k3N3t) * Dataset: [https://motchallenge.net/data/MOTS/](https://www.google.com/url?q=https%3A%2F%2Fmotchallenge.net%2Fdata%2FMOTS%2F&sa=D&sntz=1&usg=AOvVaw0YWYn2oQZiewTAxCzJH_Fo) * This benchmark extends the traditional Multi-Object Tracking benchmark to a new benchmark defined on a pixel-level with precise segmentation masks. We annotated 8 challenging video sequences (4 training, 4 test) in unconstrained environments filmed with both static and moving cameras. Tracking, segmentation and evaluation are done in image coordinates. All sequences have been annotated with high accuracy on a pixel level, strictly following a well-defined protocol. [https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD) Setup: * cd /media/farshid/exfat128/code * Conda * conda create --name CenterTrack36cuda10 python=3.6 * conda activate CenterTrack * conda install pytorch torchvision -c pytorch * pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI' * CenterTrack_ROOT=/media/farshid/exfat128/code/CenterTrack * git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT * pip install -r requirements.txt * cd $CenterTrack_ROOT/src/lib/model/networks/ * git clone https://github.com/CharlesShang/DCNv2/ * cd DCNv2 * ./make.sh * Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb). * # AWS (11 December 2020) [https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD) * Conda * conda create --name CenterTrack36cuda10 python=3.6 * conda activate CenterTrack36cuda10 * conda install pytorch torchvision cudatoolkit=10.0 -c pytorch * conda install -c conda-forge ffmpeg * pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI' * CenterTrack_ROOT=/ * git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT * pip install -r requirements.txt * cd $CenterTrack_ROOT/src/lib/model/networks/ * git clone https://github.com/CharlesShang/DCNv2/ * cd DCNv2 * ./make.sh * Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb). * ### Training * cd $CenterTrack_ROOT/src/tools/ * bash get_mot_17.sh * Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/qY1sh7akX3HXB9hYYu2OsIVsGoUlGaEsm_n- RYYv92ytQ2eJUxQxXfwWzOtvEQIYYKpMG7mSME0CWWY4Yu9QpRc=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/qY1sh7akX3HXB9hYYu2OsIVsGoUlGaEsm_n- RYYv92ytQ2eJUxQxXfwWzOtvEQIYYKpMG7mSME0CWWY4Yu9QpRc=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # compile and setup source code sudo apt-get -o Dpkg::Options::="\--force-overwrite" install --fix-broken ======================== sudo nano ~/.bashsrc export PATH=/usr/local/cuda/bin${PATH:+:${PATH}} export LD_LIBRARY_PATH=/usr/local/cuda/lib64\ ${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}} =============== nvcc -std=c++17 -arch=sm_60 test.cu # cvtest: Computer Vision Test ## Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Do you want to test your output of computer vision application which is video or images? ## Standard test for computer vision application There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil) check S3 bucket in AWS for image and video files and versioning Check Docker load balancer, memory usage, ... GPU Video Tracking on Mac 1. create conda based on python 3.6 * conda create env_full -y \--name farshid python=3.6 * conda activate farshid 2. install OpenVino from Intel for converting deep learning model based on intel chips * conda install -y openvino-ie4py -c intel 3. install video library * conda install -y -c conda-forge ffmpeg 4. install pytorch and torchvision * conda install -y pytorch torchvision -c pytorch 5. conda install -y -c conda-forge matplotlib 6. conda install -y pandas scikit-learn plotly 7. conda install -y -c conda-forge opencv seaborn 8. conda install -y -c conda-forge tensorflow pip install torch torchvision torchaudio pip install matplotlib pandas scikit-learn plotly opencv seaborn tensorflow # Test for 2021 **3D Multi-Object Tracking: A Baseline and New Evaluation Metrics (IROS 2020, ECCVW 2020)[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv)**[**https://github.com/xinshuoweng/AB3DMOT**](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv) **** Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD- ts8sZsK3X32Hb2FD)[https://github.com/elliottwu/unsup3d](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD- ts8sZsK3X32Hb2FD) This repository contains the public release of the Python implementation of our Aggregate View Object Detection (AVOD) network for 3D object detection.[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl)[https://github.com/kujason/avod](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl) 𝚙𝚒𝚙 𝚒𝚗𝚜𝚝𝚊𝚕𝚕 𝚔3𝚍 # Run on Ubuntu PC + eGPU apt search nvidia-driver apt-cache search nvidia-driver sudo apt update sudo apt upgrade sudo apt install nvidia-driver-455 sudo reboot nvidia-smi Download cuDNN v7.6.5 (November 5th, 2019), for CUDA 10.0 * tar -xzvf cudnn-10.0-linux-x64-v7.6.5.32.tgz * sudo cp cuda/include/cudnn*.h /usr/local/cuda/include * sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64 * sudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn* * sudo dpkg -i libcudnn7_7.6.5.32-1+cuda10.0_amd64.deb * sudo dpkg -i libcudnn7-dev_7.6.5.32-1+cuda10.0_amd64.deb * sudo dpkg -i libcudnn7-doc_7.6.5.32-1+cuda10.0_amd64.deb * sudo apt-get install \ apt-transport-https \ ca-certificates \ curl \ gnupg-agent \ software-properties-common curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add - sudo apt-key fingerprint 0EBFCD88 sudo add-apt-repository \ "deb [arch=amd64] https://download.docker.com/linux/ubuntu \ $(lsb_release -cs) \ stable" sudo apt-get update sudo apt-get install docker-ce docker-ce-cli containerd.io Make sure you have installed the NVIDIA driver and Docker engine for your Linux distribution Note that you do not need to install the CUDA Toolkit on the host system, but the NVIDIA driver needs to be installed distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \ && curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - \ && curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia- docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list curl -s -L https://nvidia.github.io/nvidia-container- runtime/experimental/$distribution/nvidia-container-runtime.list | sudo tee /etc/apt/sources.list.d/nvidia-container-runtime.list sudo apt-get install -y nvidia-docker2 sudo systemctl restart docker sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi [Installing on CentOS 8 (AWS)](https://www.google.com/url?q=https%3A%2F%2Fdocs.nvidia.com%2Fdatacenter%2Fcloud- native%2Fcontainer-toolkit%2Finstall- guide.html%23docker&sa=D&sntz=1&usg=AOvVaw38cOFoMlAZS6R9Z4bKrU5Q) pip install cython; pip install -U 'git+[https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fcocodataset%2Fcocoapi.git%23subdirectory%3DPythonAPI&sa=D&sntz=1&usg=AOvVaw2XHIv2zk5mbXQ5VpooNoT4)' CenterTrack_ROOT=/home/farshid/code/CenterTrack git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT cd CenterTrack_ROOT pip install -r requirements.txt cd $CenterTrack_ROOT/src/lib/model/networks/ git clone https://github.com/CharlesShang/DCNv2/ cd DCNv2 ./make.sh [https://github.com/xingyizhou/CenterTrack/blob/master/readme/MODEL_ZOO.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb) # cvat sudo groupadd docker sudo usermod -aG docker $USER sudo apt-get --no-install-recommends install -y python3-pip python3-setuptools sudo python3 -m pip install setuptools docker-compose sudo apt-get --no-install-recommends install -y git git clone https://github.com/opencv/cvat cd cvat sudo docker-compose build sudo docker-compose up -d sudo docker exec -it cvat bash -ic 'python3 ~/manage.py createsuperuser' [http://localhost:8080/](http://www.google.com/url?q=http%3A%2F%2Flocalhost%3A8080%2F&sa=D&sntz=1&usg=AOvVaw3oeouV3qFXFcGyLGuDDpKa) # Towards-Realtime-MOT * conda activate cuda100 * pip install motmetrics * pip install cython_bbox * conda install -c conda-forge ffmpeg * [https://openaccess.thecvf.com/content_CVPR_2020/papers/Xu_How_to_Train_Your_Deep_Multi- Object_Tracker_CVPR_2020_paper.pdf](https://www.google.com/url?q=https%3A%2F%2Fopenaccess.thecvf.com%2Fcontent_CVPR_2020%2Fpapers%2FXu_How_to_Train_Your_Deep_Multi- Object_Tracker_CVPR_2020_paper.pdf&sa=D&sntz=1&usg=AOvVaw0GwXtXPI4_xmM- qU7ZrLmr) [https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki) git clone https://gitlab.inria.fr/yixu/deepmot.git sudo apt-get install libpng-dev sudo apt install libfreetype6-dev pip install -r requirements.txt ImportError: torch.utils.ffi is deprecated. Please use cpp extensions instead. conda create -y --name cuda92 python=3.6 conda activate cuda92 source activate cuda92 conda install pytorch==0.4.1 torchvision==0.2.0 cudatoolkit=9.2 -c pytorch conda install -c conda-forge ffmpeg * conda create -n cuda100 * conda activate cuda100 conda install pytorch torchvision cudatoolkit=10.0 -c pytorch # [https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki) AWS # AWS ## [Towards-Realtime-MOT ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fifzhang%2FFairMOT&sa=D&sntz=1&usg=AOvVaw3KRCUNb1LrlLLYIG2wTulN) * conda create -n FairMOT * conda activate FairMOT * conda install pytorch==1.2.0 torchvision==0.4.0 cudatoolkit=10.0 -c pytorch * cd ${FAIRMOT_ROOT} * pip install -r requirements.txt * conda install -c conda-forge ffmpeg * [MOTS: Multi-Object Tracking and Segmentation](http://www.google.com/url?q=http%3A%2F%2Farxiv.org%2Fabs%2F1902.03604&sa=D&sntz=1&usg=AOvVaw08PSI8pfXxFnpxsLjY3pix) * Paper: [https://arxiv.org/pdf/1902.03604](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fpdf%2F1902.03604&sa=D&sntz=1&usg=AOvVaw2JyGSgtPJlejKIa5-k3N3t) * Dataset: [https://motchallenge.net/data/MOTS/](https://www.google.com/url?q=https%3A%2F%2Fmotchallenge.net%2Fdata%2FMOTS%2F&sa=D&sntz=1&usg=AOvVaw0YWYn2oQZiewTAxCzJH_Fo) * This benchmark extends the traditional Multi-Object Tracking benchmark to a new benchmark defined on a pixel-level with precise segmentation masks. We annotated 8 challenging video sequences (4 training, 4 test) in unconstrained environments filmed with both static and moving cameras. Tracking, segmentation and evaluation are done in image coordinates. All sequences have been annotated with high accuracy on a pixel level, strictly following a well-defined protocol. [https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD) Setup: * cd /media/farshid/exfat128/code * Conda * conda create --name CenterTrack36cuda10 python=3.6 * conda activate CenterTrack * conda install pytorch torchvision -c pytorch * pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI' * CenterTrack_ROOT=/media/farshid/exfat128/code/CenterTrack * git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT * pip install -r requirements.txt * cd $CenterTrack_ROOT/src/lib/model/networks/ * git clone https://github.com/CharlesShang/DCNv2/ * cd DCNv2 * ./make.sh * Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb). * # AWS (11 December 2020) [https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD) * Conda * conda create --name CenterTrack36cuda10 python=3.6 * conda activate CenterTrack36cuda10 * conda install pytorch torchvision cudatoolkit=10.0 -c pytorch * conda install -c conda-forge ffmpeg * pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI' * CenterTrack_ROOT=/ * git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT * pip install -r requirements.txt * cd $CenterTrack_ROOT/src/lib/model/networks/ * git clone https://github.com/CharlesShang/DCNv2/ * cd DCNv2 * ./make.sh * Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb). * ### Training * cd $CenterTrack_ROOT/src/tools/ * bash get_mot_17.sh * Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/qY1sh7akX3HXB9hYYu2OsIVsGoUlGaEsm_n- RYYv92ytQ2eJUxQxXfwWzOtvEQIYYKpMG7mSME0CWWY4Yu9QpRc=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/qY1sh7akX3HXB9hYYu2OsIVsGoUlGaEsm_n- RYYv92ytQ2eJUxQxXfwWzOtvEQIYYKpMG7mSME0CWWY4Yu9QpRc=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # compile and setup source code sudo apt-get -o Dpkg::Options::="\--force-overwrite" install --fix-broken ======================== sudo nano ~/.bashsrc export PATH=/usr/local/cuda/bin${PATH:+:${PATH}} export LD_LIBRARY_PATH=/usr/local/cuda/lib64\ ${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}} =============== nvcc -std=c++17 -arch=sm_60 test.cu # cvtest: Computer Vision Test ## Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Do you want to test your output of computer vision application which is video or images? ## Standard test for computer vision application There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil) check S3 bucket in AWS for image and video files and versioning Check Docker load balancer, memory usage, ... GPU Video Tracking on Mac 1. create conda based on python 3.6 * conda create env_full -y \--name farshid python=3.6 * conda activate farshid 2. install OpenVino from Intel for converting deep learning model based on intel chips * conda install -y openvino-ie4py -c intel 3. install video library * conda install -y -c conda-forge ffmpeg 4. install pytorch and torchvision * conda install -y pytorch torchvision -c pytorch 5. conda install -y -c conda-forge matplotlib 6. conda install -y pandas scikit-learn plotly 7. conda install -y -c conda-forge opencv seaborn 8. conda install -y -c conda-forge tensorflow pip install torch torchvision torchaudio pip install matplotlib pandas scikit-learn plotly opencv seaborn tensorflow # Test for 2021 **3D Multi-Object Tracking: A Baseline and New Evaluation Metrics (IROS 2020, ECCVW 2020)[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv)**[**https://github.com/xinshuoweng/AB3DMOT**](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv) **** Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD- ts8sZsK3X32Hb2FD)[https://github.com/elliottwu/unsup3d](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD- ts8sZsK3X32Hb2FD) This repository contains the public release of the Python implementation of our Aggregate View Object Detection (AVOD) network for 3D object detection.[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl)[https://github.com/kujason/avod](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl) 𝚙𝚒𝚙 𝚒𝚗𝚜𝚝𝚊𝚕𝚕 𝚔3𝚍 # Run on Ubuntu PC + eGPU apt search nvidia-driver apt-cache search nvidia-driver sudo apt update sudo apt upgrade sudo apt install nvidia-driver-455 sudo reboot nvidia-smi Download cuDNN v7.6.5 (November 5th, 2019), for CUDA 10.0 * tar -xzvf cudnn-10.0-linux-x64-v7.6.5.32.tgz * sudo cp cuda/include/cudnn*.h /usr/local/cuda/include * sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64 * sudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn* * sudo dpkg -i libcudnn7_7.6.5.32-1+cuda10.0_amd64.deb * sudo dpkg -i libcudnn7-dev_7.6.5.32-1+cuda10.0_amd64.deb * sudo dpkg -i libcudnn7-doc_7.6.5.32-1+cuda10.0_amd64.deb * sudo apt-get install \ apt-transport-https \ ca-certificates \ curl \ gnupg-agent \ software-properties-common curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add - sudo apt-key fingerprint 0EBFCD88 sudo add-apt-repository \ "deb [arch=amd64] https://download.docker.com/linux/ubuntu \ $(lsb_release -cs) \ stable" sudo apt-get update sudo apt-get install docker-ce docker-ce-cli containerd.io Make sure you have installed the NVIDIA driver and Docker engine for your Linux distribution Note that you do not need to install the CUDA Toolkit on the host system, but the NVIDIA driver needs to be installed distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \ && curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - \ && curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia- docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list curl -s -L https://nvidia.github.io/nvidia-container- runtime/experimental/$distribution/nvidia-container-runtime.list | sudo tee /etc/apt/sources.list.d/nvidia-container-runtime.list sudo apt-get install -y nvidia-docker2 sudo systemctl restart docker sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi [Installing on CentOS 8 (AWS)](https://www.google.com/url?q=https%3A%2F%2Fdocs.nvidia.com%2Fdatacenter%2Fcloud- native%2Fcontainer-toolkit%2Finstall- guide.html%23docker&sa=D&sntz=1&usg=AOvVaw38cOFoMlAZS6R9Z4bKrU5Q) pip install cython; pip install -U 'git+[https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fcocodataset%2Fcocoapi.git%23subdirectory%3DPythonAPI&sa=D&sntz=1&usg=AOvVaw2XHIv2zk5mbXQ5VpooNoT4)' CenterTrack_ROOT=/home/farshid/code/CenterTrack git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT cd CenterTrack_ROOT pip install -r requirements.txt cd $CenterTrack_ROOT/src/lib/model/networks/ git clone https://github.com/CharlesShang/DCNv2/ cd DCNv2 ./make.sh [https://github.com/xingyizhou/CenterTrack/blob/master/readme/MODEL_ZOO.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb) # cvat sudo groupadd docker sudo usermod -aG docker $USER sudo apt-get --no-install-recommends install -y python3-pip python3-setuptools sudo python3 -m pip install setuptools docker-compose sudo apt-get --no-install-recommends install -y git git clone https://github.com/opencv/cvat cd cvat sudo docker-compose build sudo docker-compose up -d sudo docker exec -it cvat bash -ic 'python3 ~/manage.py createsuperuser' [http://localhost:8080/](http://www.google.com/url?q=http%3A%2F%2Flocalhost%3A8080%2F&sa=D&sntz=1&usg=AOvVaw3oeouV3qFXFcGyLGuDDpKa) # Towards-Realtime-MOT * conda activate cuda100 * pip install motmetrics * pip install cython_bbox * conda install -c conda-forge ffmpeg * [https://openaccess.thecvf.com/content_CVPR_2020/papers/Xu_How_to_Train_Your_Deep_Multi- Object_Tracker_CVPR_2020_paper.pdf](https://www.google.com/url?q=https%3A%2F%2Fopenaccess.thecvf.com%2Fcontent_CVPR_2020%2Fpapers%2FXu_How_to_Train_Your_Deep_Multi- Object_Tracker_CVPR_2020_paper.pdf&sa=D&sntz=1&usg=AOvVaw0GwXtXPI4_xmM- qU7ZrLmr) [https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki) git clone https://gitlab.inria.fr/yixu/deepmot.git sudo apt-get install libpng-dev sudo apt install libfreetype6-dev pip install -r requirements.txt ImportError: torch.utils.ffi is deprecated. Please use cpp extensions instead. conda create -y --name cuda92 python=3.6 conda activate cuda92 source activate cuda92 conda install pytorch==0.4.1 torchvision==0.2.0 cudatoolkit=9.2 -c pytorch conda install -c conda-forge ffmpeg * conda create -n cuda100 * conda activate cuda100 conda install pytorch torchvision cudatoolkit=10.0 -c pytorch # [https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki) AWS # AWS ## [Towards-Realtime-MOT ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fifzhang%2FFairMOT&sa=D&sntz=1&usg=AOvVaw3KRCUNb1LrlLLYIG2wTulN) * conda create -n FairMOT * conda activate FairMOT * conda install pytorch==1.2.0 torchvision==0.4.0 cudatoolkit=10.0 -c pytorch * cd ${FAIRMOT_ROOT} * pip install -r requirements.txt * conda install -c conda-forge ffmpeg * [MOTS: Multi-Object Tracking and Segmentation](http://www.google.com/url?q=http%3A%2F%2Farxiv.org%2Fabs%2F1902.03604&sa=D&sntz=1&usg=AOvVaw08PSI8pfXxFnpxsLjY3pix) * Paper: [https://arxiv.org/pdf/1902.03604](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fpdf%2F1902.03604&sa=D&sntz=1&usg=AOvVaw2JyGSgtPJlejKIa5-k3N3t) * Dataset: [https://motchallenge.net/data/MOTS/](https://www.google.com/url?q=https%3A%2F%2Fmotchallenge.net%2Fdata%2FMOTS%2F&sa=D&sntz=1&usg=AOvVaw0YWYn2oQZiewTAxCzJH_Fo) * This benchmark extends the traditional Multi-Object Tracking benchmark to a new benchmark defined on a pixel-level with precise segmentation masks. We annotated 8 challenging video sequences (4 training, 4 test) in unconstrained environments filmed with both static and moving cameras. Tracking, segmentation and evaluation are done in image coordinates. All sequences have been annotated with high accuracy on a pixel level, strictly following a well-defined protocol. [https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD) Setup: * cd /media/farshid/exfat128/code * Conda * conda create --name CenterTrack36cuda10 python=3.6 * conda activate CenterTrack * conda install pytorch torchvision -c pytorch * pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI' * CenterTrack_ROOT=/media/farshid/exfat128/code/CenterTrack * git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT * pip install -r requirements.txt * cd $CenterTrack_ROOT/src/lib/model/networks/ * git clone https://github.com/CharlesShang/DCNv2/ * cd DCNv2 * ./make.sh * Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb). * # AWS (11 December 2020) [https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD) * Conda * conda create --name CenterTrack36cuda10 python=3.6 * conda activate CenterTrack36cuda10 * conda install pytorch torchvision cudatoolkit=10.0 -c pytorch * conda install -c conda-forge ffmpeg * pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI' * CenterTrack_ROOT=/ * git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT * pip install -r requirements.txt * cd $CenterTrack_ROOT/src/lib/model/networks/ * git clone https://github.com/CharlesShang/DCNv2/ * cd DCNv2 * ./make.sh * Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb). * ### Training * cd $CenterTrack_ROOT/src/tools/ * bash get_mot_17.sh * Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/qY1sh7akX3HXB9hYYu2OsIVsGoUlGaEsm_n- RYYv92ytQ2eJUxQxXfwWzOtvEQIYYKpMG7mSME0CWWY4Yu9QpRc=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/qY1sh7akX3HXB9hYYu2OsIVsGoUlGaEsm_n- RYYv92ytQ2eJUxQxXfwWzOtvEQIYYKpMG7mSME0CWWY4Yu9QpRc=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # compile and setup source code sudo apt-get -o Dpkg::Options::="\--force-overwrite" install --fix-broken ======================== sudo nano ~/.bashsrc export PATH=/usr/local/cuda/bin${PATH:+:${PATH}} export LD_LIBRARY_PATH=/usr/local/cuda/lib64\ ${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}} =============== nvcc -std=c++17 -arch=sm_60 test.cu # cvtest: Computer Vision Test ## Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Do you want to test your output of computer vision application which is video or images? ## Standard test for computer vision application There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil) check S3 bucket in AWS for image and video files and versioning Check Docker load balancer, memory usage, ... GPU Video Tracking on Mac 1. create conda based on python 3.6 * conda create env_full -y \--name farshid python=3.6 * conda activate farshid 2. install OpenVino from Intel for converting deep learning model based on intel chips * conda install -y openvino-ie4py -c intel 3. install video library * conda install -y -c conda-forge ffmpeg 4. install pytorch and torchvision * conda install -y pytorch torchvision -c pytorch 5. conda install -y -c conda-forge matplotlib 6. conda install -y pandas scikit-learn plotly 7. conda install -y -c conda-forge opencv seaborn 8. conda install -y -c conda-forge tensorflow pip install torch torchvision torchaudio pip install matplotlib pandas scikit-learn plotly opencv seaborn tensorflow # Test for 2021 **3D Multi-Object Tracking: A Baseline and New Evaluation Metrics (IROS 2020, ECCVW 2020)[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv)**[**https://github.com/xinshuoweng/AB3DMOT**](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv) **** Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD- ts8sZsK3X32Hb2FD)[https://github.com/elliottwu/unsup3d](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD- ts8sZsK3X32Hb2FD) This repository contains the public release of the Python implementation of our Aggregate View Object Detection (AVOD) network for 3D object detection.[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl)[https://github.com/kujason/avod](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl) 𝚙𝚒𝚙 𝚒𝚗𝚜𝚝𝚊𝚕𝚕 𝚔3𝚍 # Run on Ubuntu PC + eGPU apt search nvidia-driver apt-cache search nvidia-driver sudo apt update sudo apt upgrade sudo apt install nvidia-driver-455 sudo reboot nvidia-smi Download cuDNN v7.6.5 (November 5th, 2019), for CUDA 10.0 * tar -xzvf cudnn-10.0-linux-x64-v7.6.5.32.tgz * sudo cp cuda/include/cudnn*.h /usr/local/cuda/include * sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64 * sudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn* * sudo dpkg -i libcudnn7_7.6.5.32-1+cuda10.0_amd64.deb * sudo dpkg -i libcudnn7-dev_7.6.5.32-1+cuda10.0_amd64.deb * sudo dpkg -i libcudnn7-doc_7.6.5.32-1+cuda10.0_amd64.deb * sudo apt-get install \ apt-transport-https \ ca-certificates \ curl \ gnupg-agent \ software-properties-common curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add - sudo apt-key fingerprint 0EBFCD88 sudo add-apt-repository \ "deb [arch=amd64] https://download.docker.com/linux/ubuntu \ $(lsb_release -cs) \ stable" sudo apt-get update sudo apt-get install docker-ce docker-ce-cli containerd.io Make sure you have installed the NVIDIA driver and Docker engine for your Linux distribution Note that you do not need to install the CUDA Toolkit on the host system, but the NVIDIA driver needs to be installed distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \ && curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - \ && curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia- docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list curl -s -L https://nvidia.github.io/nvidia-container- runtime/experimental/$distribution/nvidia-container-runtime.list | sudo tee /etc/apt/sources.list.d/nvidia-container-runtime.list sudo apt-get install -y nvidia-docker2 sudo systemctl restart docker sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi [Installing on CentOS 8 (AWS)](https://www.google.com/url?q=https%3A%2F%2Fdocs.nvidia.com%2Fdatacenter%2Fcloud- native%2Fcontainer-toolkit%2Finstall- guide.html%23docker&sa=D&sntz=1&usg=AOvVaw38cOFoMlAZS6R9Z4bKrU5Q) pip install cython; pip install -U 'git+[https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fcocodataset%2Fcocoapi.git%23subdirectory%3DPythonAPI&sa=D&sntz=1&usg=AOvVaw2XHIv2zk5mbXQ5VpooNoT4)' CenterTrack_ROOT=/home/farshid/code/CenterTrack git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT cd CenterTrack_ROOT pip install -r requirements.txt cd $CenterTrack_ROOT/src/lib/model/networks/ git clone https://github.com/CharlesShang/DCNv2/ cd DCNv2 ./make.sh [https://github.com/xingyizhou/CenterTrack/blob/master/readme/MODEL_ZOO.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb) # cvat sudo groupadd docker sudo usermod -aG docker $USER sudo apt-get --no-install-recommends install -y python3-pip python3-setuptools sudo python3 -m pip install setuptools docker-compose sudo apt-get --no-install-recommends install -y git git clone https://github.com/opencv/cvat cd cvat sudo docker-compose build sudo docker-compose up -d sudo docker exec -it cvat bash -ic 'python3 ~/manage.py createsuperuser' [http://localhost:8080/](http://www.google.com/url?q=http%3A%2F%2Flocalhost%3A8080%2F&sa=D&sntz=1&usg=AOvVaw3oeouV3qFXFcGyLGuDDpKa) # Towards-Realtime-MOT * conda activate cuda100 * pip install motmetrics * pip install cython_bbox * conda install -c conda-forge ffmpeg * [https://openaccess.thecvf.com/content_CVPR_2020/papers/Xu_How_to_Train_Your_Deep_Multi- Object_Tracker_CVPR_2020_paper.pdf](https://www.google.com/url?q=https%3A%2F%2Fopenaccess.thecvf.com%2Fcontent_CVPR_2020%2Fpapers%2FXu_How_to_Train_Your_Deep_Multi- Object_Tracker_CVPR_2020_paper.pdf&sa=D&sntz=1&usg=AOvVaw0GwXtXPI4_xmM- qU7ZrLmr) [https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki) git clone https://gitlab.inria.fr/yixu/deepmot.git sudo apt-get install libpng-dev sudo apt install libfreetype6-dev pip install -r requirements.txt ImportError: torch.utils.ffi is deprecated. Please use cpp extensions instead. conda create -y --name cuda92 python=3.6 conda activate cuda92 source activate cuda92 conda install pytorch==0.4.1 torchvision==0.2.0 cudatoolkit=9.2 -c pytorch conda install -c conda-forge ffmpeg * conda create -n cuda100 * conda activate cuda100 conda install pytorch torchvision cudatoolkit=10.0 -c pytorch # [https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki) AWS # AWS ## [Towards-Realtime-MOT ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fifzhang%2FFairMOT&sa=D&sntz=1&usg=AOvVaw3KRCUNb1LrlLLYIG2wTulN) * conda create -n FairMOT * conda activate FairMOT * conda install pytorch==1.2.0 torchvision==0.4.0 cudatoolkit=10.0 -c pytorch * cd ${FAIRMOT_ROOT} * pip install -r requirements.txt * conda install -c conda-forge ffmpeg * [MOTS: Multi-Object Tracking and Segmentation](http://www.google.com/url?q=http%3A%2F%2Farxiv.org%2Fabs%2F1902.03604&sa=D&sntz=1&usg=AOvVaw08PSI8pfXxFnpxsLjY3pix) * Paper: [https://arxiv.org/pdf/1902.03604](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fpdf%2F1902.03604&sa=D&sntz=1&usg=AOvVaw2JyGSgtPJlejKIa5-k3N3t) * Dataset: [https://motchallenge.net/data/MOTS/](https://www.google.com/url?q=https%3A%2F%2Fmotchallenge.net%2Fdata%2FMOTS%2F&sa=D&sntz=1&usg=AOvVaw0YWYn2oQZiewTAxCzJH_Fo) * This benchmark extends the traditional Multi-Object Tracking benchmark to a new benchmark defined on a pixel-level with precise segmentation masks. We annotated 8 challenging video sequences (4 training, 4 test) in unconstrained environments filmed with both static and moving cameras. Tracking, segmentation and evaluation are done in image coordinates. All sequences have been annotated with high accuracy on a pixel level, strictly following a well-defined protocol. [https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD) Setup: * cd /media/farshid/exfat128/code * Conda * conda create --name CenterTrack36cuda10 python=3.6 * conda activate CenterTrack * conda install pytorch torchvision -c pytorch * pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI' * CenterTrack_ROOT=/media/farshid/exfat128/code/CenterTrack * git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT * pip install -r requirements.txt * cd $CenterTrack_ROOT/src/lib/model/networks/ * git clone https://github.com/CharlesShang/DCNv2/ * cd DCNv2 * ./make.sh * Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb). * # AWS (11 December 2020) [https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD) * Conda * conda create --name CenterTrack36cuda10 python=3.6 * conda activate CenterTrack36cuda10 * conda install pytorch torchvision cudatoolkit=10.0 -c pytorch * conda install -c conda-forge ffmpeg * pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI' * CenterTrack_ROOT=/ * git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT * pip install -r requirements.txt * cd $CenterTrack_ROOT/src/lib/model/networks/ * git clone https://github.com/CharlesShang/DCNv2/ * cd DCNv2 * ./make.sh * Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb). * ### Training * cd $CenterTrack_ROOT/src/tools/ * bash get_mot_17.sh * Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/qY1sh7akX3HXB9hYYu2OsIVsGoUlGaEsm_n- RYYv92ytQ2eJUxQxXfwWzOtvEQIYYKpMG7mSME0CWWY4Yu9QpRc=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/qY1sh7akX3HXB9hYYu2OsIVsGoUlGaEsm_n- RYYv92ytQ2eJUxQxXfwWzOtvEQIYYKpMG7mSME0CWWY4Yu9QpRc=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # compile and setup source code sudo apt-get -o Dpkg::Options::="\--force-overwrite" install --fix-broken ======================== sudo nano ~/.bashsrc export PATH=/usr/local/cuda/bin${PATH:+:${PATH}} export LD_LIBRARY_PATH=/usr/local/cuda/lib64\ ${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}} =============== nvcc -std=c++17 -arch=sm_60 test.cu # cvtest: Computer Vision Test ## Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Do you want to test your output of computer vision application which is video or images? ## Standard test for computer vision application There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil) check S3 bucket in AWS for image and video files and versioning Check Docker load balancer, memory usage, ... GPU Video Tracking on Mac 1. create conda based on python 3.6 * conda create env_full -y \--name farshid python=3.6 * conda activate farshid 2. install OpenVino from Intel for converting deep learning model based on intel chips * conda install -y openvino-ie4py -c intel 3. install video library * conda install -y -c conda-forge ffmpeg 4. install pytorch and torchvision * conda install -y pytorch torchvision -c pytorch 5. conda install -y -c conda-forge matplotlib 6. conda install -y pandas scikit-learn plotly 7. conda install -y -c conda-forge opencv seaborn 8. conda install -y -c conda-forge tensorflow pip install torch torchvision torchaudio pip install matplotlib pandas scikit-learn plotly opencv seaborn tensorflow # Test for 2021 **3D Multi-Object Tracking: A Baseline and New Evaluation Metrics (IROS 2020, ECCVW 2020)[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv)**[**https://github.com/xinshuoweng/AB3DMOT**](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv) **** Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD- ts8sZsK3X32Hb2FD)[https://github.com/elliottwu/unsup3d](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD- ts8sZsK3X32Hb2FD) This repository contains the public release of the Python implementation of our Aggregate View Object Detection (AVOD) network for 3D object detection.[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl)[https://github.com/kujason/avod](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl) 𝚙𝚒𝚙 𝚒𝚗𝚜𝚝𝚊𝚕𝚕 𝚔3𝚍 # Run on Ubuntu PC + eGPU apt search nvidia-driver apt-cache search nvidia-driver sudo apt update sudo apt upgrade sudo apt install nvidia-driver-455 sudo reboot nvidia-smi Download cuDNN v7.6.5 (November 5th, 2019), for CUDA 10.0 * tar -xzvf cudnn-10.0-linux-x64-v7.6.5.32.tgz * sudo cp cuda/include/cudnn*.h /usr/local/cuda/include * sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64 * sudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn* * sudo dpkg -i libcudnn7_7.6.5.32-1+cuda10.0_amd64.deb * sudo dpkg -i libcudnn7-dev_7.6.5.32-1+cuda10.0_amd64.deb * sudo dpkg -i libcudnn7-doc_7.6.5.32-1+cuda10.0_amd64.deb * sudo apt-get install \ apt-transport-https \ ca-certificates \ curl \ gnupg-agent \ software-properties-common curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add - sudo apt-key fingerprint 0EBFCD88 sudo add-apt-repository \ "deb [arch=amd64] https://download.docker.com/linux/ubuntu \ $(lsb_release -cs) \ stable" sudo apt-get update sudo apt-get install docker-ce docker-ce-cli containerd.io Make sure you have installed the NVIDIA driver and Docker engine for your Linux distribution Note that you do not need to install the CUDA Toolkit on the host system, but the NVIDIA driver needs to be installed distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \ && curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - \ && curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia- docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list curl -s -L https://nvidia.github.io/nvidia-container- runtime/experimental/$distribution/nvidia-container-runtime.list | sudo tee /etc/apt/sources.list.d/nvidia-container-runtime.list sudo apt-get install -y nvidia-docker2 sudo systemctl restart docker sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi [Installing on CentOS 8 (AWS)](https://www.google.com/url?q=https%3A%2F%2Fdocs.nvidia.com%2Fdatacenter%2Fcloud- native%2Fcontainer-toolkit%2Finstall- guide.html%23docker&sa=D&sntz=1&usg=AOvVaw38cOFoMlAZS6R9Z4bKrU5Q) pip install cython; pip install -U 'git+[https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fcocodataset%2Fcocoapi.git%23subdirectory%3DPythonAPI&sa=D&sntz=1&usg=AOvVaw2XHIv2zk5mbXQ5VpooNoT4)' CenterTrack_ROOT=/home/farshid/code/CenterTrack git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT cd CenterTrack_ROOT pip install -r requirements.txt cd $CenterTrack_ROOT/src/lib/model/networks/ git clone https://github.com/CharlesShang/DCNv2/ cd DCNv2 ./make.sh [https://github.com/xingyizhou/CenterTrack/blob/master/readme/MODEL_ZOO.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb) # cvat sudo groupadd docker sudo usermod -aG docker $USER sudo apt-get --no-install-recommends install -y python3-pip python3-setuptools sudo python3 -m pip install setuptools docker-compose sudo apt-get --no-install-recommends install -y git git clone https://github.com/opencv/cvat cd cvat sudo docker-compose build sudo docker-compose up -d sudo docker exec -it cvat bash -ic 'python3 ~/manage.py createsuperuser' [http://localhost:8080/](http://www.google.com/url?q=http%3A%2F%2Flocalhost%3A8080%2F&sa=D&sntz=1&usg=AOvVaw3oeouV3qFXFcGyLGuDDpKa) # Towards-Realtime-MOT * conda activate cuda100 * pip install motmetrics * pip install cython_bbox * conda install -c conda-forge ffmpeg * [https://openaccess.thecvf.com/content_CVPR_2020/papers/Xu_How_to_Train_Your_Deep_Multi- Object_Tracker_CVPR_2020_paper.pdf](https://www.google.com/url?q=https%3A%2F%2Fopenaccess.thecvf.com%2Fcontent_CVPR_2020%2Fpapers%2FXu_How_to_Train_Your_Deep_Multi- Object_Tracker_CVPR_2020_paper.pdf&sa=D&sntz=1&usg=AOvVaw0GwXtXPI4_xmM- qU7ZrLmr) [https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki) git clone https://gitlab.inria.fr/yixu/deepmot.git sudo apt-get install libpng-dev sudo apt install libfreetype6-dev pip install -r requirements.txt ImportError: torch.utils.ffi is deprecated. Please use cpp extensions instead. conda create -y --name cuda92 python=3.6 conda activate cuda92 source activate cuda92 conda install pytorch==0.4.1 torchvision==0.2.0 cudatoolkit=9.2 -c pytorch conda install -c conda-forge ffmpeg * conda create -n cuda100 * conda activate cuda100 conda install pytorch torchvision cudatoolkit=10.0 -c pytorch # [https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki) AWS # AWS ## [Towards-Realtime-MOT ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fifzhang%2FFairMOT&sa=D&sntz=1&usg=AOvVaw3KRCUNb1LrlLLYIG2wTulN) * conda create -n FairMOT * conda activate FairMOT * conda install pytorch==1.2.0 torchvision==0.4.0 cudatoolkit=10.0 -c pytorch * cd ${FAIRMOT_ROOT} * pip install -r requirements.txt * conda install -c conda-forge ffmpeg * [MOTS: Multi-Object Tracking and Segmentation](http://www.google.com/url?q=http%3A%2F%2Farxiv.org%2Fabs%2F1902.03604&sa=D&sntz=1&usg=AOvVaw08PSI8pfXxFnpxsLjY3pix) * Paper: [https://arxiv.org/pdf/1902.03604](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fpdf%2F1902.03604&sa=D&sntz=1&usg=AOvVaw2JyGSgtPJlejKIa5-k3N3t) * Dataset: [https://motchallenge.net/data/MOTS/](https://www.google.com/url?q=https%3A%2F%2Fmotchallenge.net%2Fdata%2FMOTS%2F&sa=D&sntz=1&usg=AOvVaw0YWYn2oQZiewTAxCzJH_Fo) * This benchmark extends the traditional Multi-Object Tracking benchmark to a new benchmark defined on a pixel-level with precise segmentation masks. We annotated 8 challenging video sequences (4 training, 4 test) in unconstrained environments filmed with both static and moving cameras. Tracking, segmentation and evaluation are done in image coordinates. All sequences have been annotated with high accuracy on a pixel level, strictly following a well-defined protocol. [https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD) Setup: * cd /media/farshid/exfat128/code * Conda * conda create --name CenterTrack36cuda10 python=3.6 * conda activate CenterTrack * conda install pytorch torchvision -c pytorch * pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI' * CenterTrack_ROOT=/media/farshid/exfat128/code/CenterTrack * git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT * pip install -r requirements.txt * cd $CenterTrack_ROOT/src/lib/model/networks/ * git clone https://github.com/CharlesShang/DCNv2/ * cd DCNv2 * ./make.sh * Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb). * # AWS (11 December 2020) [https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD) * Conda * conda create --name CenterTrack36cuda10 python=3.6 * conda activate CenterTrack36cuda10 * conda install pytorch torchvision cudatoolkit=10.0 -c pytorch * conda install -c conda-forge ffmpeg * pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI' * CenterTrack_ROOT=/ * git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT * pip install -r requirements.txt * cd $CenterTrack_ROOT/src/lib/model/networks/ * git clone https://github.com/CharlesShang/DCNv2/ * cd DCNv2 * ./make.sh * Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb). * ### Training * cd $CenterTrack_ROOT/src/tools/ * bash get_mot_17.sh * Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/qY1sh7akX3HXB9hYYu2OsIVsGoUlGaEsm_n- RYYv92ytQ2eJUxQxXfwWzOtvEQIYYKpMG7mSME0CWWY4Yu9QpRc=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/qY1sh7akX3HXB9hYYu2OsIVsGoUlGaEsm_n- RYYv92ytQ2eJUxQxXfwWzOtvEQIYYKpMG7mSME0CWWY4Yu9QpRc=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # compile and setup source code sudo apt-get -o Dpkg::Options::="\--force-overwrite" install --fix-broken ======================== sudo nano ~/.bashsrc export PATH=/usr/local/cuda/bin${PATH:+:${PATH}} export LD_LIBRARY_PATH=/usr/local/cuda/lib64\ ${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}} =============== nvcc -std=c++17 -arch=sm_60 test.cu # cvtest: Computer Vision Test ## Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Do you want to test your output of computer vision application which is video or images? ## Standard test for computer vision application There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil) check S3 bucket in AWS for image and video files and versioning Check Docker load balancer, memory usage, ... GPU Video Tracking on Mac 1. create conda based on python 3.6 * conda create env_full -y \--name farshid python=3.6 * conda activate farshid 2. install OpenVino from Intel for converting deep learning model based on intel chips * conda install -y openvino-ie4py -c intel 3. install video library * conda install -y -c conda-forge ffmpeg 4. install pytorch and torchvision * conda install -y pytorch torchvision -c pytorch 5. conda install -y -c conda-forge matplotlib 6. conda install -y pandas scikit-learn plotly 7. conda install -y -c conda-forge opencv seaborn 8. conda install -y -c conda-forge tensorflow pip install torch torchvision torchaudio pip install matplotlib pandas scikit-learn plotly opencv seaborn tensorflow # Test for 2021 **3D Multi-Object Tracking: A Baseline and New Evaluation Metrics (IROS 2020, ECCVW 2020)[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv)**[**https://github.com/xinshuoweng/AB3DMOT**](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv) **** Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD- ts8sZsK3X32Hb2FD)[https://github.com/elliottwu/unsup3d](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD- ts8sZsK3X32Hb2FD) This repository contains the public release of the Python implementation of our Aggregate View Object Detection (AVOD) network for 3D object detection.[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl)[https://github.com/kujason/avod](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl) 𝚙𝚒𝚙 𝚒𝚗𝚜𝚝𝚊𝚕𝚕 𝚔3𝚍 # Run on Ubuntu PC + eGPU apt search nvidia-driver apt-cache search nvidia-driver sudo apt update sudo apt upgrade sudo apt install nvidia-driver-455 sudo reboot nvidia-smi Download cuDNN v7.6.5 (November 5th, 2019), for CUDA 10.0 * tar -xzvf cudnn-10.0-linux-x64-v7.6.5.32.tgz * sudo cp cuda/include/cudnn*.h /usr/local/cuda/include * sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64 * sudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn* * sudo dpkg -i libcudnn7_7.6.5.32-1+cuda10.0_amd64.deb * sudo dpkg -i libcudnn7-dev_7.6.5.32-1+cuda10.0_amd64.deb * sudo dpkg -i libcudnn7-doc_7.6.5.32-1+cuda10.0_amd64.deb * sudo apt-get install \ apt-transport-https \ ca-certificates \ curl \ gnupg-agent \ software-properties-common curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add - sudo apt-key fingerprint 0EBFCD88 sudo add-apt-repository \ "deb [arch=amd64] https://download.docker.com/linux/ubuntu \ $(lsb_release -cs) \ stable" sudo apt-get update sudo apt-get install docker-ce docker-ce-cli containerd.io Make sure you have installed the NVIDIA driver and Docker engine for your Linux distribution Note that you do not need to install the CUDA Toolkit on the host system, but the NVIDIA driver needs to be installed distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \ && curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - \ && curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia- docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list curl -s -L https://nvidia.github.io/nvidia-container- runtime/experimental/$distribution/nvidia-container-runtime.list | sudo tee /etc/apt/sources.list.d/nvidia-container-runtime.list sudo apt-get install -y nvidia-docker2 sudo systemctl restart docker sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi [Installing on CentOS 8 (AWS)](https://www.google.com/url?q=https%3A%2F%2Fdocs.nvidia.com%2Fdatacenter%2Fcloud- native%2Fcontainer-toolkit%2Finstall- guide.html%23docker&sa=D&sntz=1&usg=AOvVaw38cOFoMlAZS6R9Z4bKrU5Q) pip install cython; pip install -U 'git+[https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fcocodataset%2Fcocoapi.git%23subdirectory%3DPythonAPI&sa=D&sntz=1&usg=AOvVaw2XHIv2zk5mbXQ5VpooNoT4)' CenterTrack_ROOT=/home/farshid/code/CenterTrack git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT cd CenterTrack_ROOT pip install -r requirements.txt cd $CenterTrack_ROOT/src/lib/model/networks/ git clone https://github.com/CharlesShang/DCNv2/ cd DCNv2 ./make.sh [https://github.com/xingyizhou/CenterTrack/blob/master/readme/MODEL_ZOO.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb) # cvat sudo groupadd docker sudo usermod -aG docker $USER sudo apt-get --no-install-recommends install -y python3-pip python3-setuptools sudo python3 -m pip install setuptools docker-compose sudo apt-get --no-install-recommends install -y git git clone https://github.com/opencv/cvat cd cvat sudo docker-compose build sudo docker-compose up -d sudo docker exec -it cvat bash -ic 'python3 ~/manage.py createsuperuser' [http://localhost:8080/](http://www.google.com/url?q=http%3A%2F%2Flocalhost%3A8080%2F&sa=D&sntz=1&usg=AOvVaw3oeouV3qFXFcGyLGuDDpKa) # Towards-Realtime-MOT * conda activate cuda100 * pip install motmetrics * pip install cython_bbox * conda install -c conda-forge ffmpeg * [https://openaccess.thecvf.com/content_CVPR_2020/papers/Xu_How_to_Train_Your_Deep_Multi- Object_Tracker_CVPR_2020_paper.pdf](https://www.google.com/url?q=https%3A%2F%2Fopenaccess.thecvf.com%2Fcontent_CVPR_2020%2Fpapers%2FXu_How_to_Train_Your_Deep_Multi- Object_Tracker_CVPR_2020_paper.pdf&sa=D&sntz=1&usg=AOvVaw0GwXtXPI4_xmM- qU7ZrLmr) [https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki) git clone https://gitlab.inria.fr/yixu/deepmot.git sudo apt-get install libpng-dev sudo apt install libfreetype6-dev pip install -r requirements.txt ImportError: torch.utils.ffi is deprecated. Please use cpp extensions instead. conda create -y --name cuda92 python=3.6 conda activate cuda92 source activate cuda92 conda install pytorch==0.4.1 torchvision==0.2.0 cudatoolkit=9.2 -c pytorch conda install -c conda-forge ffmpeg * conda create -n cuda100 * conda activate cuda100 conda install pytorch torchvision cudatoolkit=10.0 -c pytorch # [https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki) AWS # AWS ## [Towards-Realtime-MOT ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fifzhang%2FFairMOT&sa=D&sntz=1&usg=AOvVaw3KRCUNb1LrlLLYIG2wTulN) * conda create -n FairMOT * conda activate FairMOT * conda install pytorch==1.2.0 torchvision==0.4.0 cudatoolkit=10.0 -c pytorch * cd ${FAIRMOT_ROOT} * pip install -r requirements.txt * conda install -c conda-forge ffmpeg * [MOTS: Multi-Object Tracking and Segmentation](http://www.google.com/url?q=http%3A%2F%2Farxiv.org%2Fabs%2F1902.03604&sa=D&sntz=1&usg=AOvVaw08PSI8pfXxFnpxsLjY3pix) * Paper: [https://arxiv.org/pdf/1902.03604](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fpdf%2F1902.03604&sa=D&sntz=1&usg=AOvVaw2JyGSgtPJlejKIa5-k3N3t) * Dataset: [https://motchallenge.net/data/MOTS/](https://www.google.com/url?q=https%3A%2F%2Fmotchallenge.net%2Fdata%2FMOTS%2F&sa=D&sntz=1&usg=AOvVaw0YWYn2oQZiewTAxCzJH_Fo) * This benchmark extends the traditional Multi-Object Tracking benchmark to a new benchmark defined on a pixel-level with precise segmentation masks. We annotated 8 challenging video sequences (4 training, 4 test) in unconstrained environments filmed with both static and moving cameras. Tracking, segmentation and evaluation are done in image coordinates. All sequences have been annotated with high accuracy on a pixel level, strictly following a well-defined protocol. [https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD) Setup: * cd /media/farshid/exfat128/code * Conda * conda create --name CenterTrack36cuda10 python=3.6 * conda activate CenterTrack * conda install pytorch torchvision -c pytorch * pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI' * CenterTrack_ROOT=/media/farshid/exfat128/code/CenterTrack * git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT * pip install -r requirements.txt * cd $CenterTrack_ROOT/src/lib/model/networks/ * git clone https://github.com/CharlesShang/DCNv2/ * cd DCNv2 * ./make.sh * Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb). * # AWS (11 December 2020) [https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD) * Conda * conda create --name CenterTrack36cuda10 python=3.6 * conda activate CenterTrack36cuda10 * conda install pytorch torchvision cudatoolkit=10.0 -c pytorch * conda install -c conda-forge ffmpeg * pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI' * CenterTrack_ROOT=/ * git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT * pip install -r requirements.txt * cd $CenterTrack_ROOT/src/lib/model/networks/ * git clone https://github.com/CharlesShang/DCNv2/ * cd DCNv2 * ./make.sh * Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb). * ### Training * cd $CenterTrack_ROOT/src/tools/ * bash get_mot_17.sh * Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/HlmNuqyxk5gzYGxXDtUChEUYREK389bSHhqeXOjPaBaXuH- OfSzmgk4xvNu5e2EN7ntuZo418EeXtzqP8ztw9tk=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/HlmNuqyxk5gzYGxXDtUChEUYREK389bSHhqeXOjPaBaXuH- OfSzmgk4xvNu5e2EN7ntuZo418EeXtzqP8ztw9tk=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # compile and setup source code sudo apt-get -o Dpkg::Options::="\--force-overwrite" install --fix-broken ======================== sudo nano ~/.bashsrc export PATH=/usr/local/cuda/bin${PATH:+:${PATH}} export LD_LIBRARY_PATH=/usr/local/cuda/lib64\ ${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}} =============== nvcc -std=c++17 -arch=sm_60 test.cu # cvtest: Computer Vision Test ## Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Do you want to test your output of computer vision application which is video or images? ## Standard test for computer vision application There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil) check S3 bucket in AWS for image and video files and versioning Check Docker load balancer, memory usage, ... GPU Video Tracking on Mac 1. create conda based on python 3.6 * conda create env_full -y \--name farshid python=3.6 * conda activate farshid 2. install OpenVino from Intel for converting deep learning model based on intel chips * conda install -y openvino-ie4py -c intel 3. install video library * conda install -y -c conda-forge ffmpeg 4. install pytorch and torchvision * conda install -y pytorch torchvision -c pytorch 5. conda install -y -c conda-forge matplotlib 6. conda install -y pandas scikit-learn plotly 7. conda install -y -c conda-forge opencv seaborn 8. conda install -y -c conda-forge tensorflow pip install torch torchvision torchaudio pip install matplotlib pandas scikit-learn plotly opencv seaborn tensorflow # Test for 2021 **3D Multi-Object Tracking: A Baseline and New Evaluation Metrics (IROS 2020, ECCVW 2020)[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv)**[**https://github.com/xinshuoweng/AB3DMOT**](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxinshuoweng%2FAB3DMOT&sa=D&sntz=1&usg=AOvVaw2sv5blUXTNooMtyY2KFmpv) **** Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD- ts8sZsK3X32Hb2FD)[https://github.com/elliottwu/unsup3d](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Felliottwu%2Funsup3d&sa=D&sntz=1&usg=AOvVaw34XTD- ts8sZsK3X32Hb2FD) This repository contains the public release of the Python implementation of our Aggregate View Object Detection (AVOD) network for 3D object detection.[ ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl)[https://github.com/kujason/avod](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fkujason%2Favod&sa=D&sntz=1&usg=AOvVaw0rx7YKTMVcbO8JdGBBPUZl) 𝚙𝚒𝚙 𝚒𝚗𝚜𝚝𝚊𝚕𝚕 𝚔3𝚍 # Run on Ubuntu PC + eGPU apt search nvidia-driver apt-cache search nvidia-driver sudo apt update sudo apt upgrade sudo apt install nvidia-driver-455 sudo reboot nvidia-smi Download cuDNN v7.6.5 (November 5th, 2019), for CUDA 10.0 * tar -xzvf cudnn-10.0-linux-x64-v7.6.5.32.tgz * sudo cp cuda/include/cudnn*.h /usr/local/cuda/include * sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64 * sudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn* * sudo dpkg -i libcudnn7_7.6.5.32-1+cuda10.0_amd64.deb * sudo dpkg -i libcudnn7-dev_7.6.5.32-1+cuda10.0_amd64.deb * sudo dpkg -i libcudnn7-doc_7.6.5.32-1+cuda10.0_amd64.deb * sudo apt-get install \ apt-transport-https \ ca-certificates \ curl \ gnupg-agent \ software-properties-common curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add - sudo apt-key fingerprint 0EBFCD88 sudo add-apt-repository \ "deb [arch=amd64] https://download.docker.com/linux/ubuntu \ $(lsb_release -cs) \ stable" sudo apt-get update sudo apt-get install docker-ce docker-ce-cli containerd.io Make sure you have installed the NVIDIA driver and Docker engine for your Linux distribution Note that you do not need to install the CUDA Toolkit on the host system, but the NVIDIA driver needs to be installed distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \ && curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - \ && curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia- docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list curl -s -L https://nvidia.github.io/nvidia-container- runtime/experimental/$distribution/nvidia-container-runtime.list | sudo tee /etc/apt/sources.list.d/nvidia-container-runtime.list sudo apt-get install -y nvidia-docker2 sudo systemctl restart docker sudo docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi [Installing on CentOS 8 (AWS)](https://www.google.com/url?q=https%3A%2F%2Fdocs.nvidia.com%2Fdatacenter%2Fcloud- native%2Fcontainer-toolkit%2Finstall- guide.html%23docker&sa=D&sntz=1&usg=AOvVaw38cOFoMlAZS6R9Z4bKrU5Q) pip install cython; pip install -U 'git+[https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fcocodataset%2Fcocoapi.git%23subdirectory%3DPythonAPI&sa=D&sntz=1&usg=AOvVaw2XHIv2zk5mbXQ5VpooNoT4)' CenterTrack_ROOT=/home/farshid/code/CenterTrack git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT cd CenterTrack_ROOT pip install -r requirements.txt cd $CenterTrack_ROOT/src/lib/model/networks/ git clone https://github.com/CharlesShang/DCNv2/ cd DCNv2 ./make.sh [https://github.com/xingyizhou/CenterTrack/blob/master/readme/MODEL_ZOO.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb) # cvat sudo groupadd docker sudo usermod -aG docker $USER sudo apt-get --no-install-recommends install -y python3-pip python3-setuptools sudo python3 -m pip install setuptools docker-compose sudo apt-get --no-install-recommends install -y git git clone https://github.com/opencv/cvat cd cvat sudo docker-compose build sudo docker-compose up -d sudo docker exec -it cvat bash -ic 'python3 ~/manage.py createsuperuser' [http://localhost:8080/](http://www.google.com/url?q=http%3A%2F%2Flocalhost%3A8080%2F&sa=D&sntz=1&usg=AOvVaw3oeouV3qFXFcGyLGuDDpKa) # Towards-Realtime-MOT * conda activate cuda100 * pip install motmetrics * pip install cython_bbox * conda install -c conda-forge ffmpeg * [https://openaccess.thecvf.com/content_CVPR_2020/papers/Xu_How_to_Train_Your_Deep_Multi- Object_Tracker_CVPR_2020_paper.pdf](https://www.google.com/url?q=https%3A%2F%2Fopenaccess.thecvf.com%2Fcontent_CVPR_2020%2Fpapers%2FXu_How_to_Train_Your_Deep_Multi- Object_Tracker_CVPR_2020_paper.pdf&sa=D&sntz=1&usg=AOvVaw0GwXtXPI4_xmM- qU7ZrLmr) [https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki) git clone https://gitlab.inria.fr/yixu/deepmot.git sudo apt-get install libpng-dev sudo apt install libfreetype6-dev pip install -r requirements.txt ImportError: torch.utils.ffi is deprecated. Please use cpp extensions instead. conda create -y --name cuda92 python=3.6 conda activate cuda92 source activate cuda92 conda install pytorch==0.4.1 torchvision==0.2.0 cudatoolkit=9.2 -c pytorch conda install -c conda-forge ffmpeg * conda create -n cuda100 * conda activate cuda100 conda install pytorch torchvision cudatoolkit=10.0 -c pytorch # [https://gitlab.inria.fr/yixu/deepmot](https://www.google.com/url?q=https%3A%2F%2Fgitlab.inria.fr%2Fyixu%2Fdeepmot&sa=D&sntz=1&usg=AOvVaw0HbQIEu2MvpQZhQzJh2mki) AWS # AWS ## [Towards-Realtime-MOT ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fifzhang%2FFairMOT&sa=D&sntz=1&usg=AOvVaw3KRCUNb1LrlLLYIG2wTulN) * conda create -n FairMOT * conda activate FairMOT * conda install pytorch==1.2.0 torchvision==0.4.0 cudatoolkit=10.0 -c pytorch * cd ${FAIRMOT_ROOT} * pip install -r requirements.txt * conda install -c conda-forge ffmpeg * [MOTS: Multi-Object Tracking and Segmentation](http://www.google.com/url?q=http%3A%2F%2Farxiv.org%2Fabs%2F1902.03604&sa=D&sntz=1&usg=AOvVaw08PSI8pfXxFnpxsLjY3pix) * Paper: [https://arxiv.org/pdf/1902.03604](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fpdf%2F1902.03604&sa=D&sntz=1&usg=AOvVaw2JyGSgtPJlejKIa5-k3N3t) * Dataset: [https://motchallenge.net/data/MOTS/](https://www.google.com/url?q=https%3A%2F%2Fmotchallenge.net%2Fdata%2FMOTS%2F&sa=D&sntz=1&usg=AOvVaw0YWYn2oQZiewTAxCzJH_Fo) * This benchmark extends the traditional Multi-Object Tracking benchmark to a new benchmark defined on a pixel-level with precise segmentation masks. We annotated 8 challenging video sequences (4 training, 4 test) in unconstrained environments filmed with both static and moving cameras. Tracking, segmentation and evaluation are done in image coordinates. All sequences have been annotated with high accuracy on a pixel level, strictly following a well-defined protocol. [https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD) Setup: * cd /media/farshid/exfat128/code * Conda * conda create --name CenterTrack36cuda10 python=3.6 * conda activate CenterTrack * conda install pytorch torchvision -c pytorch * pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI' * CenterTrack_ROOT=/media/farshid/exfat128/code/CenterTrack * git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT * pip install -r requirements.txt * cd $CenterTrack_ROOT/src/lib/model/networks/ * git clone https://github.com/CharlesShang/DCNv2/ * cd DCNv2 * ./make.sh * Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb). * # AWS (11 December 2020) [https://github.com/xingyizhou/CenterTrack](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack&sa=D&sntz=1&usg=AOvVaw0rc6wf8IZkUL77tjl4GjnD) * Conda * conda create --name CenterTrack36cuda10 python=3.6 * conda activate CenterTrack36cuda10 * conda install pytorch torchvision cudatoolkit=10.0 -c pytorch * conda install -c conda-forge ffmpeg * pip install cython; pip install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI' * CenterTrack_ROOT=/ * git clone --recursive https://github.com/xingyizhou/CenterTrack $CenterTrack_ROOT * pip install -r requirements.txt * cd $CenterTrack_ROOT/src/lib/model/networks/ * git clone https://github.com/CharlesShang/DCNv2/ * cd DCNv2 * ./make.sh * Download pertained models for [monocular 3D tracking](https://drive.google.com/open?id=1e8zR1m1QMJne-Tjp-2iY_o81hn2CiQRt), [80-category tracking](https://drive.google.com/open?id=1tJCEJmdtYIh8VuN8CClGNws3YO7QGd40), or [pose tracking](https://drive.google.com/open?id=1H0YvFYCOIZ06EzAkC2NxECNQGXxK27hH) and move them to $CenterTrack_ROOT/models/. More models can be found in [Model zoo](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxingyizhou%2FCenterTrack%2Fblob%2Fmaster%2Freadme%2FMODEL_ZOO.md&sa=D&sntz=1&usg=AOvVaw2O5LbD1zzQ6xnG4kSfswGb). * ### Training * cd $CenterTrack_ROOT/src/tools/ * bash get_mot_17.sh * Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. 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[https://youtu.be/dgYy4Cf1qO4](https://youtu.be/dgYy4Cf1qO4) * /bin/bash -c "$(curl -fsSL [https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh](https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh))" * brew install jpeg libpng libtiff openexr * brew install opencv * * find path of OpenCV. in order to see hidden folders and file in Mac you can use "Command+Shift+Dot" * export PKG_CONFIG_PATH="/usr/local/Cellar/opencv/4.7.0_1/lib/pkgconfig:$PKG_CONFIG_PATH" * pkg-config --cflags opencv4 * s * Makefile TARGET = ./main SRCS := $(wildcard ./src/*.cpp ./*.cpp) OBJS := $(patsubst %cpp,%o,$(SRCS)) CFLG = -g -Wall -I/usr/local/Cellar/opencv/4.7.0_1/include/opencv4 -Iinc -I./ -std=c++17 LDFG = -Wl, $(shell pkg-config opencv --cflags --libs) CXX = g++ $(TARGET) : $(OBJS) $(CXX) -o $(TARGET) $(OBJS) $(LDFG) %.o:%.cpp $(CXX) $(CFLG) -c $< -o $@ clean: -rm ./*.o * tasks.json { "version": "2.0.0", "tasks": [ { "label": "Build", "type": "shell", "command": "g++", "args": [ "-std=c++17", "${file}", "-o", "${fileDirname}/${fileBasenameNoExtension}.out", "-I", "/usr/local/Cellar/opencv/4.7.0_1/include/opencv4/opencv2", "-I", "/usr/local/Cellar/opencv/4.7.0_1/include/opencv4", "-L", "/usr/local/Cellar/opencv/4.7.0_1/lib", "-l", "opencv_stitching", "-l", "opencv_superres", "-l", "opencv_videostab", "-l", "opencv_aruco", "-l", "opencv_bgsegm", "-l", "opencv_bioinspired", "-l", "opencv_ccalib", "-l", "opencv_dnn_objdetect", "-l", "opencv_dpm", "-l", "opencv_face", "-l", "opencv_fuzzy", "-l", "opencv_hfs", "-l", "opencv_img_hash", "-l", "opencv_line_descriptor", "-l", "opencv_optflow", "-l", "opencv_reg", "-l", "opencv_rgbd", "-l", "opencv_saliency", "-l", "opencv_stereo", "-l", "opencv_structured_light", "-l", "opencv_phase_unwrapping", "-l", "opencv_surface_matching", "-l", "opencv_tracking", "-l", "opencv_datasets", "-l", "opencv_dnn", "-l", "opencv_plot", "-l", "opencv_xfeatures2d", "-l", "opencv_shape", "-l", "opencv_video", "-l", "opencv_ml", "-l", "opencv_ximgproc", "-l", "opencv_xobjdetect", "-l", "opencv_objdetect", "-l", "opencv_calib3d", "-l", "opencv_features2d", "-l", "opencv_highgui", "-l", "opencv_videoio", "-l", "opencv_imgcodecs", "-l", "opencv_flann", "-l", "opencv_xphoto", "-l", "opencv_photo", "-l", "opencv_imgproc", "-l", "opencv_core", "-g" ], "group": { "kind": "build", "isDefault": true }, "problemMatcher": [ "$gcc" ] } ] } * launch.json { "version": "0.2.0", "configurations": [ { "name": "(lldb) Launch", "type": "cppdbg", "request": "launch", "program": "${fileDirname}/${fileBasenameNoExtension}.out", "args": [], "stopAtEntry": true, "cwd": "${workspaceFolder}", "environment": [], "externalConsole": true, "MIMode": "lldb", "preLaunchTask": "Build" } ] } * if you do not want external terminal (you want to use internal terminal in vscode) you may change the line * "externalConsole": false * if you do not want debug and run line by line the code and not stop in first line of code (F10 or F5 to continue) you may change the line to * "stopAtEntry": false * c_cpp_properties.json { "configurations": [ { "name": "Mac", "includePath": [ "${workspaceFolder}/**", "/usr/local/Cellar/opencv/4.7.0_1/include/opencv4", "/usr/local/Cellar/opencv/4.7.0_1/include" ], "defines": [], "macFrameworkPath": [], "compilerPath": "/usr/bin/g++", "cStandard": "c17", "cppStandard": "c++17", "intelliSenseMode": "clang-x64", "browse": { "path": [ "/usr/local/Cellar/opencv/4.7.0_1/include/opencv4" ], "limitSymbolsToIncludedHeaders": true, "databaseFilename": "" } } ], "version": 4 } * /bin/bash -c "$(curl -fsSL [https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh](https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh))" * brew install jpeg libpng libtiff openexr * brew install opencv * * export PKG_CONFIG_PATH="/usr/local/Cellar/opencv/4.7.0_1/lib/pkgconfig:$PKG_CONFIG_PATH" * pkg-config --cflags opencv4 * in VS code add below into c_cpp_properties.json file in .vscode * * "includePath": [ ... "/usr/local/opt/opencv/include/opencv4" ... Command+Shift+Dot. * /usr/local/Cellar/opencv/4.7.0_1/include/opencv4/** * copy .dylib files from lib folder (you can copy directly into your project or create folder and copy there) #include #include "opencv2/opencv.hpp" using namespace cv; using namespace std; int main(int argc, const char * argv[]) { // insert code here... cout << "OpenCV version : " << CV_VERSION << endl; cout << "Major version : " << CV_MAJOR_VERSION << endl; cout << "Minor version : " << CV_MINOR_VERSION << endl; cout << "Subminor version : " << CV_SUBMINOR_VERSION << endl; std::cout << "Hello, World!\n"; return 0; } To learn how to install OpenCV (C++) on a MacOS and utilize it in an Xcode project with a simple configuration, you can watch a video on YouTube. The video will include a download link, source code, and additional documents for reference. It is scheduled to be released in 2023. OpenCV (C++) is a popular computer vision library that allows developers to perform various image and video processing tasks such as object detection, face recognition, and image segmentation. Using OpenCV on a MacOS provides developers with a stable and reliable platform to build their computer vision applications. Additionally, MacOS has a user-friendly interface and powerful development tools, such as Xcode, which can help streamline the development process. Overall, utilizing OpenCV on a MacOS can help developers create high- performance computer vision applications with ease. Metal is a low-level graphics framework developed by Apple that can be used in conjunction with OpenCV (C++) on a MacOS. Metal provides a high-performance computing environment for developers to process large amounts of data while minimizing CPU usage. By combining the power of Metal with the feature-rich OpenCV library, developers can create high-performance computer vision applications that are capable of processing large amounts of data in real- time. Metal's efficient graphics pipeline and parallel processing capabilities make it ideal for use in computer vision applications. It provides a high level of performance and scalability for tasks such as image and video processing, object recognition, and machine learning. Additionally, Metal's seamless integration with the Xcode development environment makes it easy to use in combination with OpenCV for MacOS application development. Overall, using Metal with OpenCV (C++) on a MacOS can help developers create high-performance and efficient computer vision applications. 1. Real-time object detection: With the help of OpenCV's object detection algorithms and Metal's parallel processing capabilities, developers can build an application that can detect and track objects in real-time video streams. 2. Image segmentation: Image segmentation is the process of dividing an image into different regions or segments, each of which represents a different object or part of the image. Using OpenCV with Metal, developers can build an application that can perform image segmentation in real-time, making it useful for various applications such as medical imaging. 3. Facial recognition: With OpenCV's facial recognition algorithms and Metal's parallel processing capabilities, developers can build a facial recognition system that can quickly and accurately identify people in real-time. 4. Machine learning: OpenCV provides a rich set of machine learning tools, and Metal's parallel processing capabilities can be used to train and run machine learning models in real-time. Overall, the combination of OpenCV with Metal on a MacOS provides developers with a powerful platform to build a wide range of computer vision applications. ![](https://lh6.googleusercontent.com/oSvCHCNMDU6Fo6pKqp8WTFMfube6J9G2OUx- WfNQe75r5II935Hol1rR9d0mQMGYGGojRrrClEkHQ3cZ2q2wasMvVq0LXPohDqW2HpWVqn99wyTo_yODZhVNQiXpGNuIXw=w1280) * brew install pkg-config * export PKG_CONFIG_PATH=/usr/local/lib/pkgconfig Compile 1: sudo xcodebuild -license sudo xcode-select --install /usr/bin/ruby -e "$(curl -fsSL [https://raw.githubusercontent.com/Homebrew/install/master/install](https://raw.githubusercontent.com/Homebrew/install/master/install))" * brew install cmake pkg-config * brew install jpeg libpng libtiff openexr * brew install wget * brew install --cask yuna * sudo ./cmake-gui * brew install cmake brew install --cask cmake cd ~/ git clone https://github.com/opencv/opencv.git git clone [https://github.com/opencv/opencv_contrib.git](https://github.com/opencv/opencv_contrib.git) * git branch -a * git switch 5.x mkdir build_opencv cd build_opencv cmake gui * opencv flolder * opencv build folder * Unix makefile compiler ( do not select Xcode) * OPENCV_EXTRA_MODULES_PATH to modules * OPENCV_ENABLE_NONFREE=ON * configure again * generate * remove below items * zlib * Java = 2x * imgcode * ipp = 2x * xfeatures2d * face * wechat qrcode * imgproc * ade * make -j8 sh setup_vars.sh sudo make install Compile 3: sudo xcodebuild -license sudo xcode-select --install /usr/bin/ruby -e "$(curl -fsSL [https://raw.githubusercontent.com/Homebrew/install/master/install](https://raw.githubusercontent.com/Homebrew/install/master/install))" * brew install cmake pkg-config * brew install jpeg libpng libtiff openexr * brew install wget * brew install --cask yuna * sudo ./cmake-gui * brew install cmake brew install --cask cmake cd ~/ git clone https://github.com/opencv/opencv.git git clone [https://github.com/opencv/opencv_contrib.git](https://github.com/opencv/opencv_contrib.git) * git branch -a * git switch 5.x mkdir build_opencv cd build_opencv cmake * opencv flolder * opencv build folder * Unix makefile compiler ( do not select Xcode) * OPENCV_EXTRA_MODULES_PATH to /modules * OPENCV_ENABLE_NONFREE=ON * configure again * generate cmake -D CMAKE_BUILD_TYPE=RELEASE \ -D CMAKE_INSTALL_PREFIX=/usr/local \ -D OPENCV_EXTRA_MODULES_PATH=/Users/farshid/code/opencv_contrib/modules \ -D PYTHON3_LIBRARY=`python -c 'import subprocess ; import sys ; s = subprocess.check_output("python-config --configdir", shell=True).decode("utf-8").strip() ; (M, m) = sys.version_info[:2] ; print("{}/libpython{}.{}.dylib".format(s, M, m))'` \ -D PYTHON3_INCLUDE_DIR=`python -c 'import distutils.sysconfig as s; print(s.get_python_inc())'` \ -D PYTHON3_EXECUTABLE=$VIRTUAL_ENV/bin/python \ -D BUILD_opencv_python2=OFF \ -D BUILD_opencv_python3=ON \ -D INSTALL_PYTHON_EXAMPLES=ON \ -D INSTALL_C_EXAMPLES=OFF \ -D OPENCV_ENABLE_NONFREE=ON \ -D BUILD_EXAMPLES=ON ../opencv rm CMakeCache.txt make -j8 sh setup_vars.sh sudo make install Compile: brew install cmake brew install --cask cmake cd ~/ git clone https://github.com/opencv/opencv.git git clone [https://github.com/opencv/opencv_contrib.git](https://github.com/opencv/opencv_contrib.git) * git branch -a * git switch 5.x mkdir build_opencv cd build_opencv cmake gui * opencv flolder * opencv build folder * Unix makefile compiler ( do not select Xcode) * OPENCV_EXTRA_MODULES_PATH to /modules * configure again * generate * remove below items * zlib * Java = 2x * imgcode * ipp = 2x * xfeatures2d * face * wechat qrcode * imgproc * ade * make -j8 sh setup_vars.sh sudo make install brew install pkg-config /usr/local/include/opencv5/** [https://anuragajwani.medium.com/how-to-develop-an-opencv-c-algorithm-in- xcode-d676b9aad1b7](https://anuragajwani.medium.com/how-to-develop-an-opencv- c-algorithm-in-xcode-d676b9aad1b7) [https://dev.to/0xkoji/use-opencv-with-xcode-41n0](https://dev.to/0xkoji/use- opencv-with-xcode-41n0) [https://pyimagesearch.com/2018/08/17/install-opencv-4-on- macos/](https://pyimagesearch.com/2018/08/17/install-opencv-4-on-macos/) [https://github.com/angel- star/vscode_OpenCV_template_for_Mac](https://github.com/angel- star/vscode_OpenCV_template_for_Mac) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/Bdyq7IeZMRfpWBg_bTipaEN0RonLnAgiUdIQLZR9-Wx_X38SA993YgVrvUOq-l5saY8gWSt4673OVMEWnWRrJfs=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data 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[YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/Bdyq7IeZMRfpWBg_bTipaEN0RonLnAgiUdIQLZR9-Wx_X38SA993YgVrvUOq-l5saY8gWSt4673OVMEWnWRrJfs=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Python ![](https://lh3.googleusercontent.com/PLi43Jd4dmRotHI9WORIOIM0is9DI0VwayDejuipt_znoABjXDj5gQsqYAaLzgiMGTYz6mcc9DoAdCsX3_dGGq-n5stRfl65U7V-t7TX3jPwKE8mmst4BCcaf_u- _skIQg=w1280) ## ## global _ folder, file name, functions, const, ## doc read_all_image_in_folder recursive = True ## code ## doc manual progress bar for python based on number of images process. ## code ## doc this code shows information about image ## code import unittest for the functions: for example one time create file and then detected in between all operations in this function def setUp(self): def tearDown(self) for the class: @classmethod def setUpClass(cls): @classmethod def tearDownClass(cls): # List files and folders If you want to list directories which shows specific folder name in path in windows you can use dir /s /b /o:n /ad "farshid" > farshid.txt this command listed all directories which have "farshid" in the path and save it to the farshid.txt file in python you can use below code to search and find specific folders and files ###################################################################################### import import os import glob ###################################################################################### config root="C:\\\farshid\\\" specific_directories=root+"/**/farshid/**/*.jpg" path_dir_detection_check="" ###################################################################################### function files= glob.glob(specific_directories, recursive=True) for file in files: b=file.rfind("farshid") path_dir_detection=file[0:b-1] if (path_dir_detection != path_dir_detection_check): dirname = os.path.dirname(file) print("******************************************************* Next Directories ************************") print(dirname) path_dir_detection_check=path_dir_detection print(file) The source code can be found in [GitHub](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv_python%2Fblob%2Fmaster%2Flist_files_directories.py&sa=D&sntz=1&usg=AOvVaw3atI1rZO0cKOEBL2KZwnpr) make -j$(sysctl -n hw.physicalcpu) shift+enter -> run selection menue code-> pereferences -> user snippets -> python.json pip freeze > requirements.txt extensions Visual Studio IntelliCode SSH FS ext install Kelvin.vscode-sshfs command+ shift+ p ->SSH SF: Create new SSH SF configuration code ~/.zshrc Bracket Pair Colorizer 2 => color (){}[] different color Prettier - Code formatter => when you save. setting->(format on save) indent-rainbow shell => .code Compare Folders Command +p = all files -> if I press alt+ it open new tab the file control + ` = open terminal Command + o= open folder Command + , = open setting Command + /= # shift+enter=run one line of code option+shift+arrow down = duplicate line in code command+ click mouse => go to function command++ -> bigger command+ shift +P command+ K , command+ S => shourcuts command+ L => select currnt line command+ left/right arrow => start or end of line command+ P => go to file in search git config --global core.excludesfile ~/.gitignore code ~/.gitignore brew install pyenv brew install poetry pyenv install 3.7.5 pyenv global 3.7.5 poetry new "name of project" -> go to folder -> change python version if you want in the pyproject.toml pyenv global 3.7.5 poetry new "pytorch_pretrained" poetry install pip install --upgrade pip poetry add matplotlib numpy kubernetes=10.0.0 kfp=0.2.4 click=7.0.0 opencv- python opencv-contrib-python imutils pylint fastapi uvicorn python-dateutil seldon_core spacy sklearn torch torchvision jupyter pycocotools cython pyyaml==5.1 poetry remove torch torchvision pip install --pre torch torchvision -f[ ](https://www.google.com/url?q=https%3A%2F%2Fdownload.pytorch.org%2Fwhl%2Fnightly%2Fcpu%2Ftorch_nightly.html&sa=D&sntz=1&usg=AOvVaw3w8IL7Ros8Nwz3UDCws6is)[https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html](https://www.google.com/url?q=https%3A%2F%2Fdownload.pytorch.org%2Fwhl%2Fnightly%2Fcpu%2Ftorch_nightly.html&sa=D&sntz=1&usg=AOvVaw3w8IL7Ros8Nwz3UDCws6is) pip3 install matplotlib numpy kubernetes==10.0.0 kfp==0.2.4 click==7.0.0 opencv-python opencv-contrib-python imutils pylint fastapi uvicorn python- dateutil seldon_core spacy sklearn torch torchvision jupyter pycocotools cython pip3 install pyyaml==5.1 pip3 install 'git+https://github.com/facebookresearch/detectron2.git' pip3 install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI' poetry shell jupyter notebook pyenv pyenv install pyenv install 3.7.5 cd folder pyenv global 3.7.5 pyenv versions python -> you can see this environments poetry install (pyproject.toml) ~/.bash , .bash_profile , .zshrc poetry run which python poetry run jupyter lab pipenv install requests pyenv virtualenvs cv-endpoint pyenv activate cv-endpoint ==================== black python [https://github.com/psf/black](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpsf%2Fblack&sa=D&sntz=1&usg=AOvVaw2DSvjHmDvdtitO_AhjC1o1) The Uncompromising Code Formatter pip install black ==================== pre-commit A framework for managing and maintaining multi-language pre-commit hooks. pip install pre-commit brew install pre-commit .pre-commit-config.yaml repos: \- repo: [https://github.com/asottile/reorder_python_imports](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fasottile%2Freorder_python_imports&sa=D&sntz=1&usg=AOvVaw1gALoCbTPR6O-CSgrx7r5R) rev: v1.8.0 hooks: \- id: reorder-python-imports exclude: notebooks/ language_version: python3.7 \- repo: [https://github.com/ambv/black](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fambv%2Fblack&sa=D&sntz=1&usg=AOvVaw0k7gFGFzE6MyOzqjVo8Jto) rev: 19.10b0 hooks: \- id: black exclude: notebooks/ language_version: python3.7 \- repo: [https://github.com/pre-commit/pre-commit- hooks](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpre- commit%2Fpre-commit-hooks&sa=D&sntz=1&usg=AOvVaw3OA6YG1PaCw-Mf9zuTPohi) rev: v2.4.0 hooks: \- id: flake8 args: ['\--ignore=E203,E266,E501,W503', '\--max-line-length=88', '\--max- complexity=15', '\--select=B,C,E,F,W,T4,B9'] exclude: notebooks/ language_version: python3.7 pre-commit install pre-commit run --all-files git make file make check code . **add path** import sys sys.path.append(r'C: ) **create Mat** bin_im = np.zeros((5,16)) bin_im = bin_im.astype(np.uint8)*255 **contours** , hierarchy = cv2.findContours(opening, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) np. **savetxt** ("01-src.txt", im, fmt='%d', delimiter=', ', newline='\n', header='', footer='', comments='# ') **DLL** import ctypes my_dll = r"C:\fffffff.dll" lib = ctypes.windll.LoadLibrary(my_dll) **remove background or minimum form image** im = im - im.min() **time** e1 = cv2.getTickCount() ###### e2 = cv2.getTickCount() time = (e2 - e1)/ cv2.getTickFrequency() from scipy.signal import **find_peaks** peaks, out = find_peaks(Nf, distance=25) if peaks[0] < 25: peaks = peaks[1:] heights = Nf[peaks] **read matlab mat file to python** from scipy import io res = io.loadmat(r'MatlabResults\results2.mat', struct_as_record=False, squeeze_me=True) for k in res.keys(): print(k, res[k]) **remove background or minimum form image** im = im - im.min() **time** e1 = cv2.getTickCount() ###### e2 = cv2.getTickCount() time = (e2 - e1)/ cv2.getTickFrequency() from scipy.signal import **find_peaks** peaks, out = find_peaks(Nf, distance=25) if peaks[0] < 25: peaks = peaks[1:] heights = Nf[peaks] import sys sys.path.append(r'C: ) import sys sys.path.append(r'C: ) import sys sys.path.append(r'C: ) import sys sys.path.append(r'C: ) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/Bdyq7IeZMRfpWBg_bTipaEN0RonLnAgiUdIQLZR9-Wx_X38SA993YgVrvUOq-l5saY8gWSt4673OVMEWnWRrJfs=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data 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the functions: for example one time create file and then detected in between all operations in this function def setUp(self): def tearDown(self) for the class: @classmethod def setUpClass(cls): @classmethod def tearDownClass(cls): # List files and folders If you want to list directories which shows specific folder name in path in windows you can use dir /s /b /o:n /ad "farshid" > farshid.txt this command listed all directories which have "farshid" in the path and save it to the farshid.txt file in python you can use below code to search and find specific folders and files ###################################################################################### import import os import glob ###################################################################################### config root="C:\\\farshid\\\" specific_directories=root+"/**/farshid/**/*.jpg" path_dir_detection_check="" ###################################################################################### function files= glob.glob(specific_directories, recursive=True) for file in files: b=file.rfind("farshid") path_dir_detection=file[0:b-1] if (path_dir_detection != path_dir_detection_check): dirname = os.path.dirname(file) print("******************************************************* Next Directories ************************") print(dirname) path_dir_detection_check=path_dir_detection print(file) The source code can be found in [GitHub](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv_python%2Fblob%2Fmaster%2Flist_files_directories.py&sa=D&sntz=1&usg=AOvVaw3atI1rZO0cKOEBL2KZwnpr) make -j$(sysctl -n hw.physicalcpu) shift+enter -> run selection menue code-> pereferences -> user snippets -> python.json pip freeze > requirements.txt extensions Visual Studio IntelliCode SSH FS ext install Kelvin.vscode-sshfs command+ shift+ p ->SSH SF: Create new SSH SF configuration code ~/.zshrc Bracket Pair Colorizer 2 => color (){}[] different color Prettier - Code formatter => when you save. setting->(format on save) indent-rainbow shell => .code Compare Folders Command +p = all files -> if I press alt+ it open new tab the file control + ` = open terminal Command + o= open folder Command + , = open setting Command + /= # shift+enter=run one line of code option+shift+arrow down = duplicate line in code command+ click mouse => go to function command++ -> bigger command+ shift +P command+ K , command+ S => shourcuts command+ L => select currnt line command+ left/right arrow => start or end of line command+ P => go to file in search git config --global core.excludesfile ~/.gitignore code ~/.gitignore brew install pyenv brew install poetry pyenv install 3.7.5 pyenv global 3.7.5 poetry new "name of project" -> go to folder -> change python version if you want in the pyproject.toml pyenv global 3.7.5 poetry new "pytorch_pretrained" poetry install pip install --upgrade pip poetry add matplotlib numpy kubernetes=10.0.0 kfp=0.2.4 click=7.0.0 opencv- python opencv-contrib-python imutils pylint fastapi uvicorn python-dateutil seldon_core spacy sklearn torch torchvision jupyter pycocotools cython pyyaml==5.1 poetry remove torch torchvision pip install --pre torch torchvision -f[ ](https://www.google.com/url?q=https%3A%2F%2Fdownload.pytorch.org%2Fwhl%2Fnightly%2Fcpu%2Ftorch_nightly.html&sa=D&sntz=1&usg=AOvVaw3w8IL7Ros8Nwz3UDCws6is)[https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html](https://www.google.com/url?q=https%3A%2F%2Fdownload.pytorch.org%2Fwhl%2Fnightly%2Fcpu%2Ftorch_nightly.html&sa=D&sntz=1&usg=AOvVaw3w8IL7Ros8Nwz3UDCws6is) pip3 install matplotlib numpy kubernetes==10.0.0 kfp==0.2.4 click==7.0.0 opencv-python opencv-contrib-python imutils pylint fastapi uvicorn python- dateutil seldon_core spacy sklearn torch torchvision jupyter pycocotools cython pip3 install pyyaml==5.1 pip3 install 'git+https://github.com/facebookresearch/detectron2.git' pip3 install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI' poetry shell jupyter notebook pyenv pyenv install pyenv install 3.7.5 cd folder pyenv global 3.7.5 pyenv versions python -> you can see this environments poetry install (pyproject.toml) ~/.bash , .bash_profile , .zshrc poetry run which python poetry run jupyter lab pipenv install requests pyenv virtualenvs cv-endpoint pyenv activate cv-endpoint ==================== black python [https://github.com/psf/black](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpsf%2Fblack&sa=D&sntz=1&usg=AOvVaw2DSvjHmDvdtitO_AhjC1o1) The Uncompromising Code Formatter pip install black ==================== pre-commit A framework for managing and maintaining multi-language pre-commit hooks. pip install pre-commit brew install pre-commit .pre-commit-config.yaml repos: \- repo: [https://github.com/asottile/reorder_python_imports](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fasottile%2Freorder_python_imports&sa=D&sntz=1&usg=AOvVaw1gALoCbTPR6O-CSgrx7r5R) rev: v1.8.0 hooks: \- id: reorder-python-imports exclude: notebooks/ language_version: python3.7 \- repo: [https://github.com/ambv/black](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fambv%2Fblack&sa=D&sntz=1&usg=AOvVaw0k7gFGFzE6MyOzqjVo8Jto) rev: 19.10b0 hooks: \- id: black exclude: notebooks/ language_version: python3.7 \- repo: [https://github.com/pre-commit/pre-commit- hooks](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpre- commit%2Fpre-commit-hooks&sa=D&sntz=1&usg=AOvVaw3OA6YG1PaCw-Mf9zuTPohi) rev: v2.4.0 hooks: \- id: flake8 args: ['\--ignore=E203,E266,E501,W503', '\--max-line-length=88', '\--max- complexity=15', '\--select=B,C,E,F,W,T4,B9'] exclude: notebooks/ language_version: python3.7 pre-commit install pre-commit run --all-files git make file make check code . **add path** import sys sys.path.append(r'C: ) **create Mat** bin_im = np.zeros((5,16)) bin_im = bin_im.astype(np.uint8)*255 **contours** , hierarchy = cv2.findContours(opening, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) np. **savetxt** ("01-src.txt", im, fmt='%d', delimiter=', ', newline='\n', header='', footer='', comments='# ') **DLL** import ctypes my_dll = r"C:\fffffff.dll" lib = ctypes.windll.LoadLibrary(my_dll) **remove background or minimum form image** im = im - im.min() **time** e1 = cv2.getTickCount() ###### e2 = cv2.getTickCount() time = (e2 - e1)/ cv2.getTickFrequency() from scipy.signal import **find_peaks** peaks, out = find_peaks(Nf, distance=25) if peaks[0] < 25: peaks = peaks[1:] heights = Nf[peaks] **read matlab mat file to python** from scipy import io res = io.loadmat(r'MatlabResults\results2.mat', struct_as_record=False, squeeze_me=True) for k in res.keys(): print(k, res[k]) **remove background or minimum form image** im = im - im.min() **time** e1 = cv2.getTickCount() ###### e2 = cv2.getTickCount() time = (e2 - e1)/ cv2.getTickFrequency() from scipy.signal import **find_peaks** peaks, out = find_peaks(Nf, distance=25) if peaks[0] < 25: peaks = peaks[1:] heights = Nf[peaks] import sys sys.path.append(r'C: ) import sys sys.path.append(r'C: ) import sys sys.path.append(r'C: ) import sys sys.path.append(r'C: ) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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the functions: for example one time create file and then detected in between all operations in this function def setUp(self): def tearDown(self) for the class: @classmethod def setUpClass(cls): @classmethod def tearDownClass(cls): # List files and folders If you want to list directories which shows specific folder name in path in windows you can use dir /s /b /o:n /ad "farshid" > farshid.txt this command listed all directories which have "farshid" in the path and save it to the farshid.txt file in python you can use below code to search and find specific folders and files ###################################################################################### import import os import glob ###################################################################################### config root="C:\\\farshid\\\" specific_directories=root+"/**/farshid/**/*.jpg" path_dir_detection_check="" ###################################################################################### function files= glob.glob(specific_directories, recursive=True) for file in files: b=file.rfind("farshid") path_dir_detection=file[0:b-1] if (path_dir_detection != path_dir_detection_check): dirname = os.path.dirname(file) print("******************************************************* Next Directories ************************") print(dirname) path_dir_detection_check=path_dir_detection print(file) The source code can be found in [GitHub](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv_python%2Fblob%2Fmaster%2Flist_files_directories.py&sa=D&sntz=1&usg=AOvVaw3atI1rZO0cKOEBL2KZwnpr) make -j$(sysctl -n hw.physicalcpu) shift+enter -> run selection menue code-> pereferences -> user snippets -> python.json pip freeze > requirements.txt extensions Visual Studio IntelliCode SSH FS ext install Kelvin.vscode-sshfs command+ shift+ p ->SSH SF: Create new SSH SF configuration code ~/.zshrc Bracket Pair Colorizer 2 => color (){}[] different color Prettier - Code formatter => when you save. setting->(format on save) indent-rainbow shell => .code Compare Folders Command +p = all files -> if I press alt+ it open new tab the file control + ` = open terminal Command + o= open folder Command + , = open setting Command + /= # shift+enter=run one line of code option+shift+arrow down = duplicate line in code command+ click mouse => go to function command++ -> bigger command+ shift +P command+ K , command+ S => shourcuts command+ L => select currnt line command+ left/right arrow => start or end of line command+ P => go to file in search git config --global core.excludesfile ~/.gitignore code ~/.gitignore brew install pyenv brew install poetry pyenv install 3.7.5 pyenv global 3.7.5 poetry new "name of project" -> go to folder -> change python version if you want in the pyproject.toml pyenv global 3.7.5 poetry new "pytorch_pretrained" poetry install pip install --upgrade pip poetry add matplotlib numpy kubernetes=10.0.0 kfp=0.2.4 click=7.0.0 opencv- python opencv-contrib-python imutils pylint fastapi uvicorn python-dateutil seldon_core spacy sklearn torch torchvision jupyter pycocotools cython pyyaml==5.1 poetry remove torch torchvision pip install --pre torch torchvision -f[ ](https://www.google.com/url?q=https%3A%2F%2Fdownload.pytorch.org%2Fwhl%2Fnightly%2Fcpu%2Ftorch_nightly.html&sa=D&sntz=1&usg=AOvVaw3w8IL7Ros8Nwz3UDCws6is)[https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html](https://www.google.com/url?q=https%3A%2F%2Fdownload.pytorch.org%2Fwhl%2Fnightly%2Fcpu%2Ftorch_nightly.html&sa=D&sntz=1&usg=AOvVaw3w8IL7Ros8Nwz3UDCws6is) pip3 install matplotlib numpy kubernetes==10.0.0 kfp==0.2.4 click==7.0.0 opencv-python opencv-contrib-python imutils pylint fastapi uvicorn python- dateutil seldon_core spacy sklearn torch torchvision jupyter pycocotools cython pip3 install pyyaml==5.1 pip3 install 'git+https://github.com/facebookresearch/detectron2.git' pip3 install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI' poetry shell jupyter notebook pyenv pyenv install pyenv install 3.7.5 cd folder pyenv global 3.7.5 pyenv versions python -> you can see this environments poetry install (pyproject.toml) ~/.bash , .bash_profile , .zshrc poetry run which python poetry run jupyter lab pipenv install requests pyenv virtualenvs cv-endpoint pyenv activate cv-endpoint ==================== black python [https://github.com/psf/black](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpsf%2Fblack&sa=D&sntz=1&usg=AOvVaw2DSvjHmDvdtitO_AhjC1o1) The Uncompromising Code Formatter pip install black ==================== pre-commit A framework for managing and maintaining multi-language pre-commit hooks. pip install pre-commit brew install pre-commit .pre-commit-config.yaml repos: \- repo: [https://github.com/asottile/reorder_python_imports](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fasottile%2Freorder_python_imports&sa=D&sntz=1&usg=AOvVaw1gALoCbTPR6O-CSgrx7r5R) rev: v1.8.0 hooks: \- id: reorder-python-imports exclude: notebooks/ language_version: python3.7 \- repo: [https://github.com/ambv/black](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fambv%2Fblack&sa=D&sntz=1&usg=AOvVaw0k7gFGFzE6MyOzqjVo8Jto) rev: 19.10b0 hooks: \- id: black exclude: notebooks/ language_version: python3.7 \- repo: [https://github.com/pre-commit/pre-commit- hooks](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpre- commit%2Fpre-commit-hooks&sa=D&sntz=1&usg=AOvVaw3OA6YG1PaCw-Mf9zuTPohi) rev: v2.4.0 hooks: \- id: flake8 args: ['\--ignore=E203,E266,E501,W503', '\--max-line-length=88', '\--max- complexity=15', '\--select=B,C,E,F,W,T4,B9'] exclude: notebooks/ language_version: python3.7 pre-commit install pre-commit run --all-files git make file make check code . **add path** import sys sys.path.append(r'C: ) **create Mat** bin_im = np.zeros((5,16)) bin_im = bin_im.astype(np.uint8)*255 **contours** , hierarchy = cv2.findContours(opening, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) np. **savetxt** ("01-src.txt", im, fmt='%d', delimiter=', ', newline='\n', header='', footer='', comments='# ') **DLL** import ctypes my_dll = r"C:\fffffff.dll" lib = ctypes.windll.LoadLibrary(my_dll) **remove background or minimum form image** im = im - im.min() **time** e1 = cv2.getTickCount() ###### e2 = cv2.getTickCount() time = (e2 - e1)/ cv2.getTickFrequency() from scipy.signal import **find_peaks** peaks, out = find_peaks(Nf, distance=25) if peaks[0] < 25: peaks = peaks[1:] heights = Nf[peaks] **read matlab mat file to python** from scipy import io res = io.loadmat(r'MatlabResults\results2.mat', struct_as_record=False, squeeze_me=True) for k in res.keys(): print(k, res[k]) **remove background or minimum form image** im = im - im.min() **time** e1 = cv2.getTickCount() ###### e2 = cv2.getTickCount() time = (e2 - e1)/ cv2.getTickFrequency() from scipy.signal import **find_peaks** peaks, out = find_peaks(Nf, distance=25) if peaks[0] < 25: peaks = peaks[1:] heights = Nf[peaks] import sys sys.path.append(r'C: ) import sys sys.path.append(r'C: ) import sys sys.path.append(r'C: ) import sys sys.path.append(r'C: ) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/Bdyq7IeZMRfpWBg_bTipaEN0RonLnAgiUdIQLZR9-Wx_X38SA993YgVrvUOq-l5saY8gWSt4673OVMEWnWRrJfs=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data 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the functions: for example one time create file and then detected in between all operations in this function def setUp(self): def tearDown(self) for the class: @classmethod def setUpClass(cls): @classmethod def tearDownClass(cls): # List files and folders If you want to list directories which shows specific folder name in path in windows you can use dir /s /b /o:n /ad "farshid" > farshid.txt this command listed all directories which have "farshid" in the path and save it to the farshid.txt file in python you can use below code to search and find specific folders and files ###################################################################################### import import os import glob ###################################################################################### config root="C:\\\farshid\\\" specific_directories=root+"/**/farshid/**/*.jpg" path_dir_detection_check="" ###################################################################################### function files= glob.glob(specific_directories, recursive=True) for file in files: b=file.rfind("farshid") path_dir_detection=file[0:b-1] if (path_dir_detection != path_dir_detection_check): dirname = os.path.dirname(file) print("******************************************************* Next Directories ************************") print(dirname) path_dir_detection_check=path_dir_detection print(file) The source code can be found in [GitHub](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv_python%2Fblob%2Fmaster%2Flist_files_directories.py&sa=D&sntz=1&usg=AOvVaw3atI1rZO0cKOEBL2KZwnpr) make -j$(sysctl -n hw.physicalcpu) shift+enter -> run selection menue code-> pereferences -> user snippets -> python.json pip freeze > requirements.txt extensions Visual Studio IntelliCode SSH FS ext install Kelvin.vscode-sshfs command+ shift+ p ->SSH SF: Create new SSH SF configuration code ~/.zshrc Bracket Pair Colorizer 2 => color (){}[] different color Prettier - Code formatter => when you save. setting->(format on save) indent-rainbow shell => .code Compare Folders Command +p = all files -> if I press alt+ it open new tab the file control + ` = open terminal Command + o= open folder Command + , = open setting Command + /= # shift+enter=run one line of code option+shift+arrow down = duplicate line in code command+ click mouse => go to function command++ -> bigger command+ shift +P command+ K , command+ S => shourcuts command+ L => select currnt line command+ left/right arrow => start or end of line command+ P => go to file in search git config --global core.excludesfile ~/.gitignore code ~/.gitignore brew install pyenv brew install poetry pyenv install 3.7.5 pyenv global 3.7.5 poetry new "name of project" -> go to folder -> change python version if you want in the pyproject.toml pyenv global 3.7.5 poetry new "pytorch_pretrained" poetry install pip install --upgrade pip poetry add matplotlib numpy kubernetes=10.0.0 kfp=0.2.4 click=7.0.0 opencv- python opencv-contrib-python imutils pylint fastapi uvicorn python-dateutil seldon_core spacy sklearn torch torchvision jupyter pycocotools cython pyyaml==5.1 poetry remove torch torchvision pip install --pre torch torchvision -f[ ](https://www.google.com/url?q=https%3A%2F%2Fdownload.pytorch.org%2Fwhl%2Fnightly%2Fcpu%2Ftorch_nightly.html&sa=D&sntz=1&usg=AOvVaw3w8IL7Ros8Nwz3UDCws6is)[https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html](https://www.google.com/url?q=https%3A%2F%2Fdownload.pytorch.org%2Fwhl%2Fnightly%2Fcpu%2Ftorch_nightly.html&sa=D&sntz=1&usg=AOvVaw3w8IL7Ros8Nwz3UDCws6is) pip3 install matplotlib numpy kubernetes==10.0.0 kfp==0.2.4 click==7.0.0 opencv-python opencv-contrib-python imutils pylint fastapi uvicorn python- dateutil seldon_core spacy sklearn torch torchvision jupyter pycocotools cython pip3 install pyyaml==5.1 pip3 install 'git+https://github.com/facebookresearch/detectron2.git' pip3 install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI' poetry shell jupyter notebook pyenv pyenv install pyenv install 3.7.5 cd folder pyenv global 3.7.5 pyenv versions python -> you can see this environments poetry install (pyproject.toml) ~/.bash , .bash_profile , .zshrc poetry run which python poetry run jupyter lab pipenv install requests pyenv virtualenvs cv-endpoint pyenv activate cv-endpoint ==================== black python [https://github.com/psf/black](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpsf%2Fblack&sa=D&sntz=1&usg=AOvVaw2DSvjHmDvdtitO_AhjC1o1) The Uncompromising Code Formatter pip install black ==================== pre-commit A framework for managing and maintaining multi-language pre-commit hooks. pip install pre-commit brew install pre-commit .pre-commit-config.yaml repos: \- repo: [https://github.com/asottile/reorder_python_imports](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fasottile%2Freorder_python_imports&sa=D&sntz=1&usg=AOvVaw1gALoCbTPR6O-CSgrx7r5R) rev: v1.8.0 hooks: \- id: reorder-python-imports exclude: notebooks/ language_version: python3.7 \- repo: [https://github.com/ambv/black](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fambv%2Fblack&sa=D&sntz=1&usg=AOvVaw0k7gFGFzE6MyOzqjVo8Jto) rev: 19.10b0 hooks: \- id: black exclude: notebooks/ language_version: python3.7 \- repo: [https://github.com/pre-commit/pre-commit- hooks](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpre- commit%2Fpre-commit-hooks&sa=D&sntz=1&usg=AOvVaw3OA6YG1PaCw-Mf9zuTPohi) rev: v2.4.0 hooks: \- id: flake8 args: ['\--ignore=E203,E266,E501,W503', '\--max-line-length=88', '\--max- complexity=15', '\--select=B,C,E,F,W,T4,B9'] exclude: notebooks/ language_version: python3.7 pre-commit install pre-commit run --all-files git make file make check code . **add path** import sys sys.path.append(r'C: ) **create Mat** bin_im = np.zeros((5,16)) bin_im = bin_im.astype(np.uint8)*255 **contours** , hierarchy = cv2.findContours(opening, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) np. **savetxt** ("01-src.txt", im, fmt='%d', delimiter=', ', newline='\n', header='', footer='', comments='# ') **DLL** import ctypes my_dll = r"C:\fffffff.dll" lib = ctypes.windll.LoadLibrary(my_dll) **remove background or minimum form image** im = im - im.min() **time** e1 = cv2.getTickCount() ###### e2 = cv2.getTickCount() time = (e2 - e1)/ cv2.getTickFrequency() from scipy.signal import **find_peaks** peaks, out = find_peaks(Nf, distance=25) if peaks[0] < 25: peaks = peaks[1:] heights = Nf[peaks] **read matlab mat file to python** from scipy import io res = io.loadmat(r'MatlabResults\results2.mat', struct_as_record=False, squeeze_me=True) for k in res.keys(): print(k, res[k]) **remove background or minimum form image** im = im - im.min() **time** e1 = cv2.getTickCount() ###### e2 = cv2.getTickCount() time = (e2 - e1)/ cv2.getTickFrequency() from scipy.signal import **find_peaks** peaks, out = find_peaks(Nf, distance=25) if peaks[0] < 25: peaks = peaks[1:] heights = Nf[peaks] import sys sys.path.append(r'C: ) import sys sys.path.append(r'C: ) import sys sys.path.append(r'C: ) import sys sys.path.append(r'C: ) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/Bdyq7IeZMRfpWBg_bTipaEN0RonLnAgiUdIQLZR9-Wx_X38SA993YgVrvUOq-l5saY8gWSt4673OVMEWnWRrJfs=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data 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the functions: for example one time create file and then detected in between all operations in this function def setUp(self): def tearDown(self) for the class: @classmethod def setUpClass(cls): @classmethod def tearDownClass(cls): # List files and folders If you want to list directories which shows specific folder name in path in windows you can use dir /s /b /o:n /ad "farshid" > farshid.txt this command listed all directories which have "farshid" in the path and save it to the farshid.txt file in python you can use below code to search and find specific folders and files ###################################################################################### import import os import glob ###################################################################################### config root="C:\\\farshid\\\" specific_directories=root+"/**/farshid/**/*.jpg" path_dir_detection_check="" ###################################################################################### function files= glob.glob(specific_directories, recursive=True) for file in files: b=file.rfind("farshid") path_dir_detection=file[0:b-1] if (path_dir_detection != path_dir_detection_check): dirname = os.path.dirname(file) print("******************************************************* Next Directories ************************") print(dirname) path_dir_detection_check=path_dir_detection print(file) The source code can be found in [GitHub](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv_python%2Fblob%2Fmaster%2Flist_files_directories.py&sa=D&sntz=1&usg=AOvVaw3atI1rZO0cKOEBL2KZwnpr) make -j$(sysctl -n hw.physicalcpu) shift+enter -> run selection menue code-> pereferences -> user snippets -> python.json pip freeze > requirements.txt extensions Visual Studio IntelliCode SSH FS ext install Kelvin.vscode-sshfs command+ shift+ p ->SSH SF: Create new SSH SF configuration code ~/.zshrc Bracket Pair Colorizer 2 => color (){}[] different color Prettier - Code formatter => when you save. setting->(format on save) indent-rainbow shell => .code Compare Folders Command +p = all files -> if I press alt+ it open new tab the file control + ` = open terminal Command + o= open folder Command + , = open setting Command + /= # shift+enter=run one line of code option+shift+arrow down = duplicate line in code command+ click mouse => go to function command++ -> bigger command+ shift +P command+ K , command+ S => shourcuts command+ L => select currnt line command+ left/right arrow => start or end of line command+ P => go to file in search git config --global core.excludesfile ~/.gitignore code ~/.gitignore brew install pyenv brew install poetry pyenv install 3.7.5 pyenv global 3.7.5 poetry new "name of project" -> go to folder -> change python version if you want in the pyproject.toml pyenv global 3.7.5 poetry new "pytorch_pretrained" poetry install pip install --upgrade pip poetry add matplotlib numpy kubernetes=10.0.0 kfp=0.2.4 click=7.0.0 opencv- python opencv-contrib-python imutils pylint fastapi uvicorn python-dateutil seldon_core spacy sklearn torch torchvision jupyter pycocotools cython pyyaml==5.1 poetry remove torch torchvision pip install --pre torch torchvision -f[ ](https://www.google.com/url?q=https%3A%2F%2Fdownload.pytorch.org%2Fwhl%2Fnightly%2Fcpu%2Ftorch_nightly.html&sa=D&sntz=1&usg=AOvVaw3w8IL7Ros8Nwz3UDCws6is)[https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html](https://www.google.com/url?q=https%3A%2F%2Fdownload.pytorch.org%2Fwhl%2Fnightly%2Fcpu%2Ftorch_nightly.html&sa=D&sntz=1&usg=AOvVaw3w8IL7Ros8Nwz3UDCws6is) pip3 install matplotlib numpy kubernetes==10.0.0 kfp==0.2.4 click==7.0.0 opencv-python opencv-contrib-python imutils pylint fastapi uvicorn python- dateutil seldon_core spacy sklearn torch torchvision jupyter pycocotools cython pip3 install pyyaml==5.1 pip3 install 'git+https://github.com/facebookresearch/detectron2.git' pip3 install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI' poetry shell jupyter notebook pyenv pyenv install pyenv install 3.7.5 cd folder pyenv global 3.7.5 pyenv versions python -> you can see this environments poetry install (pyproject.toml) ~/.bash , .bash_profile , .zshrc poetry run which python poetry run jupyter lab pipenv install requests pyenv virtualenvs cv-endpoint pyenv activate cv-endpoint ==================== black python [https://github.com/psf/black](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpsf%2Fblack&sa=D&sntz=1&usg=AOvVaw2DSvjHmDvdtitO_AhjC1o1) The Uncompromising Code Formatter pip install black ==================== pre-commit A framework for managing and maintaining multi-language pre-commit hooks. pip install pre-commit brew install pre-commit .pre-commit-config.yaml repos: \- repo: [https://github.com/asottile/reorder_python_imports](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fasottile%2Freorder_python_imports&sa=D&sntz=1&usg=AOvVaw1gALoCbTPR6O-CSgrx7r5R) rev: v1.8.0 hooks: \- id: reorder-python-imports exclude: notebooks/ language_version: python3.7 \- repo: [https://github.com/ambv/black](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fambv%2Fblack&sa=D&sntz=1&usg=AOvVaw0k7gFGFzE6MyOzqjVo8Jto) rev: 19.10b0 hooks: \- id: black exclude: notebooks/ language_version: python3.7 \- repo: [https://github.com/pre-commit/pre-commit- hooks](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpre- commit%2Fpre-commit-hooks&sa=D&sntz=1&usg=AOvVaw3OA6YG1PaCw-Mf9zuTPohi) rev: v2.4.0 hooks: \- id: flake8 args: ['\--ignore=E203,E266,E501,W503', '\--max-line-length=88', '\--max- complexity=15', '\--select=B,C,E,F,W,T4,B9'] exclude: notebooks/ language_version: python3.7 pre-commit install pre-commit run --all-files git make file make check code . **add path** import sys sys.path.append(r'C: ) **create Mat** bin_im = np.zeros((5,16)) bin_im = bin_im.astype(np.uint8)*255 **contours** , hierarchy = cv2.findContours(opening, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) np. **savetxt** ("01-src.txt", im, fmt='%d', delimiter=', ', newline='\n', header='', footer='', comments='# ') **DLL** import ctypes my_dll = r"C:\fffffff.dll" lib = ctypes.windll.LoadLibrary(my_dll) **remove background or minimum form image** im = im - im.min() **time** e1 = cv2.getTickCount() ###### e2 = cv2.getTickCount() time = (e2 - e1)/ cv2.getTickFrequency() from scipy.signal import **find_peaks** peaks, out = find_peaks(Nf, distance=25) if peaks[0] < 25: peaks = peaks[1:] heights = Nf[peaks] **read matlab mat file to python** from scipy import io res = io.loadmat(r'MatlabResults\results2.mat', struct_as_record=False, squeeze_me=True) for k in res.keys(): print(k, res[k]) **remove background or minimum form image** im = im - im.min() **time** e1 = cv2.getTickCount() ###### e2 = cv2.getTickCount() time = (e2 - e1)/ cv2.getTickFrequency() from scipy.signal import **find_peaks** peaks, out = find_peaks(Nf, distance=25) if peaks[0] < 25: peaks = peaks[1:] heights = Nf[peaks] import sys sys.path.append(r'C: ) import sys sys.path.append(r'C: ) import sys sys.path.append(r'C: ) import sys sys.path.append(r'C: ) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/Bdyq7IeZMRfpWBg_bTipaEN0RonLnAgiUdIQLZR9-Wx_X38SA993YgVrvUOq-l5saY8gWSt4673OVMEWnWRrJfs=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data 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the functions: for example one time create file and then detected in between all operations in this function def setUp(self): def tearDown(self) for the class: @classmethod def setUpClass(cls): @classmethod def tearDownClass(cls): # List files and folders If you want to list directories which shows specific folder name in path in windows you can use dir /s /b /o:n /ad "farshid" > farshid.txt this command listed all directories which have "farshid" in the path and save it to the farshid.txt file in python you can use below code to search and find specific folders and files ###################################################################################### import import os import glob ###################################################################################### config root="C:\\\farshid\\\" specific_directories=root+"/**/farshid/**/*.jpg" path_dir_detection_check="" ###################################################################################### function files= glob.glob(specific_directories, recursive=True) for file in files: b=file.rfind("farshid") path_dir_detection=file[0:b-1] if (path_dir_detection != path_dir_detection_check): dirname = os.path.dirname(file) print("******************************************************* Next Directories ************************") print(dirname) path_dir_detection_check=path_dir_detection print(file) The source code can be found in [GitHub](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv_python%2Fblob%2Fmaster%2Flist_files_directories.py&sa=D&sntz=1&usg=AOvVaw3atI1rZO0cKOEBL2KZwnpr) make -j$(sysctl -n hw.physicalcpu) shift+enter -> run selection menue code-> pereferences -> user snippets -> python.json pip freeze > requirements.txt extensions Visual Studio IntelliCode SSH FS ext install Kelvin.vscode-sshfs command+ shift+ p ->SSH SF: Create new SSH SF configuration code ~/.zshrc Bracket Pair Colorizer 2 => color (){}[] different color Prettier - Code formatter => when you save. setting->(format on save) indent-rainbow shell => .code Compare Folders Command +p = all files -> if I press alt+ it open new tab the file control + ` = open terminal Command + o= open folder Command + , = open setting Command + /= # shift+enter=run one line of code option+shift+arrow down = duplicate line in code command+ click mouse => go to function command++ -> bigger command+ shift +P command+ K , command+ S => shourcuts command+ L => select currnt line command+ left/right arrow => start or end of line command+ P => go to file in search git config --global core.excludesfile ~/.gitignore code ~/.gitignore brew install pyenv brew install poetry pyenv install 3.7.5 pyenv global 3.7.5 poetry new "name of project" -> go to folder -> change python version if you want in the pyproject.toml pyenv global 3.7.5 poetry new "pytorch_pretrained" poetry install pip install --upgrade pip poetry add matplotlib numpy kubernetes=10.0.0 kfp=0.2.4 click=7.0.0 opencv- python opencv-contrib-python imutils pylint fastapi uvicorn python-dateutil seldon_core spacy sklearn torch torchvision jupyter pycocotools cython pyyaml==5.1 poetry remove torch torchvision pip install --pre torch torchvision -f[ ](https://www.google.com/url?q=https%3A%2F%2Fdownload.pytorch.org%2Fwhl%2Fnightly%2Fcpu%2Ftorch_nightly.html&sa=D&sntz=1&usg=AOvVaw3w8IL7Ros8Nwz3UDCws6is)[https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html](https://www.google.com/url?q=https%3A%2F%2Fdownload.pytorch.org%2Fwhl%2Fnightly%2Fcpu%2Ftorch_nightly.html&sa=D&sntz=1&usg=AOvVaw3w8IL7Ros8Nwz3UDCws6is) pip3 install matplotlib numpy kubernetes==10.0.0 kfp==0.2.4 click==7.0.0 opencv-python opencv-contrib-python imutils pylint fastapi uvicorn python- dateutil seldon_core spacy sklearn torch torchvision jupyter pycocotools cython pip3 install pyyaml==5.1 pip3 install 'git+https://github.com/facebookresearch/detectron2.git' pip3 install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI' poetry shell jupyter notebook pyenv pyenv install pyenv install 3.7.5 cd folder pyenv global 3.7.5 pyenv versions python -> you can see this environments poetry install (pyproject.toml) ~/.bash , .bash_profile , .zshrc poetry run which python poetry run jupyter lab pipenv install requests pyenv virtualenvs cv-endpoint pyenv activate cv-endpoint ==================== black python [https://github.com/psf/black](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpsf%2Fblack&sa=D&sntz=1&usg=AOvVaw2DSvjHmDvdtitO_AhjC1o1) The Uncompromising Code Formatter pip install black ==================== pre-commit A framework for managing and maintaining multi-language pre-commit hooks. pip install pre-commit brew install pre-commit .pre-commit-config.yaml repos: \- repo: [https://github.com/asottile/reorder_python_imports](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fasottile%2Freorder_python_imports&sa=D&sntz=1&usg=AOvVaw1gALoCbTPR6O-CSgrx7r5R) rev: v1.8.0 hooks: \- id: reorder-python-imports exclude: notebooks/ language_version: python3.7 \- repo: [https://github.com/ambv/black](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fambv%2Fblack&sa=D&sntz=1&usg=AOvVaw0k7gFGFzE6MyOzqjVo8Jto) rev: 19.10b0 hooks: \- id: black exclude: notebooks/ language_version: python3.7 \- repo: [https://github.com/pre-commit/pre-commit- hooks](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpre- commit%2Fpre-commit-hooks&sa=D&sntz=1&usg=AOvVaw3OA6YG1PaCw-Mf9zuTPohi) rev: v2.4.0 hooks: \- id: flake8 args: ['\--ignore=E203,E266,E501,W503', '\--max-line-length=88', '\--max- complexity=15', '\--select=B,C,E,F,W,T4,B9'] exclude: notebooks/ language_version: python3.7 pre-commit install pre-commit run --all-files git make file make check code . **add path** import sys sys.path.append(r'C: ) **create Mat** bin_im = np.zeros((5,16)) bin_im = bin_im.astype(np.uint8)*255 **contours** , hierarchy = cv2.findContours(opening, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) np. **savetxt** ("01-src.txt", im, fmt='%d', delimiter=', ', newline='\n', header='', footer='', comments='# ') **DLL** import ctypes my_dll = r"C:\fffffff.dll" lib = ctypes.windll.LoadLibrary(my_dll) **remove background or minimum form image** im = im - im.min() **time** e1 = cv2.getTickCount() ###### e2 = cv2.getTickCount() time = (e2 - e1)/ cv2.getTickFrequency() from scipy.signal import **find_peaks** peaks, out = find_peaks(Nf, distance=25) if peaks[0] < 25: peaks = peaks[1:] heights = Nf[peaks] **read matlab mat file to python** from scipy import io res = io.loadmat(r'MatlabResults\results2.mat', struct_as_record=False, squeeze_me=True) for k in res.keys(): print(k, res[k]) **remove background or minimum form image** im = im - im.min() **time** e1 = cv2.getTickCount() ###### e2 = cv2.getTickCount() time = (e2 - e1)/ cv2.getTickFrequency() from scipy.signal import **find_peaks** peaks, out = find_peaks(Nf, distance=25) if peaks[0] < 25: peaks = peaks[1:] heights = Nf[peaks] import sys sys.path.append(r'C: ) import sys sys.path.append(r'C: ) import sys sys.path.append(r'C: ) import sys sys.path.append(r'C: ) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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the functions: for example one time create file and then detected in between all operations in this function def setUp(self): def tearDown(self) for the class: @classmethod def setUpClass(cls): @classmethod def tearDownClass(cls): # List files and folders If you want to list directories which shows specific folder name in path in windows you can use dir /s /b /o:n /ad "farshid" > farshid.txt this command listed all directories which have "farshid" in the path and save it to the farshid.txt file in python you can use below code to search and find specific folders and files ###################################################################################### import import os import glob ###################################################################################### config root="C:\\\farshid\\\" specific_directories=root+"/**/farshid/**/*.jpg" path_dir_detection_check="" ###################################################################################### function files= glob.glob(specific_directories, recursive=True) for file in files: b=file.rfind("farshid") path_dir_detection=file[0:b-1] if (path_dir_detection != path_dir_detection_check): dirname = os.path.dirname(file) print("******************************************************* Next Directories ************************") print(dirname) path_dir_detection_check=path_dir_detection print(file) The source code can be found in [GitHub](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv_python%2Fblob%2Fmaster%2Flist_files_directories.py&sa=D&sntz=1&usg=AOvVaw3atI1rZO0cKOEBL2KZwnpr) make -j$(sysctl -n hw.physicalcpu) shift+enter -> run selection menue code-> pereferences -> user snippets -> python.json pip freeze > requirements.txt extensions Visual Studio IntelliCode SSH FS ext install Kelvin.vscode-sshfs command+ shift+ p ->SSH SF: Create new SSH SF configuration code ~/.zshrc Bracket Pair Colorizer 2 => color (){}[] different color Prettier - Code formatter => when you save. setting->(format on save) indent-rainbow shell => .code Compare Folders Command +p = all files -> if I press alt+ it open new tab the file control + ` = open terminal Command + o= open folder Command + , = open setting Command + /= # shift+enter=run one line of code option+shift+arrow down = duplicate line in code command+ click mouse => go to function command++ -> bigger command+ shift +P command+ K , command+ S => shourcuts command+ L => select currnt line command+ left/right arrow => start or end of line command+ P => go to file in search git config --global core.excludesfile ~/.gitignore code ~/.gitignore brew install pyenv brew install poetry pyenv install 3.7.5 pyenv global 3.7.5 poetry new "name of project" -> go to folder -> change python version if you want in the pyproject.toml pyenv global 3.7.5 poetry new "pytorch_pretrained" poetry install pip install --upgrade pip poetry add matplotlib numpy kubernetes=10.0.0 kfp=0.2.4 click=7.0.0 opencv- python opencv-contrib-python imutils pylint fastapi uvicorn python-dateutil seldon_core spacy sklearn torch torchvision jupyter pycocotools cython pyyaml==5.1 poetry remove torch torchvision pip install --pre torch torchvision -f[ ](https://www.google.com/url?q=https%3A%2F%2Fdownload.pytorch.org%2Fwhl%2Fnightly%2Fcpu%2Ftorch_nightly.html&sa=D&sntz=1&usg=AOvVaw3w8IL7Ros8Nwz3UDCws6is)[https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html](https://www.google.com/url?q=https%3A%2F%2Fdownload.pytorch.org%2Fwhl%2Fnightly%2Fcpu%2Ftorch_nightly.html&sa=D&sntz=1&usg=AOvVaw3w8IL7Ros8Nwz3UDCws6is) pip3 install matplotlib numpy kubernetes==10.0.0 kfp==0.2.4 click==7.0.0 opencv-python opencv-contrib-python imutils pylint fastapi uvicorn python- dateutil seldon_core spacy sklearn torch torchvision jupyter pycocotools cython pip3 install pyyaml==5.1 pip3 install 'git+https://github.com/facebookresearch/detectron2.git' pip3 install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI' poetry shell jupyter notebook pyenv pyenv install pyenv install 3.7.5 cd folder pyenv global 3.7.5 pyenv versions python -> you can see this environments poetry install (pyproject.toml) ~/.bash , .bash_profile , .zshrc poetry run which python poetry run jupyter lab pipenv install requests pyenv virtualenvs cv-endpoint pyenv activate cv-endpoint ==================== black python [https://github.com/psf/black](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpsf%2Fblack&sa=D&sntz=1&usg=AOvVaw2DSvjHmDvdtitO_AhjC1o1) The Uncompromising Code Formatter pip install black ==================== pre-commit A framework for managing and maintaining multi-language pre-commit hooks. pip install pre-commit brew install pre-commit .pre-commit-config.yaml repos: \- repo: [https://github.com/asottile/reorder_python_imports](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fasottile%2Freorder_python_imports&sa=D&sntz=1&usg=AOvVaw1gALoCbTPR6O-CSgrx7r5R) rev: v1.8.0 hooks: \- id: reorder-python-imports exclude: notebooks/ language_version: python3.7 \- repo: [https://github.com/ambv/black](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fambv%2Fblack&sa=D&sntz=1&usg=AOvVaw0k7gFGFzE6MyOzqjVo8Jto) rev: 19.10b0 hooks: \- id: black exclude: notebooks/ language_version: python3.7 \- repo: [https://github.com/pre-commit/pre-commit- hooks](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpre- commit%2Fpre-commit-hooks&sa=D&sntz=1&usg=AOvVaw3OA6YG1PaCw-Mf9zuTPohi) rev: v2.4.0 hooks: \- id: flake8 args: ['\--ignore=E203,E266,E501,W503', '\--max-line-length=88', '\--max- complexity=15', '\--select=B,C,E,F,W,T4,B9'] exclude: notebooks/ language_version: python3.7 pre-commit install pre-commit run --all-files git make file make check code . **add path** import sys sys.path.append(r'C: ) **create Mat** bin_im = np.zeros((5,16)) bin_im = bin_im.astype(np.uint8)*255 **contours** , hierarchy = cv2.findContours(opening, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) np. **savetxt** ("01-src.txt", im, fmt='%d', delimiter=', ', newline='\n', header='', footer='', comments='# ') **DLL** import ctypes my_dll = r"C:\fffffff.dll" lib = ctypes.windll.LoadLibrary(my_dll) **remove background or minimum form image** im = im - im.min() **time** e1 = cv2.getTickCount() ###### e2 = cv2.getTickCount() time = (e2 - e1)/ cv2.getTickFrequency() from scipy.signal import **find_peaks** peaks, out = find_peaks(Nf, distance=25) if peaks[0] < 25: peaks = peaks[1:] heights = Nf[peaks] **read matlab mat file to python** from scipy import io res = io.loadmat(r'MatlabResults\results2.mat', struct_as_record=False, squeeze_me=True) for k in res.keys(): print(k, res[k]) **remove background or minimum form image** im = im - im.min() **time** e1 = cv2.getTickCount() ###### e2 = cv2.getTickCount() time = (e2 - e1)/ cv2.getTickFrequency() from scipy.signal import **find_peaks** peaks, out = find_peaks(Nf, distance=25) if peaks[0] < 25: peaks = peaks[1:] heights = Nf[peaks] import sys sys.path.append(r'C: ) import sys sys.path.append(r'C: ) import sys sys.path.append(r'C: ) import sys sys.path.append(r'C: ) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/HlmNuqyxk5gzYGxXDtUChEUYREK389bSHhqeXOjPaBaXuH- OfSzmgk4xvNu5e2EN7ntuZo418EeXtzqP8ztw9tk=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data 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Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/HlmNuqyxk5gzYGxXDtUChEUYREK389bSHhqeXOjPaBaXuH- OfSzmgk4xvNu5e2EN7ntuZo418EeXtzqP8ztw9tk=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Python ![](https://lh6.googleusercontent.com/543wuBVenutUTxovMJ4gQ_HXvQVlv5EjabCjHK1vhC- xkkKoDFdARf-cue7b24QHOCB_C6YrBuHTEG6ibH4Y9_LG_zuOKZk_TGpXEo- atf3LS2Z-6Sz79nJ55c5zIwhS4Q=w1280) ## ## global _ folder, file name, functions, const, ## doc read_all_image_in_folder recursive = True ## code ## doc manual progress bar for python based on number of images process. ## code ## doc this code shows information about image ## code import unittest for the functions: for example one time create file and then detected in between all operations in this function def setUp(self): def tearDown(self) for the class: @classmethod def setUpClass(cls): @classmethod def tearDownClass(cls): # List files and folders If you want to list directories which shows specific folder name in path in windows you can use dir /s /b /o:n /ad "farshid" > farshid.txt this command listed all directories which have "farshid" in the path and save it to the farshid.txt file in python you can use below code to search and find specific folders and files ###################################################################################### import import os import glob ###################################################################################### config root="C:\\\farshid\\\" specific_directories=root+"/**/farshid/**/*.jpg" path_dir_detection_check="" ###################################################################################### function files= glob.glob(specific_directories, recursive=True) for file in files: b=file.rfind("farshid") path_dir_detection=file[0:b-1] if (path_dir_detection != path_dir_detection_check): dirname = os.path.dirname(file) print("******************************************************* Next Directories ************************") print(dirname) path_dir_detection_check=path_dir_detection print(file) The source code can be found in [GitHub](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv_python%2Fblob%2Fmaster%2Flist_files_directories.py&sa=D&sntz=1&usg=AOvVaw3atI1rZO0cKOEBL2KZwnpr) make -j$(sysctl -n hw.physicalcpu) shift+enter -> run selection menue code-> pereferences -> user snippets -> python.json pip freeze > requirements.txt extensions Visual Studio IntelliCode SSH FS ext install Kelvin.vscode-sshfs command+ shift+ p ->SSH SF: Create new SSH SF configuration code ~/.zshrc Bracket Pair Colorizer 2 => color (){}[] different color Prettier - Code formatter => when you save. setting->(format on save) indent-rainbow shell => .code Compare Folders Command +p = all files -> if I press alt+ it open new tab the file control + ` = open terminal Command + o= open folder Command + , = open setting Command + /= # shift+enter=run one line of code option+shift+arrow down = duplicate line in code command+ click mouse => go to function command++ -> bigger command+ shift +P command+ K , command+ S => shourcuts command+ L => select currnt line command+ left/right arrow => start or end of line command+ P => go to file in search git config --global core.excludesfile ~/.gitignore code ~/.gitignore brew install pyenv brew install poetry pyenv install 3.7.5 pyenv global 3.7.5 poetry new "name of project" -> go to folder -> change python version if you want in the pyproject.toml pyenv global 3.7.5 poetry new "pytorch_pretrained" poetry install pip install --upgrade pip poetry add matplotlib numpy kubernetes=10.0.0 kfp=0.2.4 click=7.0.0 opencv- python opencv-contrib-python imutils pylint fastapi uvicorn python-dateutil seldon_core spacy sklearn torch torchvision jupyter pycocotools cython pyyaml==5.1 poetry remove torch torchvision pip install --pre torch torchvision -f[ ](https://www.google.com/url?q=https%3A%2F%2Fdownload.pytorch.org%2Fwhl%2Fnightly%2Fcpu%2Ftorch_nightly.html&sa=D&sntz=1&usg=AOvVaw3w8IL7Ros8Nwz3UDCws6is)[https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html](https://www.google.com/url?q=https%3A%2F%2Fdownload.pytorch.org%2Fwhl%2Fnightly%2Fcpu%2Ftorch_nightly.html&sa=D&sntz=1&usg=AOvVaw3w8IL7Ros8Nwz3UDCws6is) pip3 install matplotlib numpy kubernetes==10.0.0 kfp==0.2.4 click==7.0.0 opencv-python opencv-contrib-python imutils pylint fastapi uvicorn python- dateutil seldon_core spacy sklearn torch torchvision jupyter pycocotools cython pip3 install pyyaml==5.1 pip3 install 'git+https://github.com/facebookresearch/detectron2.git' pip3 install -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI' poetry shell jupyter notebook pyenv pyenv install pyenv install 3.7.5 cd folder pyenv global 3.7.5 pyenv versions python -> you can see this environments poetry install (pyproject.toml) ~/.bash , .bash_profile , .zshrc poetry run which python poetry run jupyter lab pipenv install requests pyenv virtualenvs cv-endpoint pyenv activate cv-endpoint ==================== black python [https://github.com/psf/black](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpsf%2Fblack&sa=D&sntz=1&usg=AOvVaw2DSvjHmDvdtitO_AhjC1o1) The Uncompromising Code Formatter pip install black ==================== pre-commit A framework for managing and maintaining multi-language pre-commit hooks. pip install pre-commit brew install pre-commit .pre-commit-config.yaml repos: \- repo: [https://github.com/asottile/reorder_python_imports](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fasottile%2Freorder_python_imports&sa=D&sntz=1&usg=AOvVaw1gALoCbTPR6O-CSgrx7r5R) rev: v1.8.0 hooks: \- id: reorder-python-imports exclude: notebooks/ language_version: python3.7 \- repo: [https://github.com/ambv/black](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fambv%2Fblack&sa=D&sntz=1&usg=AOvVaw0k7gFGFzE6MyOzqjVo8Jto) rev: 19.10b0 hooks: \- id: black exclude: notebooks/ language_version: python3.7 \- repo: [https://github.com/pre-commit/pre-commit- hooks](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpre- commit%2Fpre-commit-hooks&sa=D&sntz=1&usg=AOvVaw3OA6YG1PaCw-Mf9zuTPohi) rev: v2.4.0 hooks: \- id: flake8 args: ['\--ignore=E203,E266,E501,W503', '\--max-line-length=88', '\--max- complexity=15', '\--select=B,C,E,F,W,T4,B9'] exclude: notebooks/ language_version: python3.7 pre-commit install pre-commit run --all-files git make file make check code . **add path** import sys sys.path.append(r'C: ) **create Mat** bin_im = np.zeros((5,16)) bin_im = bin_im.astype(np.uint8)*255 **contours** , hierarchy = cv2.findContours(opening, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE) np. **savetxt** ("01-src.txt", im, fmt='%d', delimiter=', ', newline='\n', header='', footer='', comments='# ') **DLL** import ctypes my_dll = r"C:\fffffff.dll" lib = ctypes.windll.LoadLibrary(my_dll) **remove background or minimum form image** im = im - im.min() **time** e1 = cv2.getTickCount() ###### e2 = cv2.getTickCount() time = (e2 - e1)/ cv2.getTickFrequency() from scipy.signal import **find_peaks** peaks, out = find_peaks(Nf, distance=25) if peaks[0] < 25: peaks = peaks[1:] heights = Nf[peaks] **read matlab mat file to python** from scipy import io res = io.loadmat(r'MatlabResults\results2.mat', struct_as_record=False, squeeze_me=True) for k in res.keys(): print(k, res[k]) **remove background or minimum form image** im = im - im.min() **time** e1 = cv2.getTickCount() ###### e2 = cv2.getTickCount() time = (e2 - e1)/ cv2.getTickFrequency() from scipy.signal import **find_peaks** peaks, out = find_peaks(Nf, distance=25) if peaks[0] < 25: peaks = peaks[1:] heights = Nf[peaks] import sys sys.path.append(r'C: ) import sys sys.path.append(r'C: ) import sys sys.path.append(r'C: ) import sys sys.path.append(r'C: ) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/Bdyq7IeZMRfpWBg_bTipaEN0RonLnAgiUdIQLZR9-Wx_X38SA993YgVrvUOq-l5saY8gWSt4673OVMEWnWRrJfs=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/Bdyq7IeZMRfpWBg_bTipaEN0RonLnAgiUdIQLZR9-Wx_X38SA993YgVrvUOq-l5saY8gWSt4673OVMEWnWRrJfs=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # C++ # Clean Code for Computer Vision using OpenCV and C++ When writing clean code using the OpenCV library in C++, here are some additional principles to follow: 1. Avoid using magic numbers: Instead of using hardcoded values, use named constants or named variables for numbers that have a specific meaning. 2. Keep functions focused on OpenCV operations: Limit the number of lines of code in functions and make sure each function focuses on performing OpenCV operations. 3. Use clear and descriptive names for OpenCV functions: When calling OpenCV functions, use names that are clear and descriptive of what the function does. 4. Use OpenCV data structures appropriately: Familiarize yourself with the different OpenCV data structures, like cv::Mat, cv::Point, etc., and use the appropriate one for each task. 5. Error handling: Make sure to check the return value of OpenCV functions and handle errors appropriately. 6. Make use of OpenCV's high-level functions: Whenever possible, use OpenCV's high-level functions instead of lower-level functions to simplify code and reduce the amount of boilerplate code. 7. Keep track of the image size: Make sure to keep track of the size of images, especially when performing operations like resizing, as this can affect the results. These examples demonstrate how following good coding practices and paying attention to the specific features of the OpenCV library can help you write clean, efficient, and effective code. By following these principles, you can write clean and maintainable code that makes effective use of the OpenCV library. Here are several examples of clean code in OpenCV C++: * Meaningful variable names: cv::Mat original_image = cv::imread("image.jpg"); cv::Mat resized_image; cv::resize(original_image, resized_image, cv::Size(), 0.5, 0.5, cv::INTER_AREA); * Use of high-level functions: cv::Mat src = cv::imread("image.jpg"); cv::Mat dst; cv::GaussianBlur(src, dst, cv::Size(3,3), 0); * Error handling: cv::Mat src = cv::imread("image.jpg"); if(src.empty()) { std::cout << "Error: Could not load image" << std::endl; return -1; } * Use of descriptive function names: cv::Mat src * Appropriate use of OpenCV data structures: cv::Mat src = cv::imread("image.jpg"); std::vector corners; cv::goodFeaturesToTrack(src, corners, 100, 0.01, 10); * Reusable functions: cv::Mat src = cv::imread("image.jpg"); cv::Mat gray; cv::cvtColor(src, gray, cv::COLOR_BGR2GRAY); cv::Mat sharpen_image(const cv::Mat& image) { cv::Mat result; cv::GaussianBlur(image, result, cv::Size(0,0), 3); cv::addWeighted(image, 1.5, result, -0.5, 0, result); return result; } cv::Mat sharpened = sharpen_image(gray); * Clear and concise comments: // Load the source image cv::Mat src = cv::imread("image.jpg"); // Convert the image to grayscale cv::Mat gray; cv::cvtColor(src, gray, cv::COLOR_BGR2GRAY); // Threshold the image to create a binary image cv::Mat thresholded; cv::threshold(gray, thresholded, 128, 255, cv::THRESH_BINARY); ## doc this code shows information about image ## code ## doc this code shows information about image ## code Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/Bdyq7IeZMRfpWBg_bTipaEN0RonLnAgiUdIQLZR9-Wx_X38SA993YgVrvUOq-l5saY8gWSt4673OVMEWnWRrJfs=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/Bdyq7IeZMRfpWBg_bTipaEN0RonLnAgiUdIQLZR9-Wx_X38SA993YgVrvUOq-l5saY8gWSt4673OVMEWnWRrJfs=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # C++ # Clean Code for Computer Vision using OpenCV and C++ When writing clean code using the OpenCV library in C++, here are some additional principles to follow: 1. Avoid using magic numbers: Instead of using hardcoded values, use named constants or named variables for numbers that have a specific meaning. 2. Keep functions focused on OpenCV operations: Limit the number of lines of code in functions and make sure each function focuses on performing OpenCV operations. 3. Use clear and descriptive names for OpenCV functions: When calling OpenCV functions, use names that are clear and descriptive of what the function does. 4. Use OpenCV data structures appropriately: Familiarize yourself with the different OpenCV data structures, like cv::Mat, cv::Point, etc., and use the appropriate one for each task. 5. Error handling: Make sure to check the return value of OpenCV functions and handle errors appropriately. 6. Make use of OpenCV's high-level functions: Whenever possible, use OpenCV's high-level functions instead of lower-level functions to simplify code and reduce the amount of boilerplate code. 7. Keep track of the image size: Make sure to keep track of the size of images, especially when performing operations like resizing, as this can affect the results. These examples demonstrate how following good coding practices and paying attention to the specific features of the OpenCV library can help you write clean, efficient, and effective code. By following these principles, you can write clean and maintainable code that makes effective use of the OpenCV library. Here are several examples of clean code in OpenCV C++: * Meaningful variable names: cv::Mat original_image = cv::imread("image.jpg"); cv::Mat resized_image; cv::resize(original_image, resized_image, cv::Size(), 0.5, 0.5, cv::INTER_AREA); * Use of high-level functions: cv::Mat src = cv::imread("image.jpg"); cv::Mat dst; cv::GaussianBlur(src, dst, cv::Size(3,3), 0); * Error handling: cv::Mat src = cv::imread("image.jpg"); if(src.empty()) { std::cout << "Error: Could not load image" << std::endl; return -1; } * Use of descriptive function names: cv::Mat src * Appropriate use of OpenCV data structures: cv::Mat src = cv::imread("image.jpg"); std::vector corners; cv::goodFeaturesToTrack(src, corners, 100, 0.01, 10); * Reusable functions: cv::Mat src = cv::imread("image.jpg"); cv::Mat gray; cv::cvtColor(src, gray, cv::COLOR_BGR2GRAY); cv::Mat sharpen_image(const cv::Mat& image) { cv::Mat result; cv::GaussianBlur(image, result, cv::Size(0,0), 3); cv::addWeighted(image, 1.5, result, -0.5, 0, result); return result; } cv::Mat sharpened = sharpen_image(gray); * Clear and concise comments: // Load the source image cv::Mat src = cv::imread("image.jpg"); // Convert the image to grayscale cv::Mat gray; cv::cvtColor(src, gray, cv::COLOR_BGR2GRAY); // Threshold the image to create a binary image cv::Mat thresholded; cv::threshold(gray, thresholded, 128, 255, cv::THRESH_BINARY); ## doc this code shows information about image ## code ## doc this code shows information about image ## code Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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Avoid using magic numbers: Instead of using hardcoded values, use named constants or named variables for numbers that have a specific meaning. 2. Keep functions focused on OpenCV operations: Limit the number of lines of code in functions and make sure each function focuses on performing OpenCV operations. 3. Use clear and descriptive names for OpenCV functions: When calling OpenCV functions, use names that are clear and descriptive of what the function does. 4. Use OpenCV data structures appropriately: Familiarize yourself with the different OpenCV data structures, like cv::Mat, cv::Point, etc., and use the appropriate one for each task. 5. Error handling: Make sure to check the return value of OpenCV functions and handle errors appropriately. 6. Make use of OpenCV's high-level functions: Whenever possible, use OpenCV's high-level functions instead of lower-level functions to simplify code and reduce the amount of boilerplate code. 7. Keep track of the image size: Make sure to keep track of the size of images, especially when performing operations like resizing, as this can affect the results. These examples demonstrate how following good coding practices and paying attention to the specific features of the OpenCV library can help you write clean, efficient, and effective code. By following these principles, you can write clean and maintainable code that makes effective use of the OpenCV library. Here are several examples of clean code in OpenCV C++: * Meaningful variable names: cv::Mat original_image = cv::imread("image.jpg"); cv::Mat resized_image; cv::resize(original_image, resized_image, cv::Size(), 0.5, 0.5, cv::INTER_AREA); * Use of high-level functions: cv::Mat src = cv::imread("image.jpg"); cv::Mat dst; cv::GaussianBlur(src, dst, cv::Size(3,3), 0); * Error handling: cv::Mat src = cv::imread("image.jpg"); if(src.empty()) { std::cout << "Error: Could not load image" << std::endl; return -1; } * Use of descriptive function names: cv::Mat src * Appropriate use of OpenCV data structures: cv::Mat src = cv::imread("image.jpg"); std::vector corners; cv::goodFeaturesToTrack(src, corners, 100, 0.01, 10); * Reusable functions: cv::Mat src = cv::imread("image.jpg"); cv::Mat gray; cv::cvtColor(src, gray, cv::COLOR_BGR2GRAY); cv::Mat sharpen_image(const cv::Mat& image) { cv::Mat result; cv::GaussianBlur(image, result, cv::Size(0,0), 3); cv::addWeighted(image, 1.5, result, -0.5, 0, result); return result; } cv::Mat sharpened = sharpen_image(gray); * Clear and concise comments: // Load the source image cv::Mat src = cv::imread("image.jpg"); // Convert the image to grayscale cv::Mat gray; cv::cvtColor(src, gray, cv::COLOR_BGR2GRAY); // Threshold the image to create a binary image cv::Mat thresholded; cv::threshold(gray, thresholded, 128, 255, cv::THRESH_BINARY); ## doc this code shows information about image ## code ## doc this code shows information about image ## code Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/Bdyq7IeZMRfpWBg_bTipaEN0RonLnAgiUdIQLZR9-Wx_X38SA993YgVrvUOq-l5saY8gWSt4673OVMEWnWRrJfs=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/Bdyq7IeZMRfpWBg_bTipaEN0RonLnAgiUdIQLZR9-Wx_X38SA993YgVrvUOq-l5saY8gWSt4673OVMEWnWRrJfs=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # C++ # Clean Code for Computer Vision using OpenCV and C++ When writing clean code using the OpenCV library in C++, here are some additional principles to follow: 1. Avoid using magic numbers: Instead of using hardcoded values, use named constants or named variables for numbers that have a specific meaning. 2. Keep functions focused on OpenCV operations: Limit the number of lines of code in functions and make sure each function focuses on performing OpenCV operations. 3. Use clear and descriptive names for OpenCV functions: When calling OpenCV functions, use names that are clear and descriptive of what the function does. 4. Use OpenCV data structures appropriately: Familiarize yourself with the different OpenCV data structures, like cv::Mat, cv::Point, etc., and use the appropriate one for each task. 5. Error handling: Make sure to check the return value of OpenCV functions and handle errors appropriately. 6. Make use of OpenCV's high-level functions: Whenever possible, use OpenCV's high-level functions instead of lower-level functions to simplify code and reduce the amount of boilerplate code. 7. Keep track of the image size: Make sure to keep track of the size of images, especially when performing operations like resizing, as this can affect the results. These examples demonstrate how following good coding practices and paying attention to the specific features of the OpenCV library can help you write clean, efficient, and effective code. By following these principles, you can write clean and maintainable code that makes effective use of the OpenCV library. Here are several examples of clean code in OpenCV C++: * Meaningful variable names: cv::Mat original_image = cv::imread("image.jpg"); cv::Mat resized_image; cv::resize(original_image, resized_image, cv::Size(), 0.5, 0.5, cv::INTER_AREA); * Use of high-level functions: cv::Mat src = cv::imread("image.jpg"); cv::Mat dst; cv::GaussianBlur(src, dst, cv::Size(3,3), 0); * Error handling: cv::Mat src = cv::imread("image.jpg"); if(src.empty()) { std::cout << "Error: Could not load image" << std::endl; return -1; } * Use of descriptive function names: cv::Mat src * Appropriate use of OpenCV data structures: cv::Mat src = cv::imread("image.jpg"); std::vector corners; cv::goodFeaturesToTrack(src, corners, 100, 0.01, 10); * Reusable functions: cv::Mat src = cv::imread("image.jpg"); cv::Mat gray; cv::cvtColor(src, gray, cv::COLOR_BGR2GRAY); cv::Mat sharpen_image(const cv::Mat& image) { cv::Mat result; cv::GaussianBlur(image, result, cv::Size(0,0), 3); cv::addWeighted(image, 1.5, result, -0.5, 0, result); return result; } cv::Mat sharpened = sharpen_image(gray); * Clear and concise comments: // Load the source image cv::Mat src = cv::imread("image.jpg"); // Convert the image to grayscale cv::Mat gray; cv::cvtColor(src, gray, cv::COLOR_BGR2GRAY); // Threshold the image to create a binary image cv::Mat thresholded; cv::threshold(gray, thresholded, 128, 255, cv::THRESH_BINARY); ## doc this code shows information about image ## code ## doc this code shows information about image ## code Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/Bdyq7IeZMRfpWBg_bTipaEN0RonLnAgiUdIQLZR9-Wx_X38SA993YgVrvUOq-l5saY8gWSt4673OVMEWnWRrJfs=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/Bdyq7IeZMRfpWBg_bTipaEN0RonLnAgiUdIQLZR9-Wx_X38SA993YgVrvUOq-l5saY8gWSt4673OVMEWnWRrJfs=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # C++ # Clean Code for Computer Vision using OpenCV and C++ When writing clean code using the OpenCV library in C++, here are some additional principles to follow: 1. Avoid using magic numbers: Instead of using hardcoded values, use named constants or named variables for numbers that have a specific meaning. 2. Keep functions focused on OpenCV operations: Limit the number of lines of code in functions and make sure each function focuses on performing OpenCV operations. 3. Use clear and descriptive names for OpenCV functions: When calling OpenCV functions, use names that are clear and descriptive of what the function does. 4. Use OpenCV data structures appropriately: Familiarize yourself with the different OpenCV data structures, like cv::Mat, cv::Point, etc., and use the appropriate one for each task. 5. Error handling: Make sure to check the return value of OpenCV functions and handle errors appropriately. 6. Make use of OpenCV's high-level functions: Whenever possible, use OpenCV's high-level functions instead of lower-level functions to simplify code and reduce the amount of boilerplate code. 7. Keep track of the image size: Make sure to keep track of the size of images, especially when performing operations like resizing, as this can affect the results. These examples demonstrate how following good coding practices and paying attention to the specific features of the OpenCV library can help you write clean, efficient, and effective code. By following these principles, you can write clean and maintainable code that makes effective use of the OpenCV library. Here are several examples of clean code in OpenCV C++: * Meaningful variable names: cv::Mat original_image = cv::imread("image.jpg"); cv::Mat resized_image; cv::resize(original_image, resized_image, cv::Size(), 0.5, 0.5, cv::INTER_AREA); * Use of high-level functions: cv::Mat src = cv::imread("image.jpg"); cv::Mat dst; cv::GaussianBlur(src, dst, cv::Size(3,3), 0); * Error handling: cv::Mat src = cv::imread("image.jpg"); if(src.empty()) { std::cout << "Error: Could not load image" << std::endl; return -1; } * Use of descriptive function names: cv::Mat src * Appropriate use of OpenCV data structures: cv::Mat src = cv::imread("image.jpg"); std::vector corners; cv::goodFeaturesToTrack(src, corners, 100, 0.01, 10); * Reusable functions: cv::Mat src = cv::imread("image.jpg"); cv::Mat gray; cv::cvtColor(src, gray, cv::COLOR_BGR2GRAY); cv::Mat sharpen_image(const cv::Mat& image) { cv::Mat result; cv::GaussianBlur(image, result, cv::Size(0,0), 3); cv::addWeighted(image, 1.5, result, -0.5, 0, result); return result; } cv::Mat sharpened = sharpen_image(gray); * Clear and concise comments: // Load the source image cv::Mat src = cv::imread("image.jpg"); // Convert the image to grayscale cv::Mat gray; cv::cvtColor(src, gray, cv::COLOR_BGR2GRAY); // Threshold the image to create a binary image cv::Mat thresholded; cv::threshold(gray, thresholded, 128, 255, cv::THRESH_BINARY); ## doc this code shows information about image ## code ## doc this code shows information about image ## code Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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Avoid using magic numbers: Instead of using hardcoded values, use named constants or named variables for numbers that have a specific meaning. 2. Keep functions focused on OpenCV operations: Limit the number of lines of code in functions and make sure each function focuses on performing OpenCV operations. 3. Use clear and descriptive names for OpenCV functions: When calling OpenCV functions, use names that are clear and descriptive of what the function does. 4. Use OpenCV data structures appropriately: Familiarize yourself with the different OpenCV data structures, like cv::Mat, cv::Point, etc., and use the appropriate one for each task. 5. Error handling: Make sure to check the return value of OpenCV functions and handle errors appropriately. 6. Make use of OpenCV's high-level functions: Whenever possible, use OpenCV's high-level functions instead of lower-level functions to simplify code and reduce the amount of boilerplate code. 7. Keep track of the image size: Make sure to keep track of the size of images, especially when performing operations like resizing, as this can affect the results. These examples demonstrate how following good coding practices and paying attention to the specific features of the OpenCV library can help you write clean, efficient, and effective code. By following these principles, you can write clean and maintainable code that makes effective use of the OpenCV library. Here are several examples of clean code in OpenCV C++: * Meaningful variable names: cv::Mat original_image = cv::imread("image.jpg"); cv::Mat resized_image; cv::resize(original_image, resized_image, cv::Size(), 0.5, 0.5, cv::INTER_AREA); * Use of high-level functions: cv::Mat src = cv::imread("image.jpg"); cv::Mat dst; cv::GaussianBlur(src, dst, cv::Size(3,3), 0); * Error handling: cv::Mat src = cv::imread("image.jpg"); if(src.empty()) { std::cout << "Error: Could not load image" << std::endl; return -1; } * Use of descriptive function names: cv::Mat src * Appropriate use of OpenCV data structures: cv::Mat src = cv::imread("image.jpg"); std::vector corners; cv::goodFeaturesToTrack(src, corners, 100, 0.01, 10); * Reusable functions: cv::Mat src = cv::imread("image.jpg"); cv::Mat gray; cv::cvtColor(src, gray, cv::COLOR_BGR2GRAY); cv::Mat sharpen_image(const cv::Mat& image) { cv::Mat result; cv::GaussianBlur(image, result, cv::Size(0,0), 3); cv::addWeighted(image, 1.5, result, -0.5, 0, result); return result; } cv::Mat sharpened = sharpen_image(gray); * Clear and concise comments: // Load the source image cv::Mat src = cv::imread("image.jpg"); // Convert the image to grayscale cv::Mat gray; cv::cvtColor(src, gray, cv::COLOR_BGR2GRAY); // Threshold the image to create a binary image cv::Mat thresholded; cv::threshold(gray, thresholded, 128, 255, cv::THRESH_BINARY); ## doc this code shows information about image ## code ## doc this code shows information about image ## code Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/h_FyaRuTvKPhUo69bPpCeVsZvMgj2QWV4ONYLhXJacdIZfybldOm0B_zkWDtn02V6ch5xPd9sbmAGN2yE52AHIc=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/h_FyaRuTvKPhUo69bPpCeVsZvMgj2QWV4ONYLhXJacdIZfybldOm0B_zkWDtn02V6ch5xPd9sbmAGN2yE52AHIc=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # OpenCV Download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) # [C++](/topics-and-projects/source-code/opencv/cpp) # [Python](/topics-and-projects/source-code/opencv/python) NuGet - OpenCV 5 beta cvtest: Computer Vision Test Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Standard test for computer vision application Advanced OpenCV techniques: Advanced OpenCV techniques: Advanced OpenCV techniques: balance white Advanced OpenCV techniques: contrast and brightness Advanced subpixel techniques: Shift image content Mesh grid float mesh grid int main: Tips and Tricks of OpenCV that Nobody Told You Tricks Tips Save results Error Testing for OpenCV Projects Tricks Tips Example YouTube # NuGet - OpenCV 5 beta NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022 [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static opencv library for visual studio 2022 by using NuGet package manager just in few minutes #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) #OpenCV #Farshid_PirahanSiah #pirahansiah My NuGet packages comprised of two versions for different VS version. * Visual Studio 2019 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS2019_NuGet&sa=D&sntz=1&usg=AOvVaw0B-7f3iUYsMrp8SHvJGQb9) * Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7 * Visual Studio 2022 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS22_NuGet&sa=D&sntz=1&usg=AOvVaw2tWZP3KILcTESYrI2cdofl) * Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7 _**more:**_[ _ **https://www.pirahansiah.com/topics/opencv**_](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) _ ****_ # cvtest: Computer Vision Test ## Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Do you want to test your output of computer vision application which is video or images? ## Standard test for computer vision application There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil) download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: [](https://docs.google.com/presentation/d/14HX-99rGO9x1AtOnEigZ_2gAzZaPwvxqCEnnG0dk870/present "Open Presentation, cvtest in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/slides_32dp.png)cvtest # Advanced OpenCV techniques: sub-pixel, floating points, more precise, real-valued coordinates, Changing the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and Tricks of OpenCV that Nobody Told You download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: # Advanced OpenCV techniques: Cross correlation (CC): TM_CCORR Mean shifted cross correlation (Pearson correlation coefficient): TM_CCOEFF Normalization: TM_SQDIFF_NORMED, TM_CCORR_NORMED, TM_CCOEFF_NORMED maximum absolute difference metric (MaxAD), which is also known as the uniform distance metric computeECC() and findTransformECC(). Sum of absolute differences (SAD) Cross correlation (CC) find identical regions of an image that match a template, select by giving a threshold 2D convolution It simply slides the template image over the input image (as in 2D convolution) and compares the template and patch of input image under the template image. Template matching > cv2.TM_CCOEFF > cv2.TM_CCOEFF_NORMED > cv2.TM_CCORR > cv2.TM_CCORR_NORMED < cv2.TM_SQDIFF < cv2.TM_SQDIFF_NORMED cv2.minMaxLoc() more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: [https://docs.opencv.org/master/df/dfb/group__imgproc__object.html#gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2Fmaster%2Fdf%2Fdfb%2Fgroup__imgproc__object.html%23gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65&sa=D&sntz=1&usg=AOvVaw37X4mefDnUv8PmnZK0YdoM) [https://stackoverflow.com/questions/58158129/understanding-and-evaluating- template-matching- methods](https://www.google.com/url?q=https%3A%2F%2Fstackoverflow.com%2Fquestions%2F58158129%2Funderstanding- and-evaluating-template-matching- methods&sa=D&sntz=1&usg=AOvVaw08M8efI3a63Nn7qXHfZZEY) # Advanced OpenCV techniques: balance white balance white with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: void balance_white(cv::Mat mat) { double discard_ratio = 0.05; int hists[3][256]; memset(hists, 0, 3 * 256 * sizeof(int)); for (int y = 0; y < mat.rows; ++y) { uchar* ptr = mat.ptr(y); for (int x = 0; x < mat.cols; ++x) { for (int j = 0; j < 3; ++j) { hists[j][ptr[x * 3 + j]] += 1; } } } // cumulative hist int total = mat.cols * mat.rows; int vmin[3], vmax[3]; for (int i = 0; i < 3; ++i) { for (int j = 0; j < 255; ++j) { hists[i][j + 1] += hists[i][j]; } vmin[i] = 0; vmax[i] = 255; while (hists[i][vmin[i]] < discard_ratio * total) vmin[i] += 1; while (hists[i][vmax[i]] > (1 - discard_ratio) * total) vmax[i] -= 1; if (vmax[i] < 255 - 1) vmax[i] += 1; } for (int y = 0; y < mat.rows; ++y) { uchar* ptr = mat.ptr(y); for (int x = 0; x < mat.cols; ++x) { for (int j = 0; j < 3; ++j) { int val = ptr[x * 3 + j]; if (val < vmin[j]) val = vmin[j]; if (val > vmax[j]) val = vmax[j]; ptr[x * 3 + j] = static_cast((val - vmin[j]) * 255.0 / (vmax[j] - vmin[j])); } } } } reference http://www.ipol.im/pub/art/2011/llmps-scb/ https://gist.github.com/tomykaira/94472e9f4921ec2cf582 # Advanced OpenCV techniques: contrast and brightness sub-pixel, floating points, more precise, real-valued coordinates, Changing the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: showImage.convertTo(showImage, CV_32FC3); double alpha = 2.0; /*< Simple contrast control */ int beta = 100; /*< Simple brightness control */ for (int y = 0; y < showImage.rows; y++) { for (int x = 0; x < showImage.cols; x++) { for (int c = 0; c < showImage.channels(); c++) { showImage.at(y, x)[c] =cv::saturate_cast(alpha * showImage.at(y, x)[c] + beta); } } } showImage.convertTo(showImage, CV_8UC3); cv::imshow("Changing the contrast and brightness of an image! ", showImage); cv::waitKey(0); based on [https://docs.opencv.org/3.4/d3/dc1/tutorial_basic_linear_transform.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd3%2Fdc1%2Ftutorial_basic_linear_transform.html&sa=D&sntz=1&usg=AOvVaw15cmUCYNHZx6OdaoUN0tSB) [https://blog.csdn.net/u014230360/article/details/109802229](https://www.google.com/url?q=https%3A%2F%2Fblog.csdn.net%2Fu014230360%2Farticle%2Fdetails%2F109802229&sa=D&sntz=1&usg=AOvVaw1IxjJJewGlTNahnzI7pCX7) # Advanced subpixel techniques: Shift image content sub-pixel, floating points, mesh grid, remap, more precise, real-valued coordinates, moving image pixel, Shift image content with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: ## ## Mesh grid float static void meshgrid_float(const cv::Mat& xgv, const cv::Mat& ygv, cv::Mat1f& X, cv::Mat1f& Y) { cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X); cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y); } static void meshgrid_map_float(const cv::Range& xgv, const cv::Range& ygv, cv::Mat1f& X, cv::Mat1f& Y) { std::vector t_x, t_y; for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i); for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i); meshgrid_float(cv::Mat(t_x), cv::Mat(t_y), X, Y); } ## mesh grid int static void meshgrid(const cv::Mat& xgv, const cv::Mat& ygv, cv::Mat1i& X, cv::Mat1i& Y) { cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X); cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y); } static void meshgridTest(const cv::Range& xgv, const cv::Range& ygv, cv::Mat1i& X, cv::Mat1i& Y) { std::vector t_x, t_y; for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i); for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i); meshgrid(cv::Mat(t_x), cv::Mat(t_y), X, Y); } ## main: cv::Mat1f XF, YF; //for int //meshgrid_map_float(cv::Range(0, cols_main), cv::Range(0, rows_main), XF, YF); //for float meshgrid_map_float(cv::Range(0, cols_main-1), cv::Range(0, rows_main-1), XF, YF); for (int rowsImage = 0; rowsImage < rows_main; ++rowsImage) { for (int colsImage = 0; colsImage < cols_main; ++colsImage) { XF.at(rowsImage, colsImage) += offset1; YF.at(rowsImage, colsImage) += offset2; } } cv::remap(convert_img_i, dst, XF, YF, cv::INTER_LINEAR); if (show) { cv::Mat resizedImage = dst.clone(); dst.convertTo(resizedImage, CV_8UC3); cv::resize(resizedImage, resizedImage, cv::Size(), 0.5, 0.5); std::string nameWindow = " meshgrid and remap in float "; cv::imshow(nameWindow, resizedImage); cv::waitKey(0); } # Tips and Tricks of OpenCV that Nobody Told You Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: ### Tricks cv::multiply(outMat, cv::Scalar(gain, gain, gain), outMat); //color cv::multiply(outMat, cv::Scalar(gain), outMat); //grayscale //copy small Mat to bigger Mat cv::Rect roi( cv::Point( originX, originY ), smallImage.size() ); smallImage.copyTo( bigImage( roi ) ); ### Tips * copy mat to vector need clone() ### ### Save results * save image in float * cv::imwrite("image.exr", MatImage); * save image in uncompressed format : * cv::imwrite("fa.tiff", resizedImage, { cv::IMWRITE_TIFF_COMPRESSION, 1,cv::IMWRITE_TIFF_XDPI, 300,cv::IMWRITE_TIFF_YDPI,300 }); * * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) # None * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 5)) # LZW * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 8)) # Adobe Deflate * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 32946)) # Deflate * cv::imwrite("far.png", resizedImage, { cv::IMWRITE_PNG_COMPRESSION, 0 }); * Save images in streaming * int64 t0 = cv::getTickCount(); * std::string fileName= "fashid_"+std::to_string(t0)+ ".png"; * cv::imwrite(fileName, cimMat, { cv::IMWRITE_PNG_COMPRESSION, 0 }); * Save file name: * std::filesystem::path p = std::filesystem::path(files[i]).filename(); * std::string imgFile = savePath + "/" \+ p.string() + ".tiff"; * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) ; ### Error * Exception thrown at 0x00007FFB5CB21636 (vcruntime140d.dll) in farshid.exe: 0xC0000005: Access violation writing location 0x000002B09BAC4000. * check the size of Mat * cv::Mat meanImages = cv::Mat::zeros(rows_main, cols_main, CV_32F); * cv::Mat meanImages = cv::Mat::zeros(farshidMat.size(), CV_32F); * Linker Tools Error LNK2038 & Linker Tools Error LNK2001; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for '_ITERATOR_DEBUG_LEVEL': value '2' doesn't match value '0' in Source.obj ; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for 'RuntimeLibrary': value 'MTd_StaticDebug' doesn't match value 'MT_StaticRelease' * the link files are not match based on release or debug mode. # Testing for OpenCV Projects Test, C++, Computer Vision, Image Processing, more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: * Arrange, Act and Assert (AAA) Pattern * Google C++ Test Framework * Assertions Types and Test Fixtures * ASSERT_FALSE(frame.empty()); ASSERT_NO_THROW(cap >> img); ASSERT_FALSE(img.empty()) << "idx=" << idx; ### Tricks embedded system, keep the software as small as possible, Embedding static elements in your application, [https://gstreamer.freedesktop.org/documentation/installing/index.html?gi- language=c](https://www.google.com/url?q=https%3A%2F%2Fgstreamer.freedesktop.org%2Fdocumentation%2Finstalling%2Findex.html%3Fgi- language%3Dc&sa=D&sntz=1&usg=AOvVaw1LbT7pN6fSH1VBpZ07TI2Y) ### Tips * copy mat to vector need clone() ### ### Example #include assert(!im.empty()); assert(x.size()==y.size()); assert(x.size()>2); #ifdef _DEBUG #endif #if true #else #endif # YouTube Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static opencv library for visual studio 2022 by using NuGet package manager just in few minutes #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah [https://github.com/xiaohongchen1991/meshgen](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxiaohongchen1991%2Fmeshgen&sa=D&sntz=1&usg=AOvVaw1J7_ZnsiBoB0T6RB0dNc6u) A set of C++ APIs are provided to mimic the same behaviors as the MATLAB function "linspace" and "meshgrid". 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OpenCV 5 beta cvtest: Computer Vision Test Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Standard test for computer vision application Advanced OpenCV techniques: Advanced OpenCV techniques: Advanced OpenCV techniques: balance white Advanced OpenCV techniques: contrast and brightness Advanced subpixel techniques: Shift image content Mesh grid float mesh grid int main: Tips and Tricks of OpenCV that Nobody Told You Tricks Tips Save results Error Testing for OpenCV Projects Tricks Tips Example YouTube # NuGet - OpenCV 5 beta NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022 [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static opencv library for visual studio 2022 by using NuGet package manager just in few minutes #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) #OpenCV #Farshid_PirahanSiah #pirahansiah My NuGet packages comprised of two versions for different VS version. * Visual Studio 2019 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS2019_NuGet&sa=D&sntz=1&usg=AOvVaw0B-7f3iUYsMrp8SHvJGQb9) * Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7 * Visual Studio 2022 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS22_NuGet&sa=D&sntz=1&usg=AOvVaw2tWZP3KILcTESYrI2cdofl) * Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7 _**more:**_[ _ **https://www.pirahansiah.com/topics/opencv**_](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) _ ****_ # cvtest: Computer Vision Test ## Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Do you want to test your output of computer vision application which is video or images? ## Standard test for computer vision application There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil) download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: [](https://docs.google.com/presentation/d/14HX-99rGO9x1AtOnEigZ_2gAzZaPwvxqCEnnG0dk870/present "Open Presentation, cvtest in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/slides_32dp.png)cvtest # Advanced OpenCV techniques: sub-pixel, floating points, more precise, real-valued coordinates, Changing the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and Tricks of OpenCV that Nobody Told You download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: # Advanced OpenCV techniques: Cross correlation (CC): TM_CCORR Mean shifted cross correlation (Pearson correlation coefficient): TM_CCOEFF Normalization: TM_SQDIFF_NORMED, TM_CCORR_NORMED, TM_CCOEFF_NORMED maximum absolute difference metric (MaxAD), which is also known as the uniform distance metric computeECC() and findTransformECC(). Sum of absolute differences (SAD) Cross correlation (CC) find identical regions of an image that match a template, select by giving a threshold 2D convolution It simply slides the template image over the input image (as in 2D convolution) and compares the template and patch of input image under the template image. Template matching > cv2.TM_CCOEFF > cv2.TM_CCOEFF_NORMED > cv2.TM_CCORR > cv2.TM_CCORR_NORMED < cv2.TM_SQDIFF < cv2.TM_SQDIFF_NORMED cv2.minMaxLoc() more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: [https://docs.opencv.org/master/df/dfb/group__imgproc__object.html#gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2Fmaster%2Fdf%2Fdfb%2Fgroup__imgproc__object.html%23gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65&sa=D&sntz=1&usg=AOvVaw37X4mefDnUv8PmnZK0YdoM) [https://stackoverflow.com/questions/58158129/understanding-and-evaluating- template-matching- methods](https://www.google.com/url?q=https%3A%2F%2Fstackoverflow.com%2Fquestions%2F58158129%2Funderstanding- and-evaluating-template-matching- methods&sa=D&sntz=1&usg=AOvVaw08M8efI3a63Nn7qXHfZZEY) # Advanced OpenCV techniques: balance white balance white with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: void balance_white(cv::Mat mat) { double discard_ratio = 0.05; int hists[3][256]; memset(hists, 0, 3 * 256 * sizeof(int)); for (int y = 0; y < mat.rows; ++y) { uchar* ptr = mat.ptr(y); for (int x = 0; x < mat.cols; ++x) { for (int j = 0; j < 3; ++j) { hists[j][ptr[x * 3 + j]] += 1; } } } // cumulative hist int total = mat.cols * mat.rows; int vmin[3], vmax[3]; for (int i = 0; i < 3; ++i) { for (int j = 0; j < 255; ++j) { hists[i][j + 1] += hists[i][j]; } vmin[i] = 0; vmax[i] = 255; while (hists[i][vmin[i]] < discard_ratio * total) vmin[i] += 1; while (hists[i][vmax[i]] > (1 - discard_ratio) * total) vmax[i] -= 1; if (vmax[i] < 255 - 1) vmax[i] += 1; } for (int y = 0; y < mat.rows; ++y) { uchar* ptr = mat.ptr(y); for (int x = 0; x < mat.cols; ++x) { for (int j = 0; j < 3; ++j) { int val = ptr[x * 3 + j]; if (val < vmin[j]) val = vmin[j]; if (val > vmax[j]) val = vmax[j]; ptr[x * 3 + j] = static_cast((val - vmin[j]) * 255.0 / (vmax[j] - vmin[j])); } } } } reference http://www.ipol.im/pub/art/2011/llmps-scb/ https://gist.github.com/tomykaira/94472e9f4921ec2cf582 # Advanced OpenCV techniques: contrast and brightness sub-pixel, floating points, more precise, real-valued coordinates, Changing the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: showImage.convertTo(showImage, CV_32FC3); double alpha = 2.0; /*< Simple contrast control */ int beta = 100; /*< Simple brightness control */ for (int y = 0; y < showImage.rows; y++) { for (int x = 0; x < showImage.cols; x++) { for (int c = 0; c < showImage.channels(); c++) { showImage.at(y, x)[c] =cv::saturate_cast(alpha * showImage.at(y, x)[c] + beta); } } } showImage.convertTo(showImage, CV_8UC3); cv::imshow("Changing the contrast and brightness of an image! ", showImage); cv::waitKey(0); based on [https://docs.opencv.org/3.4/d3/dc1/tutorial_basic_linear_transform.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd3%2Fdc1%2Ftutorial_basic_linear_transform.html&sa=D&sntz=1&usg=AOvVaw15cmUCYNHZx6OdaoUN0tSB) [https://blog.csdn.net/u014230360/article/details/109802229](https://www.google.com/url?q=https%3A%2F%2Fblog.csdn.net%2Fu014230360%2Farticle%2Fdetails%2F109802229&sa=D&sntz=1&usg=AOvVaw1IxjJJewGlTNahnzI7pCX7) # Advanced subpixel techniques: Shift image content sub-pixel, floating points, mesh grid, remap, more precise, real-valued coordinates, moving image pixel, Shift image content with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: ## ## Mesh grid float static void meshgrid_float(const cv::Mat& xgv, const cv::Mat& ygv, cv::Mat1f& X, cv::Mat1f& Y) { cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X); cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y); } static void meshgrid_map_float(const cv::Range& xgv, const cv::Range& ygv, cv::Mat1f& X, cv::Mat1f& Y) { std::vector t_x, t_y; for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i); for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i); meshgrid_float(cv::Mat(t_x), cv::Mat(t_y), X, Y); } ## mesh grid int static void meshgrid(const cv::Mat& xgv, const cv::Mat& ygv, cv::Mat1i& X, cv::Mat1i& Y) { cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X); cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y); } static void meshgridTest(const cv::Range& xgv, const cv::Range& ygv, cv::Mat1i& X, cv::Mat1i& Y) { std::vector t_x, t_y; for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i); for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i); meshgrid(cv::Mat(t_x), cv::Mat(t_y), X, Y); } ## main: cv::Mat1f XF, YF; //for int //meshgrid_map_float(cv::Range(0, cols_main), cv::Range(0, rows_main), XF, YF); //for float meshgrid_map_float(cv::Range(0, cols_main-1), cv::Range(0, rows_main-1), XF, YF); for (int rowsImage = 0; rowsImage < rows_main; ++rowsImage) { for (int colsImage = 0; colsImage < cols_main; ++colsImage) { XF.at(rowsImage, colsImage) += offset1; YF.at(rowsImage, colsImage) += offset2; } } cv::remap(convert_img_i, dst, XF, YF, cv::INTER_LINEAR); if (show) { cv::Mat resizedImage = dst.clone(); dst.convertTo(resizedImage, CV_8UC3); cv::resize(resizedImage, resizedImage, cv::Size(), 0.5, 0.5); std::string nameWindow = " meshgrid and remap in float "; cv::imshow(nameWindow, resizedImage); cv::waitKey(0); } # Tips and Tricks of OpenCV that Nobody Told You Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: ### Tricks cv::multiply(outMat, cv::Scalar(gain, gain, gain), outMat); //color cv::multiply(outMat, cv::Scalar(gain), outMat); //grayscale //copy small Mat to bigger Mat cv::Rect roi( cv::Point( originX, originY ), smallImage.size() ); smallImage.copyTo( bigImage( roi ) ); ### Tips * copy mat to vector need clone() ### ### Save results * save image in float * cv::imwrite("image.exr", MatImage); * save image in uncompressed format : * cv::imwrite("fa.tiff", resizedImage, { cv::IMWRITE_TIFF_COMPRESSION, 1,cv::IMWRITE_TIFF_XDPI, 300,cv::IMWRITE_TIFF_YDPI,300 }); * * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) # None * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 5)) # LZW * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 8)) # Adobe Deflate * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 32946)) # Deflate * cv::imwrite("far.png", resizedImage, { cv::IMWRITE_PNG_COMPRESSION, 0 }); * Save images in streaming * int64 t0 = cv::getTickCount(); * std::string fileName= "fashid_"+std::to_string(t0)+ ".png"; * cv::imwrite(fileName, cimMat, { cv::IMWRITE_PNG_COMPRESSION, 0 }); * Save file name: * std::filesystem::path p = std::filesystem::path(files[i]).filename(); * std::string imgFile = savePath + "/" \+ p.string() + ".tiff"; * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) ; ### Error * Exception thrown at 0x00007FFB5CB21636 (vcruntime140d.dll) in farshid.exe: 0xC0000005: Access violation writing location 0x000002B09BAC4000. * check the size of Mat * cv::Mat meanImages = cv::Mat::zeros(rows_main, cols_main, CV_32F); * cv::Mat meanImages = cv::Mat::zeros(farshidMat.size(), CV_32F); * Linker Tools Error LNK2038 & Linker Tools Error LNK2001; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for '_ITERATOR_DEBUG_LEVEL': value '2' doesn't match value '0' in Source.obj ; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for 'RuntimeLibrary': value 'MTd_StaticDebug' doesn't match value 'MT_StaticRelease' * the link files are not match based on release or debug mode. # Testing for OpenCV Projects Test, C++, Computer Vision, Image Processing, more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: * Arrange, Act and Assert (AAA) Pattern * Google C++ Test Framework * Assertions Types and Test Fixtures * ASSERT_FALSE(frame.empty()); ASSERT_NO_THROW(cap >> img); ASSERT_FALSE(img.empty()) << "idx=" << idx; ### Tricks embedded system, keep the software as small as possible, Embedding static elements in your application, [https://gstreamer.freedesktop.org/documentation/installing/index.html?gi- language=c](https://www.google.com/url?q=https%3A%2F%2Fgstreamer.freedesktop.org%2Fdocumentation%2Finstalling%2Findex.html%3Fgi- language%3Dc&sa=D&sntz=1&usg=AOvVaw1LbT7pN6fSH1VBpZ07TI2Y) ### Tips * copy mat to vector need clone() ### ### Example #include assert(!im.empty()); assert(x.size()==y.size()); assert(x.size()>2); #ifdef _DEBUG #endif #if true #else #endif # YouTube Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static opencv library for visual studio 2022 by using NuGet package manager just in few minutes #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah [https://github.com/xiaohongchen1991/meshgen](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxiaohongchen1991%2Fmeshgen&sa=D&sntz=1&usg=AOvVaw1J7_ZnsiBoB0T6RB0dNc6u) A set of C++ APIs are provided to mimic the same behaviors as the MATLAB function "linspace" and "meshgrid". Must-Read Books of All Time in Computer Vision and Machine Learning ![](https://lh6.googleusercontent.com/xmn3C-kogDWhYFajeyz5ut5C5cLqfb7h69EHu8Ugepm3dc1gqty- aVEoRWJvXvQ_aJbtlviOa76e8iH90wgfXawMGLybHWWuNxO3zKsqAL4oF1iv1X8Z-SiE6rS7qOz2xA=w1280) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. 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OpenCV 5 beta cvtest: Computer Vision Test Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Standard test for computer vision application Advanced OpenCV techniques: Advanced OpenCV techniques: Advanced OpenCV techniques: balance white Advanced OpenCV techniques: contrast and brightness Advanced subpixel techniques: Shift image content Mesh grid float mesh grid int main: Tips and Tricks of OpenCV that Nobody Told You Tricks Tips Save results Error Testing for OpenCV Projects Tricks Tips Example YouTube # NuGet - OpenCV 5 beta NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022 [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static opencv library for visual studio 2022 by using NuGet package manager just in few minutes #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) #OpenCV #Farshid_PirahanSiah #pirahansiah My NuGet packages comprised of two versions for different VS version. * Visual Studio 2019 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS2019_NuGet&sa=D&sntz=1&usg=AOvVaw0B-7f3iUYsMrp8SHvJGQb9) * Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7 * Visual Studio 2022 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS22_NuGet&sa=D&sntz=1&usg=AOvVaw2tWZP3KILcTESYrI2cdofl) * Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7 _**more:**_[ _ **https://www.pirahansiah.com/topics/opencv**_](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) _ ****_ # cvtest: Computer Vision Test ## Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Do you want to test your output of computer vision application which is video or images? ## Standard test for computer vision application There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil) download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: [](https://docs.google.com/presentation/d/14HX-99rGO9x1AtOnEigZ_2gAzZaPwvxqCEnnG0dk870/present "Open Presentation, cvtest in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/slides_32dp.png)cvtest # Advanced OpenCV techniques: sub-pixel, floating points, more precise, real-valued coordinates, Changing the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and Tricks of OpenCV that Nobody Told You download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: # Advanced OpenCV techniques: Cross correlation (CC): TM_CCORR Mean shifted cross correlation (Pearson correlation coefficient): TM_CCOEFF Normalization: TM_SQDIFF_NORMED, TM_CCORR_NORMED, TM_CCOEFF_NORMED maximum absolute difference metric (MaxAD), which is also known as the uniform distance metric computeECC() and findTransformECC(). Sum of absolute differences (SAD) Cross correlation (CC) find identical regions of an image that match a template, select by giving a threshold 2D convolution It simply slides the template image over the input image (as in 2D convolution) and compares the template and patch of input image under the template image. Template matching > cv2.TM_CCOEFF > cv2.TM_CCOEFF_NORMED > cv2.TM_CCORR > cv2.TM_CCORR_NORMED < cv2.TM_SQDIFF < cv2.TM_SQDIFF_NORMED cv2.minMaxLoc() more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: [https://docs.opencv.org/master/df/dfb/group__imgproc__object.html#gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2Fmaster%2Fdf%2Fdfb%2Fgroup__imgproc__object.html%23gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65&sa=D&sntz=1&usg=AOvVaw37X4mefDnUv8PmnZK0YdoM) [https://stackoverflow.com/questions/58158129/understanding-and-evaluating- template-matching- methods](https://www.google.com/url?q=https%3A%2F%2Fstackoverflow.com%2Fquestions%2F58158129%2Funderstanding- and-evaluating-template-matching- methods&sa=D&sntz=1&usg=AOvVaw08M8efI3a63Nn7qXHfZZEY) # Advanced OpenCV techniques: balance white balance white with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: void balance_white(cv::Mat mat) { double discard_ratio = 0.05; int hists[3][256]; memset(hists, 0, 3 * 256 * sizeof(int)); for (int y = 0; y < mat.rows; ++y) { uchar* ptr = mat.ptr(y); for (int x = 0; x < mat.cols; ++x) { for (int j = 0; j < 3; ++j) { hists[j][ptr[x * 3 + j]] += 1; } } } // cumulative hist int total = mat.cols * mat.rows; int vmin[3], vmax[3]; for (int i = 0; i < 3; ++i) { for (int j = 0; j < 255; ++j) { hists[i][j + 1] += hists[i][j]; } vmin[i] = 0; vmax[i] = 255; while (hists[i][vmin[i]] < discard_ratio * total) vmin[i] += 1; while (hists[i][vmax[i]] > (1 - discard_ratio) * total) vmax[i] -= 1; if (vmax[i] < 255 - 1) vmax[i] += 1; } for (int y = 0; y < mat.rows; ++y) { uchar* ptr = mat.ptr(y); for (int x = 0; x < mat.cols; ++x) { for (int j = 0; j < 3; ++j) { int val = ptr[x * 3 + j]; if (val < vmin[j]) val = vmin[j]; if (val > vmax[j]) val = vmax[j]; ptr[x * 3 + j] = static_cast((val - vmin[j]) * 255.0 / (vmax[j] - vmin[j])); } } } } reference http://www.ipol.im/pub/art/2011/llmps-scb/ https://gist.github.com/tomykaira/94472e9f4921ec2cf582 # Advanced OpenCV techniques: contrast and brightness sub-pixel, floating points, more precise, real-valued coordinates, Changing the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: showImage.convertTo(showImage, CV_32FC3); double alpha = 2.0; /*< Simple contrast control */ int beta = 100; /*< Simple brightness control */ for (int y = 0; y < showImage.rows; y++) { for (int x = 0; x < showImage.cols; x++) { for (int c = 0; c < showImage.channels(); c++) { showImage.at(y, x)[c] =cv::saturate_cast(alpha * showImage.at(y, x)[c] + beta); } } } showImage.convertTo(showImage, CV_8UC3); cv::imshow("Changing the contrast and brightness of an image! ", showImage); cv::waitKey(0); based on [https://docs.opencv.org/3.4/d3/dc1/tutorial_basic_linear_transform.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd3%2Fdc1%2Ftutorial_basic_linear_transform.html&sa=D&sntz=1&usg=AOvVaw15cmUCYNHZx6OdaoUN0tSB) [https://blog.csdn.net/u014230360/article/details/109802229](https://www.google.com/url?q=https%3A%2F%2Fblog.csdn.net%2Fu014230360%2Farticle%2Fdetails%2F109802229&sa=D&sntz=1&usg=AOvVaw1IxjJJewGlTNahnzI7pCX7) # Advanced subpixel techniques: Shift image content sub-pixel, floating points, mesh grid, remap, more precise, real-valued coordinates, moving image pixel, Shift image content with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: ## ## Mesh grid float static void meshgrid_float(const cv::Mat& xgv, const cv::Mat& ygv, cv::Mat1f& X, cv::Mat1f& Y) { cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X); cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y); } static void meshgrid_map_float(const cv::Range& xgv, const cv::Range& ygv, cv::Mat1f& X, cv::Mat1f& Y) { std::vector t_x, t_y; for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i); for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i); meshgrid_float(cv::Mat(t_x), cv::Mat(t_y), X, Y); } ## mesh grid int static void meshgrid(const cv::Mat& xgv, const cv::Mat& ygv, cv::Mat1i& X, cv::Mat1i& Y) { cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X); cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y); } static void meshgridTest(const cv::Range& xgv, const cv::Range& ygv, cv::Mat1i& X, cv::Mat1i& Y) { std::vector t_x, t_y; for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i); for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i); meshgrid(cv::Mat(t_x), cv::Mat(t_y), X, Y); } ## main: cv::Mat1f XF, YF; //for int //meshgrid_map_float(cv::Range(0, cols_main), cv::Range(0, rows_main), XF, YF); //for float meshgrid_map_float(cv::Range(0, cols_main-1), cv::Range(0, rows_main-1), XF, YF); for (int rowsImage = 0; rowsImage < rows_main; ++rowsImage) { for (int colsImage = 0; colsImage < cols_main; ++colsImage) { XF.at(rowsImage, colsImage) += offset1; YF.at(rowsImage, colsImage) += offset2; } } cv::remap(convert_img_i, dst, XF, YF, cv::INTER_LINEAR); if (show) { cv::Mat resizedImage = dst.clone(); dst.convertTo(resizedImage, CV_8UC3); cv::resize(resizedImage, resizedImage, cv::Size(), 0.5, 0.5); std::string nameWindow = " meshgrid and remap in float "; cv::imshow(nameWindow, resizedImage); cv::waitKey(0); } # Tips and Tricks of OpenCV that Nobody Told You Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: ### Tricks cv::multiply(outMat, cv::Scalar(gain, gain, gain), outMat); //color cv::multiply(outMat, cv::Scalar(gain), outMat); //grayscale //copy small Mat to bigger Mat cv::Rect roi( cv::Point( originX, originY ), smallImage.size() ); smallImage.copyTo( bigImage( roi ) ); ### Tips * copy mat to vector need clone() ### ### Save results * save image in float * cv::imwrite("image.exr", MatImage); * save image in uncompressed format : * cv::imwrite("fa.tiff", resizedImage, { cv::IMWRITE_TIFF_COMPRESSION, 1,cv::IMWRITE_TIFF_XDPI, 300,cv::IMWRITE_TIFF_YDPI,300 }); * * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) # None * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 5)) # LZW * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 8)) # Adobe Deflate * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 32946)) # Deflate * cv::imwrite("far.png", resizedImage, { cv::IMWRITE_PNG_COMPRESSION, 0 }); * Save images in streaming * int64 t0 = cv::getTickCount(); * std::string fileName= "fashid_"+std::to_string(t0)+ ".png"; * cv::imwrite(fileName, cimMat, { cv::IMWRITE_PNG_COMPRESSION, 0 }); * Save file name: * std::filesystem::path p = std::filesystem::path(files[i]).filename(); * std::string imgFile = savePath + "/" \+ p.string() + ".tiff"; * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) ; ### Error * Exception thrown at 0x00007FFB5CB21636 (vcruntime140d.dll) in farshid.exe: 0xC0000005: Access violation writing location 0x000002B09BAC4000. * check the size of Mat * cv::Mat meanImages = cv::Mat::zeros(rows_main, cols_main, CV_32F); * cv::Mat meanImages = cv::Mat::zeros(farshidMat.size(), CV_32F); * Linker Tools Error LNK2038 & Linker Tools Error LNK2001; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for '_ITERATOR_DEBUG_LEVEL': value '2' doesn't match value '0' in Source.obj ; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for 'RuntimeLibrary': value 'MTd_StaticDebug' doesn't match value 'MT_StaticRelease' * the link files are not match based on release or debug mode. # Testing for OpenCV Projects Test, C++, Computer Vision, Image Processing, more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: * Arrange, Act and Assert (AAA) Pattern * Google C++ Test Framework * Assertions Types and Test Fixtures * ASSERT_FALSE(frame.empty()); ASSERT_NO_THROW(cap >> img); ASSERT_FALSE(img.empty()) << "idx=" << idx; ### Tricks embedded system, keep the software as small as possible, Embedding static elements in your application, [https://gstreamer.freedesktop.org/documentation/installing/index.html?gi- language=c](https://www.google.com/url?q=https%3A%2F%2Fgstreamer.freedesktop.org%2Fdocumentation%2Finstalling%2Findex.html%3Fgi- language%3Dc&sa=D&sntz=1&usg=AOvVaw1LbT7pN6fSH1VBpZ07TI2Y) ### Tips * copy mat to vector need clone() ### ### Example #include assert(!im.empty()); assert(x.size()==y.size()); assert(x.size()>2); #ifdef _DEBUG #endif #if true #else #endif # YouTube Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static opencv library for visual studio 2022 by using NuGet package manager just in few minutes #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah [https://github.com/xiaohongchen1991/meshgen](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxiaohongchen1991%2Fmeshgen&sa=D&sntz=1&usg=AOvVaw1J7_ZnsiBoB0T6RB0dNc6u) A set of C++ APIs are provided to mimic the same behaviors as the MATLAB function "linspace" and "meshgrid". Must-Read Books of All Time in Computer Vision and Machine Learning ![](https://lh6.googleusercontent.com/xmn3C-kogDWhYFajeyz5ut5C5cLqfb7h69EHu8Ugepm3dc1gqty- aVEoRWJvXvQ_aJbtlviOa76e8iH90wgfXawMGLybHWWuNxO3zKsqAL4oF1iv1X8Z-SiE6rS7qOz2xA=w1280) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. 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OpenCV 5 beta cvtest: Computer Vision Test Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Standard test for computer vision application Advanced OpenCV techniques: Advanced OpenCV techniques: Advanced OpenCV techniques: balance white Advanced OpenCV techniques: contrast and brightness Advanced subpixel techniques: Shift image content Mesh grid float mesh grid int main: Tips and Tricks of OpenCV that Nobody Told You Tricks Tips Save results Error Testing for OpenCV Projects Tricks Tips Example YouTube # NuGet - OpenCV 5 beta NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022 [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static opencv library for visual studio 2022 by using NuGet package manager just in few minutes #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) #OpenCV #Farshid_PirahanSiah #pirahansiah My NuGet packages comprised of two versions for different VS version. * Visual Studio 2019 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS2019_NuGet&sa=D&sntz=1&usg=AOvVaw0B-7f3iUYsMrp8SHvJGQb9) * Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7 * Visual Studio 2022 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS22_NuGet&sa=D&sntz=1&usg=AOvVaw2tWZP3KILcTESYrI2cdofl) * Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7 _**more:**_[ _ **https://www.pirahansiah.com/topics/opencv**_](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) _ ****_ # cvtest: Computer Vision Test ## Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Do you want to test your output of computer vision application which is video or images? ## Standard test for computer vision application There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil) download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: [](https://docs.google.com/presentation/d/14HX-99rGO9x1AtOnEigZ_2gAzZaPwvxqCEnnG0dk870/present "Open Presentation, cvtest in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/slides_32dp.png)cvtest # Advanced OpenCV techniques: sub-pixel, floating points, more precise, real-valued coordinates, Changing the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and Tricks of OpenCV that Nobody Told You download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: # Advanced OpenCV techniques: Cross correlation (CC): TM_CCORR Mean shifted cross correlation (Pearson correlation coefficient): TM_CCOEFF Normalization: TM_SQDIFF_NORMED, TM_CCORR_NORMED, TM_CCOEFF_NORMED maximum absolute difference metric (MaxAD), which is also known as the uniform distance metric computeECC() and findTransformECC(). Sum of absolute differences (SAD) Cross correlation (CC) find identical regions of an image that match a template, select by giving a threshold 2D convolution It simply slides the template image over the input image (as in 2D convolution) and compares the template and patch of input image under the template image. Template matching > cv2.TM_CCOEFF > cv2.TM_CCOEFF_NORMED > cv2.TM_CCORR > cv2.TM_CCORR_NORMED < cv2.TM_SQDIFF < cv2.TM_SQDIFF_NORMED cv2.minMaxLoc() more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: [https://docs.opencv.org/master/df/dfb/group__imgproc__object.html#gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2Fmaster%2Fdf%2Fdfb%2Fgroup__imgproc__object.html%23gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65&sa=D&sntz=1&usg=AOvVaw37X4mefDnUv8PmnZK0YdoM) [https://stackoverflow.com/questions/58158129/understanding-and-evaluating- template-matching- methods](https://www.google.com/url?q=https%3A%2F%2Fstackoverflow.com%2Fquestions%2F58158129%2Funderstanding- and-evaluating-template-matching- methods&sa=D&sntz=1&usg=AOvVaw08M8efI3a63Nn7qXHfZZEY) # Advanced OpenCV techniques: balance white balance white with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: void balance_white(cv::Mat mat) { double discard_ratio = 0.05; int hists[3][256]; memset(hists, 0, 3 * 256 * sizeof(int)); for (int y = 0; y < mat.rows; ++y) { uchar* ptr = mat.ptr(y); for (int x = 0; x < mat.cols; ++x) { for (int j = 0; j < 3; ++j) { hists[j][ptr[x * 3 + j]] += 1; } } } // cumulative hist int total = mat.cols * mat.rows; int vmin[3], vmax[3]; for (int i = 0; i < 3; ++i) { for (int j = 0; j < 255; ++j) { hists[i][j + 1] += hists[i][j]; } vmin[i] = 0; vmax[i] = 255; while (hists[i][vmin[i]] < discard_ratio * total) vmin[i] += 1; while (hists[i][vmax[i]] > (1 - discard_ratio) * total) vmax[i] -= 1; if (vmax[i] < 255 - 1) vmax[i] += 1; } for (int y = 0; y < mat.rows; ++y) { uchar* ptr = mat.ptr(y); for (int x = 0; x < mat.cols; ++x) { for (int j = 0; j < 3; ++j) { int val = ptr[x * 3 + j]; if (val < vmin[j]) val = vmin[j]; if (val > vmax[j]) val = vmax[j]; ptr[x * 3 + j] = static_cast((val - vmin[j]) * 255.0 / (vmax[j] - vmin[j])); } } } } reference http://www.ipol.im/pub/art/2011/llmps-scb/ https://gist.github.com/tomykaira/94472e9f4921ec2cf582 # Advanced OpenCV techniques: contrast and brightness sub-pixel, floating points, more precise, real-valued coordinates, Changing the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: showImage.convertTo(showImage, CV_32FC3); double alpha = 2.0; /*< Simple contrast control */ int beta = 100; /*< Simple brightness control */ for (int y = 0; y < showImage.rows; y++) { for (int x = 0; x < showImage.cols; x++) { for (int c = 0; c < showImage.channels(); c++) { showImage.at(y, x)[c] =cv::saturate_cast(alpha * showImage.at(y, x)[c] + beta); } } } showImage.convertTo(showImage, CV_8UC3); cv::imshow("Changing the contrast and brightness of an image! ", showImage); cv::waitKey(0); based on [https://docs.opencv.org/3.4/d3/dc1/tutorial_basic_linear_transform.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd3%2Fdc1%2Ftutorial_basic_linear_transform.html&sa=D&sntz=1&usg=AOvVaw15cmUCYNHZx6OdaoUN0tSB) [https://blog.csdn.net/u014230360/article/details/109802229](https://www.google.com/url?q=https%3A%2F%2Fblog.csdn.net%2Fu014230360%2Farticle%2Fdetails%2F109802229&sa=D&sntz=1&usg=AOvVaw1IxjJJewGlTNahnzI7pCX7) # Advanced subpixel techniques: Shift image content sub-pixel, floating points, mesh grid, remap, more precise, real-valued coordinates, moving image pixel, Shift image content with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: ## ## Mesh grid float static void meshgrid_float(const cv::Mat& xgv, const cv::Mat& ygv, cv::Mat1f& X, cv::Mat1f& Y) { cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X); cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y); } static void meshgrid_map_float(const cv::Range& xgv, const cv::Range& ygv, cv::Mat1f& X, cv::Mat1f& Y) { std::vector t_x, t_y; for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i); for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i); meshgrid_float(cv::Mat(t_x), cv::Mat(t_y), X, Y); } ## mesh grid int static void meshgrid(const cv::Mat& xgv, const cv::Mat& ygv, cv::Mat1i& X, cv::Mat1i& Y) { cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X); cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y); } static void meshgridTest(const cv::Range& xgv, const cv::Range& ygv, cv::Mat1i& X, cv::Mat1i& Y) { std::vector t_x, t_y; for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i); for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i); meshgrid(cv::Mat(t_x), cv::Mat(t_y), X, Y); } ## main: cv::Mat1f XF, YF; //for int //meshgrid_map_float(cv::Range(0, cols_main), cv::Range(0, rows_main), XF, YF); //for float meshgrid_map_float(cv::Range(0, cols_main-1), cv::Range(0, rows_main-1), XF, YF); for (int rowsImage = 0; rowsImage < rows_main; ++rowsImage) { for (int colsImage = 0; colsImage < cols_main; ++colsImage) { XF.at(rowsImage, colsImage) += offset1; YF.at(rowsImage, colsImage) += offset2; } } cv::remap(convert_img_i, dst, XF, YF, cv::INTER_LINEAR); if (show) { cv::Mat resizedImage = dst.clone(); dst.convertTo(resizedImage, CV_8UC3); cv::resize(resizedImage, resizedImage, cv::Size(), 0.5, 0.5); std::string nameWindow = " meshgrid and remap in float "; cv::imshow(nameWindow, resizedImage); cv::waitKey(0); } # Tips and Tricks of OpenCV that Nobody Told You Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: ### Tricks cv::multiply(outMat, cv::Scalar(gain, gain, gain), outMat); //color cv::multiply(outMat, cv::Scalar(gain), outMat); //grayscale //copy small Mat to bigger Mat cv::Rect roi( cv::Point( originX, originY ), smallImage.size() ); smallImage.copyTo( bigImage( roi ) ); ### Tips * copy mat to vector need clone() ### ### Save results * save image in float * cv::imwrite("image.exr", MatImage); * save image in uncompressed format : * cv::imwrite("fa.tiff", resizedImage, { cv::IMWRITE_TIFF_COMPRESSION, 1,cv::IMWRITE_TIFF_XDPI, 300,cv::IMWRITE_TIFF_YDPI,300 }); * * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) # None * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 5)) # LZW * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 8)) # Adobe Deflate * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 32946)) # Deflate * cv::imwrite("far.png", resizedImage, { cv::IMWRITE_PNG_COMPRESSION, 0 }); * Save images in streaming * int64 t0 = cv::getTickCount(); * std::string fileName= "fashid_"+std::to_string(t0)+ ".png"; * cv::imwrite(fileName, cimMat, { cv::IMWRITE_PNG_COMPRESSION, 0 }); * Save file name: * std::filesystem::path p = std::filesystem::path(files[i]).filename(); * std::string imgFile = savePath + "/" \+ p.string() + ".tiff"; * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) ; ### Error * Exception thrown at 0x00007FFB5CB21636 (vcruntime140d.dll) in farshid.exe: 0xC0000005: Access violation writing location 0x000002B09BAC4000. * check the size of Mat * cv::Mat meanImages = cv::Mat::zeros(rows_main, cols_main, CV_32F); * cv::Mat meanImages = cv::Mat::zeros(farshidMat.size(), CV_32F); * Linker Tools Error LNK2038 & Linker Tools Error LNK2001; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for '_ITERATOR_DEBUG_LEVEL': value '2' doesn't match value '0' in Source.obj ; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for 'RuntimeLibrary': value 'MTd_StaticDebug' doesn't match value 'MT_StaticRelease' * the link files are not match based on release or debug mode. # Testing for OpenCV Projects Test, C++, Computer Vision, Image Processing, more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: * Arrange, Act and Assert (AAA) Pattern * Google C++ Test Framework * Assertions Types and Test Fixtures * ASSERT_FALSE(frame.empty()); ASSERT_NO_THROW(cap >> img); ASSERT_FALSE(img.empty()) << "idx=" << idx; ### Tricks embedded system, keep the software as small as possible, Embedding static elements in your application, [https://gstreamer.freedesktop.org/documentation/installing/index.html?gi- language=c](https://www.google.com/url?q=https%3A%2F%2Fgstreamer.freedesktop.org%2Fdocumentation%2Finstalling%2Findex.html%3Fgi- language%3Dc&sa=D&sntz=1&usg=AOvVaw1LbT7pN6fSH1VBpZ07TI2Y) ### Tips * copy mat to vector need clone() ### ### Example #include assert(!im.empty()); assert(x.size()==y.size()); assert(x.size()>2); #ifdef _DEBUG #endif #if true #else #endif # YouTube Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static opencv library for visual studio 2022 by using NuGet package manager just in few minutes #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah [https://github.com/xiaohongchen1991/meshgen](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxiaohongchen1991%2Fmeshgen&sa=D&sntz=1&usg=AOvVaw1J7_ZnsiBoB0T6RB0dNc6u) A set of C++ APIs are provided to mimic the same behaviors as the MATLAB function "linspace" and "meshgrid". Must-Read Books of All Time in Computer Vision and Machine Learning ![](https://lh6.googleusercontent.com/xmn3C-kogDWhYFajeyz5ut5C5cLqfb7h69EHu8Ugepm3dc1gqty- aVEoRWJvXvQ_aJbtlviOa76e8iH90wgfXawMGLybHWWuNxO3zKsqAL4oF1iv1X8Z-SiE6rS7qOz2xA=w1280) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. 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OpenCV 5 beta cvtest: Computer Vision Test Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Standard test for computer vision application Advanced OpenCV techniques: Advanced OpenCV techniques: Advanced OpenCV techniques: balance white Advanced OpenCV techniques: contrast and brightness Advanced subpixel techniques: Shift image content Mesh grid float mesh grid int main: Tips and Tricks of OpenCV that Nobody Told You Tricks Tips Save results Error Testing for OpenCV Projects Tricks Tips Example YouTube # NuGet - OpenCV 5 beta NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022 [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static opencv library for visual studio 2022 by using NuGet package manager just in few minutes #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) #OpenCV #Farshid_PirahanSiah #pirahansiah My NuGet packages comprised of two versions for different VS version. * Visual Studio 2019 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS2019_NuGet&sa=D&sntz=1&usg=AOvVaw0B-7f3iUYsMrp8SHvJGQb9) * Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7 * Visual Studio 2022 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS22_NuGet&sa=D&sntz=1&usg=AOvVaw2tWZP3KILcTESYrI2cdofl) * Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7 _**more:**_[ _ **https://www.pirahansiah.com/topics/opencv**_](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) _ ****_ # cvtest: Computer Vision Test ## Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Do you want to test your output of computer vision application which is video or images? ## Standard test for computer vision application There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil) download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: [](https://docs.google.com/presentation/d/14HX-99rGO9x1AtOnEigZ_2gAzZaPwvxqCEnnG0dk870/present "Open Presentation, cvtest in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/slides_32dp.png)cvtest # Advanced OpenCV techniques: sub-pixel, floating points, more precise, real-valued coordinates, Changing the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and Tricks of OpenCV that Nobody Told You download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: # Advanced OpenCV techniques: Cross correlation (CC): TM_CCORR Mean shifted cross correlation (Pearson correlation coefficient): TM_CCOEFF Normalization: TM_SQDIFF_NORMED, TM_CCORR_NORMED, TM_CCOEFF_NORMED maximum absolute difference metric (MaxAD), which is also known as the uniform distance metric computeECC() and findTransformECC(). Sum of absolute differences (SAD) Cross correlation (CC) find identical regions of an image that match a template, select by giving a threshold 2D convolution It simply slides the template image over the input image (as in 2D convolution) and compares the template and patch of input image under the template image. Template matching > cv2.TM_CCOEFF > cv2.TM_CCOEFF_NORMED > cv2.TM_CCORR > cv2.TM_CCORR_NORMED < cv2.TM_SQDIFF < cv2.TM_SQDIFF_NORMED cv2.minMaxLoc() more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: [https://docs.opencv.org/master/df/dfb/group__imgproc__object.html#gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2Fmaster%2Fdf%2Fdfb%2Fgroup__imgproc__object.html%23gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65&sa=D&sntz=1&usg=AOvVaw37X4mefDnUv8PmnZK0YdoM) [https://stackoverflow.com/questions/58158129/understanding-and-evaluating- template-matching- methods](https://www.google.com/url?q=https%3A%2F%2Fstackoverflow.com%2Fquestions%2F58158129%2Funderstanding- and-evaluating-template-matching- methods&sa=D&sntz=1&usg=AOvVaw08M8efI3a63Nn7qXHfZZEY) # Advanced OpenCV techniques: balance white balance white with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: void balance_white(cv::Mat mat) { double discard_ratio = 0.05; int hists[3][256]; memset(hists, 0, 3 * 256 * sizeof(int)); for (int y = 0; y < mat.rows; ++y) { uchar* ptr = mat.ptr(y); for (int x = 0; x < mat.cols; ++x) { for (int j = 0; j < 3; ++j) { hists[j][ptr[x * 3 + j]] += 1; } } } // cumulative hist int total = mat.cols * mat.rows; int vmin[3], vmax[3]; for (int i = 0; i < 3; ++i) { for (int j = 0; j < 255; ++j) { hists[i][j + 1] += hists[i][j]; } vmin[i] = 0; vmax[i] = 255; while (hists[i][vmin[i]] < discard_ratio * total) vmin[i] += 1; while (hists[i][vmax[i]] > (1 - discard_ratio) * total) vmax[i] -= 1; if (vmax[i] < 255 - 1) vmax[i] += 1; } for (int y = 0; y < mat.rows; ++y) { uchar* ptr = mat.ptr(y); for (int x = 0; x < mat.cols; ++x) { for (int j = 0; j < 3; ++j) { int val = ptr[x * 3 + j]; if (val < vmin[j]) val = vmin[j]; if (val > vmax[j]) val = vmax[j]; ptr[x * 3 + j] = static_cast((val - vmin[j]) * 255.0 / (vmax[j] - vmin[j])); } } } } reference http://www.ipol.im/pub/art/2011/llmps-scb/ https://gist.github.com/tomykaira/94472e9f4921ec2cf582 # Advanced OpenCV techniques: contrast and brightness sub-pixel, floating points, more precise, real-valued coordinates, Changing the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: showImage.convertTo(showImage, CV_32FC3); double alpha = 2.0; /*< Simple contrast control */ int beta = 100; /*< Simple brightness control */ for (int y = 0; y < showImage.rows; y++) { for (int x = 0; x < showImage.cols; x++) { for (int c = 0; c < showImage.channels(); c++) { showImage.at(y, x)[c] =cv::saturate_cast(alpha * showImage.at(y, x)[c] + beta); } } } showImage.convertTo(showImage, CV_8UC3); cv::imshow("Changing the contrast and brightness of an image! ", showImage); cv::waitKey(0); based on [https://docs.opencv.org/3.4/d3/dc1/tutorial_basic_linear_transform.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd3%2Fdc1%2Ftutorial_basic_linear_transform.html&sa=D&sntz=1&usg=AOvVaw15cmUCYNHZx6OdaoUN0tSB) [https://blog.csdn.net/u014230360/article/details/109802229](https://www.google.com/url?q=https%3A%2F%2Fblog.csdn.net%2Fu014230360%2Farticle%2Fdetails%2F109802229&sa=D&sntz=1&usg=AOvVaw1IxjJJewGlTNahnzI7pCX7) # Advanced subpixel techniques: Shift image content sub-pixel, floating points, mesh grid, remap, more precise, real-valued coordinates, moving image pixel, Shift image content with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: ## ## Mesh grid float static void meshgrid_float(const cv::Mat& xgv, const cv::Mat& ygv, cv::Mat1f& X, cv::Mat1f& Y) { cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X); cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y); } static void meshgrid_map_float(const cv::Range& xgv, const cv::Range& ygv, cv::Mat1f& X, cv::Mat1f& Y) { std::vector t_x, t_y; for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i); for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i); meshgrid_float(cv::Mat(t_x), cv::Mat(t_y), X, Y); } ## mesh grid int static void meshgrid(const cv::Mat& xgv, const cv::Mat& ygv, cv::Mat1i& X, cv::Mat1i& Y) { cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X); cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y); } static void meshgridTest(const cv::Range& xgv, const cv::Range& ygv, cv::Mat1i& X, cv::Mat1i& Y) { std::vector t_x, t_y; for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i); for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i); meshgrid(cv::Mat(t_x), cv::Mat(t_y), X, Y); } ## main: cv::Mat1f XF, YF; //for int //meshgrid_map_float(cv::Range(0, cols_main), cv::Range(0, rows_main), XF, YF); //for float meshgrid_map_float(cv::Range(0, cols_main-1), cv::Range(0, rows_main-1), XF, YF); for (int rowsImage = 0; rowsImage < rows_main; ++rowsImage) { for (int colsImage = 0; colsImage < cols_main; ++colsImage) { XF.at(rowsImage, colsImage) += offset1; YF.at(rowsImage, colsImage) += offset2; } } cv::remap(convert_img_i, dst, XF, YF, cv::INTER_LINEAR); if (show) { cv::Mat resizedImage = dst.clone(); dst.convertTo(resizedImage, CV_8UC3); cv::resize(resizedImage, resizedImage, cv::Size(), 0.5, 0.5); std::string nameWindow = " meshgrid and remap in float "; cv::imshow(nameWindow, resizedImage); cv::waitKey(0); } # Tips and Tricks of OpenCV that Nobody Told You Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: ### Tricks cv::multiply(outMat, cv::Scalar(gain, gain, gain), outMat); //color cv::multiply(outMat, cv::Scalar(gain), outMat); //grayscale //copy small Mat to bigger Mat cv::Rect roi( cv::Point( originX, originY ), smallImage.size() ); smallImage.copyTo( bigImage( roi ) ); ### Tips * copy mat to vector need clone() ### ### Save results * save image in float * cv::imwrite("image.exr", MatImage); * save image in uncompressed format : * cv::imwrite("fa.tiff", resizedImage, { cv::IMWRITE_TIFF_COMPRESSION, 1,cv::IMWRITE_TIFF_XDPI, 300,cv::IMWRITE_TIFF_YDPI,300 }); * * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) # None * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 5)) # LZW * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 8)) # Adobe Deflate * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 32946)) # Deflate * cv::imwrite("far.png", resizedImage, { cv::IMWRITE_PNG_COMPRESSION, 0 }); * Save images in streaming * int64 t0 = cv::getTickCount(); * std::string fileName= "fashid_"+std::to_string(t0)+ ".png"; * cv::imwrite(fileName, cimMat, { cv::IMWRITE_PNG_COMPRESSION, 0 }); * Save file name: * std::filesystem::path p = std::filesystem::path(files[i]).filename(); * std::string imgFile = savePath + "/" \+ p.string() + ".tiff"; * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) ; ### Error * Exception thrown at 0x00007FFB5CB21636 (vcruntime140d.dll) in farshid.exe: 0xC0000005: Access violation writing location 0x000002B09BAC4000. * check the size of Mat * cv::Mat meanImages = cv::Mat::zeros(rows_main, cols_main, CV_32F); * cv::Mat meanImages = cv::Mat::zeros(farshidMat.size(), CV_32F); * Linker Tools Error LNK2038 & Linker Tools Error LNK2001; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for '_ITERATOR_DEBUG_LEVEL': value '2' doesn't match value '0' in Source.obj ; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for 'RuntimeLibrary': value 'MTd_StaticDebug' doesn't match value 'MT_StaticRelease' * the link files are not match based on release or debug mode. # Testing for OpenCV Projects Test, C++, Computer Vision, Image Processing, more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: * Arrange, Act and Assert (AAA) Pattern * Google C++ Test Framework * Assertions Types and Test Fixtures * ASSERT_FALSE(frame.empty()); ASSERT_NO_THROW(cap >> img); ASSERT_FALSE(img.empty()) << "idx=" << idx; ### Tricks embedded system, keep the software as small as possible, Embedding static elements in your application, [https://gstreamer.freedesktop.org/documentation/installing/index.html?gi- language=c](https://www.google.com/url?q=https%3A%2F%2Fgstreamer.freedesktop.org%2Fdocumentation%2Finstalling%2Findex.html%3Fgi- language%3Dc&sa=D&sntz=1&usg=AOvVaw1LbT7pN6fSH1VBpZ07TI2Y) ### Tips * copy mat to vector need clone() ### ### Example #include assert(!im.empty()); assert(x.size()==y.size()); assert(x.size()>2); #ifdef _DEBUG #endif #if true #else #endif # YouTube Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static opencv library for visual studio 2022 by using NuGet package manager just in few minutes #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah [https://github.com/xiaohongchen1991/meshgen](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxiaohongchen1991%2Fmeshgen&sa=D&sntz=1&usg=AOvVaw1J7_ZnsiBoB0T6RB0dNc6u) A set of C++ APIs are provided to mimic the same behaviors as the MATLAB function "linspace" and "meshgrid". Must-Read Books of All Time in Computer Vision and Machine Learning ![](https://lh6.googleusercontent.com/xmn3C-kogDWhYFajeyz5ut5C5cLqfb7h69EHu8Ugepm3dc1gqty- aVEoRWJvXvQ_aJbtlviOa76e8iH90wgfXawMGLybHWWuNxO3zKsqAL4oF1iv1X8Z-SiE6rS7qOz2xA=w1280) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. 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OpenCV 5 beta cvtest: Computer Vision Test Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Standard test for computer vision application Advanced OpenCV techniques: Advanced OpenCV techniques: Advanced OpenCV techniques: balance white Advanced OpenCV techniques: contrast and brightness Advanced subpixel techniques: Shift image content Mesh grid float mesh grid int main: Tips and Tricks of OpenCV that Nobody Told You Tricks Tips Save results Error Testing for OpenCV Projects Tricks Tips Example YouTube # NuGet - OpenCV 5 beta NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022 [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static opencv library for visual studio 2022 by using NuGet package manager just in few minutes #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) #OpenCV #Farshid_PirahanSiah #pirahansiah My NuGet packages comprised of two versions for different VS version. * Visual Studio 2019 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS2019_NuGet&sa=D&sntz=1&usg=AOvVaw0B-7f3iUYsMrp8SHvJGQb9) * Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7 * Visual Studio 2022 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS22_NuGet&sa=D&sntz=1&usg=AOvVaw2tWZP3KILcTESYrI2cdofl) * Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7 _**more:**_[ _ **https://www.pirahansiah.com/topics/opencv**_](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) _ ****_ # cvtest: Computer Vision Test ## Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Do you want to test your output of computer vision application which is video or images? ## Standard test for computer vision application There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil) download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: [](https://docs.google.com/presentation/d/14HX-99rGO9x1AtOnEigZ_2gAzZaPwvxqCEnnG0dk870/present "Open Presentation, cvtest in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/slides_32dp.png)cvtest # Advanced OpenCV techniques: sub-pixel, floating points, more precise, real-valued coordinates, Changing the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and Tricks of OpenCV that Nobody Told You download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: # Advanced OpenCV techniques: Cross correlation (CC): TM_CCORR Mean shifted cross correlation (Pearson correlation coefficient): TM_CCOEFF Normalization: TM_SQDIFF_NORMED, TM_CCORR_NORMED, TM_CCOEFF_NORMED maximum absolute difference metric (MaxAD), which is also known as the uniform distance metric computeECC() and findTransformECC(). Sum of absolute differences (SAD) Cross correlation (CC) find identical regions of an image that match a template, select by giving a threshold 2D convolution It simply slides the template image over the input image (as in 2D convolution) and compares the template and patch of input image under the template image. Template matching > cv2.TM_CCOEFF > cv2.TM_CCOEFF_NORMED > cv2.TM_CCORR > cv2.TM_CCORR_NORMED < cv2.TM_SQDIFF < cv2.TM_SQDIFF_NORMED cv2.minMaxLoc() more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: [https://docs.opencv.org/master/df/dfb/group__imgproc__object.html#gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2Fmaster%2Fdf%2Fdfb%2Fgroup__imgproc__object.html%23gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65&sa=D&sntz=1&usg=AOvVaw37X4mefDnUv8PmnZK0YdoM) [https://stackoverflow.com/questions/58158129/understanding-and-evaluating- template-matching- methods](https://www.google.com/url?q=https%3A%2F%2Fstackoverflow.com%2Fquestions%2F58158129%2Funderstanding- and-evaluating-template-matching- methods&sa=D&sntz=1&usg=AOvVaw08M8efI3a63Nn7qXHfZZEY) # Advanced OpenCV techniques: balance white balance white with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: void balance_white(cv::Mat mat) { double discard_ratio = 0.05; int hists[3][256]; memset(hists, 0, 3 * 256 * sizeof(int)); for (int y = 0; y < mat.rows; ++y) { uchar* ptr = mat.ptr(y); for (int x = 0; x < mat.cols; ++x) { for (int j = 0; j < 3; ++j) { hists[j][ptr[x * 3 + j]] += 1; } } } // cumulative hist int total = mat.cols * mat.rows; int vmin[3], vmax[3]; for (int i = 0; i < 3; ++i) { for (int j = 0; j < 255; ++j) { hists[i][j + 1] += hists[i][j]; } vmin[i] = 0; vmax[i] = 255; while (hists[i][vmin[i]] < discard_ratio * total) vmin[i] += 1; while (hists[i][vmax[i]] > (1 - discard_ratio) * total) vmax[i] -= 1; if (vmax[i] < 255 - 1) vmax[i] += 1; } for (int y = 0; y < mat.rows; ++y) { uchar* ptr = mat.ptr(y); for (int x = 0; x < mat.cols; ++x) { for (int j = 0; j < 3; ++j) { int val = ptr[x * 3 + j]; if (val < vmin[j]) val = vmin[j]; if (val > vmax[j]) val = vmax[j]; ptr[x * 3 + j] = static_cast((val - vmin[j]) * 255.0 / (vmax[j] - vmin[j])); } } } } reference http://www.ipol.im/pub/art/2011/llmps-scb/ https://gist.github.com/tomykaira/94472e9f4921ec2cf582 # Advanced OpenCV techniques: contrast and brightness sub-pixel, floating points, more precise, real-valued coordinates, Changing the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: showImage.convertTo(showImage, CV_32FC3); double alpha = 2.0; /*< Simple contrast control */ int beta = 100; /*< Simple brightness control */ for (int y = 0; y < showImage.rows; y++) { for (int x = 0; x < showImage.cols; x++) { for (int c = 0; c < showImage.channels(); c++) { showImage.at(y, x)[c] =cv::saturate_cast(alpha * showImage.at(y, x)[c] + beta); } } } showImage.convertTo(showImage, CV_8UC3); cv::imshow("Changing the contrast and brightness of an image! ", showImage); cv::waitKey(0); based on [https://docs.opencv.org/3.4/d3/dc1/tutorial_basic_linear_transform.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd3%2Fdc1%2Ftutorial_basic_linear_transform.html&sa=D&sntz=1&usg=AOvVaw15cmUCYNHZx6OdaoUN0tSB) [https://blog.csdn.net/u014230360/article/details/109802229](https://www.google.com/url?q=https%3A%2F%2Fblog.csdn.net%2Fu014230360%2Farticle%2Fdetails%2F109802229&sa=D&sntz=1&usg=AOvVaw1IxjJJewGlTNahnzI7pCX7) # Advanced subpixel techniques: Shift image content sub-pixel, floating points, mesh grid, remap, more precise, real-valued coordinates, moving image pixel, Shift image content with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: ## ## Mesh grid float static void meshgrid_float(const cv::Mat& xgv, const cv::Mat& ygv, cv::Mat1f& X, cv::Mat1f& Y) { cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X); cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y); } static void meshgrid_map_float(const cv::Range& xgv, const cv::Range& ygv, cv::Mat1f& X, cv::Mat1f& Y) { std::vector t_x, t_y; for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i); for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i); meshgrid_float(cv::Mat(t_x), cv::Mat(t_y), X, Y); } ## mesh grid int static void meshgrid(const cv::Mat& xgv, const cv::Mat& ygv, cv::Mat1i& X, cv::Mat1i& Y) { cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X); cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y); } static void meshgridTest(const cv::Range& xgv, const cv::Range& ygv, cv::Mat1i& X, cv::Mat1i& Y) { std::vector t_x, t_y; for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i); for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i); meshgrid(cv::Mat(t_x), cv::Mat(t_y), X, Y); } ## main: cv::Mat1f XF, YF; //for int //meshgrid_map_float(cv::Range(0, cols_main), cv::Range(0, rows_main), XF, YF); //for float meshgrid_map_float(cv::Range(0, cols_main-1), cv::Range(0, rows_main-1), XF, YF); for (int rowsImage = 0; rowsImage < rows_main; ++rowsImage) { for (int colsImage = 0; colsImage < cols_main; ++colsImage) { XF.at(rowsImage, colsImage) += offset1; YF.at(rowsImage, colsImage) += offset2; } } cv::remap(convert_img_i, dst, XF, YF, cv::INTER_LINEAR); if (show) { cv::Mat resizedImage = dst.clone(); dst.convertTo(resizedImage, CV_8UC3); cv::resize(resizedImage, resizedImage, cv::Size(), 0.5, 0.5); std::string nameWindow = " meshgrid and remap in float "; cv::imshow(nameWindow, resizedImage); cv::waitKey(0); } # Tips and Tricks of OpenCV that Nobody Told You Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: ### Tricks cv::multiply(outMat, cv::Scalar(gain, gain, gain), outMat); //color cv::multiply(outMat, cv::Scalar(gain), outMat); //grayscale //copy small Mat to bigger Mat cv::Rect roi( cv::Point( originX, originY ), smallImage.size() ); smallImage.copyTo( bigImage( roi ) ); ### Tips * copy mat to vector need clone() ### ### Save results * save image in float * cv::imwrite("image.exr", MatImage); * save image in uncompressed format : * cv::imwrite("fa.tiff", resizedImage, { cv::IMWRITE_TIFF_COMPRESSION, 1,cv::IMWRITE_TIFF_XDPI, 300,cv::IMWRITE_TIFF_YDPI,300 }); * * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) # None * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 5)) # LZW * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 8)) # Adobe Deflate * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 32946)) # Deflate * cv::imwrite("far.png", resizedImage, { cv::IMWRITE_PNG_COMPRESSION, 0 }); * Save images in streaming * int64 t0 = cv::getTickCount(); * std::string fileName= "fashid_"+std::to_string(t0)+ ".png"; * cv::imwrite(fileName, cimMat, { cv::IMWRITE_PNG_COMPRESSION, 0 }); * Save file name: * std::filesystem::path p = std::filesystem::path(files[i]).filename(); * std::string imgFile = savePath + "/" \+ p.string() + ".tiff"; * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) ; ### Error * Exception thrown at 0x00007FFB5CB21636 (vcruntime140d.dll) in farshid.exe: 0xC0000005: Access violation writing location 0x000002B09BAC4000. * check the size of Mat * cv::Mat meanImages = cv::Mat::zeros(rows_main, cols_main, CV_32F); * cv::Mat meanImages = cv::Mat::zeros(farshidMat.size(), CV_32F); * Linker Tools Error LNK2038 & Linker Tools Error LNK2001; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for '_ITERATOR_DEBUG_LEVEL': value '2' doesn't match value '0' in Source.obj ; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for 'RuntimeLibrary': value 'MTd_StaticDebug' doesn't match value 'MT_StaticRelease' * the link files are not match based on release or debug mode. # Testing for OpenCV Projects Test, C++, Computer Vision, Image Processing, more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: * Arrange, Act and Assert (AAA) Pattern * Google C++ Test Framework * Assertions Types and Test Fixtures * ASSERT_FALSE(frame.empty()); ASSERT_NO_THROW(cap >> img); ASSERT_FALSE(img.empty()) << "idx=" << idx; ### Tricks embedded system, keep the software as small as possible, Embedding static elements in your application, [https://gstreamer.freedesktop.org/documentation/installing/index.html?gi- language=c](https://www.google.com/url?q=https%3A%2F%2Fgstreamer.freedesktop.org%2Fdocumentation%2Finstalling%2Findex.html%3Fgi- language%3Dc&sa=D&sntz=1&usg=AOvVaw1LbT7pN6fSH1VBpZ07TI2Y) ### Tips * copy mat to vector need clone() ### ### Example #include assert(!im.empty()); assert(x.size()==y.size()); assert(x.size()>2); #ifdef _DEBUG #endif #if true #else #endif # YouTube Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static opencv library for visual studio 2022 by using NuGet package manager just in few minutes #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah [https://github.com/xiaohongchen1991/meshgen](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxiaohongchen1991%2Fmeshgen&sa=D&sntz=1&usg=AOvVaw1J7_ZnsiBoB0T6RB0dNc6u) A set of C++ APIs are provided to mimic the same behaviors as the MATLAB function "linspace" and "meshgrid". 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OpenCV 5 beta cvtest: Computer Vision Test Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Standard test for computer vision application Advanced OpenCV techniques: Advanced OpenCV techniques: Advanced OpenCV techniques: balance white Advanced OpenCV techniques: contrast and brightness Advanced subpixel techniques: Shift image content Mesh grid float mesh grid int main: Tips and Tricks of OpenCV that Nobody Told You Tricks Tips Save results Error Testing for OpenCV Projects Tricks Tips Example YouTube # NuGet - OpenCV 5 beta NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022 [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static opencv library for visual studio 2022 by using NuGet package manager just in few minutes #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) #OpenCV #Farshid_PirahanSiah #pirahansiah My NuGet packages comprised of two versions for different VS version. * Visual Studio 2019 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS2019_NuGet&sa=D&sntz=1&usg=AOvVaw0B-7f3iUYsMrp8SHvJGQb9) * Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7 * Visual Studio 2022 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS22_NuGet&sa=D&sntz=1&usg=AOvVaw2tWZP3KILcTESYrI2cdofl) * Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7 _**more:**_[ _ **https://www.pirahansiah.com/topics/opencv**_](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) _ ****_ # cvtest: Computer Vision Test ## Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Do you want to test your output of computer vision application which is video or images? ## Standard test for computer vision application There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil) download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: [](https://docs.google.com/presentation/d/14HX-99rGO9x1AtOnEigZ_2gAzZaPwvxqCEnnG0dk870/present "Open Presentation, cvtest in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/slides_32dp.png)cvtest # Advanced OpenCV techniques: sub-pixel, floating points, more precise, real-valued coordinates, Changing the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and Tricks of OpenCV that Nobody Told You download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: # Advanced OpenCV techniques: Cross correlation (CC): TM_CCORR Mean shifted cross correlation (Pearson correlation coefficient): TM_CCOEFF Normalization: TM_SQDIFF_NORMED, TM_CCORR_NORMED, TM_CCOEFF_NORMED maximum absolute difference metric (MaxAD), which is also known as the uniform distance metric computeECC() and findTransformECC(). Sum of absolute differences (SAD) Cross correlation (CC) find identical regions of an image that match a template, select by giving a threshold 2D convolution It simply slides the template image over the input image (as in 2D convolution) and compares the template and patch of input image under the template image. Template matching > cv2.TM_CCOEFF > cv2.TM_CCOEFF_NORMED > cv2.TM_CCORR > cv2.TM_CCORR_NORMED < cv2.TM_SQDIFF < cv2.TM_SQDIFF_NORMED cv2.minMaxLoc() more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: [https://docs.opencv.org/master/df/dfb/group__imgproc__object.html#gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2Fmaster%2Fdf%2Fdfb%2Fgroup__imgproc__object.html%23gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65&sa=D&sntz=1&usg=AOvVaw37X4mefDnUv8PmnZK0YdoM) [https://stackoverflow.com/questions/58158129/understanding-and-evaluating- template-matching- methods](https://www.google.com/url?q=https%3A%2F%2Fstackoverflow.com%2Fquestions%2F58158129%2Funderstanding- and-evaluating-template-matching- methods&sa=D&sntz=1&usg=AOvVaw08M8efI3a63Nn7qXHfZZEY) # Advanced OpenCV techniques: balance white balance white with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: void balance_white(cv::Mat mat) { double discard_ratio = 0.05; int hists[3][256]; memset(hists, 0, 3 * 256 * sizeof(int)); for (int y = 0; y < mat.rows; ++y) { uchar* ptr = mat.ptr(y); for (int x = 0; x < mat.cols; ++x) { for (int j = 0; j < 3; ++j) { hists[j][ptr[x * 3 + j]] += 1; } } } // cumulative hist int total = mat.cols * mat.rows; int vmin[3], vmax[3]; for (int i = 0; i < 3; ++i) { for (int j = 0; j < 255; ++j) { hists[i][j + 1] += hists[i][j]; } vmin[i] = 0; vmax[i] = 255; while (hists[i][vmin[i]] < discard_ratio * total) vmin[i] += 1; while (hists[i][vmax[i]] > (1 - discard_ratio) * total) vmax[i] -= 1; if (vmax[i] < 255 - 1) vmax[i] += 1; } for (int y = 0; y < mat.rows; ++y) { uchar* ptr = mat.ptr(y); for (int x = 0; x < mat.cols; ++x) { for (int j = 0; j < 3; ++j) { int val = ptr[x * 3 + j]; if (val < vmin[j]) val = vmin[j]; if (val > vmax[j]) val = vmax[j]; ptr[x * 3 + j] = static_cast((val - vmin[j]) * 255.0 / (vmax[j] - vmin[j])); } } } } reference http://www.ipol.im/pub/art/2011/llmps-scb/ https://gist.github.com/tomykaira/94472e9f4921ec2cf582 # Advanced OpenCV techniques: contrast and brightness sub-pixel, floating points, more precise, real-valued coordinates, Changing the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: showImage.convertTo(showImage, CV_32FC3); double alpha = 2.0; /*< Simple contrast control */ int beta = 100; /*< Simple brightness control */ for (int y = 0; y < showImage.rows; y++) { for (int x = 0; x < showImage.cols; x++) { for (int c = 0; c < showImage.channels(); c++) { showImage.at(y, x)[c] =cv::saturate_cast(alpha * showImage.at(y, x)[c] + beta); } } } showImage.convertTo(showImage, CV_8UC3); cv::imshow("Changing the contrast and brightness of an image! ", showImage); cv::waitKey(0); based on [https://docs.opencv.org/3.4/d3/dc1/tutorial_basic_linear_transform.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd3%2Fdc1%2Ftutorial_basic_linear_transform.html&sa=D&sntz=1&usg=AOvVaw15cmUCYNHZx6OdaoUN0tSB) [https://blog.csdn.net/u014230360/article/details/109802229](https://www.google.com/url?q=https%3A%2F%2Fblog.csdn.net%2Fu014230360%2Farticle%2Fdetails%2F109802229&sa=D&sntz=1&usg=AOvVaw1IxjJJewGlTNahnzI7pCX7) # Advanced subpixel techniques: Shift image content sub-pixel, floating points, mesh grid, remap, more precise, real-valued coordinates, moving image pixel, Shift image content with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: ## ## Mesh grid float static void meshgrid_float(const cv::Mat& xgv, const cv::Mat& ygv, cv::Mat1f& X, cv::Mat1f& Y) { cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X); cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y); } static void meshgrid_map_float(const cv::Range& xgv, const cv::Range& ygv, cv::Mat1f& X, cv::Mat1f& Y) { std::vector t_x, t_y; for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i); for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i); meshgrid_float(cv::Mat(t_x), cv::Mat(t_y), X, Y); } ## mesh grid int static void meshgrid(const cv::Mat& xgv, const cv::Mat& ygv, cv::Mat1i& X, cv::Mat1i& Y) { cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X); cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y); } static void meshgridTest(const cv::Range& xgv, const cv::Range& ygv, cv::Mat1i& X, cv::Mat1i& Y) { std::vector t_x, t_y; for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i); for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i); meshgrid(cv::Mat(t_x), cv::Mat(t_y), X, Y); } ## main: cv::Mat1f XF, YF; //for int //meshgrid_map_float(cv::Range(0, cols_main), cv::Range(0, rows_main), XF, YF); //for float meshgrid_map_float(cv::Range(0, cols_main-1), cv::Range(0, rows_main-1), XF, YF); for (int rowsImage = 0; rowsImage < rows_main; ++rowsImage) { for (int colsImage = 0; colsImage < cols_main; ++colsImage) { XF.at(rowsImage, colsImage) += offset1; YF.at(rowsImage, colsImage) += offset2; } } cv::remap(convert_img_i, dst, XF, YF, cv::INTER_LINEAR); if (show) { cv::Mat resizedImage = dst.clone(); dst.convertTo(resizedImage, CV_8UC3); cv::resize(resizedImage, resizedImage, cv::Size(), 0.5, 0.5); std::string nameWindow = " meshgrid and remap in float "; cv::imshow(nameWindow, resizedImage); cv::waitKey(0); } # Tips and Tricks of OpenCV that Nobody Told You Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: ### Tricks cv::multiply(outMat, cv::Scalar(gain, gain, gain), outMat); //color cv::multiply(outMat, cv::Scalar(gain), outMat); //grayscale //copy small Mat to bigger Mat cv::Rect roi( cv::Point( originX, originY ), smallImage.size() ); smallImage.copyTo( bigImage( roi ) ); ### Tips * copy mat to vector need clone() ### ### Save results * save image in float * cv::imwrite("image.exr", MatImage); * save image in uncompressed format : * cv::imwrite("fa.tiff", resizedImage, { cv::IMWRITE_TIFF_COMPRESSION, 1,cv::IMWRITE_TIFF_XDPI, 300,cv::IMWRITE_TIFF_YDPI,300 }); * * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) # None * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 5)) # LZW * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 8)) # Adobe Deflate * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 32946)) # Deflate * cv::imwrite("far.png", resizedImage, { cv::IMWRITE_PNG_COMPRESSION, 0 }); * Save images in streaming * int64 t0 = cv::getTickCount(); * std::string fileName= "fashid_"+std::to_string(t0)+ ".png"; * cv::imwrite(fileName, cimMat, { cv::IMWRITE_PNG_COMPRESSION, 0 }); * Save file name: * std::filesystem::path p = std::filesystem::path(files[i]).filename(); * std::string imgFile = savePath + "/" \+ p.string() + ".tiff"; * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) ; ### Error * Exception thrown at 0x00007FFB5CB21636 (vcruntime140d.dll) in farshid.exe: 0xC0000005: Access violation writing location 0x000002B09BAC4000. * check the size of Mat * cv::Mat meanImages = cv::Mat::zeros(rows_main, cols_main, CV_32F); * cv::Mat meanImages = cv::Mat::zeros(farshidMat.size(), CV_32F); * Linker Tools Error LNK2038 & Linker Tools Error LNK2001; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for '_ITERATOR_DEBUG_LEVEL': value '2' doesn't match value '0' in Source.obj ; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for 'RuntimeLibrary': value 'MTd_StaticDebug' doesn't match value 'MT_StaticRelease' * the link files are not match based on release or debug mode. # Testing for OpenCV Projects Test, C++, Computer Vision, Image Processing, more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: * Arrange, Act and Assert (AAA) Pattern * Google C++ Test Framework * Assertions Types and Test Fixtures * ASSERT_FALSE(frame.empty()); ASSERT_NO_THROW(cap >> img); ASSERT_FALSE(img.empty()) << "idx=" << idx; ### Tricks embedded system, keep the software as small as possible, Embedding static elements in your application, [https://gstreamer.freedesktop.org/documentation/installing/index.html?gi- language=c](https://www.google.com/url?q=https%3A%2F%2Fgstreamer.freedesktop.org%2Fdocumentation%2Finstalling%2Findex.html%3Fgi- language%3Dc&sa=D&sntz=1&usg=AOvVaw1LbT7pN6fSH1VBpZ07TI2Y) ### Tips * copy mat to vector need clone() ### ### Example #include assert(!im.empty()); assert(x.size()==y.size()); assert(x.size()>2); #ifdef _DEBUG #endif #if true #else #endif # YouTube Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static opencv library for visual studio 2022 by using NuGet package manager just in few minutes #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah [https://github.com/xiaohongchen1991/meshgen](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxiaohongchen1991%2Fmeshgen&sa=D&sntz=1&usg=AOvVaw1J7_ZnsiBoB0T6RB0dNc6u) A set of C++ APIs are provided to mimic the same behaviors as the MATLAB function "linspace" and "meshgrid". 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OpenCV 5 beta cvtest: Computer Vision Test Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Standard test for computer vision application Advanced OpenCV techniques: Advanced OpenCV techniques: Advanced OpenCV techniques: balance white Advanced OpenCV techniques: contrast and brightness Advanced subpixel techniques: Shift image content Mesh grid float mesh grid int main: Tips and Tricks of OpenCV that Nobody Told You Tricks Tips Save results Error Testing for OpenCV Projects Tricks Tips Example YouTube # NuGet - OpenCV 5 beta NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022 [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static opencv library for visual studio 2022 by using NuGet package manager just in few minutes #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) #OpenCV #Farshid_PirahanSiah #pirahansiah My NuGet packages comprised of two versions for different VS version. * Visual Studio 2019 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS2019_NuGet&sa=D&sntz=1&usg=AOvVaw0B-7f3iUYsMrp8SHvJGQb9) * Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7 * Visual Studio 2022 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS22_NuGet&sa=D&sntz=1&usg=AOvVaw2tWZP3KILcTESYrI2cdofl) * Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7 _**more:**_[ _ **https://www.pirahansiah.com/topics/opencv**_](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) _ ****_ # cvtest: Computer Vision Test ## Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Do you want to test your output of computer vision application which is video or images? ## Standard test for computer vision application There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil) download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: [](https://docs.google.com/presentation/d/14HX-99rGO9x1AtOnEigZ_2gAzZaPwvxqCEnnG0dk870/present "Open Presentation, cvtest in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/slides_32dp.png)cvtest # Advanced OpenCV techniques: sub-pixel, floating points, more precise, real-valued coordinates, Changing the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and Tricks of OpenCV that Nobody Told You download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: # Advanced OpenCV techniques: Cross correlation (CC): TM_CCORR Mean shifted cross correlation (Pearson correlation coefficient): TM_CCOEFF Normalization: TM_SQDIFF_NORMED, TM_CCORR_NORMED, TM_CCOEFF_NORMED maximum absolute difference metric (MaxAD), which is also known as the uniform distance metric computeECC() and findTransformECC(). Sum of absolute differences (SAD) Cross correlation (CC) find identical regions of an image that match a template, select by giving a threshold 2D convolution It simply slides the template image over the input image (as in 2D convolution) and compares the template and patch of input image under the template image. Template matching > cv2.TM_CCOEFF > cv2.TM_CCOEFF_NORMED > cv2.TM_CCORR > cv2.TM_CCORR_NORMED < cv2.TM_SQDIFF < cv2.TM_SQDIFF_NORMED cv2.minMaxLoc() more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: [https://docs.opencv.org/master/df/dfb/group__imgproc__object.html#gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2Fmaster%2Fdf%2Fdfb%2Fgroup__imgproc__object.html%23gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65&sa=D&sntz=1&usg=AOvVaw37X4mefDnUv8PmnZK0YdoM) [https://stackoverflow.com/questions/58158129/understanding-and-evaluating- template-matching- methods](https://www.google.com/url?q=https%3A%2F%2Fstackoverflow.com%2Fquestions%2F58158129%2Funderstanding- and-evaluating-template-matching- methods&sa=D&sntz=1&usg=AOvVaw08M8efI3a63Nn7qXHfZZEY) # Advanced OpenCV techniques: balance white balance white with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: void balance_white(cv::Mat mat) { double discard_ratio = 0.05; int hists[3][256]; memset(hists, 0, 3 * 256 * sizeof(int)); for (int y = 0; y < mat.rows; ++y) { uchar* ptr = mat.ptr(y); for (int x = 0; x < mat.cols; ++x) { for (int j = 0; j < 3; ++j) { hists[j][ptr[x * 3 + j]] += 1; } } } // cumulative hist int total = mat.cols * mat.rows; int vmin[3], vmax[3]; for (int i = 0; i < 3; ++i) { for (int j = 0; j < 255; ++j) { hists[i][j + 1] += hists[i][j]; } vmin[i] = 0; vmax[i] = 255; while (hists[i][vmin[i]] < discard_ratio * total) vmin[i] += 1; while (hists[i][vmax[i]] > (1 - discard_ratio) * total) vmax[i] -= 1; if (vmax[i] < 255 - 1) vmax[i] += 1; } for (int y = 0; y < mat.rows; ++y) { uchar* ptr = mat.ptr(y); for (int x = 0; x < mat.cols; ++x) { for (int j = 0; j < 3; ++j) { int val = ptr[x * 3 + j]; if (val < vmin[j]) val = vmin[j]; if (val > vmax[j]) val = vmax[j]; ptr[x * 3 + j] = static_cast((val - vmin[j]) * 255.0 / (vmax[j] - vmin[j])); } } } } reference http://www.ipol.im/pub/art/2011/llmps-scb/ https://gist.github.com/tomykaira/94472e9f4921ec2cf582 # Advanced OpenCV techniques: contrast and brightness sub-pixel, floating points, more precise, real-valued coordinates, Changing the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: showImage.convertTo(showImage, CV_32FC3); double alpha = 2.0; /*< Simple contrast control */ int beta = 100; /*< Simple brightness control */ for (int y = 0; y < showImage.rows; y++) { for (int x = 0; x < showImage.cols; x++) { for (int c = 0; c < showImage.channels(); c++) { showImage.at(y, x)[c] =cv::saturate_cast(alpha * showImage.at(y, x)[c] + beta); } } } showImage.convertTo(showImage, CV_8UC3); cv::imshow("Changing the contrast and brightness of an image! ", showImage); cv::waitKey(0); based on [https://docs.opencv.org/3.4/d3/dc1/tutorial_basic_linear_transform.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd3%2Fdc1%2Ftutorial_basic_linear_transform.html&sa=D&sntz=1&usg=AOvVaw15cmUCYNHZx6OdaoUN0tSB) [https://blog.csdn.net/u014230360/article/details/109802229](https://www.google.com/url?q=https%3A%2F%2Fblog.csdn.net%2Fu014230360%2Farticle%2Fdetails%2F109802229&sa=D&sntz=1&usg=AOvVaw1IxjJJewGlTNahnzI7pCX7) # Advanced subpixel techniques: Shift image content sub-pixel, floating points, mesh grid, remap, more precise, real-valued coordinates, moving image pixel, Shift image content with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: ## ## Mesh grid float static void meshgrid_float(const cv::Mat& xgv, const cv::Mat& ygv, cv::Mat1f& X, cv::Mat1f& Y) { cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X); cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y); } static void meshgrid_map_float(const cv::Range& xgv, const cv::Range& ygv, cv::Mat1f& X, cv::Mat1f& Y) { std::vector t_x, t_y; for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i); for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i); meshgrid_float(cv::Mat(t_x), cv::Mat(t_y), X, Y); } ## mesh grid int static void meshgrid(const cv::Mat& xgv, const cv::Mat& ygv, cv::Mat1i& X, cv::Mat1i& Y) { cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X); cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y); } static void meshgridTest(const cv::Range& xgv, const cv::Range& ygv, cv::Mat1i& X, cv::Mat1i& Y) { std::vector t_x, t_y; for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i); for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i); meshgrid(cv::Mat(t_x), cv::Mat(t_y), X, Y); } ## main: cv::Mat1f XF, YF; //for int //meshgrid_map_float(cv::Range(0, cols_main), cv::Range(0, rows_main), XF, YF); //for float meshgrid_map_float(cv::Range(0, cols_main-1), cv::Range(0, rows_main-1), XF, YF); for (int rowsImage = 0; rowsImage < rows_main; ++rowsImage) { for (int colsImage = 0; colsImage < cols_main; ++colsImage) { XF.at(rowsImage, colsImage) += offset1; YF.at(rowsImage, colsImage) += offset2; } } cv::remap(convert_img_i, dst, XF, YF, cv::INTER_LINEAR); if (show) { cv::Mat resizedImage = dst.clone(); dst.convertTo(resizedImage, CV_8UC3); cv::resize(resizedImage, resizedImage, cv::Size(), 0.5, 0.5); std::string nameWindow = " meshgrid and remap in float "; cv::imshow(nameWindow, resizedImage); cv::waitKey(0); } # Tips and Tricks of OpenCV that Nobody Told You Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: ### Tricks cv::multiply(outMat, cv::Scalar(gain, gain, gain), outMat); //color cv::multiply(outMat, cv::Scalar(gain), outMat); //grayscale //copy small Mat to bigger Mat cv::Rect roi( cv::Point( originX, originY ), smallImage.size() ); smallImage.copyTo( bigImage( roi ) ); ### Tips * copy mat to vector need clone() ### ### Save results * save image in float * cv::imwrite("image.exr", MatImage); * save image in uncompressed format : * cv::imwrite("fa.tiff", resizedImage, { cv::IMWRITE_TIFF_COMPRESSION, 1,cv::IMWRITE_TIFF_XDPI, 300,cv::IMWRITE_TIFF_YDPI,300 }); * * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) # None * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 5)) # LZW * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 8)) # Adobe Deflate * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 32946)) # Deflate * cv::imwrite("far.png", resizedImage, { cv::IMWRITE_PNG_COMPRESSION, 0 }); * Save images in streaming * int64 t0 = cv::getTickCount(); * std::string fileName= "fashid_"+std::to_string(t0)+ ".png"; * cv::imwrite(fileName, cimMat, { cv::IMWRITE_PNG_COMPRESSION, 0 }); * Save file name: * std::filesystem::path p = std::filesystem::path(files[i]).filename(); * std::string imgFile = savePath + "/" \+ p.string() + ".tiff"; * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) ; ### Error * Exception thrown at 0x00007FFB5CB21636 (vcruntime140d.dll) in farshid.exe: 0xC0000005: Access violation writing location 0x000002B09BAC4000. * check the size of Mat * cv::Mat meanImages = cv::Mat::zeros(rows_main, cols_main, CV_32F); * cv::Mat meanImages = cv::Mat::zeros(farshidMat.size(), CV_32F); * Linker Tools Error LNK2038 & Linker Tools Error LNK2001; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for '_ITERATOR_DEBUG_LEVEL': value '2' doesn't match value '0' in Source.obj ; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for 'RuntimeLibrary': value 'MTd_StaticDebug' doesn't match value 'MT_StaticRelease' * the link files are not match based on release or debug mode. # Testing for OpenCV Projects Test, C++, Computer Vision, Image Processing, more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: * Arrange, Act and Assert (AAA) Pattern * Google C++ Test Framework * Assertions Types and Test Fixtures * ASSERT_FALSE(frame.empty()); ASSERT_NO_THROW(cap >> img); ASSERT_FALSE(img.empty()) << "idx=" << idx; ### Tricks embedded system, keep the software as small as possible, Embedding static elements in your application, [https://gstreamer.freedesktop.org/documentation/installing/index.html?gi- language=c](https://www.google.com/url?q=https%3A%2F%2Fgstreamer.freedesktop.org%2Fdocumentation%2Finstalling%2Findex.html%3Fgi- language%3Dc&sa=D&sntz=1&usg=AOvVaw1LbT7pN6fSH1VBpZ07TI2Y) ### Tips * copy mat to vector need clone() ### ### Example #include assert(!im.empty()); assert(x.size()==y.size()); assert(x.size()>2); #ifdef _DEBUG #endif #if true #else #endif # YouTube Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static opencv library for visual studio 2022 by using NuGet package manager just in few minutes #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah [https://github.com/xiaohongchen1991/meshgen](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxiaohongchen1991%2Fmeshgen&sa=D&sntz=1&usg=AOvVaw1J7_ZnsiBoB0T6RB0dNc6u) A set of C++ APIs are provided to mimic the same behaviors as the MATLAB function "linspace" and "meshgrid". Must-Read Books of All Time in Computer Vision and Machine Learning ![](https://lh6.googleusercontent.com/xmn3C-kogDWhYFajeyz5ut5C5cLqfb7h69EHu8Ugepm3dc1gqty- aVEoRWJvXvQ_aJbtlviOa76e8iH90wgfXawMGLybHWWuNxO3zKsqAL4oF1iv1X8Z-SiE6rS7qOz2xA=w1280) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. 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OpenCV 5 beta cvtest: Computer Vision Test Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Standard test for computer vision application Advanced OpenCV techniques: Advanced OpenCV techniques: Advanced OpenCV techniques: balance white Advanced OpenCV techniques: contrast and brightness Advanced subpixel techniques: Shift image content Mesh grid float mesh grid int main: Tips and Tricks of OpenCV that Nobody Told You Tricks Tips Save results Error Testing for OpenCV Projects Tricks Tips Example YouTube # NuGet - OpenCV 5 beta NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022 [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static opencv library for visual studio 2022 by using NuGet package manager just in few minutes #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) #OpenCV #Farshid_PirahanSiah #pirahansiah My NuGet packages comprised of two versions for different VS version. * Visual Studio 2019 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS2019_NuGet&sa=D&sntz=1&usg=AOvVaw0B-7f3iUYsMrp8SHvJGQb9) * Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7 * Visual Studio 2022 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS22_NuGet&sa=D&sntz=1&usg=AOvVaw2tWZP3KILcTESYrI2cdofl) * Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7 _**more:**_[ _ **https://www.pirahansiah.com/topics/opencv**_](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) _ ****_ # cvtest: Computer Vision Test ## Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Do you want to test your output of computer vision application which is video or images? ## Standard test for computer vision application There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil) download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: [](https://docs.google.com/presentation/d/14HX-99rGO9x1AtOnEigZ_2gAzZaPwvxqCEnnG0dk870/present "Open Presentation, cvtest in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/slides_32dp.png)cvtest # Advanced OpenCV techniques: sub-pixel, floating points, more precise, real-valued coordinates, Changing the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and Tricks of OpenCV that Nobody Told You download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: # Advanced OpenCV techniques: Cross correlation (CC): TM_CCORR Mean shifted cross correlation (Pearson correlation coefficient): TM_CCOEFF Normalization: TM_SQDIFF_NORMED, TM_CCORR_NORMED, TM_CCOEFF_NORMED maximum absolute difference metric (MaxAD), which is also known as the uniform distance metric computeECC() and findTransformECC(). Sum of absolute differences (SAD) Cross correlation (CC) find identical regions of an image that match a template, select by giving a threshold 2D convolution It simply slides the template image over the input image (as in 2D convolution) and compares the template and patch of input image under the template image. Template matching > cv2.TM_CCOEFF > cv2.TM_CCOEFF_NORMED > cv2.TM_CCORR > cv2.TM_CCORR_NORMED < cv2.TM_SQDIFF < cv2.TM_SQDIFF_NORMED cv2.minMaxLoc() more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: [https://docs.opencv.org/master/df/dfb/group__imgproc__object.html#gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2Fmaster%2Fdf%2Fdfb%2Fgroup__imgproc__object.html%23gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65&sa=D&sntz=1&usg=AOvVaw37X4mefDnUv8PmnZK0YdoM) [https://stackoverflow.com/questions/58158129/understanding-and-evaluating- template-matching- methods](https://www.google.com/url?q=https%3A%2F%2Fstackoverflow.com%2Fquestions%2F58158129%2Funderstanding- and-evaluating-template-matching- methods&sa=D&sntz=1&usg=AOvVaw08M8efI3a63Nn7qXHfZZEY) # Advanced OpenCV techniques: balance white balance white with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: void balance_white(cv::Mat mat) { double discard_ratio = 0.05; int hists[3][256]; memset(hists, 0, 3 * 256 * sizeof(int)); for (int y = 0; y < mat.rows; ++y) { uchar* ptr = mat.ptr(y); for (int x = 0; x < mat.cols; ++x) { for (int j = 0; j < 3; ++j) { hists[j][ptr[x * 3 + j]] += 1; } } } // cumulative hist int total = mat.cols * mat.rows; int vmin[3], vmax[3]; for (int i = 0; i < 3; ++i) { for (int j = 0; j < 255; ++j) { hists[i][j + 1] += hists[i][j]; } vmin[i] = 0; vmax[i] = 255; while (hists[i][vmin[i]] < discard_ratio * total) vmin[i] += 1; while (hists[i][vmax[i]] > (1 - discard_ratio) * total) vmax[i] -= 1; if (vmax[i] < 255 - 1) vmax[i] += 1; } for (int y = 0; y < mat.rows; ++y) { uchar* ptr = mat.ptr(y); for (int x = 0; x < mat.cols; ++x) { for (int j = 0; j < 3; ++j) { int val = ptr[x * 3 + j]; if (val < vmin[j]) val = vmin[j]; if (val > vmax[j]) val = vmax[j]; ptr[x * 3 + j] = static_cast((val - vmin[j]) * 255.0 / (vmax[j] - vmin[j])); } } } } reference http://www.ipol.im/pub/art/2011/llmps-scb/ https://gist.github.com/tomykaira/94472e9f4921ec2cf582 # Advanced OpenCV techniques: contrast and brightness sub-pixel, floating points, more precise, real-valued coordinates, Changing the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: showImage.convertTo(showImage, CV_32FC3); double alpha = 2.0; /*< Simple contrast control */ int beta = 100; /*< Simple brightness control */ for (int y = 0; y < showImage.rows; y++) { for (int x = 0; x < showImage.cols; x++) { for (int c = 0; c < showImage.channels(); c++) { showImage.at(y, x)[c] =cv::saturate_cast(alpha * showImage.at(y, x)[c] + beta); } } } showImage.convertTo(showImage, CV_8UC3); cv::imshow("Changing the contrast and brightness of an image! ", showImage); cv::waitKey(0); based on [https://docs.opencv.org/3.4/d3/dc1/tutorial_basic_linear_transform.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd3%2Fdc1%2Ftutorial_basic_linear_transform.html&sa=D&sntz=1&usg=AOvVaw15cmUCYNHZx6OdaoUN0tSB) [https://blog.csdn.net/u014230360/article/details/109802229](https://www.google.com/url?q=https%3A%2F%2Fblog.csdn.net%2Fu014230360%2Farticle%2Fdetails%2F109802229&sa=D&sntz=1&usg=AOvVaw1IxjJJewGlTNahnzI7pCX7) # Advanced subpixel techniques: Shift image content sub-pixel, floating points, mesh grid, remap, more precise, real-valued coordinates, moving image pixel, Shift image content with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: ## ## Mesh grid float static void meshgrid_float(const cv::Mat& xgv, const cv::Mat& ygv, cv::Mat1f& X, cv::Mat1f& Y) { cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X); cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y); } static void meshgrid_map_float(const cv::Range& xgv, const cv::Range& ygv, cv::Mat1f& X, cv::Mat1f& Y) { std::vector t_x, t_y; for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i); for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i); meshgrid_float(cv::Mat(t_x), cv::Mat(t_y), X, Y); } ## mesh grid int static void meshgrid(const cv::Mat& xgv, const cv::Mat& ygv, cv::Mat1i& X, cv::Mat1i& Y) { cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X); cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y); } static void meshgridTest(const cv::Range& xgv, const cv::Range& ygv, cv::Mat1i& X, cv::Mat1i& Y) { std::vector t_x, t_y; for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i); for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i); meshgrid(cv::Mat(t_x), cv::Mat(t_y), X, Y); } ## main: cv::Mat1f XF, YF; //for int //meshgrid_map_float(cv::Range(0, cols_main), cv::Range(0, rows_main), XF, YF); //for float meshgrid_map_float(cv::Range(0, cols_main-1), cv::Range(0, rows_main-1), XF, YF); for (int rowsImage = 0; rowsImage < rows_main; ++rowsImage) { for (int colsImage = 0; colsImage < cols_main; ++colsImage) { XF.at(rowsImage, colsImage) += offset1; YF.at(rowsImage, colsImage) += offset2; } } cv::remap(convert_img_i, dst, XF, YF, cv::INTER_LINEAR); if (show) { cv::Mat resizedImage = dst.clone(); dst.convertTo(resizedImage, CV_8UC3); cv::resize(resizedImage, resizedImage, cv::Size(), 0.5, 0.5); std::string nameWindow = " meshgrid and remap in float "; cv::imshow(nameWindow, resizedImage); cv::waitKey(0); } # Tips and Tricks of OpenCV that Nobody Told You Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: ### Tricks cv::multiply(outMat, cv::Scalar(gain, gain, gain), outMat); //color cv::multiply(outMat, cv::Scalar(gain), outMat); //grayscale //copy small Mat to bigger Mat cv::Rect roi( cv::Point( originX, originY ), smallImage.size() ); smallImage.copyTo( bigImage( roi ) ); ### Tips * copy mat to vector need clone() ### ### Save results * save image in float * cv::imwrite("image.exr", MatImage); * save image in uncompressed format : * cv::imwrite("fa.tiff", resizedImage, { cv::IMWRITE_TIFF_COMPRESSION, 1,cv::IMWRITE_TIFF_XDPI, 300,cv::IMWRITE_TIFF_YDPI,300 }); * * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) # None * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 5)) # LZW * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 8)) # Adobe Deflate * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 32946)) # Deflate * cv::imwrite("far.png", resizedImage, { cv::IMWRITE_PNG_COMPRESSION, 0 }); * Save images in streaming * int64 t0 = cv::getTickCount(); * std::string fileName= "fashid_"+std::to_string(t0)+ ".png"; * cv::imwrite(fileName, cimMat, { cv::IMWRITE_PNG_COMPRESSION, 0 }); * Save file name: * std::filesystem::path p = std::filesystem::path(files[i]).filename(); * std::string imgFile = savePath + "/" \+ p.string() + ".tiff"; * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) ; ### Error * Exception thrown at 0x00007FFB5CB21636 (vcruntime140d.dll) in farshid.exe: 0xC0000005: Access violation writing location 0x000002B09BAC4000. * check the size of Mat * cv::Mat meanImages = cv::Mat::zeros(rows_main, cols_main, CV_32F); * cv::Mat meanImages = cv::Mat::zeros(farshidMat.size(), CV_32F); * Linker Tools Error LNK2038 & Linker Tools Error LNK2001; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for '_ITERATOR_DEBUG_LEVEL': value '2' doesn't match value '0' in Source.obj ; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for 'RuntimeLibrary': value 'MTd_StaticDebug' doesn't match value 'MT_StaticRelease' * the link files are not match based on release or debug mode. # Testing for OpenCV Projects Test, C++, Computer Vision, Image Processing, more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: * Arrange, Act and Assert (AAA) Pattern * Google C++ Test Framework * Assertions Types and Test Fixtures * ASSERT_FALSE(frame.empty()); ASSERT_NO_THROW(cap >> img); ASSERT_FALSE(img.empty()) << "idx=" << idx; ### Tricks embedded system, keep the software as small as possible, Embedding static elements in your application, [https://gstreamer.freedesktop.org/documentation/installing/index.html?gi- language=c](https://www.google.com/url?q=https%3A%2F%2Fgstreamer.freedesktop.org%2Fdocumentation%2Finstalling%2Findex.html%3Fgi- language%3Dc&sa=D&sntz=1&usg=AOvVaw1LbT7pN6fSH1VBpZ07TI2Y) ### Tips * copy mat to vector need clone() ### ### Example #include assert(!im.empty()); assert(x.size()==y.size()); assert(x.size()>2); #ifdef _DEBUG #endif #if true #else #endif # YouTube Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static opencv library for visual studio 2022 by using NuGet package manager just in few minutes #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah [https://github.com/xiaohongchen1991/meshgen](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxiaohongchen1991%2Fmeshgen&sa=D&sntz=1&usg=AOvVaw1J7_ZnsiBoB0T6RB0dNc6u) A set of C++ APIs are provided to mimic the same behaviors as the MATLAB function "linspace" and "meshgrid". Must-Read Books of All Time in Computer Vision and Machine Learning ![](https://lh6.googleusercontent.com/xmn3C-kogDWhYFajeyz5ut5C5cLqfb7h69EHu8Ugepm3dc1gqty- aVEoRWJvXvQ_aJbtlviOa76e8iH90wgfXawMGLybHWWuNxO3zKsqAL4oF1iv1X8Z-SiE6rS7qOz2xA=w1280) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. 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OpenCV 5 beta cvtest: Computer Vision Test Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Standard test for computer vision application Advanced OpenCV techniques: Advanced OpenCV techniques: Advanced OpenCV techniques: balance white Advanced OpenCV techniques: contrast and brightness Advanced subpixel techniques: Shift image content Mesh grid float mesh grid int main: Tips and Tricks of OpenCV that Nobody Told You Tricks Tips Save results Error Testing for OpenCV Projects Tricks Tips Example YouTube # NuGet - OpenCV 5 beta NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022 [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static opencv library for visual studio 2022 by using NuGet package manager just in few minutes #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) #OpenCV #Farshid_PirahanSiah #pirahansiah My NuGet packages comprised of two versions for different VS version. * Visual Studio 2019 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS2019_NuGet&sa=D&sntz=1&usg=AOvVaw0B-7f3iUYsMrp8SHvJGQb9) * Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7 * Visual Studio 2022 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS22_NuGet&sa=D&sntz=1&usg=AOvVaw2tWZP3KILcTESYrI2cdofl) * Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7 _**more:**_[ _ **https://www.pirahansiah.com/topics/opencv**_](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) _ ****_ # cvtest: Computer Vision Test ## Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Do you want to test your output of computer vision application which is video or images? ## Standard test for computer vision application There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil) download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: [](https://docs.google.com/presentation/d/14HX-99rGO9x1AtOnEigZ_2gAzZaPwvxqCEnnG0dk870/present "Open Presentation, cvtest in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/slides_32dp.png)cvtest # Advanced OpenCV techniques: sub-pixel, floating points, more precise, real-valued coordinates, Changing the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and Tricks of OpenCV that Nobody Told You download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: # Advanced OpenCV techniques: Cross correlation (CC): TM_CCORR Mean shifted cross correlation (Pearson correlation coefficient): TM_CCOEFF Normalization: TM_SQDIFF_NORMED, TM_CCORR_NORMED, TM_CCOEFF_NORMED maximum absolute difference metric (MaxAD), which is also known as the uniform distance metric computeECC() and findTransformECC(). Sum of absolute differences (SAD) Cross correlation (CC) find identical regions of an image that match a template, select by giving a threshold 2D convolution It simply slides the template image over the input image (as in 2D convolution) and compares the template and patch of input image under the template image. Template matching > cv2.TM_CCOEFF > cv2.TM_CCOEFF_NORMED > cv2.TM_CCORR > cv2.TM_CCORR_NORMED < cv2.TM_SQDIFF < cv2.TM_SQDIFF_NORMED cv2.minMaxLoc() more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: [https://docs.opencv.org/master/df/dfb/group__imgproc__object.html#gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2Fmaster%2Fdf%2Fdfb%2Fgroup__imgproc__object.html%23gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65&sa=D&sntz=1&usg=AOvVaw37X4mefDnUv8PmnZK0YdoM) [https://stackoverflow.com/questions/58158129/understanding-and-evaluating- template-matching- methods](https://www.google.com/url?q=https%3A%2F%2Fstackoverflow.com%2Fquestions%2F58158129%2Funderstanding- and-evaluating-template-matching- methods&sa=D&sntz=1&usg=AOvVaw08M8efI3a63Nn7qXHfZZEY) # Advanced OpenCV techniques: balance white balance white with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: void balance_white(cv::Mat mat) { double discard_ratio = 0.05; int hists[3][256]; memset(hists, 0, 3 * 256 * sizeof(int)); for (int y = 0; y < mat.rows; ++y) { uchar* ptr = mat.ptr(y); for (int x = 0; x < mat.cols; ++x) { for (int j = 0; j < 3; ++j) { hists[j][ptr[x * 3 + j]] += 1; } } } // cumulative hist int total = mat.cols * mat.rows; int vmin[3], vmax[3]; for (int i = 0; i < 3; ++i) { for (int j = 0; j < 255; ++j) { hists[i][j + 1] += hists[i][j]; } vmin[i] = 0; vmax[i] = 255; while (hists[i][vmin[i]] < discard_ratio * total) vmin[i] += 1; while (hists[i][vmax[i]] > (1 - discard_ratio) * total) vmax[i] -= 1; if (vmax[i] < 255 - 1) vmax[i] += 1; } for (int y = 0; y < mat.rows; ++y) { uchar* ptr = mat.ptr(y); for (int x = 0; x < mat.cols; ++x) { for (int j = 0; j < 3; ++j) { int val = ptr[x * 3 + j]; if (val < vmin[j]) val = vmin[j]; if (val > vmax[j]) val = vmax[j]; ptr[x * 3 + j] = static_cast((val - vmin[j]) * 255.0 / (vmax[j] - vmin[j])); } } } } reference http://www.ipol.im/pub/art/2011/llmps-scb/ https://gist.github.com/tomykaira/94472e9f4921ec2cf582 # Advanced OpenCV techniques: contrast and brightness sub-pixel, floating points, more precise, real-valued coordinates, Changing the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: showImage.convertTo(showImage, CV_32FC3); double alpha = 2.0; /*< Simple contrast control */ int beta = 100; /*< Simple brightness control */ for (int y = 0; y < showImage.rows; y++) { for (int x = 0; x < showImage.cols; x++) { for (int c = 0; c < showImage.channels(); c++) { showImage.at(y, x)[c] =cv::saturate_cast(alpha * showImage.at(y, x)[c] + beta); } } } showImage.convertTo(showImage, CV_8UC3); cv::imshow("Changing the contrast and brightness of an image! ", showImage); cv::waitKey(0); based on [https://docs.opencv.org/3.4/d3/dc1/tutorial_basic_linear_transform.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd3%2Fdc1%2Ftutorial_basic_linear_transform.html&sa=D&sntz=1&usg=AOvVaw15cmUCYNHZx6OdaoUN0tSB) [https://blog.csdn.net/u014230360/article/details/109802229](https://www.google.com/url?q=https%3A%2F%2Fblog.csdn.net%2Fu014230360%2Farticle%2Fdetails%2F109802229&sa=D&sntz=1&usg=AOvVaw1IxjJJewGlTNahnzI7pCX7) # Advanced subpixel techniques: Shift image content sub-pixel, floating points, mesh grid, remap, more precise, real-valued coordinates, moving image pixel, Shift image content with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: ## ## Mesh grid float static void meshgrid_float(const cv::Mat& xgv, const cv::Mat& ygv, cv::Mat1f& X, cv::Mat1f& Y) { cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X); cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y); } static void meshgrid_map_float(const cv::Range& xgv, const cv::Range& ygv, cv::Mat1f& X, cv::Mat1f& Y) { std::vector t_x, t_y; for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i); for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i); meshgrid_float(cv::Mat(t_x), cv::Mat(t_y), X, Y); } ## mesh grid int static void meshgrid(const cv::Mat& xgv, const cv::Mat& ygv, cv::Mat1i& X, cv::Mat1i& Y) { cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X); cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y); } static void meshgridTest(const cv::Range& xgv, const cv::Range& ygv, cv::Mat1i& X, cv::Mat1i& Y) { std::vector t_x, t_y; for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i); for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i); meshgrid(cv::Mat(t_x), cv::Mat(t_y), X, Y); } ## main: cv::Mat1f XF, YF; //for int //meshgrid_map_float(cv::Range(0, cols_main), cv::Range(0, rows_main), XF, YF); //for float meshgrid_map_float(cv::Range(0, cols_main-1), cv::Range(0, rows_main-1), XF, YF); for (int rowsImage = 0; rowsImage < rows_main; ++rowsImage) { for (int colsImage = 0; colsImage < cols_main; ++colsImage) { XF.at(rowsImage, colsImage) += offset1; YF.at(rowsImage, colsImage) += offset2; } } cv::remap(convert_img_i, dst, XF, YF, cv::INTER_LINEAR); if (show) { cv::Mat resizedImage = dst.clone(); dst.convertTo(resizedImage, CV_8UC3); cv::resize(resizedImage, resizedImage, cv::Size(), 0.5, 0.5); std::string nameWindow = " meshgrid and remap in float "; cv::imshow(nameWindow, resizedImage); cv::waitKey(0); } # Tips and Tricks of OpenCV that Nobody Told You Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: ### Tricks cv::multiply(outMat, cv::Scalar(gain, gain, gain), outMat); //color cv::multiply(outMat, cv::Scalar(gain), outMat); //grayscale //copy small Mat to bigger Mat cv::Rect roi( cv::Point( originX, originY ), smallImage.size() ); smallImage.copyTo( bigImage( roi ) ); ### Tips * copy mat to vector need clone() ### ### Save results * save image in float * cv::imwrite("image.exr", MatImage); * save image in uncompressed format : * cv::imwrite("fa.tiff", resizedImage, { cv::IMWRITE_TIFF_COMPRESSION, 1,cv::IMWRITE_TIFF_XDPI, 300,cv::IMWRITE_TIFF_YDPI,300 }); * * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) # None * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 5)) # LZW * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 8)) # Adobe Deflate * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 32946)) # Deflate * cv::imwrite("far.png", resizedImage, { cv::IMWRITE_PNG_COMPRESSION, 0 }); * Save images in streaming * int64 t0 = cv::getTickCount(); * std::string fileName= "fashid_"+std::to_string(t0)+ ".png"; * cv::imwrite(fileName, cimMat, { cv::IMWRITE_PNG_COMPRESSION, 0 }); * Save file name: * std::filesystem::path p = std::filesystem::path(files[i]).filename(); * std::string imgFile = savePath + "/" \+ p.string() + ".tiff"; * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) ; ### Error * Exception thrown at 0x00007FFB5CB21636 (vcruntime140d.dll) in farshid.exe: 0xC0000005: Access violation writing location 0x000002B09BAC4000. * check the size of Mat * cv::Mat meanImages = cv::Mat::zeros(rows_main, cols_main, CV_32F); * cv::Mat meanImages = cv::Mat::zeros(farshidMat.size(), CV_32F); * Linker Tools Error LNK2038 & Linker Tools Error LNK2001; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for '_ITERATOR_DEBUG_LEVEL': value '2' doesn't match value '0' in Source.obj ; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for 'RuntimeLibrary': value 'MTd_StaticDebug' doesn't match value 'MT_StaticRelease' * the link files are not match based on release or debug mode. # Testing for OpenCV Projects Test, C++, Computer Vision, Image Processing, more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: * Arrange, Act and Assert (AAA) Pattern * Google C++ Test Framework * Assertions Types and Test Fixtures * ASSERT_FALSE(frame.empty()); ASSERT_NO_THROW(cap >> img); ASSERT_FALSE(img.empty()) << "idx=" << idx; ### Tricks embedded system, keep the software as small as possible, Embedding static elements in your application, [https://gstreamer.freedesktop.org/documentation/installing/index.html?gi- language=c](https://www.google.com/url?q=https%3A%2F%2Fgstreamer.freedesktop.org%2Fdocumentation%2Finstalling%2Findex.html%3Fgi- language%3Dc&sa=D&sntz=1&usg=AOvVaw1LbT7pN6fSH1VBpZ07TI2Y) ### Tips * copy mat to vector need clone() ### ### Example #include assert(!im.empty()); assert(x.size()==y.size()); assert(x.size()>2); #ifdef _DEBUG #endif #if true #else #endif # YouTube Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static opencv library for visual studio 2022 by using NuGet package manager just in few minutes #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah [https://github.com/xiaohongchen1991/meshgen](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxiaohongchen1991%2Fmeshgen&sa=D&sntz=1&usg=AOvVaw1J7_ZnsiBoB0T6RB0dNc6u) A set of C++ APIs are provided to mimic the same behaviors as the MATLAB function "linspace" and "meshgrid". Must-Read Books of All Time in Computer Vision and Machine Learning ![](https://lh6.googleusercontent.com/xmn3C-kogDWhYFajeyz5ut5C5cLqfb7h69EHu8Ugepm3dc1gqty- aVEoRWJvXvQ_aJbtlviOa76e8iH90wgfXawMGLybHWWuNxO3zKsqAL4oF1iv1X8Z-SiE6rS7qOz2xA=w1280) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. 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OpenCV 5 beta cvtest: Computer Vision Test Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Standard test for computer vision application Advanced OpenCV techniques: Advanced OpenCV techniques: Advanced OpenCV techniques: balance white Advanced OpenCV techniques: contrast and brightness Advanced subpixel techniques: Shift image content Mesh grid float mesh grid int main: Tips and Tricks of OpenCV that Nobody Told You Tricks Tips Save results Error Testing for OpenCV Projects Tricks Tips Example YouTube # NuGet - OpenCV 5 beta NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022 [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static opencv library for visual studio 2022 by using NuGet package manager just in few minutes #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) #OpenCV #Farshid_PirahanSiah #pirahansiah My NuGet packages comprised of two versions for different VS version. * Visual Studio 2019 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS2019_NuGet&sa=D&sntz=1&usg=AOvVaw0B-7f3iUYsMrp8SHvJGQb9) * Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7 * Visual Studio 2022 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS22_NuGet&sa=D&sntz=1&usg=AOvVaw2tWZP3KILcTESYrI2cdofl) * Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7 _**more:**_[ _ **https://www.pirahansiah.com/topics/opencv**_](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) _ ****_ # cvtest: Computer Vision Test ## Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Do you want to test your output of computer vision application which is video or images? ## Standard test for computer vision application There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil) download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: [](https://docs.google.com/presentation/d/14HX-99rGO9x1AtOnEigZ_2gAzZaPwvxqCEnnG0dk870/present "Open Presentation, cvtest in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/slides_32dp.png)cvtest # Advanced OpenCV techniques: sub-pixel, floating points, more precise, real-valued coordinates, Changing the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and Tricks of OpenCV that Nobody Told You download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: # Advanced OpenCV techniques: Cross correlation (CC): TM_CCORR Mean shifted cross correlation (Pearson correlation coefficient): TM_CCOEFF Normalization: TM_SQDIFF_NORMED, TM_CCORR_NORMED, TM_CCOEFF_NORMED maximum absolute difference metric (MaxAD), which is also known as the uniform distance metric computeECC() and findTransformECC(). Sum of absolute differences (SAD) Cross correlation (CC) find identical regions of an image that match a template, select by giving a threshold 2D convolution It simply slides the template image over the input image (as in 2D convolution) and compares the template and patch of input image under the template image. Template matching > cv2.TM_CCOEFF > cv2.TM_CCOEFF_NORMED > cv2.TM_CCORR > cv2.TM_CCORR_NORMED < cv2.TM_SQDIFF < cv2.TM_SQDIFF_NORMED cv2.minMaxLoc() more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: [https://docs.opencv.org/master/df/dfb/group__imgproc__object.html#gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2Fmaster%2Fdf%2Fdfb%2Fgroup__imgproc__object.html%23gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65&sa=D&sntz=1&usg=AOvVaw37X4mefDnUv8PmnZK0YdoM) [https://stackoverflow.com/questions/58158129/understanding-and-evaluating- template-matching- methods](https://www.google.com/url?q=https%3A%2F%2Fstackoverflow.com%2Fquestions%2F58158129%2Funderstanding- and-evaluating-template-matching- methods&sa=D&sntz=1&usg=AOvVaw08M8efI3a63Nn7qXHfZZEY) # Advanced OpenCV techniques: balance white balance white with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: void balance_white(cv::Mat mat) { double discard_ratio = 0.05; int hists[3][256]; memset(hists, 0, 3 * 256 * sizeof(int)); for (int y = 0; y < mat.rows; ++y) { uchar* ptr = mat.ptr(y); for (int x = 0; x < mat.cols; ++x) { for (int j = 0; j < 3; ++j) { hists[j][ptr[x * 3 + j]] += 1; } } } // cumulative hist int total = mat.cols * mat.rows; int vmin[3], vmax[3]; for (int i = 0; i < 3; ++i) { for (int j = 0; j < 255; ++j) { hists[i][j + 1] += hists[i][j]; } vmin[i] = 0; vmax[i] = 255; while (hists[i][vmin[i]] < discard_ratio * total) vmin[i] += 1; while (hists[i][vmax[i]] > (1 - discard_ratio) * total) vmax[i] -= 1; if (vmax[i] < 255 - 1) vmax[i] += 1; } for (int y = 0; y < mat.rows; ++y) { uchar* ptr = mat.ptr(y); for (int x = 0; x < mat.cols; ++x) { for (int j = 0; j < 3; ++j) { int val = ptr[x * 3 + j]; if (val < vmin[j]) val = vmin[j]; if (val > vmax[j]) val = vmax[j]; ptr[x * 3 + j] = static_cast((val - vmin[j]) * 255.0 / (vmax[j] - vmin[j])); } } } } reference http://www.ipol.im/pub/art/2011/llmps-scb/ https://gist.github.com/tomykaira/94472e9f4921ec2cf582 # Advanced OpenCV techniques: contrast and brightness sub-pixel, floating points, more precise, real-valued coordinates, Changing the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: showImage.convertTo(showImage, CV_32FC3); double alpha = 2.0; /*< Simple contrast control */ int beta = 100; /*< Simple brightness control */ for (int y = 0; y < showImage.rows; y++) { for (int x = 0; x < showImage.cols; x++) { for (int c = 0; c < showImage.channels(); c++) { showImage.at(y, x)[c] =cv::saturate_cast(alpha * showImage.at(y, x)[c] + beta); } } } showImage.convertTo(showImage, CV_8UC3); cv::imshow("Changing the contrast and brightness of an image! ", showImage); cv::waitKey(0); based on [https://docs.opencv.org/3.4/d3/dc1/tutorial_basic_linear_transform.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd3%2Fdc1%2Ftutorial_basic_linear_transform.html&sa=D&sntz=1&usg=AOvVaw15cmUCYNHZx6OdaoUN0tSB) [https://blog.csdn.net/u014230360/article/details/109802229](https://www.google.com/url?q=https%3A%2F%2Fblog.csdn.net%2Fu014230360%2Farticle%2Fdetails%2F109802229&sa=D&sntz=1&usg=AOvVaw1IxjJJewGlTNahnzI7pCX7) # Advanced subpixel techniques: Shift image content sub-pixel, floating points, mesh grid, remap, more precise, real-valued coordinates, moving image pixel, Shift image content with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: ## ## Mesh grid float static void meshgrid_float(const cv::Mat& xgv, const cv::Mat& ygv, cv::Mat1f& X, cv::Mat1f& Y) { cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X); cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y); } static void meshgrid_map_float(const cv::Range& xgv, const cv::Range& ygv, cv::Mat1f& X, cv::Mat1f& Y) { std::vector t_x, t_y; for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i); for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i); meshgrid_float(cv::Mat(t_x), cv::Mat(t_y), X, Y); } ## mesh grid int static void meshgrid(const cv::Mat& xgv, const cv::Mat& ygv, cv::Mat1i& X, cv::Mat1i& Y) { cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X); cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y); } static void meshgridTest(const cv::Range& xgv, const cv::Range& ygv, cv::Mat1i& X, cv::Mat1i& Y) { std::vector t_x, t_y; for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i); for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i); meshgrid(cv::Mat(t_x), cv::Mat(t_y), X, Y); } ## main: cv::Mat1f XF, YF; //for int //meshgrid_map_float(cv::Range(0, cols_main), cv::Range(0, rows_main), XF, YF); //for float meshgrid_map_float(cv::Range(0, cols_main-1), cv::Range(0, rows_main-1), XF, YF); for (int rowsImage = 0; rowsImage < rows_main; ++rowsImage) { for (int colsImage = 0; colsImage < cols_main; ++colsImage) { XF.at(rowsImage, colsImage) += offset1; YF.at(rowsImage, colsImage) += offset2; } } cv::remap(convert_img_i, dst, XF, YF, cv::INTER_LINEAR); if (show) { cv::Mat resizedImage = dst.clone(); dst.convertTo(resizedImage, CV_8UC3); cv::resize(resizedImage, resizedImage, cv::Size(), 0.5, 0.5); std::string nameWindow = " meshgrid and remap in float "; cv::imshow(nameWindow, resizedImage); cv::waitKey(0); } # Tips and Tricks of OpenCV that Nobody Told You Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: ### Tricks cv::multiply(outMat, cv::Scalar(gain, gain, gain), outMat); //color cv::multiply(outMat, cv::Scalar(gain), outMat); //grayscale //copy small Mat to bigger Mat cv::Rect roi( cv::Point( originX, originY ), smallImage.size() ); smallImage.copyTo( bigImage( roi ) ); ### Tips * copy mat to vector need clone() ### ### Save results * save image in float * cv::imwrite("image.exr", MatImage); * save image in uncompressed format : * cv::imwrite("fa.tiff", resizedImage, { cv::IMWRITE_TIFF_COMPRESSION, 1,cv::IMWRITE_TIFF_XDPI, 300,cv::IMWRITE_TIFF_YDPI,300 }); * * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) # None * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 5)) # LZW * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 8)) # Adobe Deflate * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 32946)) # Deflate * cv::imwrite("far.png", resizedImage, { cv::IMWRITE_PNG_COMPRESSION, 0 }); * Save images in streaming * int64 t0 = cv::getTickCount(); * std::string fileName= "fashid_"+std::to_string(t0)+ ".png"; * cv::imwrite(fileName, cimMat, { cv::IMWRITE_PNG_COMPRESSION, 0 }); * Save file name: * std::filesystem::path p = std::filesystem::path(files[i]).filename(); * std::string imgFile = savePath + "/" \+ p.string() + ".tiff"; * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) ; ### Error * Exception thrown at 0x00007FFB5CB21636 (vcruntime140d.dll) in farshid.exe: 0xC0000005: Access violation writing location 0x000002B09BAC4000. * check the size of Mat * cv::Mat meanImages = cv::Mat::zeros(rows_main, cols_main, CV_32F); * cv::Mat meanImages = cv::Mat::zeros(farshidMat.size(), CV_32F); * Linker Tools Error LNK2038 & Linker Tools Error LNK2001; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for '_ITERATOR_DEBUG_LEVEL': value '2' doesn't match value '0' in Source.obj ; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for 'RuntimeLibrary': value 'MTd_StaticDebug' doesn't match value 'MT_StaticRelease' * the link files are not match based on release or debug mode. # Testing for OpenCV Projects Test, C++, Computer Vision, Image Processing, more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: * Arrange, Act and Assert (AAA) Pattern * Google C++ Test Framework * Assertions Types and Test Fixtures * ASSERT_FALSE(frame.empty()); ASSERT_NO_THROW(cap >> img); ASSERT_FALSE(img.empty()) << "idx=" << idx; ### Tricks embedded system, keep the software as small as possible, Embedding static elements in your application, [https://gstreamer.freedesktop.org/documentation/installing/index.html?gi- language=c](https://www.google.com/url?q=https%3A%2F%2Fgstreamer.freedesktop.org%2Fdocumentation%2Finstalling%2Findex.html%3Fgi- language%3Dc&sa=D&sntz=1&usg=AOvVaw1LbT7pN6fSH1VBpZ07TI2Y) ### Tips * copy mat to vector need clone() ### ### Example #include assert(!im.empty()); assert(x.size()==y.size()); assert(x.size()>2); #ifdef _DEBUG #endif #if true #else #endif # YouTube Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static opencv library for visual studio 2022 by using NuGet package manager just in few minutes #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah [https://github.com/xiaohongchen1991/meshgen](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxiaohongchen1991%2Fmeshgen&sa=D&sntz=1&usg=AOvVaw1J7_ZnsiBoB0T6RB0dNc6u) A set of C++ APIs are provided to mimic the same behaviors as the MATLAB function "linspace" and "meshgrid". Must-Read Books of All Time in Computer Vision and Machine Learning ![](https://lh6.googleusercontent.com/xmn3C-kogDWhYFajeyz5ut5C5cLqfb7h69EHu8Ugepm3dc1gqty- aVEoRWJvXvQ_aJbtlviOa76e8iH90wgfXawMGLybHWWuNxO3zKsqAL4oF1iv1X8Z-SiE6rS7qOz2xA=w1280) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. 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OpenCV 5 beta cvtest: Computer Vision Test Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Standard test for computer vision application Advanced OpenCV techniques: Advanced OpenCV techniques: Advanced OpenCV techniques: balance white Advanced OpenCV techniques: contrast and brightness Advanced subpixel techniques: Shift image content Mesh grid float mesh grid int main: Tips and Tricks of OpenCV that Nobody Told You Tricks Tips Save results Error Testing for OpenCV Projects Tricks Tips Example YouTube # NuGet - OpenCV 5 beta NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022 [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static opencv library for visual studio 2022 by using NuGet package manager just in few minutes #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) #OpenCV #Farshid_PirahanSiah #pirahansiah My NuGet packages comprised of two versions for different VS version. * Visual Studio 2019 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS2019_NuGet&sa=D&sntz=1&usg=AOvVaw0B-7f3iUYsMrp8SHvJGQb9) * Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7 * Visual Studio 2022 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS22_NuGet&sa=D&sntz=1&usg=AOvVaw2tWZP3KILcTESYrI2cdofl) * Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7 _**more:**_[ _ **https://www.pirahansiah.com/topics/opencv**_](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) _ ****_ # cvtest: Computer Vision Test ## Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Do you want to test your output of computer vision application which is video or images? ## Standard test for computer vision application There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil) download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: [](https://docs.google.com/presentation/d/14HX-99rGO9x1AtOnEigZ_2gAzZaPwvxqCEnnG0dk870/present "Open Presentation, cvtest in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/slides_32dp.png)cvtest # Advanced OpenCV techniques: sub-pixel, floating points, more precise, real-valued coordinates, Changing the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and Tricks of OpenCV that Nobody Told You download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: # Advanced OpenCV techniques: Cross correlation (CC): TM_CCORR Mean shifted cross correlation (Pearson correlation coefficient): TM_CCOEFF Normalization: TM_SQDIFF_NORMED, TM_CCORR_NORMED, TM_CCOEFF_NORMED maximum absolute difference metric (MaxAD), which is also known as the uniform distance metric computeECC() and findTransformECC(). Sum of absolute differences (SAD) Cross correlation (CC) find identical regions of an image that match a template, select by giving a threshold 2D convolution It simply slides the template image over the input image (as in 2D convolution) and compares the template and patch of input image under the template image. Template matching > cv2.TM_CCOEFF > cv2.TM_CCOEFF_NORMED > cv2.TM_CCORR > cv2.TM_CCORR_NORMED < cv2.TM_SQDIFF < cv2.TM_SQDIFF_NORMED cv2.minMaxLoc() more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: [https://docs.opencv.org/master/df/dfb/group__imgproc__object.html#gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2Fmaster%2Fdf%2Fdfb%2Fgroup__imgproc__object.html%23gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65&sa=D&sntz=1&usg=AOvVaw37X4mefDnUv8PmnZK0YdoM) [https://stackoverflow.com/questions/58158129/understanding-and-evaluating- template-matching- methods](https://www.google.com/url?q=https%3A%2F%2Fstackoverflow.com%2Fquestions%2F58158129%2Funderstanding- and-evaluating-template-matching- methods&sa=D&sntz=1&usg=AOvVaw08M8efI3a63Nn7qXHfZZEY) # Advanced OpenCV techniques: balance white balance white with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: void balance_white(cv::Mat mat) { double discard_ratio = 0.05; int hists[3][256]; memset(hists, 0, 3 * 256 * sizeof(int)); for (int y = 0; y < mat.rows; ++y) { uchar* ptr = mat.ptr(y); for (int x = 0; x < mat.cols; ++x) { for (int j = 0; j < 3; ++j) { hists[j][ptr[x * 3 + j]] += 1; } } } // cumulative hist int total = mat.cols * mat.rows; int vmin[3], vmax[3]; for (int i = 0; i < 3; ++i) { for (int j = 0; j < 255; ++j) { hists[i][j + 1] += hists[i][j]; } vmin[i] = 0; vmax[i] = 255; while (hists[i][vmin[i]] < discard_ratio * total) vmin[i] += 1; while (hists[i][vmax[i]] > (1 - discard_ratio) * total) vmax[i] -= 1; if (vmax[i] < 255 - 1) vmax[i] += 1; } for (int y = 0; y < mat.rows; ++y) { uchar* ptr = mat.ptr(y); for (int x = 0; x < mat.cols; ++x) { for (int j = 0; j < 3; ++j) { int val = ptr[x * 3 + j]; if (val < vmin[j]) val = vmin[j]; if (val > vmax[j]) val = vmax[j]; ptr[x * 3 + j] = static_cast((val - vmin[j]) * 255.0 / (vmax[j] - vmin[j])); } } } } reference http://www.ipol.im/pub/art/2011/llmps-scb/ https://gist.github.com/tomykaira/94472e9f4921ec2cf582 # Advanced OpenCV techniques: contrast and brightness sub-pixel, floating points, more precise, real-valued coordinates, Changing the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: showImage.convertTo(showImage, CV_32FC3); double alpha = 2.0; /*< Simple contrast control */ int beta = 100; /*< Simple brightness control */ for (int y = 0; y < showImage.rows; y++) { for (int x = 0; x < showImage.cols; x++) { for (int c = 0; c < showImage.channels(); c++) { showImage.at(y, x)[c] =cv::saturate_cast(alpha * showImage.at(y, x)[c] + beta); } } } showImage.convertTo(showImage, CV_8UC3); cv::imshow("Changing the contrast and brightness of an image! ", showImage); cv::waitKey(0); based on [https://docs.opencv.org/3.4/d3/dc1/tutorial_basic_linear_transform.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd3%2Fdc1%2Ftutorial_basic_linear_transform.html&sa=D&sntz=1&usg=AOvVaw15cmUCYNHZx6OdaoUN0tSB) [https://blog.csdn.net/u014230360/article/details/109802229](https://www.google.com/url?q=https%3A%2F%2Fblog.csdn.net%2Fu014230360%2Farticle%2Fdetails%2F109802229&sa=D&sntz=1&usg=AOvVaw1IxjJJewGlTNahnzI7pCX7) # Advanced subpixel techniques: Shift image content sub-pixel, floating points, mesh grid, remap, more precise, real-valued coordinates, moving image pixel, Shift image content with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: ## ## Mesh grid float static void meshgrid_float(const cv::Mat& xgv, const cv::Mat& ygv, cv::Mat1f& X, cv::Mat1f& Y) { cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X); cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y); } static void meshgrid_map_float(const cv::Range& xgv, const cv::Range& ygv, cv::Mat1f& X, cv::Mat1f& Y) { std::vector t_x, t_y; for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i); for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i); meshgrid_float(cv::Mat(t_x), cv::Mat(t_y), X, Y); } ## mesh grid int static void meshgrid(const cv::Mat& xgv, const cv::Mat& ygv, cv::Mat1i& X, cv::Mat1i& Y) { cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X); cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y); } static void meshgridTest(const cv::Range& xgv, const cv::Range& ygv, cv::Mat1i& X, cv::Mat1i& Y) { std::vector t_x, t_y; for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i); for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i); meshgrid(cv::Mat(t_x), cv::Mat(t_y), X, Y); } ## main: cv::Mat1f XF, YF; //for int //meshgrid_map_float(cv::Range(0, cols_main), cv::Range(0, rows_main), XF, YF); //for float meshgrid_map_float(cv::Range(0, cols_main-1), cv::Range(0, rows_main-1), XF, YF); for (int rowsImage = 0; rowsImage < rows_main; ++rowsImage) { for (int colsImage = 0; colsImage < cols_main; ++colsImage) { XF.at(rowsImage, colsImage) += offset1; YF.at(rowsImage, colsImage) += offset2; } } cv::remap(convert_img_i, dst, XF, YF, cv::INTER_LINEAR); if (show) { cv::Mat resizedImage = dst.clone(); dst.convertTo(resizedImage, CV_8UC3); cv::resize(resizedImage, resizedImage, cv::Size(), 0.5, 0.5); std::string nameWindow = " meshgrid and remap in float "; cv::imshow(nameWindow, resizedImage); cv::waitKey(0); } # Tips and Tricks of OpenCV that Nobody Told You Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: ### Tricks cv::multiply(outMat, cv::Scalar(gain, gain, gain), outMat); //color cv::multiply(outMat, cv::Scalar(gain), outMat); //grayscale //copy small Mat to bigger Mat cv::Rect roi( cv::Point( originX, originY ), smallImage.size() ); smallImage.copyTo( bigImage( roi ) ); ### Tips * copy mat to vector need clone() ### ### Save results * save image in float * cv::imwrite("image.exr", MatImage); * save image in uncompressed format : * cv::imwrite("fa.tiff", resizedImage, { cv::IMWRITE_TIFF_COMPRESSION, 1,cv::IMWRITE_TIFF_XDPI, 300,cv::IMWRITE_TIFF_YDPI,300 }); * * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) # None * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 5)) # LZW * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 8)) # Adobe Deflate * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 32946)) # Deflate * cv::imwrite("far.png", resizedImage, { cv::IMWRITE_PNG_COMPRESSION, 0 }); * Save images in streaming * int64 t0 = cv::getTickCount(); * std::string fileName= "fashid_"+std::to_string(t0)+ ".png"; * cv::imwrite(fileName, cimMat, { cv::IMWRITE_PNG_COMPRESSION, 0 }); * Save file name: * std::filesystem::path p = std::filesystem::path(files[i]).filename(); * std::string imgFile = savePath + "/" \+ p.string() + ".tiff"; * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) ; ### Error * Exception thrown at 0x00007FFB5CB21636 (vcruntime140d.dll) in farshid.exe: 0xC0000005: Access violation writing location 0x000002B09BAC4000. * check the size of Mat * cv::Mat meanImages = cv::Mat::zeros(rows_main, cols_main, CV_32F); * cv::Mat meanImages = cv::Mat::zeros(farshidMat.size(), CV_32F); * Linker Tools Error LNK2038 & Linker Tools Error LNK2001; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for '_ITERATOR_DEBUG_LEVEL': value '2' doesn't match value '0' in Source.obj ; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for 'RuntimeLibrary': value 'MTd_StaticDebug' doesn't match value 'MT_StaticRelease' * the link files are not match based on release or debug mode. # Testing for OpenCV Projects Test, C++, Computer Vision, Image Processing, more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: * Arrange, Act and Assert (AAA) Pattern * Google C++ Test Framework * Assertions Types and Test Fixtures * ASSERT_FALSE(frame.empty()); ASSERT_NO_THROW(cap >> img); ASSERT_FALSE(img.empty()) << "idx=" << idx; ### Tricks embedded system, keep the software as small as possible, Embedding static elements in your application, [https://gstreamer.freedesktop.org/documentation/installing/index.html?gi- language=c](https://www.google.com/url?q=https%3A%2F%2Fgstreamer.freedesktop.org%2Fdocumentation%2Finstalling%2Findex.html%3Fgi- language%3Dc&sa=D&sntz=1&usg=AOvVaw1LbT7pN6fSH1VBpZ07TI2Y) ### Tips * copy mat to vector need clone() ### ### Example #include assert(!im.empty()); assert(x.size()==y.size()); assert(x.size()>2); #ifdef _DEBUG #endif #if true #else #endif # YouTube Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static opencv library for visual studio 2022 by using NuGet package manager just in few minutes #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah [https://github.com/xiaohongchen1991/meshgen](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxiaohongchen1991%2Fmeshgen&sa=D&sntz=1&usg=AOvVaw1J7_ZnsiBoB0T6RB0dNc6u) A set of C++ APIs are provided to mimic the same behaviors as the MATLAB function "linspace" and "meshgrid". 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OpenCV 5 beta cvtest: Computer Vision Test Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Standard test for computer vision application Advanced OpenCV techniques: Advanced OpenCV techniques: Advanced OpenCV techniques: balance white Advanced OpenCV techniques: contrast and brightness Advanced subpixel techniques: Shift image content Mesh grid float mesh grid int main: Tips and Tricks of OpenCV that Nobody Told You Tricks Tips Save results Error Testing for OpenCV Projects Tricks Tips Example YouTube # NuGet - OpenCV 5 beta NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022 [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static opencv library for visual studio 2022 by using NuGet package manager just in few minutes #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) #OpenCV #Farshid_PirahanSiah #pirahansiah My NuGet packages comprised of two versions for different VS version. * Visual Studio 2019 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS2019_NuGet&sa=D&sntz=1&usg=AOvVaw0B-7f3iUYsMrp8SHvJGQb9) * Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7 * Visual Studio 2022 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS22_NuGet&sa=D&sntz=1&usg=AOvVaw2tWZP3KILcTESYrI2cdofl) * Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7 _**more:**_[ _ **https://www.pirahansiah.com/topics/opencv**_](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) _ ****_ # cvtest: Computer Vision Test ## Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Do you want to test your output of computer vision application which is video or images? ## Standard test for computer vision application There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil) download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: [](https://docs.google.com/presentation/d/14HX-99rGO9x1AtOnEigZ_2gAzZaPwvxqCEnnG0dk870/present "Open Presentation, cvtest in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/slides_32dp.png)cvtest # Advanced OpenCV techniques: sub-pixel, floating points, more precise, real-valued coordinates, Changing the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and Tricks of OpenCV that Nobody Told You download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: # Advanced OpenCV techniques: Cross correlation (CC): TM_CCORR Mean shifted cross correlation (Pearson correlation coefficient): TM_CCOEFF Normalization: TM_SQDIFF_NORMED, TM_CCORR_NORMED, TM_CCOEFF_NORMED maximum absolute difference metric (MaxAD), which is also known as the uniform distance metric computeECC() and findTransformECC(). Sum of absolute differences (SAD) Cross correlation (CC) find identical regions of an image that match a template, select by giving a threshold 2D convolution It simply slides the template image over the input image (as in 2D convolution) and compares the template and patch of input image under the template image. Template matching > cv2.TM_CCOEFF > cv2.TM_CCOEFF_NORMED > cv2.TM_CCORR > cv2.TM_CCORR_NORMED < cv2.TM_SQDIFF < cv2.TM_SQDIFF_NORMED cv2.minMaxLoc() more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: [https://docs.opencv.org/master/df/dfb/group__imgproc__object.html#gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2Fmaster%2Fdf%2Fdfb%2Fgroup__imgproc__object.html%23gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65&sa=D&sntz=1&usg=AOvVaw37X4mefDnUv8PmnZK0YdoM) [https://stackoverflow.com/questions/58158129/understanding-and-evaluating- template-matching- methods](https://www.google.com/url?q=https%3A%2F%2Fstackoverflow.com%2Fquestions%2F58158129%2Funderstanding- and-evaluating-template-matching- methods&sa=D&sntz=1&usg=AOvVaw08M8efI3a63Nn7qXHfZZEY) # Advanced OpenCV techniques: balance white balance white with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: void balance_white(cv::Mat mat) { double discard_ratio = 0.05; int hists[3][256]; memset(hists, 0, 3 * 256 * sizeof(int)); for (int y = 0; y < mat.rows; ++y) { uchar* ptr = mat.ptr(y); for (int x = 0; x < mat.cols; ++x) { for (int j = 0; j < 3; ++j) { hists[j][ptr[x * 3 + j]] += 1; } } } // cumulative hist int total = mat.cols * mat.rows; int vmin[3], vmax[3]; for (int i = 0; i < 3; ++i) { for (int j = 0; j < 255; ++j) { hists[i][j + 1] += hists[i][j]; } vmin[i] = 0; vmax[i] = 255; while (hists[i][vmin[i]] < discard_ratio * total) vmin[i] += 1; while (hists[i][vmax[i]] > (1 - discard_ratio) * total) vmax[i] -= 1; if (vmax[i] < 255 - 1) vmax[i] += 1; } for (int y = 0; y < mat.rows; ++y) { uchar* ptr = mat.ptr(y); for (int x = 0; x < mat.cols; ++x) { for (int j = 0; j < 3; ++j) { int val = ptr[x * 3 + j]; if (val < vmin[j]) val = vmin[j]; if (val > vmax[j]) val = vmax[j]; ptr[x * 3 + j] = static_cast((val - vmin[j]) * 255.0 / (vmax[j] - vmin[j])); } } } } reference http://www.ipol.im/pub/art/2011/llmps-scb/ https://gist.github.com/tomykaira/94472e9f4921ec2cf582 # Advanced OpenCV techniques: contrast and brightness sub-pixel, floating points, more precise, real-valued coordinates, Changing the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: showImage.convertTo(showImage, CV_32FC3); double alpha = 2.0; /*< Simple contrast control */ int beta = 100; /*< Simple brightness control */ for (int y = 0; y < showImage.rows; y++) { for (int x = 0; x < showImage.cols; x++) { for (int c = 0; c < showImage.channels(); c++) { showImage.at(y, x)[c] =cv::saturate_cast(alpha * showImage.at(y, x)[c] + beta); } } } showImage.convertTo(showImage, CV_8UC3); cv::imshow("Changing the contrast and brightness of an image! ", showImage); cv::waitKey(0); based on [https://docs.opencv.org/3.4/d3/dc1/tutorial_basic_linear_transform.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd3%2Fdc1%2Ftutorial_basic_linear_transform.html&sa=D&sntz=1&usg=AOvVaw15cmUCYNHZx6OdaoUN0tSB) [https://blog.csdn.net/u014230360/article/details/109802229](https://www.google.com/url?q=https%3A%2F%2Fblog.csdn.net%2Fu014230360%2Farticle%2Fdetails%2F109802229&sa=D&sntz=1&usg=AOvVaw1IxjJJewGlTNahnzI7pCX7) # Advanced subpixel techniques: Shift image content sub-pixel, floating points, mesh grid, remap, more precise, real-valued coordinates, moving image pixel, Shift image content with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: ## ## Mesh grid float static void meshgrid_float(const cv::Mat& xgv, const cv::Mat& ygv, cv::Mat1f& X, cv::Mat1f& Y) { cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X); cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y); } static void meshgrid_map_float(const cv::Range& xgv, const cv::Range& ygv, cv::Mat1f& X, cv::Mat1f& Y) { std::vector t_x, t_y; for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i); for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i); meshgrid_float(cv::Mat(t_x), cv::Mat(t_y), X, Y); } ## mesh grid int static void meshgrid(const cv::Mat& xgv, const cv::Mat& ygv, cv::Mat1i& X, cv::Mat1i& Y) { cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X); cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y); } static void meshgridTest(const cv::Range& xgv, const cv::Range& ygv, cv::Mat1i& X, cv::Mat1i& Y) { std::vector t_x, t_y; for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i); for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i); meshgrid(cv::Mat(t_x), cv::Mat(t_y), X, Y); } ## main: cv::Mat1f XF, YF; //for int //meshgrid_map_float(cv::Range(0, cols_main), cv::Range(0, rows_main), XF, YF); //for float meshgrid_map_float(cv::Range(0, cols_main-1), cv::Range(0, rows_main-1), XF, YF); for (int rowsImage = 0; rowsImage < rows_main; ++rowsImage) { for (int colsImage = 0; colsImage < cols_main; ++colsImage) { XF.at(rowsImage, colsImage) += offset1; YF.at(rowsImage, colsImage) += offset2; } } cv::remap(convert_img_i, dst, XF, YF, cv::INTER_LINEAR); if (show) { cv::Mat resizedImage = dst.clone(); dst.convertTo(resizedImage, CV_8UC3); cv::resize(resizedImage, resizedImage, cv::Size(), 0.5, 0.5); std::string nameWindow = " meshgrid and remap in float "; cv::imshow(nameWindow, resizedImage); cv::waitKey(0); } # Tips and Tricks of OpenCV that Nobody Told You Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: ### Tricks cv::multiply(outMat, cv::Scalar(gain, gain, gain), outMat); //color cv::multiply(outMat, cv::Scalar(gain), outMat); //grayscale //copy small Mat to bigger Mat cv::Rect roi( cv::Point( originX, originY ), smallImage.size() ); smallImage.copyTo( bigImage( roi ) ); ### Tips * copy mat to vector need clone() ### ### Save results * save image in float * cv::imwrite("image.exr", MatImage); * save image in uncompressed format : * cv::imwrite("fa.tiff", resizedImage, { cv::IMWRITE_TIFF_COMPRESSION, 1,cv::IMWRITE_TIFF_XDPI, 300,cv::IMWRITE_TIFF_YDPI,300 }); * * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) # None * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 5)) # LZW * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 8)) # Adobe Deflate * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 32946)) # Deflate * cv::imwrite("far.png", resizedImage, { cv::IMWRITE_PNG_COMPRESSION, 0 }); * Save images in streaming * int64 t0 = cv::getTickCount(); * std::string fileName= "fashid_"+std::to_string(t0)+ ".png"; * cv::imwrite(fileName, cimMat, { cv::IMWRITE_PNG_COMPRESSION, 0 }); * Save file name: * std::filesystem::path p = std::filesystem::path(files[i]).filename(); * std::string imgFile = savePath + "/" \+ p.string() + ".tiff"; * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) ; ### Error * Exception thrown at 0x00007FFB5CB21636 (vcruntime140d.dll) in farshid.exe: 0xC0000005: Access violation writing location 0x000002B09BAC4000. * check the size of Mat * cv::Mat meanImages = cv::Mat::zeros(rows_main, cols_main, CV_32F); * cv::Mat meanImages = cv::Mat::zeros(farshidMat.size(), CV_32F); * Linker Tools Error LNK2038 & Linker Tools Error LNK2001; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for '_ITERATOR_DEBUG_LEVEL': value '2' doesn't match value '0' in Source.obj ; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for 'RuntimeLibrary': value 'MTd_StaticDebug' doesn't match value 'MT_StaticRelease' * the link files are not match based on release or debug mode. # Testing for OpenCV Projects Test, C++, Computer Vision, Image Processing, more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: * Arrange, Act and Assert (AAA) Pattern * Google C++ Test Framework * Assertions Types and Test Fixtures * ASSERT_FALSE(frame.empty()); ASSERT_NO_THROW(cap >> img); ASSERT_FALSE(img.empty()) << "idx=" << idx; ### Tricks embedded system, keep the software as small as possible, Embedding static elements in your application, [https://gstreamer.freedesktop.org/documentation/installing/index.html?gi- language=c](https://www.google.com/url?q=https%3A%2F%2Fgstreamer.freedesktop.org%2Fdocumentation%2Finstalling%2Findex.html%3Fgi- language%3Dc&sa=D&sntz=1&usg=AOvVaw1LbT7pN6fSH1VBpZ07TI2Y) ### Tips * copy mat to vector need clone() ### ### Example #include assert(!im.empty()); assert(x.size()==y.size()); assert(x.size()>2); #ifdef _DEBUG #endif #if true #else #endif # YouTube Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static opencv library for visual studio 2022 by using NuGet package manager just in few minutes #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah [https://github.com/xiaohongchen1991/meshgen](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxiaohongchen1991%2Fmeshgen&sa=D&sntz=1&usg=AOvVaw1J7_ZnsiBoB0T6RB0dNc6u) A set of C++ APIs are provided to mimic the same behaviors as the MATLAB function "linspace" and "meshgrid". 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OpenCV 5 beta cvtest: Computer Vision Test Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Standard test for computer vision application Advanced OpenCV techniques: Advanced OpenCV techniques: Advanced OpenCV techniques: balance white Advanced OpenCV techniques: contrast and brightness Advanced subpixel techniques: Shift image content Mesh grid float mesh grid int main: Tips and Tricks of OpenCV that Nobody Told You Tricks Tips Save results Error Testing for OpenCV Projects Tricks Tips Example YouTube # NuGet - OpenCV 5 beta NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022 [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static opencv library for visual studio 2022 by using NuGet package manager just in few minutes #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) #OpenCV #Farshid_PirahanSiah #pirahansiah My NuGet packages comprised of two versions for different VS version. * Visual Studio 2019 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS2019_NuGet&sa=D&sntz=1&usg=AOvVaw0B-7f3iUYsMrp8SHvJGQb9) * Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7 * Visual Studio 2022 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS22_NuGet&sa=D&sntz=1&usg=AOvVaw2tWZP3KILcTESYrI2cdofl) * Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7 _**more:**_[ _ **https://www.pirahansiah.com/topics/opencv**_](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) _ ****_ # cvtest: Computer Vision Test ## Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Do you want to test your output of computer vision application which is video or images? ## Standard test for computer vision application There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil) download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: [](https://docs.google.com/presentation/d/14HX-99rGO9x1AtOnEigZ_2gAzZaPwvxqCEnnG0dk870/present "Open Presentation, cvtest in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/slides_32dp.png)cvtest # Advanced OpenCV techniques: sub-pixel, floating points, more precise, real-valued coordinates, Changing the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and Tricks of OpenCV that Nobody Told You download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: # Advanced OpenCV techniques: Cross correlation (CC): TM_CCORR Mean shifted cross correlation (Pearson correlation coefficient): TM_CCOEFF Normalization: TM_SQDIFF_NORMED, TM_CCORR_NORMED, TM_CCOEFF_NORMED maximum absolute difference metric (MaxAD), which is also known as the uniform distance metric computeECC() and findTransformECC(). Sum of absolute differences (SAD) Cross correlation (CC) find identical regions of an image that match a template, select by giving a threshold 2D convolution It simply slides the template image over the input image (as in 2D convolution) and compares the template and patch of input image under the template image. Template matching > cv2.TM_CCOEFF > cv2.TM_CCOEFF_NORMED > cv2.TM_CCORR > cv2.TM_CCORR_NORMED < cv2.TM_SQDIFF < cv2.TM_SQDIFF_NORMED cv2.minMaxLoc() more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: [https://docs.opencv.org/master/df/dfb/group__imgproc__object.html#gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2Fmaster%2Fdf%2Fdfb%2Fgroup__imgproc__object.html%23gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65&sa=D&sntz=1&usg=AOvVaw37X4mefDnUv8PmnZK0YdoM) [https://stackoverflow.com/questions/58158129/understanding-and-evaluating- template-matching- methods](https://www.google.com/url?q=https%3A%2F%2Fstackoverflow.com%2Fquestions%2F58158129%2Funderstanding- and-evaluating-template-matching- methods&sa=D&sntz=1&usg=AOvVaw08M8efI3a63Nn7qXHfZZEY) # Advanced OpenCV techniques: balance white balance white with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: void balance_white(cv::Mat mat) { double discard_ratio = 0.05; int hists[3][256]; memset(hists, 0, 3 * 256 * sizeof(int)); for (int y = 0; y < mat.rows; ++y) { uchar* ptr = mat.ptr(y); for (int x = 0; x < mat.cols; ++x) { for (int j = 0; j < 3; ++j) { hists[j][ptr[x * 3 + j]] += 1; } } } // cumulative hist int total = mat.cols * mat.rows; int vmin[3], vmax[3]; for (int i = 0; i < 3; ++i) { for (int j = 0; j < 255; ++j) { hists[i][j + 1] += hists[i][j]; } vmin[i] = 0; vmax[i] = 255; while (hists[i][vmin[i]] < discard_ratio * total) vmin[i] += 1; while (hists[i][vmax[i]] > (1 - discard_ratio) * total) vmax[i] -= 1; if (vmax[i] < 255 - 1) vmax[i] += 1; } for (int y = 0; y < mat.rows; ++y) { uchar* ptr = mat.ptr(y); for (int x = 0; x < mat.cols; ++x) { for (int j = 0; j < 3; ++j) { int val = ptr[x * 3 + j]; if (val < vmin[j]) val = vmin[j]; if (val > vmax[j]) val = vmax[j]; ptr[x * 3 + j] = static_cast((val - vmin[j]) * 255.0 / (vmax[j] - vmin[j])); } } } } reference http://www.ipol.im/pub/art/2011/llmps-scb/ https://gist.github.com/tomykaira/94472e9f4921ec2cf582 # Advanced OpenCV techniques: contrast and brightness sub-pixel, floating points, more precise, real-valued coordinates, Changing the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: showImage.convertTo(showImage, CV_32FC3); double alpha = 2.0; /*< Simple contrast control */ int beta = 100; /*< Simple brightness control */ for (int y = 0; y < showImage.rows; y++) { for (int x = 0; x < showImage.cols; x++) { for (int c = 0; c < showImage.channels(); c++) { showImage.at(y, x)[c] =cv::saturate_cast(alpha * showImage.at(y, x)[c] + beta); } } } showImage.convertTo(showImage, CV_8UC3); cv::imshow("Changing the contrast and brightness of an image! ", showImage); cv::waitKey(0); based on [https://docs.opencv.org/3.4/d3/dc1/tutorial_basic_linear_transform.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd3%2Fdc1%2Ftutorial_basic_linear_transform.html&sa=D&sntz=1&usg=AOvVaw15cmUCYNHZx6OdaoUN0tSB) [https://blog.csdn.net/u014230360/article/details/109802229](https://www.google.com/url?q=https%3A%2F%2Fblog.csdn.net%2Fu014230360%2Farticle%2Fdetails%2F109802229&sa=D&sntz=1&usg=AOvVaw1IxjJJewGlTNahnzI7pCX7) # Advanced subpixel techniques: Shift image content sub-pixel, floating points, mesh grid, remap, more precise, real-valued coordinates, moving image pixel, Shift image content with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: ## ## Mesh grid float static void meshgrid_float(const cv::Mat& xgv, const cv::Mat& ygv, cv::Mat1f& X, cv::Mat1f& Y) { cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X); cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y); } static void meshgrid_map_float(const cv::Range& xgv, const cv::Range& ygv, cv::Mat1f& X, cv::Mat1f& Y) { std::vector t_x, t_y; for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i); for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i); meshgrid_float(cv::Mat(t_x), cv::Mat(t_y), X, Y); } ## mesh grid int static void meshgrid(const cv::Mat& xgv, const cv::Mat& ygv, cv::Mat1i& X, cv::Mat1i& Y) { cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X); cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y); } static void meshgridTest(const cv::Range& xgv, const cv::Range& ygv, cv::Mat1i& X, cv::Mat1i& Y) { std::vector t_x, t_y; for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i); for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i); meshgrid(cv::Mat(t_x), cv::Mat(t_y), X, Y); } ## main: cv::Mat1f XF, YF; //for int //meshgrid_map_float(cv::Range(0, cols_main), cv::Range(0, rows_main), XF, YF); //for float meshgrid_map_float(cv::Range(0, cols_main-1), cv::Range(0, rows_main-1), XF, YF); for (int rowsImage = 0; rowsImage < rows_main; ++rowsImage) { for (int colsImage = 0; colsImage < cols_main; ++colsImage) { XF.at(rowsImage, colsImage) += offset1; YF.at(rowsImage, colsImage) += offset2; } } cv::remap(convert_img_i, dst, XF, YF, cv::INTER_LINEAR); if (show) { cv::Mat resizedImage = dst.clone(); dst.convertTo(resizedImage, CV_8UC3); cv::resize(resizedImage, resizedImage, cv::Size(), 0.5, 0.5); std::string nameWindow = " meshgrid and remap in float "; cv::imshow(nameWindow, resizedImage); cv::waitKey(0); } # Tips and Tricks of OpenCV that Nobody Told You Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: ### Tricks cv::multiply(outMat, cv::Scalar(gain, gain, gain), outMat); //color cv::multiply(outMat, cv::Scalar(gain), outMat); //grayscale //copy small Mat to bigger Mat cv::Rect roi( cv::Point( originX, originY ), smallImage.size() ); smallImage.copyTo( bigImage( roi ) ); ### Tips * copy mat to vector need clone() ### ### Save results * save image in float * cv::imwrite("image.exr", MatImage); * save image in uncompressed format : * cv::imwrite("fa.tiff", resizedImage, { cv::IMWRITE_TIFF_COMPRESSION, 1,cv::IMWRITE_TIFF_XDPI, 300,cv::IMWRITE_TIFF_YDPI,300 }); * * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) # None * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 5)) # LZW * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 8)) # Adobe Deflate * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 32946)) # Deflate * cv::imwrite("far.png", resizedImage, { cv::IMWRITE_PNG_COMPRESSION, 0 }); * Save images in streaming * int64 t0 = cv::getTickCount(); * std::string fileName= "fashid_"+std::to_string(t0)+ ".png"; * cv::imwrite(fileName, cimMat, { cv::IMWRITE_PNG_COMPRESSION, 0 }); * Save file name: * std::filesystem::path p = std::filesystem::path(files[i]).filename(); * std::string imgFile = savePath + "/" \+ p.string() + ".tiff"; * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) ; ### Error * Exception thrown at 0x00007FFB5CB21636 (vcruntime140d.dll) in farshid.exe: 0xC0000005: Access violation writing location 0x000002B09BAC4000. * check the size of Mat * cv::Mat meanImages = cv::Mat::zeros(rows_main, cols_main, CV_32F); * cv::Mat meanImages = cv::Mat::zeros(farshidMat.size(), CV_32F); * Linker Tools Error LNK2038 & Linker Tools Error LNK2001; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for '_ITERATOR_DEBUG_LEVEL': value '2' doesn't match value '0' in Source.obj ; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for 'RuntimeLibrary': value 'MTd_StaticDebug' doesn't match value 'MT_StaticRelease' * the link files are not match based on release or debug mode. # Testing for OpenCV Projects Test, C++, Computer Vision, Image Processing, more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: * Arrange, Act and Assert (AAA) Pattern * Google C++ Test Framework * Assertions Types and Test Fixtures * ASSERT_FALSE(frame.empty()); ASSERT_NO_THROW(cap >> img); ASSERT_FALSE(img.empty()) << "idx=" << idx; ### Tricks embedded system, keep the software as small as possible, Embedding static elements in your application, [https://gstreamer.freedesktop.org/documentation/installing/index.html?gi- language=c](https://www.google.com/url?q=https%3A%2F%2Fgstreamer.freedesktop.org%2Fdocumentation%2Finstalling%2Findex.html%3Fgi- language%3Dc&sa=D&sntz=1&usg=AOvVaw1LbT7pN6fSH1VBpZ07TI2Y) ### Tips * copy mat to vector need clone() ### ### Example #include assert(!im.empty()); assert(x.size()==y.size()); assert(x.size()>2); #ifdef _DEBUG #endif #if true #else #endif # YouTube Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static opencv library for visual studio 2022 by using NuGet package manager just in few minutes #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah [https://github.com/xiaohongchen1991/meshgen](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxiaohongchen1991%2Fmeshgen&sa=D&sntz=1&usg=AOvVaw1J7_ZnsiBoB0T6RB0dNc6u) A set of C++ APIs are provided to mimic the same behaviors as the MATLAB function "linspace" and "meshgrid". Must-Read Books of All Time in Computer Vision and Machine Learning ![](https://lh6.googleusercontent.com/xmn3C-kogDWhYFajeyz5ut5C5cLqfb7h69EHu8Ugepm3dc1gqty- aVEoRWJvXvQ_aJbtlviOa76e8iH90wgfXawMGLybHWWuNxO3zKsqAL4oF1iv1X8Z-SiE6rS7qOz2xA=w1280) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. 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OpenCV 5 beta cvtest: Computer Vision Test Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Standard test for computer vision application Advanced OpenCV techniques: Advanced OpenCV techniques: Advanced OpenCV techniques: balance white Advanced OpenCV techniques: contrast and brightness Advanced subpixel techniques: Shift image content Mesh grid float mesh grid int main: Tips and Tricks of OpenCV that Nobody Told You Tricks Tips Save results Error Testing for OpenCV Projects Tricks Tips Example YouTube # NuGet - OpenCV 5 beta NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022 [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static opencv library for visual studio 2022 by using NuGet package manager just in few minutes #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) #OpenCV #Farshid_PirahanSiah #pirahansiah My NuGet packages comprised of two versions for different VS version. * Visual Studio 2019 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS2019_NuGet&sa=D&sntz=1&usg=AOvVaw0B-7f3iUYsMrp8SHvJGQb9) * Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7 * Visual Studio 2022 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS22_NuGet&sa=D&sntz=1&usg=AOvVaw2tWZP3KILcTESYrI2cdofl) * Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7 _**more:**_[ _ **https://www.pirahansiah.com/topics/opencv**_](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) _ ****_ # cvtest: Computer Vision Test ## Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Do you want to test your output of computer vision application which is video or images? ## Standard test for computer vision application There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil) download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: [](https://docs.google.com/presentation/d/14HX-99rGO9x1AtOnEigZ_2gAzZaPwvxqCEnnG0dk870/present "Open Presentation, cvtest in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/slides_32dp.png)cvtest # Advanced OpenCV techniques: sub-pixel, floating points, more precise, real-valued coordinates, Changing the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and Tricks of OpenCV that Nobody Told You download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: # Advanced OpenCV techniques: Cross correlation (CC): TM_CCORR Mean shifted cross correlation (Pearson correlation coefficient): TM_CCOEFF Normalization: TM_SQDIFF_NORMED, TM_CCORR_NORMED, TM_CCOEFF_NORMED maximum absolute difference metric (MaxAD), which is also known as the uniform distance metric computeECC() and findTransformECC(). Sum of absolute differences (SAD) Cross correlation (CC) find identical regions of an image that match a template, select by giving a threshold 2D convolution It simply slides the template image over the input image (as in 2D convolution) and compares the template and patch of input image under the template image. Template matching > cv2.TM_CCOEFF > cv2.TM_CCOEFF_NORMED > cv2.TM_CCORR > cv2.TM_CCORR_NORMED < cv2.TM_SQDIFF < cv2.TM_SQDIFF_NORMED cv2.minMaxLoc() more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: [https://docs.opencv.org/master/df/dfb/group__imgproc__object.html#gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2Fmaster%2Fdf%2Fdfb%2Fgroup__imgproc__object.html%23gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65&sa=D&sntz=1&usg=AOvVaw37X4mefDnUv8PmnZK0YdoM) [https://stackoverflow.com/questions/58158129/understanding-and-evaluating- template-matching- methods](https://www.google.com/url?q=https%3A%2F%2Fstackoverflow.com%2Fquestions%2F58158129%2Funderstanding- and-evaluating-template-matching- methods&sa=D&sntz=1&usg=AOvVaw08M8efI3a63Nn7qXHfZZEY) # Advanced OpenCV techniques: balance white balance white with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: void balance_white(cv::Mat mat) { double discard_ratio = 0.05; int hists[3][256]; memset(hists, 0, 3 * 256 * sizeof(int)); for (int y = 0; y < mat.rows; ++y) { uchar* ptr = mat.ptr(y); for (int x = 0; x < mat.cols; ++x) { for (int j = 0; j < 3; ++j) { hists[j][ptr[x * 3 + j]] += 1; } } } // cumulative hist int total = mat.cols * mat.rows; int vmin[3], vmax[3]; for (int i = 0; i < 3; ++i) { for (int j = 0; j < 255; ++j) { hists[i][j + 1] += hists[i][j]; } vmin[i] = 0; vmax[i] = 255; while (hists[i][vmin[i]] < discard_ratio * total) vmin[i] += 1; while (hists[i][vmax[i]] > (1 - discard_ratio) * total) vmax[i] -= 1; if (vmax[i] < 255 - 1) vmax[i] += 1; } for (int y = 0; y < mat.rows; ++y) { uchar* ptr = mat.ptr(y); for (int x = 0; x < mat.cols; ++x) { for (int j = 0; j < 3; ++j) { int val = ptr[x * 3 + j]; if (val < vmin[j]) val = vmin[j]; if (val > vmax[j]) val = vmax[j]; ptr[x * 3 + j] = static_cast((val - vmin[j]) * 255.0 / (vmax[j] - vmin[j])); } } } } reference http://www.ipol.im/pub/art/2011/llmps-scb/ https://gist.github.com/tomykaira/94472e9f4921ec2cf582 # Advanced OpenCV techniques: contrast and brightness sub-pixel, floating points, more precise, real-valued coordinates, Changing the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: showImage.convertTo(showImage, CV_32FC3); double alpha = 2.0; /*< Simple contrast control */ int beta = 100; /*< Simple brightness control */ for (int y = 0; y < showImage.rows; y++) { for (int x = 0; x < showImage.cols; x++) { for (int c = 0; c < showImage.channels(); c++) { showImage.at(y, x)[c] =cv::saturate_cast(alpha * showImage.at(y, x)[c] + beta); } } } showImage.convertTo(showImage, CV_8UC3); cv::imshow("Changing the contrast and brightness of an image! ", showImage); cv::waitKey(0); based on [https://docs.opencv.org/3.4/d3/dc1/tutorial_basic_linear_transform.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd3%2Fdc1%2Ftutorial_basic_linear_transform.html&sa=D&sntz=1&usg=AOvVaw15cmUCYNHZx6OdaoUN0tSB) [https://blog.csdn.net/u014230360/article/details/109802229](https://www.google.com/url?q=https%3A%2F%2Fblog.csdn.net%2Fu014230360%2Farticle%2Fdetails%2F109802229&sa=D&sntz=1&usg=AOvVaw1IxjJJewGlTNahnzI7pCX7) # Advanced subpixel techniques: Shift image content sub-pixel, floating points, mesh grid, remap, more precise, real-valued coordinates, moving image pixel, Shift image content with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: ## ## Mesh grid float static void meshgrid_float(const cv::Mat& xgv, const cv::Mat& ygv, cv::Mat1f& X, cv::Mat1f& Y) { cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X); cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y); } static void meshgrid_map_float(const cv::Range& xgv, const cv::Range& ygv, cv::Mat1f& X, cv::Mat1f& Y) { std::vector t_x, t_y; for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i); for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i); meshgrid_float(cv::Mat(t_x), cv::Mat(t_y), X, Y); } ## mesh grid int static void meshgrid(const cv::Mat& xgv, const cv::Mat& ygv, cv::Mat1i& X, cv::Mat1i& Y) { cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X); cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y); } static void meshgridTest(const cv::Range& xgv, const cv::Range& ygv, cv::Mat1i& X, cv::Mat1i& Y) { std::vector t_x, t_y; for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i); for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i); meshgrid(cv::Mat(t_x), cv::Mat(t_y), X, Y); } ## main: cv::Mat1f XF, YF; //for int //meshgrid_map_float(cv::Range(0, cols_main), cv::Range(0, rows_main), XF, YF); //for float meshgrid_map_float(cv::Range(0, cols_main-1), cv::Range(0, rows_main-1), XF, YF); for (int rowsImage = 0; rowsImage < rows_main; ++rowsImage) { for (int colsImage = 0; colsImage < cols_main; ++colsImage) { XF.at(rowsImage, colsImage) += offset1; YF.at(rowsImage, colsImage) += offset2; } } cv::remap(convert_img_i, dst, XF, YF, cv::INTER_LINEAR); if (show) { cv::Mat resizedImage = dst.clone(); dst.convertTo(resizedImage, CV_8UC3); cv::resize(resizedImage, resizedImage, cv::Size(), 0.5, 0.5); std::string nameWindow = " meshgrid and remap in float "; cv::imshow(nameWindow, resizedImage); cv::waitKey(0); } # Tips and Tricks of OpenCV that Nobody Told You Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: ### Tricks cv::multiply(outMat, cv::Scalar(gain, gain, gain), outMat); //color cv::multiply(outMat, cv::Scalar(gain), outMat); //grayscale //copy small Mat to bigger Mat cv::Rect roi( cv::Point( originX, originY ), smallImage.size() ); smallImage.copyTo( bigImage( roi ) ); ### Tips * copy mat to vector need clone() ### ### Save results * save image in float * cv::imwrite("image.exr", MatImage); * save image in uncompressed format : * cv::imwrite("fa.tiff", resizedImage, { cv::IMWRITE_TIFF_COMPRESSION, 1,cv::IMWRITE_TIFF_XDPI, 300,cv::IMWRITE_TIFF_YDPI,300 }); * * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) # None * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 5)) # LZW * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 8)) # Adobe Deflate * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 32946)) # Deflate * cv::imwrite("far.png", resizedImage, { cv::IMWRITE_PNG_COMPRESSION, 0 }); * Save images in streaming * int64 t0 = cv::getTickCount(); * std::string fileName= "fashid_"+std::to_string(t0)+ ".png"; * cv::imwrite(fileName, cimMat, { cv::IMWRITE_PNG_COMPRESSION, 0 }); * Save file name: * std::filesystem::path p = std::filesystem::path(files[i]).filename(); * std::string imgFile = savePath + "/" \+ p.string() + ".tiff"; * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) ; ### Error * Exception thrown at 0x00007FFB5CB21636 (vcruntime140d.dll) in farshid.exe: 0xC0000005: Access violation writing location 0x000002B09BAC4000. * check the size of Mat * cv::Mat meanImages = cv::Mat::zeros(rows_main, cols_main, CV_32F); * cv::Mat meanImages = cv::Mat::zeros(farshidMat.size(), CV_32F); * Linker Tools Error LNK2038 & Linker Tools Error LNK2001; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for '_ITERATOR_DEBUG_LEVEL': value '2' doesn't match value '0' in Source.obj ; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for 'RuntimeLibrary': value 'MTd_StaticDebug' doesn't match value 'MT_StaticRelease' * the link files are not match based on release or debug mode. # Testing for OpenCV Projects Test, C++, Computer Vision, Image Processing, more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: * Arrange, Act and Assert (AAA) Pattern * Google C++ Test Framework * Assertions Types and Test Fixtures * ASSERT_FALSE(frame.empty()); ASSERT_NO_THROW(cap >> img); ASSERT_FALSE(img.empty()) << "idx=" << idx; ### Tricks embedded system, keep the software as small as possible, Embedding static elements in your application, [https://gstreamer.freedesktop.org/documentation/installing/index.html?gi- language=c](https://www.google.com/url?q=https%3A%2F%2Fgstreamer.freedesktop.org%2Fdocumentation%2Finstalling%2Findex.html%3Fgi- language%3Dc&sa=D&sntz=1&usg=AOvVaw1LbT7pN6fSH1VBpZ07TI2Y) ### Tips * copy mat to vector need clone() ### ### Example #include assert(!im.empty()); assert(x.size()==y.size()); assert(x.size()>2); #ifdef _DEBUG #endif #if true #else #endif # YouTube Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static opencv library for visual studio 2022 by using NuGet package manager just in few minutes #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah [https://github.com/xiaohongchen1991/meshgen](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxiaohongchen1991%2Fmeshgen&sa=D&sntz=1&usg=AOvVaw1J7_ZnsiBoB0T6RB0dNc6u) A set of C++ APIs are provided to mimic the same behaviors as the MATLAB function "linspace" and "meshgrid". Must-Read Books of All Time in Computer Vision and Machine Learning ![](https://lh6.googleusercontent.com/xmn3C-kogDWhYFajeyz5ut5C5cLqfb7h69EHu8Ugepm3dc1gqty- aVEoRWJvXvQ_aJbtlviOa76e8iH90wgfXawMGLybHWWuNxO3zKsqAL4oF1iv1X8Z-SiE6rS7qOz2xA=w1280) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. 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OpenCV 5 beta cvtest: Computer Vision Test Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Standard test for computer vision application Advanced OpenCV techniques: Advanced OpenCV techniques: Advanced OpenCV techniques: balance white Advanced OpenCV techniques: contrast and brightness Advanced subpixel techniques: Shift image content Mesh grid float mesh grid int main: Tips and Tricks of OpenCV that Nobody Told You Tricks Tips Save results Error Testing for OpenCV Projects Tricks Tips Example YouTube # NuGet - OpenCV 5 beta NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022 [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static opencv library for visual studio 2022 by using NuGet package manager just in few minutes #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) #OpenCV #Farshid_PirahanSiah #pirahansiah My NuGet packages comprised of two versions for different VS version. * Visual Studio 2019 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS2019_NuGet&sa=D&sntz=1&usg=AOvVaw0B-7f3iUYsMrp8SHvJGQb9) * Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7 * Visual Studio 2022 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS22_NuGet&sa=D&sntz=1&usg=AOvVaw2tWZP3KILcTESYrI2cdofl) * Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7 _**more:**_[ _ **https://www.pirahansiah.com/topics/opencv**_](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) _ ****_ # cvtest: Computer Vision Test ## Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Do you want to test your output of computer vision application which is video or images? ## Standard test for computer vision application There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil) download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: [](https://docs.google.com/presentation/d/14HX-99rGO9x1AtOnEigZ_2gAzZaPwvxqCEnnG0dk870/present "Open Presentation, cvtest in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/slides_32dp.png)cvtest # Advanced OpenCV techniques: sub-pixel, floating points, more precise, real-valued coordinates, Changing the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and Tricks of OpenCV that Nobody Told You download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: # Advanced OpenCV techniques: Cross correlation (CC): TM_CCORR Mean shifted cross correlation (Pearson correlation coefficient): TM_CCOEFF Normalization: TM_SQDIFF_NORMED, TM_CCORR_NORMED, TM_CCOEFF_NORMED maximum absolute difference metric (MaxAD), which is also known as the uniform distance metric computeECC() and findTransformECC(). Sum of absolute differences (SAD) Cross correlation (CC) find identical regions of an image that match a template, select by giving a threshold 2D convolution It simply slides the template image over the input image (as in 2D convolution) and compares the template and patch of input image under the template image. Template matching > cv2.TM_CCOEFF > cv2.TM_CCOEFF_NORMED > cv2.TM_CCORR > cv2.TM_CCORR_NORMED < cv2.TM_SQDIFF < cv2.TM_SQDIFF_NORMED cv2.minMaxLoc() more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: [https://docs.opencv.org/master/df/dfb/group__imgproc__object.html#gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2Fmaster%2Fdf%2Fdfb%2Fgroup__imgproc__object.html%23gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65&sa=D&sntz=1&usg=AOvVaw37X4mefDnUv8PmnZK0YdoM) [https://stackoverflow.com/questions/58158129/understanding-and-evaluating- template-matching- methods](https://www.google.com/url?q=https%3A%2F%2Fstackoverflow.com%2Fquestions%2F58158129%2Funderstanding- and-evaluating-template-matching- methods&sa=D&sntz=1&usg=AOvVaw08M8efI3a63Nn7qXHfZZEY) # Advanced OpenCV techniques: balance white balance white with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: void balance_white(cv::Mat mat) { double discard_ratio = 0.05; int hists[3][256]; memset(hists, 0, 3 * 256 * sizeof(int)); for (int y = 0; y < mat.rows; ++y) { uchar* ptr = mat.ptr(y); for (int x = 0; x < mat.cols; ++x) { for (int j = 0; j < 3; ++j) { hists[j][ptr[x * 3 + j]] += 1; } } } // cumulative hist int total = mat.cols * mat.rows; int vmin[3], vmax[3]; for (int i = 0; i < 3; ++i) { for (int j = 0; j < 255; ++j) { hists[i][j + 1] += hists[i][j]; } vmin[i] = 0; vmax[i] = 255; while (hists[i][vmin[i]] < discard_ratio * total) vmin[i] += 1; while (hists[i][vmax[i]] > (1 - discard_ratio) * total) vmax[i] -= 1; if (vmax[i] < 255 - 1) vmax[i] += 1; } for (int y = 0; y < mat.rows; ++y) { uchar* ptr = mat.ptr(y); for (int x = 0; x < mat.cols; ++x) { for (int j = 0; j < 3; ++j) { int val = ptr[x * 3 + j]; if (val < vmin[j]) val = vmin[j]; if (val > vmax[j]) val = vmax[j]; ptr[x * 3 + j] = static_cast((val - vmin[j]) * 255.0 / (vmax[j] - vmin[j])); } } } } reference http://www.ipol.im/pub/art/2011/llmps-scb/ https://gist.github.com/tomykaira/94472e9f4921ec2cf582 # Advanced OpenCV techniques: contrast and brightness sub-pixel, floating points, more precise, real-valued coordinates, Changing the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: showImage.convertTo(showImage, CV_32FC3); double alpha = 2.0; /*< Simple contrast control */ int beta = 100; /*< Simple brightness control */ for (int y = 0; y < showImage.rows; y++) { for (int x = 0; x < showImage.cols; x++) { for (int c = 0; c < showImage.channels(); c++) { showImage.at(y, x)[c] =cv::saturate_cast(alpha * showImage.at(y, x)[c] + beta); } } } showImage.convertTo(showImage, CV_8UC3); cv::imshow("Changing the contrast and brightness of an image! ", showImage); cv::waitKey(0); based on [https://docs.opencv.org/3.4/d3/dc1/tutorial_basic_linear_transform.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd3%2Fdc1%2Ftutorial_basic_linear_transform.html&sa=D&sntz=1&usg=AOvVaw15cmUCYNHZx6OdaoUN0tSB) [https://blog.csdn.net/u014230360/article/details/109802229](https://www.google.com/url?q=https%3A%2F%2Fblog.csdn.net%2Fu014230360%2Farticle%2Fdetails%2F109802229&sa=D&sntz=1&usg=AOvVaw1IxjJJewGlTNahnzI7pCX7) # Advanced subpixel techniques: Shift image content sub-pixel, floating points, mesh grid, remap, more precise, real-valued coordinates, moving image pixel, Shift image content with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: ## ## Mesh grid float static void meshgrid_float(const cv::Mat& xgv, const cv::Mat& ygv, cv::Mat1f& X, cv::Mat1f& Y) { cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X); cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y); } static void meshgrid_map_float(const cv::Range& xgv, const cv::Range& ygv, cv::Mat1f& X, cv::Mat1f& Y) { std::vector t_x, t_y; for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i); for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i); meshgrid_float(cv::Mat(t_x), cv::Mat(t_y), X, Y); } ## mesh grid int static void meshgrid(const cv::Mat& xgv, const cv::Mat& ygv, cv::Mat1i& X, cv::Mat1i& Y) { cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X); cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y); } static void meshgridTest(const cv::Range& xgv, const cv::Range& ygv, cv::Mat1i& X, cv::Mat1i& Y) { std::vector t_x, t_y; for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i); for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i); meshgrid(cv::Mat(t_x), cv::Mat(t_y), X, Y); } ## main: cv::Mat1f XF, YF; //for int //meshgrid_map_float(cv::Range(0, cols_main), cv::Range(0, rows_main), XF, YF); //for float meshgrid_map_float(cv::Range(0, cols_main-1), cv::Range(0, rows_main-1), XF, YF); for (int rowsImage = 0; rowsImage < rows_main; ++rowsImage) { for (int colsImage = 0; colsImage < cols_main; ++colsImage) { XF.at(rowsImage, colsImage) += offset1; YF.at(rowsImage, colsImage) += offset2; } } cv::remap(convert_img_i, dst, XF, YF, cv::INTER_LINEAR); if (show) { cv::Mat resizedImage = dst.clone(); dst.convertTo(resizedImage, CV_8UC3); cv::resize(resizedImage, resizedImage, cv::Size(), 0.5, 0.5); std::string nameWindow = " meshgrid and remap in float "; cv::imshow(nameWindow, resizedImage); cv::waitKey(0); } # Tips and Tricks of OpenCV that Nobody Told You Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: ### Tricks cv::multiply(outMat, cv::Scalar(gain, gain, gain), outMat); //color cv::multiply(outMat, cv::Scalar(gain), outMat); //grayscale //copy small Mat to bigger Mat cv::Rect roi( cv::Point( originX, originY ), smallImage.size() ); smallImage.copyTo( bigImage( roi ) ); ### Tips * copy mat to vector need clone() ### ### Save results * save image in float * cv::imwrite("image.exr", MatImage); * save image in uncompressed format : * cv::imwrite("fa.tiff", resizedImage, { cv::IMWRITE_TIFF_COMPRESSION, 1,cv::IMWRITE_TIFF_XDPI, 300,cv::IMWRITE_TIFF_YDPI,300 }); * * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) # None * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 5)) # LZW * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 8)) # Adobe Deflate * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 32946)) # Deflate * cv::imwrite("far.png", resizedImage, { cv::IMWRITE_PNG_COMPRESSION, 0 }); * Save images in streaming * int64 t0 = cv::getTickCount(); * std::string fileName= "fashid_"+std::to_string(t0)+ ".png"; * cv::imwrite(fileName, cimMat, { cv::IMWRITE_PNG_COMPRESSION, 0 }); * Save file name: * std::filesystem::path p = std::filesystem::path(files[i]).filename(); * std::string imgFile = savePath + "/" \+ p.string() + ".tiff"; * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) ; ### Error * Exception thrown at 0x00007FFB5CB21636 (vcruntime140d.dll) in farshid.exe: 0xC0000005: Access violation writing location 0x000002B09BAC4000. * check the size of Mat * cv::Mat meanImages = cv::Mat::zeros(rows_main, cols_main, CV_32F); * cv::Mat meanImages = cv::Mat::zeros(farshidMat.size(), CV_32F); * Linker Tools Error LNK2038 & Linker Tools Error LNK2001; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for '_ITERATOR_DEBUG_LEVEL': value '2' doesn't match value '0' in Source.obj ; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for 'RuntimeLibrary': value 'MTd_StaticDebug' doesn't match value 'MT_StaticRelease' * the link files are not match based on release or debug mode. # Testing for OpenCV Projects Test, C++, Computer Vision, Image Processing, more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: * Arrange, Act and Assert (AAA) Pattern * Google C++ Test Framework * Assertions Types and Test Fixtures * ASSERT_FALSE(frame.empty()); ASSERT_NO_THROW(cap >> img); ASSERT_FALSE(img.empty()) << "idx=" << idx; ### Tricks embedded system, keep the software as small as possible, Embedding static elements in your application, [https://gstreamer.freedesktop.org/documentation/installing/index.html?gi- language=c](https://www.google.com/url?q=https%3A%2F%2Fgstreamer.freedesktop.org%2Fdocumentation%2Finstalling%2Findex.html%3Fgi- language%3Dc&sa=D&sntz=1&usg=AOvVaw1LbT7pN6fSH1VBpZ07TI2Y) ### Tips * copy mat to vector need clone() ### ### Example #include assert(!im.empty()); assert(x.size()==y.size()); assert(x.size()>2); #ifdef _DEBUG #endif #if true #else #endif # YouTube Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static opencv library for visual studio 2022 by using NuGet package manager just in few minutes #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah [https://github.com/xiaohongchen1991/meshgen](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxiaohongchen1991%2Fmeshgen&sa=D&sntz=1&usg=AOvVaw1J7_ZnsiBoB0T6RB0dNc6u) A set of C++ APIs are provided to mimic the same behaviors as the MATLAB function "linspace" and "meshgrid". Must-Read Books of All Time in Computer Vision and Machine Learning ![](https://lh6.googleusercontent.com/xmn3C-kogDWhYFajeyz5ut5C5cLqfb7h69EHu8Ugepm3dc1gqty- aVEoRWJvXvQ_aJbtlviOa76e8iH90wgfXawMGLybHWWuNxO3zKsqAL4oF1iv1X8Z-SiE6rS7qOz2xA=w1280) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. 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OpenCV 5 beta cvtest: Computer Vision Test Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Standard test for computer vision application Advanced OpenCV techniques: Advanced OpenCV techniques: Advanced OpenCV techniques: balance white Advanced OpenCV techniques: contrast and brightness Advanced subpixel techniques: Shift image content Mesh grid float mesh grid int main: Tips and Tricks of OpenCV that Nobody Told You Tricks Tips Save results Error Testing for OpenCV Projects Tricks Tips Example YouTube # NuGet - OpenCV 5 beta NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022 [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static opencv library for visual studio 2022 by using NuGet package manager just in few minutes #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) #OpenCV #Farshid_PirahanSiah #pirahansiah My NuGet packages comprised of two versions for different VS version. * Visual Studio 2019 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS2019_NuGet&sa=D&sntz=1&usg=AOvVaw0B-7f3iUYsMrp8SHvJGQb9) * Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7 * Visual Studio 2022 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS22_NuGet&sa=D&sntz=1&usg=AOvVaw2tWZP3KILcTESYrI2cdofl) * Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7 _**more:**_[ _ **https://www.pirahansiah.com/topics/opencv**_](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) _ ****_ # cvtest: Computer Vision Test ## Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Do you want to test your output of computer vision application which is video or images? ## Standard test for computer vision application There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil) download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: [](https://docs.google.com/presentation/d/14HX-99rGO9x1AtOnEigZ_2gAzZaPwvxqCEnnG0dk870/present "Open Presentation, cvtest in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/slides_32dp.png)cvtest # Advanced OpenCV techniques: sub-pixel, floating points, more precise, real-valued coordinates, Changing the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and Tricks of OpenCV that Nobody Told You download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: # Advanced OpenCV techniques: Cross correlation (CC): TM_CCORR Mean shifted cross correlation (Pearson correlation coefficient): TM_CCOEFF Normalization: TM_SQDIFF_NORMED, TM_CCORR_NORMED, TM_CCOEFF_NORMED maximum absolute difference metric (MaxAD), which is also known as the uniform distance metric computeECC() and findTransformECC(). Sum of absolute differences (SAD) Cross correlation (CC) find identical regions of an image that match a template, select by giving a threshold 2D convolution It simply slides the template image over the input image (as in 2D convolution) and compares the template and patch of input image under the template image. Template matching > cv2.TM_CCOEFF > cv2.TM_CCOEFF_NORMED > cv2.TM_CCORR > cv2.TM_CCORR_NORMED < cv2.TM_SQDIFF < cv2.TM_SQDIFF_NORMED cv2.minMaxLoc() more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: [https://docs.opencv.org/master/df/dfb/group__imgproc__object.html#gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2Fmaster%2Fdf%2Fdfb%2Fgroup__imgproc__object.html%23gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65&sa=D&sntz=1&usg=AOvVaw37X4mefDnUv8PmnZK0YdoM) [https://stackoverflow.com/questions/58158129/understanding-and-evaluating- template-matching- methods](https://www.google.com/url?q=https%3A%2F%2Fstackoverflow.com%2Fquestions%2F58158129%2Funderstanding- and-evaluating-template-matching- methods&sa=D&sntz=1&usg=AOvVaw08M8efI3a63Nn7qXHfZZEY) # Advanced OpenCV techniques: balance white balance white with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: void balance_white(cv::Mat mat) { double discard_ratio = 0.05; int hists[3][256]; memset(hists, 0, 3 * 256 * sizeof(int)); for (int y = 0; y < mat.rows; ++y) { uchar* ptr = mat.ptr(y); for (int x = 0; x < mat.cols; ++x) { for (int j = 0; j < 3; ++j) { hists[j][ptr[x * 3 + j]] += 1; } } } // cumulative hist int total = mat.cols * mat.rows; int vmin[3], vmax[3]; for (int i = 0; i < 3; ++i) { for (int j = 0; j < 255; ++j) { hists[i][j + 1] += hists[i][j]; } vmin[i] = 0; vmax[i] = 255; while (hists[i][vmin[i]] < discard_ratio * total) vmin[i] += 1; while (hists[i][vmax[i]] > (1 - discard_ratio) * total) vmax[i] -= 1; if (vmax[i] < 255 - 1) vmax[i] += 1; } for (int y = 0; y < mat.rows; ++y) { uchar* ptr = mat.ptr(y); for (int x = 0; x < mat.cols; ++x) { for (int j = 0; j < 3; ++j) { int val = ptr[x * 3 + j]; if (val < vmin[j]) val = vmin[j]; if (val > vmax[j]) val = vmax[j]; ptr[x * 3 + j] = static_cast((val - vmin[j]) * 255.0 / (vmax[j] - vmin[j])); } } } } reference http://www.ipol.im/pub/art/2011/llmps-scb/ https://gist.github.com/tomykaira/94472e9f4921ec2cf582 # Advanced OpenCV techniques: contrast and brightness sub-pixel, floating points, more precise, real-valued coordinates, Changing the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: showImage.convertTo(showImage, CV_32FC3); double alpha = 2.0; /*< Simple contrast control */ int beta = 100; /*< Simple brightness control */ for (int y = 0; y < showImage.rows; y++) { for (int x = 0; x < showImage.cols; x++) { for (int c = 0; c < showImage.channels(); c++) { showImage.at(y, x)[c] =cv::saturate_cast(alpha * showImage.at(y, x)[c] + beta); } } } showImage.convertTo(showImage, CV_8UC3); cv::imshow("Changing the contrast and brightness of an image! ", showImage); cv::waitKey(0); based on [https://docs.opencv.org/3.4/d3/dc1/tutorial_basic_linear_transform.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd3%2Fdc1%2Ftutorial_basic_linear_transform.html&sa=D&sntz=1&usg=AOvVaw15cmUCYNHZx6OdaoUN0tSB) [https://blog.csdn.net/u014230360/article/details/109802229](https://www.google.com/url?q=https%3A%2F%2Fblog.csdn.net%2Fu014230360%2Farticle%2Fdetails%2F109802229&sa=D&sntz=1&usg=AOvVaw1IxjJJewGlTNahnzI7pCX7) # Advanced subpixel techniques: Shift image content sub-pixel, floating points, mesh grid, remap, more precise, real-valued coordinates, moving image pixel, Shift image content with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: ## ## Mesh grid float static void meshgrid_float(const cv::Mat& xgv, const cv::Mat& ygv, cv::Mat1f& X, cv::Mat1f& Y) { cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X); cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y); } static void meshgrid_map_float(const cv::Range& xgv, const cv::Range& ygv, cv::Mat1f& X, cv::Mat1f& Y) { std::vector t_x, t_y; for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i); for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i); meshgrid_float(cv::Mat(t_x), cv::Mat(t_y), X, Y); } ## mesh grid int static void meshgrid(const cv::Mat& xgv, const cv::Mat& ygv, cv::Mat1i& X, cv::Mat1i& Y) { cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X); cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y); } static void meshgridTest(const cv::Range& xgv, const cv::Range& ygv, cv::Mat1i& X, cv::Mat1i& Y) { std::vector t_x, t_y; for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i); for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i); meshgrid(cv::Mat(t_x), cv::Mat(t_y), X, Y); } ## main: cv::Mat1f XF, YF; //for int //meshgrid_map_float(cv::Range(0, cols_main), cv::Range(0, rows_main), XF, YF); //for float meshgrid_map_float(cv::Range(0, cols_main-1), cv::Range(0, rows_main-1), XF, YF); for (int rowsImage = 0; rowsImage < rows_main; ++rowsImage) { for (int colsImage = 0; colsImage < cols_main; ++colsImage) { XF.at(rowsImage, colsImage) += offset1; YF.at(rowsImage, colsImage) += offset2; } } cv::remap(convert_img_i, dst, XF, YF, cv::INTER_LINEAR); if (show) { cv::Mat resizedImage = dst.clone(); dst.convertTo(resizedImage, CV_8UC3); cv::resize(resizedImage, resizedImage, cv::Size(), 0.5, 0.5); std::string nameWindow = " meshgrid and remap in float "; cv::imshow(nameWindow, resizedImage); cv::waitKey(0); } # Tips and Tricks of OpenCV that Nobody Told You Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: ### Tricks cv::multiply(outMat, cv::Scalar(gain, gain, gain), outMat); //color cv::multiply(outMat, cv::Scalar(gain), outMat); //grayscale //copy small Mat to bigger Mat cv::Rect roi( cv::Point( originX, originY ), smallImage.size() ); smallImage.copyTo( bigImage( roi ) ); ### Tips * copy mat to vector need clone() ### ### Save results * save image in float * cv::imwrite("image.exr", MatImage); * save image in uncompressed format : * cv::imwrite("fa.tiff", resizedImage, { cv::IMWRITE_TIFF_COMPRESSION, 1,cv::IMWRITE_TIFF_XDPI, 300,cv::IMWRITE_TIFF_YDPI,300 }); * * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) # None * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 5)) # LZW * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 8)) # Adobe Deflate * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 32946)) # Deflate * cv::imwrite("far.png", resizedImage, { cv::IMWRITE_PNG_COMPRESSION, 0 }); * Save images in streaming * int64 t0 = cv::getTickCount(); * std::string fileName= "fashid_"+std::to_string(t0)+ ".png"; * cv::imwrite(fileName, cimMat, { cv::IMWRITE_PNG_COMPRESSION, 0 }); * Save file name: * std::filesystem::path p = std::filesystem::path(files[i]).filename(); * std::string imgFile = savePath + "/" \+ p.string() + ".tiff"; * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) ; ### Error * Exception thrown at 0x00007FFB5CB21636 (vcruntime140d.dll) in farshid.exe: 0xC0000005: Access violation writing location 0x000002B09BAC4000. * check the size of Mat * cv::Mat meanImages = cv::Mat::zeros(rows_main, cols_main, CV_32F); * cv::Mat meanImages = cv::Mat::zeros(farshidMat.size(), CV_32F); * Linker Tools Error LNK2038 & Linker Tools Error LNK2001; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for '_ITERATOR_DEBUG_LEVEL': value '2' doesn't match value '0' in Source.obj ; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for 'RuntimeLibrary': value 'MTd_StaticDebug' doesn't match value 'MT_StaticRelease' * the link files are not match based on release or debug mode. # Testing for OpenCV Projects Test, C++, Computer Vision, Image Processing, more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: * Arrange, Act and Assert (AAA) Pattern * Google C++ Test Framework * Assertions Types and Test Fixtures * ASSERT_FALSE(frame.empty()); ASSERT_NO_THROW(cap >> img); ASSERT_FALSE(img.empty()) << "idx=" << idx; ### Tricks embedded system, keep the software as small as possible, Embedding static elements in your application, [https://gstreamer.freedesktop.org/documentation/installing/index.html?gi- language=c](https://www.google.com/url?q=https%3A%2F%2Fgstreamer.freedesktop.org%2Fdocumentation%2Finstalling%2Findex.html%3Fgi- language%3Dc&sa=D&sntz=1&usg=AOvVaw1LbT7pN6fSH1VBpZ07TI2Y) ### Tips * copy mat to vector need clone() ### ### Example #include assert(!im.empty()); assert(x.size()==y.size()); assert(x.size()>2); #ifdef _DEBUG #endif #if true #else #endif # YouTube Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static opencv library for visual studio 2022 by using NuGet package manager just in few minutes #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah [https://github.com/xiaohongchen1991/meshgen](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxiaohongchen1991%2Fmeshgen&sa=D&sntz=1&usg=AOvVaw1J7_ZnsiBoB0T6RB0dNc6u) A set of C++ APIs are provided to mimic the same behaviors as the MATLAB function "linspace" and "meshgrid". Must-Read Books of All Time in Computer Vision and Machine Learning ![](https://lh6.googleusercontent.com/xmn3C-kogDWhYFajeyz5ut5C5cLqfb7h69EHu8Ugepm3dc1gqty- aVEoRWJvXvQ_aJbtlviOa76e8iH90wgfXawMGLybHWWuNxO3zKsqAL4oF1iv1X8Z-SiE6rS7qOz2xA=w1280) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. 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OpenCV 5 beta cvtest: Computer Vision Test Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Standard test for computer vision application Advanced OpenCV techniques: Advanced OpenCV techniques: Advanced OpenCV techniques: balance white Advanced OpenCV techniques: contrast and brightness Advanced subpixel techniques: Shift image content Mesh grid float mesh grid int main: Tips and Tricks of OpenCV that Nobody Told You Tricks Tips Save results Error Testing for OpenCV Projects Tricks Tips Example YouTube # NuGet - OpenCV 5 beta NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022 [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static opencv library for visual studio 2022 by using NuGet package manager just in few minutes #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) #OpenCV #Farshid_PirahanSiah #pirahansiah My NuGet packages comprised of two versions for different VS version. * Visual Studio 2019 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS2019_NuGet&sa=D&sntz=1&usg=AOvVaw0B-7f3iUYsMrp8SHvJGQb9) * Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7 * Visual Studio 2022 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS22_NuGet&sa=D&sntz=1&usg=AOvVaw2tWZP3KILcTESYrI2cdofl) * Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7 _**more:**_[ _ **https://www.pirahansiah.com/topics/opencv**_](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) _ ****_ # cvtest: Computer Vision Test ## Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Do you want to test your output of computer vision application which is video or images? ## Standard test for computer vision application There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil) download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: [](https://docs.google.com/presentation/d/14HX-99rGO9x1AtOnEigZ_2gAzZaPwvxqCEnnG0dk870/present "Open Presentation, cvtest in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/slides_32dp.png)cvtest # Advanced OpenCV techniques: sub-pixel, floating points, more precise, real-valued coordinates, Changing the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and Tricks of OpenCV that Nobody Told You download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: # Advanced OpenCV techniques: Cross correlation (CC): TM_CCORR Mean shifted cross correlation (Pearson correlation coefficient): TM_CCOEFF Normalization: TM_SQDIFF_NORMED, TM_CCORR_NORMED, TM_CCOEFF_NORMED maximum absolute difference metric (MaxAD), which is also known as the uniform distance metric computeECC() and findTransformECC(). Sum of absolute differences (SAD) Cross correlation (CC) find identical regions of an image that match a template, select by giving a threshold 2D convolution It simply slides the template image over the input image (as in 2D convolution) and compares the template and patch of input image under the template image. Template matching > cv2.TM_CCOEFF > cv2.TM_CCOEFF_NORMED > cv2.TM_CCORR > cv2.TM_CCORR_NORMED < cv2.TM_SQDIFF < cv2.TM_SQDIFF_NORMED cv2.minMaxLoc() more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: [https://docs.opencv.org/master/df/dfb/group__imgproc__object.html#gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2Fmaster%2Fdf%2Fdfb%2Fgroup__imgproc__object.html%23gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65&sa=D&sntz=1&usg=AOvVaw37X4mefDnUv8PmnZK0YdoM) [https://stackoverflow.com/questions/58158129/understanding-and-evaluating- template-matching- methods](https://www.google.com/url?q=https%3A%2F%2Fstackoverflow.com%2Fquestions%2F58158129%2Funderstanding- and-evaluating-template-matching- methods&sa=D&sntz=1&usg=AOvVaw08M8efI3a63Nn7qXHfZZEY) # Advanced OpenCV techniques: balance white balance white with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: void balance_white(cv::Mat mat) { double discard_ratio = 0.05; int hists[3][256]; memset(hists, 0, 3 * 256 * sizeof(int)); for (int y = 0; y < mat.rows; ++y) { uchar* ptr = mat.ptr(y); for (int x = 0; x < mat.cols; ++x) { for (int j = 0; j < 3; ++j) { hists[j][ptr[x * 3 + j]] += 1; } } } // cumulative hist int total = mat.cols * mat.rows; int vmin[3], vmax[3]; for (int i = 0; i < 3; ++i) { for (int j = 0; j < 255; ++j) { hists[i][j + 1] += hists[i][j]; } vmin[i] = 0; vmax[i] = 255; while (hists[i][vmin[i]] < discard_ratio * total) vmin[i] += 1; while (hists[i][vmax[i]] > (1 - discard_ratio) * total) vmax[i] -= 1; if (vmax[i] < 255 - 1) vmax[i] += 1; } for (int y = 0; y < mat.rows; ++y) { uchar* ptr = mat.ptr(y); for (int x = 0; x < mat.cols; ++x) { for (int j = 0; j < 3; ++j) { int val = ptr[x * 3 + j]; if (val < vmin[j]) val = vmin[j]; if (val > vmax[j]) val = vmax[j]; ptr[x * 3 + j] = static_cast((val - vmin[j]) * 255.0 / (vmax[j] - vmin[j])); } } } } reference http://www.ipol.im/pub/art/2011/llmps-scb/ https://gist.github.com/tomykaira/94472e9f4921ec2cf582 # Advanced OpenCV techniques: contrast and brightness sub-pixel, floating points, more precise, real-valued coordinates, Changing the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: showImage.convertTo(showImage, CV_32FC3); double alpha = 2.0; /*< Simple contrast control */ int beta = 100; /*< Simple brightness control */ for (int y = 0; y < showImage.rows; y++) { for (int x = 0; x < showImage.cols; x++) { for (int c = 0; c < showImage.channels(); c++) { showImage.at(y, x)[c] =cv::saturate_cast(alpha * showImage.at(y, x)[c] + beta); } } } showImage.convertTo(showImage, CV_8UC3); cv::imshow("Changing the contrast and brightness of an image! ", showImage); cv::waitKey(0); based on [https://docs.opencv.org/3.4/d3/dc1/tutorial_basic_linear_transform.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd3%2Fdc1%2Ftutorial_basic_linear_transform.html&sa=D&sntz=1&usg=AOvVaw15cmUCYNHZx6OdaoUN0tSB) [https://blog.csdn.net/u014230360/article/details/109802229](https://www.google.com/url?q=https%3A%2F%2Fblog.csdn.net%2Fu014230360%2Farticle%2Fdetails%2F109802229&sa=D&sntz=1&usg=AOvVaw1IxjJJewGlTNahnzI7pCX7) # Advanced subpixel techniques: Shift image content sub-pixel, floating points, mesh grid, remap, more precise, real-valued coordinates, moving image pixel, Shift image content with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: ## ## Mesh grid float static void meshgrid_float(const cv::Mat& xgv, const cv::Mat& ygv, cv::Mat1f& X, cv::Mat1f& Y) { cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X); cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y); } static void meshgrid_map_float(const cv::Range& xgv, const cv::Range& ygv, cv::Mat1f& X, cv::Mat1f& Y) { std::vector t_x, t_y; for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i); for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i); meshgrid_float(cv::Mat(t_x), cv::Mat(t_y), X, Y); } ## mesh grid int static void meshgrid(const cv::Mat& xgv, const cv::Mat& ygv, cv::Mat1i& X, cv::Mat1i& Y) { cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X); cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y); } static void meshgridTest(const cv::Range& xgv, const cv::Range& ygv, cv::Mat1i& X, cv::Mat1i& Y) { std::vector t_x, t_y; for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i); for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i); meshgrid(cv::Mat(t_x), cv::Mat(t_y), X, Y); } ## main: cv::Mat1f XF, YF; //for int //meshgrid_map_float(cv::Range(0, cols_main), cv::Range(0, rows_main), XF, YF); //for float meshgrid_map_float(cv::Range(0, cols_main-1), cv::Range(0, rows_main-1), XF, YF); for (int rowsImage = 0; rowsImage < rows_main; ++rowsImage) { for (int colsImage = 0; colsImage < cols_main; ++colsImage) { XF.at(rowsImage, colsImage) += offset1; YF.at(rowsImage, colsImage) += offset2; } } cv::remap(convert_img_i, dst, XF, YF, cv::INTER_LINEAR); if (show) { cv::Mat resizedImage = dst.clone(); dst.convertTo(resizedImage, CV_8UC3); cv::resize(resizedImage, resizedImage, cv::Size(), 0.5, 0.5); std::string nameWindow = " meshgrid and remap in float "; cv::imshow(nameWindow, resizedImage); cv::waitKey(0); } # Tips and Tricks of OpenCV that Nobody Told You Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: ### Tricks cv::multiply(outMat, cv::Scalar(gain, gain, gain), outMat); //color cv::multiply(outMat, cv::Scalar(gain), outMat); //grayscale //copy small Mat to bigger Mat cv::Rect roi( cv::Point( originX, originY ), smallImage.size() ); smallImage.copyTo( bigImage( roi ) ); ### Tips * copy mat to vector need clone() ### ### Save results * save image in float * cv::imwrite("image.exr", MatImage); * save image in uncompressed format : * cv::imwrite("fa.tiff", resizedImage, { cv::IMWRITE_TIFF_COMPRESSION, 1,cv::IMWRITE_TIFF_XDPI, 300,cv::IMWRITE_TIFF_YDPI,300 }); * * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) # None * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 5)) # LZW * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 8)) # Adobe Deflate * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 32946)) # Deflate * cv::imwrite("far.png", resizedImage, { cv::IMWRITE_PNG_COMPRESSION, 0 }); * Save images in streaming * int64 t0 = cv::getTickCount(); * std::string fileName= "fashid_"+std::to_string(t0)+ ".png"; * cv::imwrite(fileName, cimMat, { cv::IMWRITE_PNG_COMPRESSION, 0 }); * Save file name: * std::filesystem::path p = std::filesystem::path(files[i]).filename(); * std::string imgFile = savePath + "/" \+ p.string() + ".tiff"; * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) ; ### Error * Exception thrown at 0x00007FFB5CB21636 (vcruntime140d.dll) in farshid.exe: 0xC0000005: Access violation writing location 0x000002B09BAC4000. * check the size of Mat * cv::Mat meanImages = cv::Mat::zeros(rows_main, cols_main, CV_32F); * cv::Mat meanImages = cv::Mat::zeros(farshidMat.size(), CV_32F); * Linker Tools Error LNK2038 & Linker Tools Error LNK2001; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for '_ITERATOR_DEBUG_LEVEL': value '2' doesn't match value '0' in Source.obj ; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for 'RuntimeLibrary': value 'MTd_StaticDebug' doesn't match value 'MT_StaticRelease' * the link files are not match based on release or debug mode. # Testing for OpenCV Projects Test, C++, Computer Vision, Image Processing, more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: * Arrange, Act and Assert (AAA) Pattern * Google C++ Test Framework * Assertions Types and Test Fixtures * ASSERT_FALSE(frame.empty()); ASSERT_NO_THROW(cap >> img); ASSERT_FALSE(img.empty()) << "idx=" << idx; ### Tricks embedded system, keep the software as small as possible, Embedding static elements in your application, [https://gstreamer.freedesktop.org/documentation/installing/index.html?gi- language=c](https://www.google.com/url?q=https%3A%2F%2Fgstreamer.freedesktop.org%2Fdocumentation%2Finstalling%2Findex.html%3Fgi- language%3Dc&sa=D&sntz=1&usg=AOvVaw1LbT7pN6fSH1VBpZ07TI2Y) ### Tips * copy mat to vector need clone() ### ### Example #include assert(!im.empty()); assert(x.size()==y.size()); assert(x.size()>2); #ifdef _DEBUG #endif #if true #else #endif # YouTube Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static opencv library for visual studio 2022 by using NuGet package manager just in few minutes #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah [https://github.com/xiaohongchen1991/meshgen](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxiaohongchen1991%2Fmeshgen&sa=D&sntz=1&usg=AOvVaw1J7_ZnsiBoB0T6RB0dNc6u) A set of C++ APIs are provided to mimic the same behaviors as the MATLAB function "linspace" and "meshgrid". 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OpenCV 5 beta cvtest: Computer Vision Test Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Standard test for computer vision application Advanced OpenCV techniques: Advanced OpenCV techniques: Advanced OpenCV techniques: balance white Advanced OpenCV techniques: contrast and brightness Advanced subpixel techniques: Shift image content Mesh grid float mesh grid int main: Tips and Tricks of OpenCV that Nobody Told You Tricks Tips Save results Error Testing for OpenCV Projects Tricks Tips Example YouTube # NuGet - OpenCV 5 beta NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022 [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static opencv library for visual studio 2022 by using NuGet package manager just in few minutes #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) #OpenCV #Farshid_PirahanSiah #pirahansiah My NuGet packages comprised of two versions for different VS version. * Visual Studio 2019 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS2019_NuGet&sa=D&sntz=1&usg=AOvVaw0B-7f3iUYsMrp8SHvJGQb9) * Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7 * Visual Studio 2022 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS22_NuGet&sa=D&sntz=1&usg=AOvVaw2tWZP3KILcTESYrI2cdofl) * Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7 _**more:**_[ _ **https://www.pirahansiah.com/topics/opencv**_](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) _ ****_ # cvtest: Computer Vision Test ## Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Do you want to test your output of computer vision application which is video or images? ## Standard test for computer vision application There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil) download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: [](https://docs.google.com/presentation/d/14HX-99rGO9x1AtOnEigZ_2gAzZaPwvxqCEnnG0dk870/present "Open Presentation, cvtest in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/slides_32dp.png)cvtest # Advanced OpenCV techniques: sub-pixel, floating points, more precise, real-valued coordinates, Changing the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and Tricks of OpenCV that Nobody Told You download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: # Advanced OpenCV techniques: Cross correlation (CC): TM_CCORR Mean shifted cross correlation (Pearson correlation coefficient): TM_CCOEFF Normalization: TM_SQDIFF_NORMED, TM_CCORR_NORMED, TM_CCOEFF_NORMED maximum absolute difference metric (MaxAD), which is also known as the uniform distance metric computeECC() and findTransformECC(). Sum of absolute differences (SAD) Cross correlation (CC) find identical regions of an image that match a template, select by giving a threshold 2D convolution It simply slides the template image over the input image (as in 2D convolution) and compares the template and patch of input image under the template image. Template matching > cv2.TM_CCOEFF > cv2.TM_CCOEFF_NORMED > cv2.TM_CCORR > cv2.TM_CCORR_NORMED < cv2.TM_SQDIFF < cv2.TM_SQDIFF_NORMED cv2.minMaxLoc() more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: [https://docs.opencv.org/master/df/dfb/group__imgproc__object.html#gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2Fmaster%2Fdf%2Fdfb%2Fgroup__imgproc__object.html%23gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65&sa=D&sntz=1&usg=AOvVaw37X4mefDnUv8PmnZK0YdoM) [https://stackoverflow.com/questions/58158129/understanding-and-evaluating- template-matching- methods](https://www.google.com/url?q=https%3A%2F%2Fstackoverflow.com%2Fquestions%2F58158129%2Funderstanding- and-evaluating-template-matching- methods&sa=D&sntz=1&usg=AOvVaw08M8efI3a63Nn7qXHfZZEY) # Advanced OpenCV techniques: balance white balance white with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: void balance_white(cv::Mat mat) { double discard_ratio = 0.05; int hists[3][256]; memset(hists, 0, 3 * 256 * sizeof(int)); for (int y = 0; y < mat.rows; ++y) { uchar* ptr = mat.ptr(y); for (int x = 0; x < mat.cols; ++x) { for (int j = 0; j < 3; ++j) { hists[j][ptr[x * 3 + j]] += 1; } } } // cumulative hist int total = mat.cols * mat.rows; int vmin[3], vmax[3]; for (int i = 0; i < 3; ++i) { for (int j = 0; j < 255; ++j) { hists[i][j + 1] += hists[i][j]; } vmin[i] = 0; vmax[i] = 255; while (hists[i][vmin[i]] < discard_ratio * total) vmin[i] += 1; while (hists[i][vmax[i]] > (1 - discard_ratio) * total) vmax[i] -= 1; if (vmax[i] < 255 - 1) vmax[i] += 1; } for (int y = 0; y < mat.rows; ++y) { uchar* ptr = mat.ptr(y); for (int x = 0; x < mat.cols; ++x) { for (int j = 0; j < 3; ++j) { int val = ptr[x * 3 + j]; if (val < vmin[j]) val = vmin[j]; if (val > vmax[j]) val = vmax[j]; ptr[x * 3 + j] = static_cast((val - vmin[j]) * 255.0 / (vmax[j] - vmin[j])); } } } } reference http://www.ipol.im/pub/art/2011/llmps-scb/ https://gist.github.com/tomykaira/94472e9f4921ec2cf582 # Advanced OpenCV techniques: contrast and brightness sub-pixel, floating points, more precise, real-valued coordinates, Changing the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: showImage.convertTo(showImage, CV_32FC3); double alpha = 2.0; /*< Simple contrast control */ int beta = 100; /*< Simple brightness control */ for (int y = 0; y < showImage.rows; y++) { for (int x = 0; x < showImage.cols; x++) { for (int c = 0; c < showImage.channels(); c++) { showImage.at(y, x)[c] =cv::saturate_cast(alpha * showImage.at(y, x)[c] + beta); } } } showImage.convertTo(showImage, CV_8UC3); cv::imshow("Changing the contrast and brightness of an image! ", showImage); cv::waitKey(0); based on [https://docs.opencv.org/3.4/d3/dc1/tutorial_basic_linear_transform.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd3%2Fdc1%2Ftutorial_basic_linear_transform.html&sa=D&sntz=1&usg=AOvVaw15cmUCYNHZx6OdaoUN0tSB) [https://blog.csdn.net/u014230360/article/details/109802229](https://www.google.com/url?q=https%3A%2F%2Fblog.csdn.net%2Fu014230360%2Farticle%2Fdetails%2F109802229&sa=D&sntz=1&usg=AOvVaw1IxjJJewGlTNahnzI7pCX7) # Advanced subpixel techniques: Shift image content sub-pixel, floating points, mesh grid, remap, more precise, real-valued coordinates, moving image pixel, Shift image content with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: ## ## Mesh grid float static void meshgrid_float(const cv::Mat& xgv, const cv::Mat& ygv, cv::Mat1f& X, cv::Mat1f& Y) { cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X); cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y); } static void meshgrid_map_float(const cv::Range& xgv, const cv::Range& ygv, cv::Mat1f& X, cv::Mat1f& Y) { std::vector t_x, t_y; for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i); for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i); meshgrid_float(cv::Mat(t_x), cv::Mat(t_y), X, Y); } ## mesh grid int static void meshgrid(const cv::Mat& xgv, const cv::Mat& ygv, cv::Mat1i& X, cv::Mat1i& Y) { cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X); cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y); } static void meshgridTest(const cv::Range& xgv, const cv::Range& ygv, cv::Mat1i& X, cv::Mat1i& Y) { std::vector t_x, t_y; for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i); for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i); meshgrid(cv::Mat(t_x), cv::Mat(t_y), X, Y); } ## main: cv::Mat1f XF, YF; //for int //meshgrid_map_float(cv::Range(0, cols_main), cv::Range(0, rows_main), XF, YF); //for float meshgrid_map_float(cv::Range(0, cols_main-1), cv::Range(0, rows_main-1), XF, YF); for (int rowsImage = 0; rowsImage < rows_main; ++rowsImage) { for (int colsImage = 0; colsImage < cols_main; ++colsImage) { XF.at(rowsImage, colsImage) += offset1; YF.at(rowsImage, colsImage) += offset2; } } cv::remap(convert_img_i, dst, XF, YF, cv::INTER_LINEAR); if (show) { cv::Mat resizedImage = dst.clone(); dst.convertTo(resizedImage, CV_8UC3); cv::resize(resizedImage, resizedImage, cv::Size(), 0.5, 0.5); std::string nameWindow = " meshgrid and remap in float "; cv::imshow(nameWindow, resizedImage); cv::waitKey(0); } # Tips and Tricks of OpenCV that Nobody Told You Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: ### Tricks cv::multiply(outMat, cv::Scalar(gain, gain, gain), outMat); //color cv::multiply(outMat, cv::Scalar(gain), outMat); //grayscale //copy small Mat to bigger Mat cv::Rect roi( cv::Point( originX, originY ), smallImage.size() ); smallImage.copyTo( bigImage( roi ) ); ### Tips * copy mat to vector need clone() ### ### Save results * save image in float * cv::imwrite("image.exr", MatImage); * save image in uncompressed format : * cv::imwrite("fa.tiff", resizedImage, { cv::IMWRITE_TIFF_COMPRESSION, 1,cv::IMWRITE_TIFF_XDPI, 300,cv::IMWRITE_TIFF_YDPI,300 }); * * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) # None * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 5)) # LZW * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 8)) # Adobe Deflate * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 32946)) # Deflate * cv::imwrite("far.png", resizedImage, { cv::IMWRITE_PNG_COMPRESSION, 0 }); * Save images in streaming * int64 t0 = cv::getTickCount(); * std::string fileName= "fashid_"+std::to_string(t0)+ ".png"; * cv::imwrite(fileName, cimMat, { cv::IMWRITE_PNG_COMPRESSION, 0 }); * Save file name: * std::filesystem::path p = std::filesystem::path(files[i]).filename(); * std::string imgFile = savePath + "/" \+ p.string() + ".tiff"; * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) ; ### Error * Exception thrown at 0x00007FFB5CB21636 (vcruntime140d.dll) in farshid.exe: 0xC0000005: Access violation writing location 0x000002B09BAC4000. * check the size of Mat * cv::Mat meanImages = cv::Mat::zeros(rows_main, cols_main, CV_32F); * cv::Mat meanImages = cv::Mat::zeros(farshidMat.size(), CV_32F); * Linker Tools Error LNK2038 & Linker Tools Error LNK2001; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for '_ITERATOR_DEBUG_LEVEL': value '2' doesn't match value '0' in Source.obj ; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for 'RuntimeLibrary': value 'MTd_StaticDebug' doesn't match value 'MT_StaticRelease' * the link files are not match based on release or debug mode. # Testing for OpenCV Projects Test, C++, Computer Vision, Image Processing, more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: * Arrange, Act and Assert (AAA) Pattern * Google C++ Test Framework * Assertions Types and Test Fixtures * ASSERT_FALSE(frame.empty()); ASSERT_NO_THROW(cap >> img); ASSERT_FALSE(img.empty()) << "idx=" << idx; ### Tricks embedded system, keep the software as small as possible, Embedding static elements in your application, [https://gstreamer.freedesktop.org/documentation/installing/index.html?gi- language=c](https://www.google.com/url?q=https%3A%2F%2Fgstreamer.freedesktop.org%2Fdocumentation%2Finstalling%2Findex.html%3Fgi- language%3Dc&sa=D&sntz=1&usg=AOvVaw1LbT7pN6fSH1VBpZ07TI2Y) ### Tips * copy mat to vector need clone() ### ### Example #include assert(!im.empty()); assert(x.size()==y.size()); assert(x.size()>2); #ifdef _DEBUG #endif #if true #else #endif # YouTube Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static opencv library for visual studio 2022 by using NuGet package manager just in few minutes #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah [https://github.com/xiaohongchen1991/meshgen](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxiaohongchen1991%2Fmeshgen&sa=D&sntz=1&usg=AOvVaw1J7_ZnsiBoB0T6RB0dNc6u) A set of C++ APIs are provided to mimic the same behaviors as the MATLAB function "linspace" and "meshgrid". 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OpenCV 5 beta cvtest: Computer Vision Test Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Standard test for computer vision application Advanced OpenCV techniques: Advanced OpenCV techniques: Advanced OpenCV techniques: balance white Advanced OpenCV techniques: contrast and brightness Advanced subpixel techniques: Shift image content Mesh grid float mesh grid int main: Tips and Tricks of OpenCV that Nobody Told You Tricks Tips Save results Error Testing for OpenCV Projects Tricks Tips Example YouTube # NuGet - OpenCV 5 beta NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022 [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static opencv library for visual studio 2022 by using NuGet package manager just in few minutes #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) #OpenCV #Farshid_PirahanSiah #pirahansiah My NuGet packages comprised of two versions for different VS version. * Visual Studio 2019 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS2019_NuGet&sa=D&sntz=1&usg=AOvVaw0B-7f3iUYsMrp8SHvJGQb9) * Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7 * Visual Studio 2022 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS22_NuGet&sa=D&sntz=1&usg=AOvVaw2tWZP3KILcTESYrI2cdofl) * Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7 _**more:**_[ _ **https://www.pirahansiah.com/topics/opencv**_](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) _ ****_ # cvtest: Computer Vision Test ## Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Do you want to test your output of computer vision application which is video or images? ## Standard test for computer vision application There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil) download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: [](https://docs.google.com/presentation/d/14HX-99rGO9x1AtOnEigZ_2gAzZaPwvxqCEnnG0dk870/present "Open Presentation, cvtest in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/slides_32dp.png)cvtest # Advanced OpenCV techniques: sub-pixel, floating points, more precise, real-valued coordinates, Changing the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and Tricks of OpenCV that Nobody Told You download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: # Advanced OpenCV techniques: Cross correlation (CC): TM_CCORR Mean shifted cross correlation (Pearson correlation coefficient): TM_CCOEFF Normalization: TM_SQDIFF_NORMED, TM_CCORR_NORMED, TM_CCOEFF_NORMED maximum absolute difference metric (MaxAD), which is also known as the uniform distance metric computeECC() and findTransformECC(). Sum of absolute differences (SAD) Cross correlation (CC) find identical regions of an image that match a template, select by giving a threshold 2D convolution It simply slides the template image over the input image (as in 2D convolution) and compares the template and patch of input image under the template image. Template matching > cv2.TM_CCOEFF > cv2.TM_CCOEFF_NORMED > cv2.TM_CCORR > cv2.TM_CCORR_NORMED < cv2.TM_SQDIFF < cv2.TM_SQDIFF_NORMED cv2.minMaxLoc() more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: [https://docs.opencv.org/master/df/dfb/group__imgproc__object.html#gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2Fmaster%2Fdf%2Fdfb%2Fgroup__imgproc__object.html%23gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65&sa=D&sntz=1&usg=AOvVaw37X4mefDnUv8PmnZK0YdoM) [https://stackoverflow.com/questions/58158129/understanding-and-evaluating- template-matching- methods](https://www.google.com/url?q=https%3A%2F%2Fstackoverflow.com%2Fquestions%2F58158129%2Funderstanding- and-evaluating-template-matching- methods&sa=D&sntz=1&usg=AOvVaw08M8efI3a63Nn7qXHfZZEY) # Advanced OpenCV techniques: balance white balance white with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: void balance_white(cv::Mat mat) { double discard_ratio = 0.05; int hists[3][256]; memset(hists, 0, 3 * 256 * sizeof(int)); for (int y = 0; y < mat.rows; ++y) { uchar* ptr = mat.ptr(y); for (int x = 0; x < mat.cols; ++x) { for (int j = 0; j < 3; ++j) { hists[j][ptr[x * 3 + j]] += 1; } } } // cumulative hist int total = mat.cols * mat.rows; int vmin[3], vmax[3]; for (int i = 0; i < 3; ++i) { for (int j = 0; j < 255; ++j) { hists[i][j + 1] += hists[i][j]; } vmin[i] = 0; vmax[i] = 255; while (hists[i][vmin[i]] < discard_ratio * total) vmin[i] += 1; while (hists[i][vmax[i]] > (1 - discard_ratio) * total) vmax[i] -= 1; if (vmax[i] < 255 - 1) vmax[i] += 1; } for (int y = 0; y < mat.rows; ++y) { uchar* ptr = mat.ptr(y); for (int x = 0; x < mat.cols; ++x) { for (int j = 0; j < 3; ++j) { int val = ptr[x * 3 + j]; if (val < vmin[j]) val = vmin[j]; if (val > vmax[j]) val = vmax[j]; ptr[x * 3 + j] = static_cast((val - vmin[j]) * 255.0 / (vmax[j] - vmin[j])); } } } } reference http://www.ipol.im/pub/art/2011/llmps-scb/ https://gist.github.com/tomykaira/94472e9f4921ec2cf582 # Advanced OpenCV techniques: contrast and brightness sub-pixel, floating points, more precise, real-valued coordinates, Changing the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: showImage.convertTo(showImage, CV_32FC3); double alpha = 2.0; /*< Simple contrast control */ int beta = 100; /*< Simple brightness control */ for (int y = 0; y < showImage.rows; y++) { for (int x = 0; x < showImage.cols; x++) { for (int c = 0; c < showImage.channels(); c++) { showImage.at(y, x)[c] =cv::saturate_cast(alpha * showImage.at(y, x)[c] + beta); } } } showImage.convertTo(showImage, CV_8UC3); cv::imshow("Changing the contrast and brightness of an image! ", showImage); cv::waitKey(0); based on [https://docs.opencv.org/3.4/d3/dc1/tutorial_basic_linear_transform.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd3%2Fdc1%2Ftutorial_basic_linear_transform.html&sa=D&sntz=1&usg=AOvVaw15cmUCYNHZx6OdaoUN0tSB) [https://blog.csdn.net/u014230360/article/details/109802229](https://www.google.com/url?q=https%3A%2F%2Fblog.csdn.net%2Fu014230360%2Farticle%2Fdetails%2F109802229&sa=D&sntz=1&usg=AOvVaw1IxjJJewGlTNahnzI7pCX7) # Advanced subpixel techniques: Shift image content sub-pixel, floating points, mesh grid, remap, more precise, real-valued coordinates, moving image pixel, Shift image content with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: ## ## Mesh grid float static void meshgrid_float(const cv::Mat& xgv, const cv::Mat& ygv, cv::Mat1f& X, cv::Mat1f& Y) { cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X); cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y); } static void meshgrid_map_float(const cv::Range& xgv, const cv::Range& ygv, cv::Mat1f& X, cv::Mat1f& Y) { std::vector t_x, t_y; for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i); for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i); meshgrid_float(cv::Mat(t_x), cv::Mat(t_y), X, Y); } ## mesh grid int static void meshgrid(const cv::Mat& xgv, const cv::Mat& ygv, cv::Mat1i& X, cv::Mat1i& Y) { cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X); cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y); } static void meshgridTest(const cv::Range& xgv, const cv::Range& ygv, cv::Mat1i& X, cv::Mat1i& Y) { std::vector t_x, t_y; for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i); for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i); meshgrid(cv::Mat(t_x), cv::Mat(t_y), X, Y); } ## main: cv::Mat1f XF, YF; //for int //meshgrid_map_float(cv::Range(0, cols_main), cv::Range(0, rows_main), XF, YF); //for float meshgrid_map_float(cv::Range(0, cols_main-1), cv::Range(0, rows_main-1), XF, YF); for (int rowsImage = 0; rowsImage < rows_main; ++rowsImage) { for (int colsImage = 0; colsImage < cols_main; ++colsImage) { XF.at(rowsImage, colsImage) += offset1; YF.at(rowsImage, colsImage) += offset2; } } cv::remap(convert_img_i, dst, XF, YF, cv::INTER_LINEAR); if (show) { cv::Mat resizedImage = dst.clone(); dst.convertTo(resizedImage, CV_8UC3); cv::resize(resizedImage, resizedImage, cv::Size(), 0.5, 0.5); std::string nameWindow = " meshgrid and remap in float "; cv::imshow(nameWindow, resizedImage); cv::waitKey(0); } # Tips and Tricks of OpenCV that Nobody Told You Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: ### Tricks cv::multiply(outMat, cv::Scalar(gain, gain, gain), outMat); //color cv::multiply(outMat, cv::Scalar(gain), outMat); //grayscale //copy small Mat to bigger Mat cv::Rect roi( cv::Point( originX, originY ), smallImage.size() ); smallImage.copyTo( bigImage( roi ) ); ### Tips * copy mat to vector need clone() ### ### Save results * save image in float * cv::imwrite("image.exr", MatImage); * save image in uncompressed format : * cv::imwrite("fa.tiff", resizedImage, { cv::IMWRITE_TIFF_COMPRESSION, 1,cv::IMWRITE_TIFF_XDPI, 300,cv::IMWRITE_TIFF_YDPI,300 }); * * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) # None * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 5)) # LZW * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 8)) # Adobe Deflate * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 32946)) # Deflate * cv::imwrite("far.png", resizedImage, { cv::IMWRITE_PNG_COMPRESSION, 0 }); * Save images in streaming * int64 t0 = cv::getTickCount(); * std::string fileName= "fashid_"+std::to_string(t0)+ ".png"; * cv::imwrite(fileName, cimMat, { cv::IMWRITE_PNG_COMPRESSION, 0 }); * Save file name: * std::filesystem::path p = std::filesystem::path(files[i]).filename(); * std::string imgFile = savePath + "/" \+ p.string() + ".tiff"; * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) ; ### Error * Exception thrown at 0x00007FFB5CB21636 (vcruntime140d.dll) in farshid.exe: 0xC0000005: Access violation writing location 0x000002B09BAC4000. * check the size of Mat * cv::Mat meanImages = cv::Mat::zeros(rows_main, cols_main, CV_32F); * cv::Mat meanImages = cv::Mat::zeros(farshidMat.size(), CV_32F); * Linker Tools Error LNK2038 & Linker Tools Error LNK2001; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for '_ITERATOR_DEBUG_LEVEL': value '2' doesn't match value '0' in Source.obj ; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for 'RuntimeLibrary': value 'MTd_StaticDebug' doesn't match value 'MT_StaticRelease' * the link files are not match based on release or debug mode. # Testing for OpenCV Projects Test, C++, Computer Vision, Image Processing, more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: * Arrange, Act and Assert (AAA) Pattern * Google C++ Test Framework * Assertions Types and Test Fixtures * ASSERT_FALSE(frame.empty()); ASSERT_NO_THROW(cap >> img); ASSERT_FALSE(img.empty()) << "idx=" << idx; ### Tricks embedded system, keep the software as small as possible, Embedding static elements in your application, [https://gstreamer.freedesktop.org/documentation/installing/index.html?gi- language=c](https://www.google.com/url?q=https%3A%2F%2Fgstreamer.freedesktop.org%2Fdocumentation%2Finstalling%2Findex.html%3Fgi- language%3Dc&sa=D&sntz=1&usg=AOvVaw1LbT7pN6fSH1VBpZ07TI2Y) ### Tips * copy mat to vector need clone() ### ### Example #include assert(!im.empty()); assert(x.size()==y.size()); assert(x.size()>2); #ifdef _DEBUG #endif #if true #else #endif # YouTube Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static opencv library for visual studio 2022 by using NuGet package manager just in few minutes #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah [https://github.com/xiaohongchen1991/meshgen](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxiaohongchen1991%2Fmeshgen&sa=D&sntz=1&usg=AOvVaw1J7_ZnsiBoB0T6RB0dNc6u) A set of C++ APIs are provided to mimic the same behaviors as the MATLAB function "linspace" and "meshgrid". Must-Read Books of All Time in Computer Vision and Machine Learning ![](https://lh3.googleusercontent.com/PLi43Jd4dmRotHI9WORIOIM0is9DI0VwayDejuipt_znoABjXDj5gQsqYAaLzgiMGTYz6mcc9DoAdCsX3_dGGq-n5stRfl65U7V-t7TX3jPwKE8mmst4BCcaf_u- _skIQg=w1280) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. 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OpenCV 5 beta cvtest: Computer Vision Test Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Standard test for computer vision application Advanced OpenCV techniques: Advanced OpenCV techniques: Advanced OpenCV techniques: balance white Advanced OpenCV techniques: contrast and brightness Advanced subpixel techniques: Shift image content Mesh grid float mesh grid int main: Tips and Tricks of OpenCV that Nobody Told You Tricks Tips Save results Error Testing for OpenCV Projects Tricks Tips Example YouTube # NuGet - OpenCV 5 beta NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022 [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static opencv library for visual studio 2022 by using NuGet package manager just in few minutes #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) #OpenCV #Farshid_PirahanSiah #pirahansiah My NuGet packages comprised of two versions for different VS version. * Visual Studio 2019 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS2019_NuGet&sa=D&sntz=1&usg=AOvVaw0B-7f3iUYsMrp8SHvJGQb9) * Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7 * Visual Studio 2022 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS22_NuGet&sa=D&sntz=1&usg=AOvVaw2tWZP3KILcTESYrI2cdofl) * Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7 _**more:**_[ _ **https://www.pirahansiah.com/topics/opencv**_](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) _ ****_ # cvtest: Computer Vision Test ## Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Do you want to test your output of computer vision application which is video or images? ## Standard test for computer vision application There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil) download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: [](https://docs.google.com/presentation/d/14HX-99rGO9x1AtOnEigZ_2gAzZaPwvxqCEnnG0dk870/present "Open Presentation, cvtest in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/slides_32dp.png)cvtest # Advanced OpenCV techniques: sub-pixel, floating points, more precise, real-valued coordinates, Changing the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and Tricks of OpenCV that Nobody Told You download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: # Advanced OpenCV techniques: Cross correlation (CC): TM_CCORR Mean shifted cross correlation (Pearson correlation coefficient): TM_CCOEFF Normalization: TM_SQDIFF_NORMED, TM_CCORR_NORMED, TM_CCOEFF_NORMED maximum absolute difference metric (MaxAD), which is also known as the uniform distance metric computeECC() and findTransformECC(). Sum of absolute differences (SAD) Cross correlation (CC) find identical regions of an image that match a template, select by giving a threshold 2D convolution It simply slides the template image over the input image (as in 2D convolution) and compares the template and patch of input image under the template image. Template matching > cv2.TM_CCOEFF > cv2.TM_CCOEFF_NORMED > cv2.TM_CCORR > cv2.TM_CCORR_NORMED < cv2.TM_SQDIFF < cv2.TM_SQDIFF_NORMED cv2.minMaxLoc() more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: [https://docs.opencv.org/master/df/dfb/group__imgproc__object.html#gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2Fmaster%2Fdf%2Fdfb%2Fgroup__imgproc__object.html%23gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65&sa=D&sntz=1&usg=AOvVaw37X4mefDnUv8PmnZK0YdoM) [https://stackoverflow.com/questions/58158129/understanding-and-evaluating- template-matching- methods](https://www.google.com/url?q=https%3A%2F%2Fstackoverflow.com%2Fquestions%2F58158129%2Funderstanding- and-evaluating-template-matching- methods&sa=D&sntz=1&usg=AOvVaw08M8efI3a63Nn7qXHfZZEY) # Advanced OpenCV techniques: balance white balance white with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: void balance_white(cv::Mat mat) { double discard_ratio = 0.05; int hists[3][256]; memset(hists, 0, 3 * 256 * sizeof(int)); for (int y = 0; y < mat.rows; ++y) { uchar* ptr = mat.ptr(y); for (int x = 0; x < mat.cols; ++x) { for (int j = 0; j < 3; ++j) { hists[j][ptr[x * 3 + j]] += 1; } } } // cumulative hist int total = mat.cols * mat.rows; int vmin[3], vmax[3]; for (int i = 0; i < 3; ++i) { for (int j = 0; j < 255; ++j) { hists[i][j + 1] += hists[i][j]; } vmin[i] = 0; vmax[i] = 255; while (hists[i][vmin[i]] < discard_ratio * total) vmin[i] += 1; while (hists[i][vmax[i]] > (1 - discard_ratio) * total) vmax[i] -= 1; if (vmax[i] < 255 - 1) vmax[i] += 1; } for (int y = 0; y < mat.rows; ++y) { uchar* ptr = mat.ptr(y); for (int x = 0; x < mat.cols; ++x) { for (int j = 0; j < 3; ++j) { int val = ptr[x * 3 + j]; if (val < vmin[j]) val = vmin[j]; if (val > vmax[j]) val = vmax[j]; ptr[x * 3 + j] = static_cast((val - vmin[j]) * 255.0 / (vmax[j] - vmin[j])); } } } } reference http://www.ipol.im/pub/art/2011/llmps-scb/ https://gist.github.com/tomykaira/94472e9f4921ec2cf582 # Advanced OpenCV techniques: contrast and brightness sub-pixel, floating points, more precise, real-valued coordinates, Changing the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: showImage.convertTo(showImage, CV_32FC3); double alpha = 2.0; /*< Simple contrast control */ int beta = 100; /*< Simple brightness control */ for (int y = 0; y < showImage.rows; y++) { for (int x = 0; x < showImage.cols; x++) { for (int c = 0; c < showImage.channels(); c++) { showImage.at(y, x)[c] =cv::saturate_cast(alpha * showImage.at(y, x)[c] + beta); } } } showImage.convertTo(showImage, CV_8UC3); cv::imshow("Changing the contrast and brightness of an image! ", showImage); cv::waitKey(0); based on [https://docs.opencv.org/3.4/d3/dc1/tutorial_basic_linear_transform.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd3%2Fdc1%2Ftutorial_basic_linear_transform.html&sa=D&sntz=1&usg=AOvVaw15cmUCYNHZx6OdaoUN0tSB) [https://blog.csdn.net/u014230360/article/details/109802229](https://www.google.com/url?q=https%3A%2F%2Fblog.csdn.net%2Fu014230360%2Farticle%2Fdetails%2F109802229&sa=D&sntz=1&usg=AOvVaw1IxjJJewGlTNahnzI7pCX7) # Advanced subpixel techniques: Shift image content sub-pixel, floating points, mesh grid, remap, more precise, real-valued coordinates, moving image pixel, Shift image content with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: ## ## Mesh grid float static void meshgrid_float(const cv::Mat& xgv, const cv::Mat& ygv, cv::Mat1f& X, cv::Mat1f& Y) { cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X); cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y); } static void meshgrid_map_float(const cv::Range& xgv, const cv::Range& ygv, cv::Mat1f& X, cv::Mat1f& Y) { std::vector t_x, t_y; for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i); for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i); meshgrid_float(cv::Mat(t_x), cv::Mat(t_y), X, Y); } ## mesh grid int static void meshgrid(const cv::Mat& xgv, const cv::Mat& ygv, cv::Mat1i& X, cv::Mat1i& Y) { cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X); cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y); } static void meshgridTest(const cv::Range& xgv, const cv::Range& ygv, cv::Mat1i& X, cv::Mat1i& Y) { std::vector t_x, t_y; for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i); for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i); meshgrid(cv::Mat(t_x), cv::Mat(t_y), X, Y); } ## main: cv::Mat1f XF, YF; //for int //meshgrid_map_float(cv::Range(0, cols_main), cv::Range(0, rows_main), XF, YF); //for float meshgrid_map_float(cv::Range(0, cols_main-1), cv::Range(0, rows_main-1), XF, YF); for (int rowsImage = 0; rowsImage < rows_main; ++rowsImage) { for (int colsImage = 0; colsImage < cols_main; ++colsImage) { XF.at(rowsImage, colsImage) += offset1; YF.at(rowsImage, colsImage) += offset2; } } cv::remap(convert_img_i, dst, XF, YF, cv::INTER_LINEAR); if (show) { cv::Mat resizedImage = dst.clone(); dst.convertTo(resizedImage, CV_8UC3); cv::resize(resizedImage, resizedImage, cv::Size(), 0.5, 0.5); std::string nameWindow = " meshgrid and remap in float "; cv::imshow(nameWindow, resizedImage); cv::waitKey(0); } # Tips and Tricks of OpenCV that Nobody Told You Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: ### Tricks cv::multiply(outMat, cv::Scalar(gain, gain, gain), outMat); //color cv::multiply(outMat, cv::Scalar(gain), outMat); //grayscale //copy small Mat to bigger Mat cv::Rect roi( cv::Point( originX, originY ), smallImage.size() ); smallImage.copyTo( bigImage( roi ) ); ### Tips * copy mat to vector need clone() ### ### Save results * save image in float * cv::imwrite("image.exr", MatImage); * save image in uncompressed format : * cv::imwrite("fa.tiff", resizedImage, { cv::IMWRITE_TIFF_COMPRESSION, 1,cv::IMWRITE_TIFF_XDPI, 300,cv::IMWRITE_TIFF_YDPI,300 }); * * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) # None * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 5)) # LZW * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 8)) # Adobe Deflate * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 32946)) # Deflate * cv::imwrite("far.png", resizedImage, { cv::IMWRITE_PNG_COMPRESSION, 0 }); * Save images in streaming * int64 t0 = cv::getTickCount(); * std::string fileName= "fashid_"+std::to_string(t0)+ ".png"; * cv::imwrite(fileName, cimMat, { cv::IMWRITE_PNG_COMPRESSION, 0 }); * Save file name: * std::filesystem::path p = std::filesystem::path(files[i]).filename(); * std::string imgFile = savePath + "/" \+ p.string() + ".tiff"; * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) ; ### Error * Exception thrown at 0x00007FFB5CB21636 (vcruntime140d.dll) in farshid.exe: 0xC0000005: Access violation writing location 0x000002B09BAC4000. * check the size of Mat * cv::Mat meanImages = cv::Mat::zeros(rows_main, cols_main, CV_32F); * cv::Mat meanImages = cv::Mat::zeros(farshidMat.size(), CV_32F); * Linker Tools Error LNK2038 & Linker Tools Error LNK2001; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for '_ITERATOR_DEBUG_LEVEL': value '2' doesn't match value '0' in Source.obj ; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for 'RuntimeLibrary': value 'MTd_StaticDebug' doesn't match value 'MT_StaticRelease' * the link files are not match based on release or debug mode. # Testing for OpenCV Projects Test, C++, Computer Vision, Image Processing, more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: * Arrange, Act and Assert (AAA) Pattern * Google C++ Test Framework * Assertions Types and Test Fixtures * ASSERT_FALSE(frame.empty()); ASSERT_NO_THROW(cap >> img); ASSERT_FALSE(img.empty()) << "idx=" << idx; ### Tricks embedded system, keep the software as small as possible, Embedding static elements in your application, [https://gstreamer.freedesktop.org/documentation/installing/index.html?gi- language=c](https://www.google.com/url?q=https%3A%2F%2Fgstreamer.freedesktop.org%2Fdocumentation%2Finstalling%2Findex.html%3Fgi- language%3Dc&sa=D&sntz=1&usg=AOvVaw1LbT7pN6fSH1VBpZ07TI2Y) ### Tips * copy mat to vector need clone() ### ### Example #include assert(!im.empty()); assert(x.size()==y.size()); assert(x.size()>2); #ifdef _DEBUG #endif #if true #else #endif # YouTube Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static opencv library for visual studio 2022 by using NuGet package manager just in few minutes #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah [https://github.com/xiaohongchen1991/meshgen](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxiaohongchen1991%2Fmeshgen&sa=D&sntz=1&usg=AOvVaw1J7_ZnsiBoB0T6RB0dNc6u) A set of C++ APIs are provided to mimic the same behaviors as the MATLAB function "linspace" and "meshgrid". Must-Read Books of All Time in Computer Vision and Machine Learning ![](https://lh3.googleusercontent.com/PLi43Jd4dmRotHI9WORIOIM0is9DI0VwayDejuipt_znoABjXDj5gQsqYAaLzgiMGTYz6mcc9DoAdCsX3_dGGq-n5stRfl65U7V-t7TX3jPwKE8mmst4BCcaf_u- _skIQg=w1280) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. 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OpenCV 5 beta cvtest: Computer Vision Test Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Standard test for computer vision application Advanced OpenCV techniques: Advanced OpenCV techniques: Advanced OpenCV techniques: balance white Advanced OpenCV techniques: contrast and brightness Advanced subpixel techniques: Shift image content Mesh grid float mesh grid int main: Tips and Tricks of OpenCV that Nobody Told You Tricks Tips Save results Error Testing for OpenCV Projects Tricks Tips Example YouTube # NuGet - OpenCV 5 beta NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022 [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static opencv library for visual studio 2022 by using NuGet package manager just in few minutes #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) #OpenCV #Farshid_PirahanSiah #pirahansiah My NuGet packages comprised of two versions for different VS version. * Visual Studio 2019 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS2019_NuGet&sa=D&sntz=1&usg=AOvVaw0B-7f3iUYsMrp8SHvJGQb9) * Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7 * Visual Studio 2022 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS22_NuGet&sa=D&sntz=1&usg=AOvVaw2tWZP3KILcTESYrI2cdofl) * Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7 _**more:**_[ _ **https://www.pirahansiah.com/topics/opencv**_](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) _ ****_ # cvtest: Computer Vision Test ## Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Do you want to test your output of computer vision application which is video or images? ## Standard test for computer vision application There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil) download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: [](https://docs.google.com/presentation/d/14HX-99rGO9x1AtOnEigZ_2gAzZaPwvxqCEnnG0dk870/present "Open Presentation, cvtest in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/slides_32dp.png)cvtest # Advanced OpenCV techniques: sub-pixel, floating points, more precise, real-valued coordinates, Changing the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and Tricks of OpenCV that Nobody Told You download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: # Advanced OpenCV techniques: Cross correlation (CC): TM_CCORR Mean shifted cross correlation (Pearson correlation coefficient): TM_CCOEFF Normalization: TM_SQDIFF_NORMED, TM_CCORR_NORMED, TM_CCOEFF_NORMED maximum absolute difference metric (MaxAD), which is also known as the uniform distance metric computeECC() and findTransformECC(). Sum of absolute differences (SAD) Cross correlation (CC) find identical regions of an image that match a template, select by giving a threshold 2D convolution It simply slides the template image over the input image (as in 2D convolution) and compares the template and patch of input image under the template image. Template matching > cv2.TM_CCOEFF > cv2.TM_CCOEFF_NORMED > cv2.TM_CCORR > cv2.TM_CCORR_NORMED < cv2.TM_SQDIFF < cv2.TM_SQDIFF_NORMED cv2.minMaxLoc() more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: [https://docs.opencv.org/master/df/dfb/group__imgproc__object.html#gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2Fmaster%2Fdf%2Fdfb%2Fgroup__imgproc__object.html%23gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65&sa=D&sntz=1&usg=AOvVaw37X4mefDnUv8PmnZK0YdoM) [https://stackoverflow.com/questions/58158129/understanding-and-evaluating- template-matching- methods](https://www.google.com/url?q=https%3A%2F%2Fstackoverflow.com%2Fquestions%2F58158129%2Funderstanding- and-evaluating-template-matching- methods&sa=D&sntz=1&usg=AOvVaw08M8efI3a63Nn7qXHfZZEY) # Advanced OpenCV techniques: balance white balance white with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: void balance_white(cv::Mat mat) { double discard_ratio = 0.05; int hists[3][256]; memset(hists, 0, 3 * 256 * sizeof(int)); for (int y = 0; y < mat.rows; ++y) { uchar* ptr = mat.ptr(y); for (int x = 0; x < mat.cols; ++x) { for (int j = 0; j < 3; ++j) { hists[j][ptr[x * 3 + j]] += 1; } } } // cumulative hist int total = mat.cols * mat.rows; int vmin[3], vmax[3]; for (int i = 0; i < 3; ++i) { for (int j = 0; j < 255; ++j) { hists[i][j + 1] += hists[i][j]; } vmin[i] = 0; vmax[i] = 255; while (hists[i][vmin[i]] < discard_ratio * total) vmin[i] += 1; while (hists[i][vmax[i]] > (1 - discard_ratio) * total) vmax[i] -= 1; if (vmax[i] < 255 - 1) vmax[i] += 1; } for (int y = 0; y < mat.rows; ++y) { uchar* ptr = mat.ptr(y); for (int x = 0; x < mat.cols; ++x) { for (int j = 0; j < 3; ++j) { int val = ptr[x * 3 + j]; if (val < vmin[j]) val = vmin[j]; if (val > vmax[j]) val = vmax[j]; ptr[x * 3 + j] = static_cast((val - vmin[j]) * 255.0 / (vmax[j] - vmin[j])); } } } } reference http://www.ipol.im/pub/art/2011/llmps-scb/ https://gist.github.com/tomykaira/94472e9f4921ec2cf582 # Advanced OpenCV techniques: contrast and brightness sub-pixel, floating points, more precise, real-valued coordinates, Changing the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: showImage.convertTo(showImage, CV_32FC3); double alpha = 2.0; /*< Simple contrast control */ int beta = 100; /*< Simple brightness control */ for (int y = 0; y < showImage.rows; y++) { for (int x = 0; x < showImage.cols; x++) { for (int c = 0; c < showImage.channels(); c++) { showImage.at(y, x)[c] =cv::saturate_cast(alpha * showImage.at(y, x)[c] + beta); } } } showImage.convertTo(showImage, CV_8UC3); cv::imshow("Changing the contrast and brightness of an image! ", showImage); cv::waitKey(0); based on [https://docs.opencv.org/3.4/d3/dc1/tutorial_basic_linear_transform.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd3%2Fdc1%2Ftutorial_basic_linear_transform.html&sa=D&sntz=1&usg=AOvVaw15cmUCYNHZx6OdaoUN0tSB) [https://blog.csdn.net/u014230360/article/details/109802229](https://www.google.com/url?q=https%3A%2F%2Fblog.csdn.net%2Fu014230360%2Farticle%2Fdetails%2F109802229&sa=D&sntz=1&usg=AOvVaw1IxjJJewGlTNahnzI7pCX7) # Advanced subpixel techniques: Shift image content sub-pixel, floating points, mesh grid, remap, more precise, real-valued coordinates, moving image pixel, Shift image content with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: ## ## Mesh grid float static void meshgrid_float(const cv::Mat& xgv, const cv::Mat& ygv, cv::Mat1f& X, cv::Mat1f& Y) { cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X); cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y); } static void meshgrid_map_float(const cv::Range& xgv, const cv::Range& ygv, cv::Mat1f& X, cv::Mat1f& Y) { std::vector t_x, t_y; for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i); for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i); meshgrid_float(cv::Mat(t_x), cv::Mat(t_y), X, Y); } ## mesh grid int static void meshgrid(const cv::Mat& xgv, const cv::Mat& ygv, cv::Mat1i& X, cv::Mat1i& Y) { cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X); cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y); } static void meshgridTest(const cv::Range& xgv, const cv::Range& ygv, cv::Mat1i& X, cv::Mat1i& Y) { std::vector t_x, t_y; for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i); for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i); meshgrid(cv::Mat(t_x), cv::Mat(t_y), X, Y); } ## main: cv::Mat1f XF, YF; //for int //meshgrid_map_float(cv::Range(0, cols_main), cv::Range(0, rows_main), XF, YF); //for float meshgrid_map_float(cv::Range(0, cols_main-1), cv::Range(0, rows_main-1), XF, YF); for (int rowsImage = 0; rowsImage < rows_main; ++rowsImage) { for (int colsImage = 0; colsImage < cols_main; ++colsImage) { XF.at(rowsImage, colsImage) += offset1; YF.at(rowsImage, colsImage) += offset2; } } cv::remap(convert_img_i, dst, XF, YF, cv::INTER_LINEAR); if (show) { cv::Mat resizedImage = dst.clone(); dst.convertTo(resizedImage, CV_8UC3); cv::resize(resizedImage, resizedImage, cv::Size(), 0.5, 0.5); std::string nameWindow = " meshgrid and remap in float "; cv::imshow(nameWindow, resizedImage); cv::waitKey(0); } # Tips and Tricks of OpenCV that Nobody Told You Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: ### Tricks cv::multiply(outMat, cv::Scalar(gain, gain, gain), outMat); //color cv::multiply(outMat, cv::Scalar(gain), outMat); //grayscale //copy small Mat to bigger Mat cv::Rect roi( cv::Point( originX, originY ), smallImage.size() ); smallImage.copyTo( bigImage( roi ) ); ### Tips * copy mat to vector need clone() ### ### Save results * save image in float * cv::imwrite("image.exr", MatImage); * save image in uncompressed format : * cv::imwrite("fa.tiff", resizedImage, { cv::IMWRITE_TIFF_COMPRESSION, 1,cv::IMWRITE_TIFF_XDPI, 300,cv::IMWRITE_TIFF_YDPI,300 }); * * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) # None * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 5)) # LZW * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 8)) # Adobe Deflate * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 32946)) # Deflate * cv::imwrite("far.png", resizedImage, { cv::IMWRITE_PNG_COMPRESSION, 0 }); * Save images in streaming * int64 t0 = cv::getTickCount(); * std::string fileName= "fashid_"+std::to_string(t0)+ ".png"; * cv::imwrite(fileName, cimMat, { cv::IMWRITE_PNG_COMPRESSION, 0 }); * Save file name: * std::filesystem::path p = std::filesystem::path(files[i]).filename(); * std::string imgFile = savePath + "/" \+ p.string() + ".tiff"; * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) ; ### Error * Exception thrown at 0x00007FFB5CB21636 (vcruntime140d.dll) in farshid.exe: 0xC0000005: Access violation writing location 0x000002B09BAC4000. * check the size of Mat * cv::Mat meanImages = cv::Mat::zeros(rows_main, cols_main, CV_32F); * cv::Mat meanImages = cv::Mat::zeros(farshidMat.size(), CV_32F); * Linker Tools Error LNK2038 & Linker Tools Error LNK2001; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for '_ITERATOR_DEBUG_LEVEL': value '2' doesn't match value '0' in Source.obj ; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for 'RuntimeLibrary': value 'MTd_StaticDebug' doesn't match value 'MT_StaticRelease' * the link files are not match based on release or debug mode. # Testing for OpenCV Projects Test, C++, Computer Vision, Image Processing, more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: * Arrange, Act and Assert (AAA) Pattern * Google C++ Test Framework * Assertions Types and Test Fixtures * ASSERT_FALSE(frame.empty()); ASSERT_NO_THROW(cap >> img); ASSERT_FALSE(img.empty()) << "idx=" << idx; ### Tricks embedded system, keep the software as small as possible, Embedding static elements in your application, [https://gstreamer.freedesktop.org/documentation/installing/index.html?gi- language=c](https://www.google.com/url?q=https%3A%2F%2Fgstreamer.freedesktop.org%2Fdocumentation%2Finstalling%2Findex.html%3Fgi- language%3Dc&sa=D&sntz=1&usg=AOvVaw1LbT7pN6fSH1VBpZ07TI2Y) ### Tips * copy mat to vector need clone() ### ### Example #include assert(!im.empty()); assert(x.size()==y.size()); assert(x.size()>2); #ifdef _DEBUG #endif #if true #else #endif # YouTube Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static opencv library for visual studio 2022 by using NuGet package manager just in few minutes #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah [https://github.com/xiaohongchen1991/meshgen](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxiaohongchen1991%2Fmeshgen&sa=D&sntz=1&usg=AOvVaw1J7_ZnsiBoB0T6RB0dNc6u) A set of C++ APIs are provided to mimic the same behaviors as the MATLAB function "linspace" and "meshgrid". Must-Read Books of All Time in Computer Vision and Machine Learning ![](https://lh3.googleusercontent.com/PLi43Jd4dmRotHI9WORIOIM0is9DI0VwayDejuipt_znoABjXDj5gQsqYAaLzgiMGTYz6mcc9DoAdCsX3_dGGq-n5stRfl65U7V-t7TX3jPwKE8mmst4BCcaf_u- _skIQg=w1280) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. 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OpenCV 5 beta cvtest: Computer Vision Test Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Standard test for computer vision application Advanced OpenCV techniques: Advanced OpenCV techniques: Advanced OpenCV techniques: balance white Advanced OpenCV techniques: contrast and brightness Advanced subpixel techniques: Shift image content Mesh grid float mesh grid int main: Tips and Tricks of OpenCV that Nobody Told You Tricks Tips Save results Error Testing for OpenCV Projects Tricks Tips Example YouTube # NuGet - OpenCV 5 beta NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022 [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static opencv library for visual studio 2022 by using NuGet package manager just in few minutes #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) #OpenCV #Farshid_PirahanSiah #pirahansiah My NuGet packages comprised of two versions for different VS version. * Visual Studio 2019 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS2019_NuGet&sa=D&sntz=1&usg=AOvVaw0B-7f3iUYsMrp8SHvJGQb9) * Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7 * Visual Studio 2022 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.google.com/url?q=https%3A%2F%2Fwww.nuget.org%2Fpackages%2FOpenCV5_StaticLib_VS22_NuGet&sa=D&sntz=1&usg=AOvVaw2tWZP3KILcTESYrI2cdofl) * Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7 _**more:**_[ _ **https://www.pirahansiah.com/topics/opencv**_](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) _ ****_ # cvtest: Computer Vision Test ## Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Do you want to test your output of computer vision application which is video or images? ## Standard test for computer vision application There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil) download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: [](https://docs.google.com/presentation/d/14HX-99rGO9x1AtOnEigZ_2gAzZaPwvxqCEnnG0dk870/present "Open Presentation, cvtest in new window") ![](https://www.gstatic.com/images/icons/material/product/1x/slides_32dp.png)cvtest # Advanced OpenCV techniques: sub-pixel, floating points, more precise, real-valued coordinates, Changing the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and Tricks of OpenCV that Nobody Told You download source code (GitHub): [https://github.com/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fopencv- cpp&sa=D&sntz=1&usg=AOvVaw1qtDzteOp2A8jYBk5BdBfj) more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: # Advanced OpenCV techniques: Cross correlation (CC): TM_CCORR Mean shifted cross correlation (Pearson correlation coefficient): TM_CCOEFF Normalization: TM_SQDIFF_NORMED, TM_CCORR_NORMED, TM_CCOEFF_NORMED maximum absolute difference metric (MaxAD), which is also known as the uniform distance metric computeECC() and findTransformECC(). Sum of absolute differences (SAD) Cross correlation (CC) find identical regions of an image that match a template, select by giving a threshold 2D convolution It simply slides the template image over the input image (as in 2D convolution) and compares the template and patch of input image under the template image. Template matching > cv2.TM_CCOEFF > cv2.TM_CCOEFF_NORMED > cv2.TM_CCORR > cv2.TM_CCORR_NORMED < cv2.TM_SQDIFF < cv2.TM_SQDIFF_NORMED cv2.minMaxLoc() more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: [https://docs.opencv.org/master/df/dfb/group__imgproc__object.html#gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2Fmaster%2Fdf%2Fdfb%2Fgroup__imgproc__object.html%23gga3a7850640f1fe1f58fe91a2d7583695da5be00b45a4d99b5e42625b4400bfde65&sa=D&sntz=1&usg=AOvVaw37X4mefDnUv8PmnZK0YdoM) [https://stackoverflow.com/questions/58158129/understanding-and-evaluating- template-matching- methods](https://www.google.com/url?q=https%3A%2F%2Fstackoverflow.com%2Fquestions%2F58158129%2Funderstanding- and-evaluating-template-matching- methods&sa=D&sntz=1&usg=AOvVaw08M8efI3a63Nn7qXHfZZEY) # Advanced OpenCV techniques: balance white balance white with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: void balance_white(cv::Mat mat) { double discard_ratio = 0.05; int hists[3][256]; memset(hists, 0, 3 * 256 * sizeof(int)); for (int y = 0; y < mat.rows; ++y) { uchar* ptr = mat.ptr(y); for (int x = 0; x < mat.cols; ++x) { for (int j = 0; j < 3; ++j) { hists[j][ptr[x * 3 + j]] += 1; } } } // cumulative hist int total = mat.cols * mat.rows; int vmin[3], vmax[3]; for (int i = 0; i < 3; ++i) { for (int j = 0; j < 255; ++j) { hists[i][j + 1] += hists[i][j]; } vmin[i] = 0; vmax[i] = 255; while (hists[i][vmin[i]] < discard_ratio * total) vmin[i] += 1; while (hists[i][vmax[i]] > (1 - discard_ratio) * total) vmax[i] -= 1; if (vmax[i] < 255 - 1) vmax[i] += 1; } for (int y = 0; y < mat.rows; ++y) { uchar* ptr = mat.ptr(y); for (int x = 0; x < mat.cols; ++x) { for (int j = 0; j < 3; ++j) { int val = ptr[x * 3 + j]; if (val < vmin[j]) val = vmin[j]; if (val > vmax[j]) val = vmax[j]; ptr[x * 3 + j] = static_cast((val - vmin[j]) * 255.0 / (vmax[j] - vmin[j])); } } } } reference http://www.ipol.im/pub/art/2011/llmps-scb/ https://gist.github.com/tomykaira/94472e9f4921ec2cf582 # Advanced OpenCV techniques: contrast and brightness sub-pixel, floating points, more precise, real-valued coordinates, Changing the contrast and brightness of an image in CV_32FC3 with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: showImage.convertTo(showImage, CV_32FC3); double alpha = 2.0; /*< Simple contrast control */ int beta = 100; /*< Simple brightness control */ for (int y = 0; y < showImage.rows; y++) { for (int x = 0; x < showImage.cols; x++) { for (int c = 0; c < showImage.channels(); c++) { showImage.at(y, x)[c] =cv::saturate_cast(alpha * showImage.at(y, x)[c] + beta); } } } showImage.convertTo(showImage, CV_8UC3); cv::imshow("Changing the contrast and brightness of an image! ", showImage); cv::waitKey(0); based on [https://docs.opencv.org/3.4/d3/dc1/tutorial_basic_linear_transform.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.opencv.org%2F3.4%2Fd3%2Fdc1%2Ftutorial_basic_linear_transform.html&sa=D&sntz=1&usg=AOvVaw15cmUCYNHZx6OdaoUN0tSB) [https://blog.csdn.net/u014230360/article/details/109802229](https://www.google.com/url?q=https%3A%2F%2Fblog.csdn.net%2Fu014230360%2Farticle%2Fdetails%2F109802229&sa=D&sntz=1&usg=AOvVaw1IxjJJewGlTNahnzI7pCX7) # Advanced subpixel techniques: Shift image content sub-pixel, floating points, mesh grid, remap, more precise, real-valued coordinates, moving image pixel, Shift image content with OpenCV, Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: ## ## Mesh grid float static void meshgrid_float(const cv::Mat& xgv, const cv::Mat& ygv, cv::Mat1f& X, cv::Mat1f& Y) { cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X); cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y); } static void meshgrid_map_float(const cv::Range& xgv, const cv::Range& ygv, cv::Mat1f& X, cv::Mat1f& Y) { std::vector t_x, t_y; for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i); for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i); meshgrid_float(cv::Mat(t_x), cv::Mat(t_y), X, Y); } ## mesh grid int static void meshgrid(const cv::Mat& xgv, const cv::Mat& ygv, cv::Mat1i& X, cv::Mat1i& Y) { cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X); cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y); } static void meshgridTest(const cv::Range& xgv, const cv::Range& ygv, cv::Mat1i& X, cv::Mat1i& Y) { std::vector t_x, t_y; for (int i = xgv.start; i <= xgv.end; i++) t_x.push_back(i); for (int i = ygv.start; i <= ygv.end; i++) t_y.push_back(i); meshgrid(cv::Mat(t_x), cv::Mat(t_y), X, Y); } ## main: cv::Mat1f XF, YF; //for int //meshgrid_map_float(cv::Range(0, cols_main), cv::Range(0, rows_main), XF, YF); //for float meshgrid_map_float(cv::Range(0, cols_main-1), cv::Range(0, rows_main-1), XF, YF); for (int rowsImage = 0; rowsImage < rows_main; ++rowsImage) { for (int colsImage = 0; colsImage < cols_main; ++colsImage) { XF.at(rowsImage, colsImage) += offset1; YF.at(rowsImage, colsImage) += offset2; } } cv::remap(convert_img_i, dst, XF, YF, cv::INTER_LINEAR); if (show) { cv::Mat resizedImage = dst.clone(); dst.convertTo(resizedImage, CV_8UC3); cv::resize(resizedImage, resizedImage, cv::Size(), 0.5, 0.5); std::string nameWindow = " meshgrid and remap in float "; cv::imshow(nameWindow, resizedImage); cv::waitKey(0); } # Tips and Tricks of OpenCV that Nobody Told You Tips and Tricks of OpenCV that Nobody Told You more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: ### Tricks cv::multiply(outMat, cv::Scalar(gain, gain, gain), outMat); //color cv::multiply(outMat, cv::Scalar(gain), outMat); //grayscale //copy small Mat to bigger Mat cv::Rect roi( cv::Point( originX, originY ), smallImage.size() ); smallImage.copyTo( bigImage( roi ) ); ### Tips * copy mat to vector need clone() ### ### Save results * save image in float * cv::imwrite("image.exr", MatImage); * save image in uncompressed format : * cv::imwrite("fa.tiff", resizedImage, { cv::IMWRITE_TIFF_COMPRESSION, 1,cv::IMWRITE_TIFF_XDPI, 300,cv::IMWRITE_TIFF_YDPI,300 }); * * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) # None * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 5)) # LZW * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 8)) # Adobe Deflate * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 32946)) # Deflate * cv::imwrite("far.png", resizedImage, { cv::IMWRITE_PNG_COMPRESSION, 0 }); * Save images in streaming * int64 t0 = cv::getTickCount(); * std::string fileName= "fashid_"+std::to_string(t0)+ ".png"; * cv::imwrite(fileName, cimMat, { cv::IMWRITE_PNG_COMPRESSION, 0 }); * Save file name: * std::filesystem::path p = std::filesystem::path(files[i]).filename(); * std::string imgFile = savePath + "/" \+ p.string() + ".tiff"; * cv2.imwrite(filename, array, params=(cv2.IMWRITE_TIFF_COMPRESSION, 1)) ; ### Error * Exception thrown at 0x00007FFB5CB21636 (vcruntime140d.dll) in farshid.exe: 0xC0000005: Access violation writing location 0x000002B09BAC4000. * check the size of Mat * cv::Mat meanImages = cv::Mat::zeros(rows_main, cols_main, CV_32F); * cv::Mat meanImages = cv::Mat::zeros(farshidMat.size(), CV_32F); * Linker Tools Error LNK2038 & Linker Tools Error LNK2001; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for '_ITERATOR_DEBUG_LEVEL': value '2' doesn't match value '0' in Source.obj ; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for 'RuntimeLibrary': value 'MTd_StaticDebug' doesn't match value 'MT_StaticRelease' * the link files are not match based on release or debug mode. # Testing for OpenCV Projects Test, C++, Computer Vision, Image Processing, more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah sample code in C++: * Arrange, Act and Assert (AAA) Pattern * Google C++ Test Framework * Assertions Types and Test Fixtures * ASSERT_FALSE(frame.empty()); ASSERT_NO_THROW(cap >> img); ASSERT_FALSE(img.empty()) << "idx=" << idx; ### Tricks embedded system, keep the software as small as possible, Embedding static elements in your application, [https://gstreamer.freedesktop.org/documentation/installing/index.html?gi- language=c](https://www.google.com/url?q=https%3A%2F%2Fgstreamer.freedesktop.org%2Fdocumentation%2Finstalling%2Findex.html%3Fgi- language%3Dc&sa=D&sntz=1&usg=AOvVaw1LbT7pN6fSH1VBpZ07TI2Y) ### Tips * copy mat to vector need clone() ### ### Example #include assert(!im.empty()); assert(x.size()==y.size()); assert(x.size()>2); #ifdef _DEBUG #endif #if true #else #endif # YouTube Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static opencv library for visual studio 2022 by using NuGet package manager just in few minutes #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, more: [https://www.pirahansiah.com/topics/opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fopencv&sa=D&sntz=1&usg=AOvVaw3hsu8OV4KaU1u_rEzwiaiT) #OpenCV #Farshid_PirahanSiah #pirahansiah [https://github.com/xiaohongchen1991/meshgen](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fxiaohongchen1991%2Fmeshgen&sa=D&sntz=1&usg=AOvVaw1J7_ZnsiBoB0T6RB0dNc6u) A set of C++ APIs are provided to mimic the same behaviors as the MATLAB function "linspace" and "meshgrid". Must-Read Books of All Time in Computer Vision and Machine Learning ![](https://lh6.googleusercontent.com/543wuBVenutUTxovMJ4gQ_HXvQVlv5EjabCjHK1vhC- xkkKoDFdARf-cue7b24QHOCB_C6YrBuHTEG6ibH4Y9_LG_zuOKZk_TGpXEo- atf3LS2Z-6Sz79nJ55c5zIwhS4Q=w1280) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/4UjqR1S9XsZ8zEvQ9qAgiEqvXOYUyjjcULaZlxDawTwL1VRIewpNKgopuhxbzkxZfOWjg2dlB7JHhxX2KjllGbU=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/4UjqR1S9XsZ8zEvQ9qAgiEqvXOYUyjjcULaZlxDawTwL1VRIewpNKgopuhxbzkxZfOWjg2dlB7JHhxX2KjllGbU=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Source Code # [Essential Python Tips And Tricks For advance computer vision Programmers](/topics-and-projects/source-code/opencv/python) # [Essential Tips And Tricks For ](/topics-and-projects/source- code/compile)[compiling code ](/topics-and-projects/source- code/compile)[computer vision ](/topics-and-projects/source- code/compile)[projects ](/topics-and-projects/source-code/compile) # cvtest: Computer Vision Test ## Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Do you want to test your output of computer vision application which is video or images? ## Standard test for computer vision application There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil) check S3 bucket in AWS for image and video files and versioning Check Docker load balancer, memory usage, ... GPU In general I would create a wrapper/adapter that only exposes the needed functionality of such an external dependency. Apart from being able to easer adapt to changes of the external dependency, we can also mock the adapter in our tests and let it do things we could not do so easily with the dependency itself. For our example, we could it have return a predefined image in our test and it is also easier to test if our code behaves properly in presence of failures (that are usually hard to trigger with the real thing. How to convert programming languages: Matlab/Python/C/C++ and modern C++ 23 1\. trace the images: what happens to each image and memory and copy and transit to that image 1\. Using a table and each column transitions for each image 2\. always check the call by reference or call by value 3\. check the naming similarity 2\. compare the output of each step: store data, matrix, array, and vector to the text file and compare compliantly also compare the differences because in image processing some times not exactly output same and different version of OpenCV also has different result 1\. the version of the library 2\. the function use which library 3\. the input/output of the function and call by reference or call by value 3\. The same function in Matlab, Python, C++, and OpenCV is different even change OS also make difference in output 4\. test ... test ... line by line Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/4UjqR1S9XsZ8zEvQ9qAgiEqvXOYUyjjcULaZlxDawTwL1VRIewpNKgopuhxbzkxZfOWjg2dlB7JHhxX2KjllGbU=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/4UjqR1S9XsZ8zEvQ9qAgiEqvXOYUyjjcULaZlxDawTwL1VRIewpNKgopuhxbzkxZfOWjg2dlB7JHhxX2KjllGbU=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Source Code # [Essential Python Tips And Tricks For advance computer vision Programmers](/topics-and-projects/source-code/opencv/python) # [Essential Tips And Tricks For ](/topics-and-projects/source- code/compile)[compiling code ](/topics-and-projects/source- code/compile)[computer vision ](/topics-and-projects/source- code/compile)[projects ](/topics-and-projects/source-code/compile) # cvtest: Computer Vision Test ## Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Do you want to test your output of computer vision application which is video or images? ## Standard test for computer vision application There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil) check S3 bucket in AWS for image and video files and versioning Check Docker load balancer, memory usage, ... GPU In general I would create a wrapper/adapter that only exposes the needed functionality of such an external dependency. Apart from being able to easer adapt to changes of the external dependency, we can also mock the adapter in our tests and let it do things we could not do so easily with the dependency itself. For our example, we could it have return a predefined image in our test and it is also easier to test if our code behaves properly in presence of failures (that are usually hard to trigger with the real thing. How to convert programming languages: Matlab/Python/C/C++ and modern C++ 23 1\. trace the images: what happens to each image and memory and copy and transit to that image 1\. Using a table and each column transitions for each image 2\. always check the call by reference or call by value 3\. check the naming similarity 2\. compare the output of each step: store data, matrix, array, and vector to the text file and compare compliantly also compare the differences because in image processing some times not exactly output same and different version of OpenCV also has different result 1\. the version of the library 2\. the function use which library 3\. the input/output of the function and call by reference or call by value 3\. The same function in Matlab, Python, C++, and OpenCV is different even change OS also make difference in output 4\. test ... test ... line by line Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/4UjqR1S9XsZ8zEvQ9qAgiEqvXOYUyjjcULaZlxDawTwL1VRIewpNKgopuhxbzkxZfOWjg2dlB7JHhxX2KjllGbU=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/4UjqR1S9XsZ8zEvQ9qAgiEqvXOYUyjjcULaZlxDawTwL1VRIewpNKgopuhxbzkxZfOWjg2dlB7JHhxX2KjllGbU=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Source Code # [Essential Python Tips And Tricks For advance computer vision Programmers](/topics-and-projects/source-code/opencv/python) # [Essential Tips And Tricks For ](/topics-and-projects/source- code/compile)[compiling code ](/topics-and-projects/source- code/compile)[computer vision ](/topics-and-projects/source- code/compile)[projects ](/topics-and-projects/source-code/compile) # cvtest: Computer Vision Test ## Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Do you want to test your output of computer vision application which is video or images? ## Standard test for computer vision application There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil) check S3 bucket in AWS for image and video files and versioning Check Docker load balancer, memory usage, ... GPU In general I would create a wrapper/adapter that only exposes the needed functionality of such an external dependency. Apart from being able to easer adapt to changes of the external dependency, we can also mock the adapter in our tests and let it do things we could not do so easily with the dependency itself. For our example, we could it have return a predefined image in our test and it is also easier to test if our code behaves properly in presence of failures (that are usually hard to trigger with the real thing. How to convert programming languages: Matlab/Python/C/C++ and modern C++ 23 1\. trace the images: what happens to each image and memory and copy and transit to that image 1\. Using a table and each column transitions for each image 2\. always check the call by reference or call by value 3\. check the naming similarity 2\. compare the output of each step: store data, matrix, array, and vector to the text file and compare compliantly also compare the differences because in image processing some times not exactly output same and different version of OpenCV also has different result 1\. the version of the library 2\. the function use which library 3\. the input/output of the function and call by reference or call by value 3\. The same function in Matlab, Python, C++, and OpenCV is different even change OS also make difference in output 4\. test ... test ... line by line Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/4UjqR1S9XsZ8zEvQ9qAgiEqvXOYUyjjcULaZlxDawTwL1VRIewpNKgopuhxbzkxZfOWjg2dlB7JHhxX2KjllGbU=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/4UjqR1S9XsZ8zEvQ9qAgiEqvXOYUyjjcULaZlxDawTwL1VRIewpNKgopuhxbzkxZfOWjg2dlB7JHhxX2KjllGbU=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Source Code # [Essential Python Tips And Tricks For advance computer vision Programmers](/topics-and-projects/source-code/opencv/python) # [Essential Tips And Tricks For ](/topics-and-projects/source- code/compile)[compiling code ](/topics-and-projects/source- code/compile)[computer vision ](/topics-and-projects/source- code/compile)[projects ](/topics-and-projects/source-code/compile) # cvtest: Computer Vision Test ## Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Do you want to test your output of computer vision application which is video or images? ## Standard test for computer vision application There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil) check S3 bucket in AWS for image and video files and versioning Check Docker load balancer, memory usage, ... GPU In general I would create a wrapper/adapter that only exposes the needed functionality of such an external dependency. Apart from being able to easer adapt to changes of the external dependency, we can also mock the adapter in our tests and let it do things we could not do so easily with the dependency itself. For our example, we could it have return a predefined image in our test and it is also easier to test if our code behaves properly in presence of failures (that are usually hard to trigger with the real thing. How to convert programming languages: Matlab/Python/C/C++ and modern C++ 23 1\. trace the images: what happens to each image and memory and copy and transit to that image 1\. Using a table and each column transitions for each image 2\. always check the call by reference or call by value 3\. check the naming similarity 2\. compare the output of each step: store data, matrix, array, and vector to the text file and compare compliantly also compare the differences because in image processing some times not exactly output same and different version of OpenCV also has different result 1\. the version of the library 2\. the function use which library 3\. the input/output of the function and call by reference or call by value 3\. The same function in Matlab, Python, C++, and OpenCV is different even change OS also make difference in output 4\. test ... test ... line by line Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/4UjqR1S9XsZ8zEvQ9qAgiEqvXOYUyjjcULaZlxDawTwL1VRIewpNKgopuhxbzkxZfOWjg2dlB7JHhxX2KjllGbU=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/4UjqR1S9XsZ8zEvQ9qAgiEqvXOYUyjjcULaZlxDawTwL1VRIewpNKgopuhxbzkxZfOWjg2dlB7JHhxX2KjllGbU=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Source Code # [Essential Python Tips And Tricks For advance computer vision Programmers](/topics-and-projects/source-code/opencv/python) # [Essential Tips And Tricks For ](/topics-and-projects/source- code/compile)[compiling code ](/topics-and-projects/source- code/compile)[computer vision ](/topics-and-projects/source- code/compile)[projects ](/topics-and-projects/source-code/compile) # cvtest: Computer Vision Test ## Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Do you want to test your output of computer vision application which is video or images? ## Standard test for computer vision application There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil) check S3 bucket in AWS for image and video files and versioning Check Docker load balancer, memory usage, ... GPU In general I would create a wrapper/adapter that only exposes the needed functionality of such an external dependency. Apart from being able to easer adapt to changes of the external dependency, we can also mock the adapter in our tests and let it do things we could not do so easily with the dependency itself. For our example, we could it have return a predefined image in our test and it is also easier to test if our code behaves properly in presence of failures (that are usually hard to trigger with the real thing. How to convert programming languages: Matlab/Python/C/C++ and modern C++ 23 1\. trace the images: what happens to each image and memory and copy and transit to that image 1\. Using a table and each column transitions for each image 2\. always check the call by reference or call by value 3\. check the naming similarity 2\. compare the output of each step: store data, matrix, array, and vector to the text file and compare compliantly also compare the differences because in image processing some times not exactly output same and different version of OpenCV also has different result 1\. the version of the library 2\. the function use which library 3\. the input/output of the function and call by reference or call by value 3\. The same function in Matlab, Python, C++, and OpenCV is different even change OS also make difference in output 4\. test ... test ... line by line Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/HlmNuqyxk5gzYGxXDtUChEUYREK389bSHhqeXOjPaBaXuH- OfSzmgk4xvNu5e2EN7ntuZo418EeXtzqP8ztw9tk=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/HlmNuqyxk5gzYGxXDtUChEUYREK389bSHhqeXOjPaBaXuH- OfSzmgk4xvNu5e2EN7ntuZo418EeXtzqP8ztw9tk=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Source Code # [Essential Python Tips And Tricks For advance computer vision Programmers](/topics-and-projects/source-code/opencv/python) # [Essential Tips And Tricks For ](/topics-and-projects/source- code/compile)[compiling code ](/topics-and-projects/source- code/compile)[computer vision ](/topics-and-projects/source- code/compile)[projects ](/topics-and-projects/source-code/compile) # cvtest: Computer Vision Test ## Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Do you want to test your output of computer vision application which is video or images? ## Standard test for computer vision application There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest%2Fblob%2Fmain%2FREADME.md&sa=D&sntz=1&usg=AOvVaw2fKJDIiWJAbefBUgwvpWil) check S3 bucket in AWS for image and video files and versioning Check Docker load balancer, memory usage, ... GPU In general I would create a wrapper/adapter that only exposes the needed functionality of such an external dependency. Apart from being able to easer adapt to changes of the external dependency, we can also mock the adapter in our tests and let it do things we could not do so easily with the dependency itself. For our example, we could it have return a predefined image in our test and it is also easier to test if our code behaves properly in presence of failures (that are usually hard to trigger with the real thing. How to convert programming languages: Matlab/Python/C/C++ and modern C++ 23 1\. trace the images: what happens to each image and memory and copy and transit to that image 1\. Using a table and each column transitions for each image 2\. always check the call by reference or call by value 3\. check the naming similarity 2\. compare the output of each step: store data, matrix, array, and vector to the text file and compare compliantly also compare the differences because in image processing some times not exactly output same and different version of OpenCV also has different result 1\. the version of the library 2\. the function use which library 3\. the input/output of the function and call by reference or call by value 3\. The same function in Matlab, Python, C++, and OpenCV is different even change OS also make difference in output 4\. test ... test ... line by line Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/HlmNuqyxk5gzYGxXDtUChEUYREK389bSHhqeXOjPaBaXuH- OfSzmgk4xvNu5e2EN7ntuZo418EeXtzqP8ztw9tk=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/HlmNuqyxk5gzYGxXDtUChEUYREK389bSHhqeXOjPaBaXuH- OfSzmgk4xvNu5e2EN7ntuZo418EeXtzqP8ztw9tk=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Roadmap for Image Processing ![](https://lh5.googleusercontent.com/FsGZTS3jJ54cmjmzyCcbvBqlJE- Cc6PRb60SJxZiDyvv5FBYYCHkRUP4pptNHZQXBMImsJcR5cbb5nyNWm3S2Mr4U0uBEL0hAtxgDJlKGn92uJsCO7i_m0mugN8zMhJv5A=w1280) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/4UjqR1S9XsZ8zEvQ9qAgiEqvXOYUyjjcULaZlxDawTwL1VRIewpNKgopuhxbzkxZfOWjg2dlB7JHhxX2KjllGbU=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/4UjqR1S9XsZ8zEvQ9qAgiEqvXOYUyjjcULaZlxDawTwL1VRIewpNKgopuhxbzkxZfOWjg2dlB7JHhxX2KjllGbU=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Software ## [https://www.pirahansiah.com/describe/how-to- start/software](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Fdescribe%2Fhow- to-start%2Fsoftware&sa=D&sntz=1&usg=AOvVaw0ZgcCXt9KaLELNwqn9fZAW) ## List of application for work * OneNote * OneDrive * Notion * Google (Gmail, Calendar, Meet, Doc, Sheet, ...) * Collaborate for free with online versions of Microsoft Word, PowerPoint, Excel, Outlook,Teams and OneNote * [https://miro.com](https://www.google.com/url?q=https%3A%2F%2Fmiro.com&sa=D&sntz=1&usg=AOvVaw2DKqx4VxR24Vi2CZIvCBto) * [Code Quality and Code Security ](https://www.google.com/url?q=https%3A%2F%2Fwww.sonarqube.org&sa=D&sntz=1&usg=AOvVaw0yshUZ-MnuVjgwrV7M9pNA) * [Rocket.Chat: Communications Platform You Can Fully Trust](https://www.google.com/url?q=https%3A%2F%2Fwww.rocket.chat&sa=D&sntz=1&usg=AOvVaw2Y5ilKZMg5N3rTI7FxxnSl) * [Jitsi Meet Components](https://www.google.com/url?q=https%3A%2F%2Fjaas.8x8.vc%2F%23%2F&sa=D&sntz=1&usg=AOvVaw2UGk4KmZ-jHsGLgx1eejNZ) * [https://www.linkedin.com/in/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Fin%2Fpirahansiah%2F&sa=D&sntz=1&usg=AOvVaw0ETpuSejDWH6Dz0IId5L5j) * Okta * GitHub * Amazon Drive * Discord ## List of application for work in phone * Okta Verify * Authy * Authenticator * Webex Meet * Zoom * **HabitShare** * Meetup * Drafts [Peeking Into People’s Second Brains: 6 Videos to Inspire Your Second Brain Setup](https://www.google.com/url?q=https%3A%2F%2Ffortelabs.co%2Fblog%2Fpeeking- into-peoples-second-brains-6-videos-to-inspire-your-second-brain- setup%2F&sa=D&sntz=1&usg=AOvVaw2LrepWFA7liNzmLcU7thxS) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[https://www.pirahansiah.com/describe/how-to- start/software](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Fdescribe%2Fhow- to-start%2Fsoftware&sa=D&sntz=1&usg=AOvVaw0ZgcCXt9KaLELNwqn9fZAW) ## List of application for work * OneNote * OneDrive * Notion * Google (Gmail, Calendar, Meet, Doc, Sheet, ...) * Collaborate for free with online versions of Microsoft Word, PowerPoint, Excel, Outlook,Teams and OneNote * [https://miro.com](https://www.google.com/url?q=https%3A%2F%2Fmiro.com&sa=D&sntz=1&usg=AOvVaw2DKqx4VxR24Vi2CZIvCBto) * [Code Quality and Code Security ](https://www.google.com/url?q=https%3A%2F%2Fwww.sonarqube.org&sa=D&sntz=1&usg=AOvVaw0yshUZ-MnuVjgwrV7M9pNA) * [Rocket.Chat: Communications Platform You Can Fully Trust](https://www.google.com/url?q=https%3A%2F%2Fwww.rocket.chat&sa=D&sntz=1&usg=AOvVaw2Y5ilKZMg5N3rTI7FxxnSl) * [Jitsi Meet Components](https://www.google.com/url?q=https%3A%2F%2Fjaas.8x8.vc%2F%23%2F&sa=D&sntz=1&usg=AOvVaw2UGk4KmZ-jHsGLgx1eejNZ) * [https://www.linkedin.com/in/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Fin%2Fpirahansiah%2F&sa=D&sntz=1&usg=AOvVaw0ETpuSejDWH6Dz0IId5L5j) * Okta * GitHub * Amazon Drive * Discord ## 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[https://www.pirahansiah.com/describe/how-to- start/software](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Fdescribe%2Fhow- to-start%2Fsoftware&sa=D&sntz=1&usg=AOvVaw0ZgcCXt9KaLELNwqn9fZAW) ## List of application for work * OneNote * OneDrive * Notion * Google (Gmail, Calendar, Meet, Doc, Sheet, ...) * Collaborate for free with online versions of Microsoft Word, PowerPoint, Excel, Outlook,Teams and OneNote * [https://miro.com](https://www.google.com/url?q=https%3A%2F%2Fmiro.com&sa=D&sntz=1&usg=AOvVaw2DKqx4VxR24Vi2CZIvCBto) * [Code Quality and Code Security ](https://www.google.com/url?q=https%3A%2F%2Fwww.sonarqube.org&sa=D&sntz=1&usg=AOvVaw0yshUZ-MnuVjgwrV7M9pNA) * [Rocket.Chat: Communications Platform You Can Fully Trust](https://www.google.com/url?q=https%3A%2F%2Fwww.rocket.chat&sa=D&sntz=1&usg=AOvVaw2Y5ilKZMg5N3rTI7FxxnSl) * [Jitsi Meet Components](https://www.google.com/url?q=https%3A%2F%2Fjaas.8x8.vc%2F%23%2F&sa=D&sntz=1&usg=AOvVaw2UGk4KmZ-jHsGLgx1eejNZ) * [https://www.linkedin.com/in/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Fin%2Fpirahansiah%2F&sa=D&sntz=1&usg=AOvVaw0ETpuSejDWH6Dz0IId5L5j) * Okta * GitHub * Amazon Drive * Discord ## 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[https://www.pirahansiah.com/describe/how-to- start/software](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Fdescribe%2Fhow- to-start%2Fsoftware&sa=D&sntz=1&usg=AOvVaw0ZgcCXt9KaLELNwqn9fZAW) ## List of application for work * OneNote * OneDrive * Notion * Google (Gmail, Calendar, Meet, Doc, Sheet, ...) * Collaborate for free with online versions of Microsoft Word, PowerPoint, Excel, Outlook,Teams and OneNote * [https://miro.com](https://www.google.com/url?q=https%3A%2F%2Fmiro.com&sa=D&sntz=1&usg=AOvVaw2DKqx4VxR24Vi2CZIvCBto) * [Code Quality and Code Security ](https://www.google.com/url?q=https%3A%2F%2Fwww.sonarqube.org&sa=D&sntz=1&usg=AOvVaw0yshUZ-MnuVjgwrV7M9pNA) * [Rocket.Chat: Communications Platform You Can Fully Trust](https://www.google.com/url?q=https%3A%2F%2Fwww.rocket.chat&sa=D&sntz=1&usg=AOvVaw2Y5ilKZMg5N3rTI7FxxnSl) * [Jitsi Meet Components](https://www.google.com/url?q=https%3A%2F%2Fjaas.8x8.vc%2F%23%2F&sa=D&sntz=1&usg=AOvVaw2UGk4KmZ-jHsGLgx1eejNZ) * [https://www.linkedin.com/in/pirahansiah/](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Fin%2Fpirahansiah%2F&sa=D&sntz=1&usg=AOvVaw0ETpuSejDWH6Dz0IId5L5j) * Okta * GitHub * Amazon Drive * Discord ## List of application for work in phone * Okta Verify * Authy * Authenticator * Webex Meet * Zoom * **HabitShare** * Meetup * Drafts [Peeking Into People’s Second Brains: 6 Videos to Inspire Your Second Brain Setup](https://www.google.com/url?q=https%3A%2F%2Ffortelabs.co%2Fblog%2Fpeeking- into-peoples-second-brains-6-videos-to-inspire-your-second-brain- setup%2F&sa=D&sntz=1&usg=AOvVaw2LrepWFA7liNzmLcU7thxS) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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part 1 - compile OpenCV for Deep Learning]( ) [Setup Visual Studio 2017 (C++) for OpenCV 4, Deep]( ) [Build OpenCV 4 with Visual Studio 2017 (C++) OpenCV]( ) ## OpenCV 3 [(2018) How to build OpenCV 3.4 , Visual Studio ]( ) [OpenCV (All Versions of 3.x),Easy Installation Guide and Sample Project (VS 2015 C++), tutorial 1]( ) [compile OpenCV 3.2 windows 10 (64 bit) visual studio]( ) ## Deep Learning frameworks and environments [Deep Learning by OpenCV 4 (2018) - part 1 - compile]( ) [0- Using Deep Learning for Computer Vision Applications ]( ) [1- 2018 - How to compile OpenCV 3.4 , VisualStudio]( ) [2- 2018- How to setup visual studio project ]( ) [3 - OpenCV 4 , Deep Learning for Computer Vision]( ) [4-opencv4 Using TensorFlow model in OpenCV 4]( ) [2018 NVidia Caffe Ubuntu 16 CUDA 9 GTX1080 cudnn d]( ) [2018 NVidia DIGITS Ubuntu16 CUDA9 GTX1080 with Caf]( ) [2018 -torch ubuntu 16 cuda 9 cudd GTX 1080]( ) [2018- TensorFlow installation ubuntu gtx 1080 cuda]( ) [Compile OpenCV 3.2 with Visual Studio 2017 (C++) ]( ) [TensorFlow in OpenCV 3.2 Visual Studio 2017 (C++ ]( ) [Compile Caffe v1.0 on Ubuntu 16 (2017) Deep Learning]( ) [build Torch in Ubuntu 16, Deep Learning for computer visio]( ) [How to install DIGITS 6.0 based on TensorFlow 1.3]( ) [DIGITS 6.0 TensorFlow 1.3 Ubuntu 16 Deep Learning]( ) Traditional Computer Vision and Image Processing algorithms [Camera Calibration camera resectioning (image processing with opencv 3 & c++ , computer vision)]( ) [optical flow implementation (all methods and algorithms) on opencv 3 visual studio 2015 win 64x]( ) [Pedestrian Detection MFC visual C++ Opencv 3 human detection webcam,video,motion,frame,edge,vector]( ) [video processing by opencv 3.1 vc++ 2015 win 64x]( ) [Implementation of image pyramid in OpenCV (3.x) and Visual Studio 2015]( ) [part 2 image pyramid opencv 3 visual C++ 2015 64 bit gaussina pyramid laplacian pyramid optical flow]( ) [opencv 310 vs 2015 facedetection]( ) [opencv 3.1 VS 2015 thresholding algorithm]( ) [2018 video contents search based on deep learning]( ) ## Deep Learning in Computer Vision Applications [tutorial how to apply neural style transfer to images using OpenCV 4, C++, and deep learning (torch)]( ) [Deep Learning on MacOS (MacBook) with TensorFlow ]( ) [using trained caffe model in opencv application]( ) #pirahansiah #www.pirahansiah.com #ComputerVision #DeepLearning #AI #IoT #robot #VideoObjectTracking #Multi-Class&Multi-Object&Multi-Camera-Tracking #MCMOMCT Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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part 1 - compile OpenCV for Deep Learning]( ) [Setup Visual Studio 2017 (C++) for OpenCV 4, Deep]( ) [Build OpenCV 4 with Visual Studio 2017 (C++) OpenCV]( ) ## OpenCV 3 [(2018) How to build OpenCV 3.4 , Visual Studio ]( ) [OpenCV (All Versions of 3.x),Easy Installation Guide and Sample Project (VS 2015 C++), tutorial 1]( ) [compile OpenCV 3.2 windows 10 (64 bit) visual studio]( ) ## Deep Learning frameworks and environments [Deep Learning by OpenCV 4 (2018) - part 1 - compile]( ) [0- Using Deep Learning for Computer Vision Applications ]( ) [1- 2018 - How to compile OpenCV 3.4 , VisualStudio]( ) [2- 2018- How to setup visual studio project ]( ) [3 - OpenCV 4 , Deep Learning for Computer Vision]( ) [4-opencv4 Using TensorFlow model in OpenCV 4]( ) [2018 NVidia Caffe Ubuntu 16 CUDA 9 GTX1080 cudnn d]( ) [2018 NVidia DIGITS Ubuntu16 CUDA9 GTX1080 with Caf]( ) [2018 -torch ubuntu 16 cuda 9 cudd GTX 1080]( ) [2018- TensorFlow installation ubuntu gtx 1080 cuda]( ) [Compile OpenCV 3.2 with Visual Studio 2017 (C++) ]( ) [TensorFlow in OpenCV 3.2 Visual Studio 2017 (C++ ]( ) [Compile Caffe v1.0 on Ubuntu 16 (2017) Deep Learning]( ) [build Torch in Ubuntu 16, Deep Learning for computer visio]( ) [How to install DIGITS 6.0 based on TensorFlow 1.3]( ) [DIGITS 6.0 TensorFlow 1.3 Ubuntu 16 Deep Learning]( ) Traditional Computer Vision and Image Processing algorithms [Camera Calibration camera resectioning (image processing with opencv 3 & c++ , computer vision)]( ) [optical flow implementation (all methods and algorithms) on opencv 3 visual studio 2015 win 64x]( ) [Pedestrian Detection MFC visual C++ Opencv 3 human detection webcam,video,motion,frame,edge,vector]( ) [video processing by opencv 3.1 vc++ 2015 win 64x]( ) [Implementation of image pyramid in OpenCV (3.x) and Visual Studio 2015]( ) [part 2 image pyramid opencv 3 visual C++ 2015 64 bit gaussina pyramid laplacian pyramid optical flow]( ) [opencv 310 vs 2015 facedetection]( ) [opencv 3.1 VS 2015 thresholding algorithm]( ) [2018 video contents search based on deep learning]( ) ## Deep Learning in Computer Vision Applications [tutorial how to apply neural style transfer to images using OpenCV 4, C++, and deep learning (torch)]( ) [Deep Learning on MacOS (MacBook) with TensorFlow ]( ) [using trained caffe model in opencv application]( ) #pirahansiah #www.pirahansiah.com #ComputerVision #DeepLearning #AI #IoT #robot #VideoObjectTracking #Multi-Class&Multi-Object&Multi-Camera-Tracking #MCMOMCT Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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part 1 - compile OpenCV for Deep Learning]( ) [Setup Visual Studio 2017 (C++) for OpenCV 4, Deep]( ) [Build OpenCV 4 with Visual Studio 2017 (C++) OpenCV]( ) ## OpenCV 3 [(2018) How to build OpenCV 3.4 , Visual Studio ]( ) [OpenCV (All Versions of 3.x),Easy Installation Guide and Sample Project (VS 2015 C++), tutorial 1]( ) [compile OpenCV 3.2 windows 10 (64 bit) visual studio]( ) ## Deep Learning frameworks and environments [Deep Learning by OpenCV 4 (2018) - part 1 - compile]( ) [0- Using Deep Learning for Computer Vision Applications ]( ) [1- 2018 - How to compile OpenCV 3.4 , VisualStudio]( ) [2- 2018- How to setup visual studio project ]( ) [3 - OpenCV 4 , Deep Learning for Computer Vision]( ) [4-opencv4 Using TensorFlow model in OpenCV 4]( ) [2018 NVidia Caffe Ubuntu 16 CUDA 9 GTX1080 cudnn d]( ) [2018 NVidia DIGITS Ubuntu16 CUDA9 GTX1080 with Caf]( ) [2018 -torch ubuntu 16 cuda 9 cudd GTX 1080]( ) [2018- TensorFlow installation ubuntu gtx 1080 cuda]( ) [Compile OpenCV 3.2 with Visual Studio 2017 (C++) ]( ) [TensorFlow in OpenCV 3.2 Visual Studio 2017 (C++ ]( ) [Compile Caffe v1.0 on Ubuntu 16 (2017) Deep Learning]( ) [build Torch in Ubuntu 16, Deep Learning for computer visio]( ) [How to install DIGITS 6.0 based on TensorFlow 1.3]( ) [DIGITS 6.0 TensorFlow 1.3 Ubuntu 16 Deep Learning]( ) Traditional Computer Vision and Image Processing algorithms [Camera Calibration camera resectioning (image processing with opencv 3 & c++ , computer vision)]( ) [optical flow implementation (all methods and algorithms) on opencv 3 visual studio 2015 win 64x]( ) [Pedestrian Detection MFC visual C++ Opencv 3 human detection webcam,video,motion,frame,edge,vector]( ) [video processing by opencv 3.1 vc++ 2015 win 64x]( ) [Implementation of image pyramid in OpenCV (3.x) and Visual Studio 2015]( ) [part 2 image pyramid opencv 3 visual C++ 2015 64 bit gaussina pyramid laplacian pyramid optical flow]( ) [opencv 310 vs 2015 facedetection]( ) [opencv 3.1 VS 2015 thresholding algorithm]( ) [2018 video contents search based on deep learning]( ) ## Deep Learning in Computer Vision Applications [tutorial how to apply neural style transfer to images using OpenCV 4, C++, and deep learning (torch)]( ) [Deep Learning on MacOS (MacBook) with TensorFlow ]( ) [using trained caffe model in opencv application]( ) #pirahansiah #www.pirahansiah.com #ComputerVision #DeepLearning #AI #IoT #robot #VideoObjectTracking #Multi-Class&Multi-Object&Multi-Camera-Tracking #MCMOMCT Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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part 1 - compile OpenCV for Deep Learning]( ) [Setup Visual Studio 2017 (C++) for OpenCV 4, Deep]( ) [Build OpenCV 4 with Visual Studio 2017 (C++) OpenCV]( ) ## OpenCV 3 [(2018) How to build OpenCV 3.4 , Visual Studio ]( ) [OpenCV (All Versions of 3.x),Easy Installation Guide and Sample Project (VS 2015 C++), tutorial 1]( ) [compile OpenCV 3.2 windows 10 (64 bit) visual studio]( ) ## Deep Learning frameworks and environments [Deep Learning by OpenCV 4 (2018) - part 1 - compile]( ) [0- Using Deep Learning for Computer Vision Applications ]( ) [1- 2018 - How to compile OpenCV 3.4 , VisualStudio]( ) [2- 2018- How to setup visual studio project ]( ) [3 - OpenCV 4 , Deep Learning for Computer Vision]( ) [4-opencv4 Using TensorFlow model in OpenCV 4]( ) [2018 NVidia Caffe Ubuntu 16 CUDA 9 GTX1080 cudnn d]( ) [2018 NVidia DIGITS Ubuntu16 CUDA9 GTX1080 with Caf]( ) [2018 -torch ubuntu 16 cuda 9 cudd GTX 1080]( ) [2018- TensorFlow installation ubuntu gtx 1080 cuda]( ) [Compile OpenCV 3.2 with Visual Studio 2017 (C++) ]( ) [TensorFlow in OpenCV 3.2 Visual Studio 2017 (C++ ]( ) [Compile Caffe v1.0 on Ubuntu 16 (2017) Deep Learning]( ) [build Torch in Ubuntu 16, Deep Learning for computer visio]( ) [How to install DIGITS 6.0 based on TensorFlow 1.3]( ) [DIGITS 6.0 TensorFlow 1.3 Ubuntu 16 Deep Learning]( ) Traditional Computer Vision and Image Processing algorithms [Camera Calibration camera resectioning (image processing with opencv 3 & c++ , computer vision)]( ) [optical flow implementation (all methods and algorithms) on opencv 3 visual studio 2015 win 64x]( ) [Pedestrian Detection MFC visual C++ Opencv 3 human detection webcam,video,motion,frame,edge,vector]( ) [video processing by opencv 3.1 vc++ 2015 win 64x]( ) [Implementation of image pyramid in OpenCV (3.x) and Visual Studio 2015]( ) [part 2 image pyramid opencv 3 visual C++ 2015 64 bit gaussina pyramid laplacian pyramid optical flow]( ) [opencv 310 vs 2015 facedetection]( ) [opencv 3.1 VS 2015 thresholding algorithm]( ) [2018 video contents search based on deep learning]( ) ## Deep Learning in Computer Vision Applications [tutorial how to apply neural style transfer to images using OpenCV 4, C++, and deep learning (torch)]( ) [Deep Learning on MacOS (MacBook) with TensorFlow ]( ) [using trained caffe model in opencv application]( ) #pirahansiah #www.pirahansiah.com #ComputerVision #DeepLearning #AI #IoT #robot #VideoObjectTracking #Multi-Class&Multi-Object&Multi-Camera-Tracking #MCMOMCT Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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part 1 - compile OpenCV for Deep Learning]( ) [Setup Visual Studio 2017 (C++) for OpenCV 4, Deep]( ) [Build OpenCV 4 with Visual Studio 2017 (C++) OpenCV]( ) ## OpenCV 3 [(2018) How to build OpenCV 3.4 , Visual Studio ]( ) [OpenCV (All Versions of 3.x),Easy Installation Guide and Sample Project (VS 2015 C++), tutorial 1]( ) [compile OpenCV 3.2 windows 10 (64 bit) visual studio]( ) ## Deep Learning frameworks and environments [Deep Learning by OpenCV 4 (2018) - part 1 - compile]( ) [0- Using Deep Learning for Computer Vision Applications ]( ) [1- 2018 - How to compile OpenCV 3.4 , VisualStudio]( ) [2- 2018- How to setup visual studio project ]( ) [3 - OpenCV 4 , Deep Learning for Computer Vision]( ) [4-opencv4 Using TensorFlow model in OpenCV 4]( ) [2018 NVidia Caffe Ubuntu 16 CUDA 9 GTX1080 cudnn d]( ) [2018 NVidia DIGITS Ubuntu16 CUDA9 GTX1080 with Caf]( ) [2018 -torch ubuntu 16 cuda 9 cudd GTX 1080]( ) [2018- TensorFlow installation ubuntu gtx 1080 cuda]( ) [Compile OpenCV 3.2 with Visual Studio 2017 (C++) ]( ) [TensorFlow in OpenCV 3.2 Visual Studio 2017 (C++ ]( ) [Compile Caffe v1.0 on Ubuntu 16 (2017) Deep Learning]( ) [build Torch in Ubuntu 16, Deep Learning for computer visio]( ) [How to install DIGITS 6.0 based on TensorFlow 1.3]( ) [DIGITS 6.0 TensorFlow 1.3 Ubuntu 16 Deep Learning]( ) Traditional Computer Vision and Image Processing algorithms [Camera Calibration camera resectioning (image processing with opencv 3 & c++ , computer vision)]( ) [optical flow implementation (all methods and algorithms) on opencv 3 visual studio 2015 win 64x]( ) [Pedestrian Detection MFC visual C++ Opencv 3 human detection webcam,video,motion,frame,edge,vector]( ) [video processing by opencv 3.1 vc++ 2015 win 64x]( ) [Implementation of image pyramid in OpenCV (3.x) and Visual Studio 2015]( ) [part 2 image pyramid opencv 3 visual C++ 2015 64 bit gaussina pyramid laplacian pyramid optical flow]( ) [opencv 310 vs 2015 facedetection]( ) [opencv 3.1 VS 2015 thresholding algorithm]( ) [2018 video contents search based on deep learning]( ) ## Deep Learning in Computer Vision Applications [tutorial how to apply neural style transfer to images using OpenCV 4, C++, and deep learning (torch)]( ) [Deep Learning on MacOS (MacBook) with TensorFlow ]( ) [using trained caffe model in opencv application]( ) #pirahansiah #www.pirahansiah.com #ComputerVision #DeepLearning #AI #IoT #robot #VideoObjectTracking #Multi-Class&Multi-Object&Multi-Camera-Tracking #MCMOMCT Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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Computer Vision, Thresholding, overview, machine vision, segmentation, jupyter lab, python 2020 , machine vision, deep learning, cnn, drl, Tools, data augmentation, optimization, build dl 2020 - Pattern Recognition, style transfer, Deep Reinforcement Learning, unsupervised learning, GANs 2020 - Basic, Deep Learning, supervised, deep reinforcement learning, unsupervised, GANs, DCNN, 2020 - machine vision, camera calibration, stereo vision, video stabilization, video analytic 2020 - Computer Vision Color Thresholding Transformation Histogram Image Pyramid Motion Estimation 2020 - Deep Learning in Computer Vision overview survey complete guide tutorial categories 2020 - OneNote 2020 Management Plan Organisation Classification Nots and Documents Top Tips Guide 2020 - OneNote as team management and daily planner to optimise your day, tackle goals and roadmap OneNote 2020, Tips Guide Tag Shortcuts How to use Online Free Managing Notes and documents Top Best Thresholding image segmentation opencv python Binarization separate objects from the background Top features, extensions and plugins for visual studio code and terminal in MacOS for developer Computer Vision Using Fast API, Docker, Postman, Docker-compose cv-ml-pipline based on Seldon Core, Docker, Image Processing by python and OpenCV developer setup for MacOS (iTerm, Visual Code, Brew, python) Computer Vision and Deep Learning, AWS Advanced tools for python developer, Computer Vision, Deep Learning, AWS, MacOS, Terminal, Command Advanced Setup for Terminal based for Developer ubuntu optimization packages for deep learning and OpenCV - 1 hardware for Deep Learning Raspberry pi 4, Intel Neural Stick 2, Google Coral, Nvidia Jetson Nano Using ffmpeg library to send and receive video/camera streaming Install and Test GStreamer for Windows, Mac, Linux, Raspberry Pi Compile Optimize OpenCV for best performance in computer vision and deep learning for IoT tutorial how to apply neural style transfer to images using OpenCV 4, C++, and deep learning (torch) Deep Learning by OpenCV 4 (2018) - part 1 - compile OpenCV for Deep Learning 0- Using Deep Learning for Computer Vision Applications, TensorFlow, Caffe, OpenCV 4 4-opencv4 Using TensorFlow model in OpenCV 4 3 - OpenCV 4 , Deep Learning for Computer Vision #pirahansiah #www.pirahansiah.com #ComputerVision #DeepLearning #AI #IoT #robot #VideoObjectTracking #Multi-Class&Multi-Object&Multi-Camera-Tracking #MCMOMCT Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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Computer Vision, Thresholding, overview, machine vision, segmentation, jupyter lab, python 2020 , machine vision, deep learning, cnn, drl, Tools, data augmentation, optimization, build dl 2020 - Pattern Recognition, style transfer, Deep Reinforcement Learning, unsupervised learning, GANs 2020 - Basic, Deep Learning, supervised, deep reinforcement learning, unsupervised, GANs, DCNN, 2020 - machine vision, camera calibration, stereo vision, video stabilization, video analytic 2020 - Computer Vision Color Thresholding Transformation Histogram Image Pyramid Motion Estimation 2020 - Deep Learning in Computer Vision overview survey complete guide tutorial categories 2020 - OneNote 2020 Management Plan Organisation Classification Nots and Documents Top Tips Guide 2020 - OneNote as team management and daily planner to optimise your day, tackle goals and roadmap OneNote 2020, Tips Guide Tag Shortcuts How to use Online Free Managing Notes and documents Top Best Thresholding image segmentation opencv python Binarization separate objects from the background Top features, extensions and plugins for visual studio code and terminal in MacOS for developer Computer Vision Using Fast API, Docker, Postman, Docker-compose cv-ml-pipline based on Seldon Core, Docker, Image Processing by python and OpenCV developer setup for MacOS (iTerm, Visual Code, Brew, python) Computer Vision and Deep Learning, AWS Advanced tools for python developer, Computer Vision, Deep Learning, AWS, MacOS, Terminal, Command Advanced Setup for Terminal based for Developer ubuntu optimization packages for deep learning and OpenCV - 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Computer Vision, Thresholding, overview, machine vision, segmentation, jupyter lab, python 2020 , machine vision, deep learning, cnn, drl, Tools, data augmentation, optimization, build dl 2020 - Pattern Recognition, style transfer, Deep Reinforcement Learning, unsupervised learning, GANs 2020 - Basic, Deep Learning, supervised, deep reinforcement learning, unsupervised, GANs, DCNN, 2020 - machine vision, camera calibration, stereo vision, video stabilization, video analytic 2020 - Computer Vision Color Thresholding Transformation Histogram Image Pyramid Motion Estimation 2020 - Deep Learning in Computer Vision overview survey complete guide tutorial categories 2020 - OneNote 2020 Management Plan Organisation Classification Nots and Documents Top Tips Guide 2020 - OneNote as team management and daily planner to optimise your day, tackle goals and roadmap OneNote 2020, Tips Guide Tag Shortcuts How to use Online Free Managing Notes and documents Top Best Thresholding image segmentation opencv python Binarization separate objects from the background Top features, extensions and plugins for visual studio code and terminal in MacOS for developer Computer Vision Using Fast API, Docker, Postman, Docker-compose cv-ml-pipline based on Seldon Core, Docker, Image Processing by python and OpenCV developer setup for MacOS (iTerm, Visual Code, Brew, python) Computer Vision and Deep Learning, AWS Advanced tools for python developer, Computer Vision, Deep Learning, AWS, MacOS, Terminal, Command Advanced Setup for Terminal based for Developer ubuntu optimization packages for deep learning and OpenCV - 1 hardware for Deep Learning Raspberry pi 4, Intel Neural Stick 2, Google Coral, Nvidia Jetson Nano Using ffmpeg library to send and receive video/camera streaming Install and Test GStreamer for Windows, Mac, Linux, Raspberry Pi Compile Optimize OpenCV for best performance in computer vision and deep learning for IoT tutorial how to apply neural style transfer to images using OpenCV 4, C++, and deep learning (torch) Deep Learning by OpenCV 4 (2018) - part 1 - compile OpenCV for Deep Learning 0- Using Deep Learning for Computer Vision Applications, TensorFlow, Caffe, OpenCV 4 4-opencv4 Using TensorFlow model in OpenCV 4 3 - OpenCV 4 , Deep Learning for Computer Vision #pirahansiah #www.pirahansiah.com #ComputerVision #DeepLearning #AI #IoT #robot #VideoObjectTracking #Multi-Class&Multi-Object&Multi-Camera-Tracking #MCMOMCT Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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Computer Vision, Thresholding, overview, machine vision, segmentation, jupyter lab, python 2020 , machine vision, deep learning, cnn, drl, Tools, data augmentation, optimization, build dl 2020 - Pattern Recognition, style transfer, Deep Reinforcement Learning, unsupervised learning, GANs 2020 - Basic, Deep Learning, supervised, deep reinforcement learning, unsupervised, GANs, DCNN, 2020 - machine vision, camera calibration, stereo vision, video stabilization, video analytic 2020 - Computer Vision Color Thresholding Transformation Histogram Image Pyramid Motion Estimation 2020 - Deep Learning in Computer Vision overview survey complete guide tutorial categories 2020 - OneNote 2020 Management Plan Organisation Classification Nots and Documents Top Tips Guide 2020 - OneNote as team management and daily planner to optimise your day, tackle goals and roadmap OneNote 2020, Tips Guide Tag Shortcuts How to use Online Free Managing Notes and documents Top Best Thresholding image segmentation opencv python Binarization separate objects from the background Top features, extensions and plugins for visual studio code and terminal in MacOS for developer Computer Vision Using Fast API, Docker, Postman, Docker-compose cv-ml-pipline based on Seldon Core, Docker, Image Processing by python and OpenCV developer setup for MacOS (iTerm, Visual Code, Brew, python) Computer Vision and Deep Learning, AWS Advanced tools for python developer, Computer Vision, Deep Learning, AWS, MacOS, Terminal, Command Advanced Setup for Terminal based for Developer ubuntu optimization packages for deep learning and OpenCV - 1 hardware for Deep Learning Raspberry pi 4, Intel Neural Stick 2, Google Coral, Nvidia Jetson Nano Using ffmpeg library to send and receive video/camera streaming Install and Test GStreamer for Windows, Mac, Linux, Raspberry Pi Compile Optimize OpenCV for best performance in computer vision and deep learning for IoT tutorial how to apply neural style transfer to images using OpenCV 4, C++, and deep learning (torch) Deep Learning by OpenCV 4 (2018) - part 1 - compile OpenCV for Deep Learning 0- Using Deep Learning for Computer Vision Applications, TensorFlow, Caffe, OpenCV 4 4-opencv4 Using TensorFlow model in OpenCV 4 3 - OpenCV 4 , Deep Learning for Computer Vision #pirahansiah #www.pirahansiah.com #ComputerVision #DeepLearning #AI #IoT #robot #VideoObjectTracking #Multi-Class&Multi-Object&Multi-Camera-Tracking #MCMOMCT Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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Computer Vision, Thresholding, overview, machine vision, segmentation, jupyter lab, python 2020 , machine vision, deep learning, cnn, drl, Tools, data augmentation, optimization, build dl 2020 - Pattern Recognition, style transfer, Deep Reinforcement Learning, unsupervised learning, GANs 2020 - Basic, Deep Learning, supervised, deep reinforcement learning, unsupervised, GANs, DCNN, 2020 - machine vision, camera calibration, stereo vision, video stabilization, video analytic 2020 - Computer Vision Color Thresholding Transformation Histogram Image Pyramid Motion Estimation 2020 - Deep Learning in Computer Vision overview survey complete guide tutorial categories 2020 - OneNote 2020 Management Plan Organisation Classification Nots and Documents Top Tips Guide 2020 - OneNote as team management and daily planner to optimise your day, tackle goals and roadmap OneNote 2020, Tips Guide Tag Shortcuts How to use Online Free Managing Notes and documents Top Best Thresholding image segmentation opencv python Binarization separate objects from the background Top features, extensions and plugins for visual studio code and terminal in MacOS for developer Computer Vision Using Fast API, Docker, Postman, Docker-compose cv-ml-pipline based on Seldon Core, Docker, Image Processing by python and OpenCV developer setup for MacOS (iTerm, Visual Code, Brew, python) Computer Vision and Deep Learning, AWS Advanced tools for python developer, Computer Vision, Deep Learning, AWS, MacOS, Terminal, Command Advanced Setup for Terminal based for Developer ubuntu optimization packages for deep learning and OpenCV - 1 hardware for Deep Learning Raspberry pi 4, Intel Neural Stick 2, Google Coral, Nvidia Jetson Nano Using ffmpeg library to send and receive video/camera streaming Install and Test GStreamer for Windows, Mac, Linux, Raspberry Pi Compile Optimize OpenCV for best performance in computer vision and deep learning for IoT tutorial how to apply neural style transfer to images using OpenCV 4, C++, and deep learning (torch) Deep Learning by OpenCV 4 (2018) - part 1 - compile OpenCV for Deep Learning 0- Using Deep Learning for Computer Vision Applications, TensorFlow, Caffe, OpenCV 4 4-opencv4 Using TensorFlow model in OpenCV 4 3 - OpenCV 4 , Deep Learning for Computer Vision #pirahansiah #www.pirahansiah.com #ComputerVision #DeepLearning #AI #IoT #robot #VideoObjectTracking #Multi-Class&Multi-Object&Multi-Camera-Tracking #MCMOMCT Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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Computer Vision, Thresholding, overview, machine vision, segmentation, jupyter lab, python 2020 , machine vision, deep learning, cnn, drl, Tools, data augmentation, optimization, build dl 2020 - Pattern Recognition, style transfer, Deep Reinforcement Learning, unsupervised learning, GANs 2020 - Basic, Deep Learning, supervised, deep reinforcement learning, unsupervised, GANs, DCNN, 2020 - machine vision, camera calibration, stereo vision, video stabilization, video analytic 2020 - Computer Vision Color Thresholding Transformation Histogram Image Pyramid Motion Estimation 2020 - Deep Learning in Computer Vision overview survey complete guide tutorial categories 2020 - OneNote 2020 Management Plan Organisation Classification Nots and Documents Top Tips Guide 2020 - OneNote as team management and daily planner to optimise your day, tackle goals and roadmap OneNote 2020, Tips Guide Tag Shortcuts How to use Online Free Managing Notes and documents Top Best Thresholding image segmentation opencv python Binarization separate objects from the background Top features, extensions and plugins for visual studio code and terminal in MacOS for developer Computer Vision Using Fast API, Docker, Postman, Docker-compose cv-ml-pipline based on Seldon Core, Docker, Image Processing by python and OpenCV developer setup for MacOS (iTerm, Visual Code, Brew, python) Computer Vision and Deep Learning, AWS Advanced tools for python developer, Computer Vision, Deep Learning, AWS, MacOS, Terminal, Command Advanced Setup for Terminal based for Developer ubuntu optimization packages for deep learning and OpenCV - 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/qDZvojKIjUt_BmKSFeFiLcko8E9gUx2UPvpr5lUZqjKQRQ0VpykBihMNMzKE- OOh8UqTvee2oKfKd28QF1FzbWk=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/qDZvojKIjUt_BmKSFeFiLcko8E9gUx2UPvpr5lUZqjKQRQ0VpykBihMNMzKE- OOh8UqTvee2oKfKd28QF1FzbWk=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # ChatGPT ![](https://lh5.googleusercontent.com/W1pJt7EPyHfrb_HwJ7uA5ELTTmGX8w69ieURpOXQ1Bb5Tz3TBUZZc7gtYYa2svJvkH0TMez5TZpCgd2cXRlAR7oXDjirkED6Xw5bQX2N-o1FBCCJ55YEqF3LJOgsziWfSw=w1280) [https://www.pirahansiah.com/topics-and- projects/chatgpt](https://www.pirahansiah.com/topics-and-projects/chatgpt) MindMap of ChatGPT Prompt Engineering for Developers by deeplearning.ai [https://learn.deeplearning.ai/chatgpt-prompt- eng](https://learn.deeplearning.ai/chatgpt-prompt-eng) #ChatGPT #LLM #pirahansiah Download source : [https://github.com/pirahansiah/pirahansiah/blob/main/ChatGPT%20Prompt%20Engineering%20for%20Developers.md](https://github.com/pirahansiah/pirahansiah/blob/main/ChatGPT%20Prompt%20Engineering%20for%20Developers.md) # ChatGPT * introduction * two model * based llm * predict next word, based on text training data * instruction tuned llm * tries to follow instructions * fine-tune on instructions and good attempts at following those instructions * RLHF: reinforcement learning with human feedback * principles * 1: write clear and specific instructions * 2: give the model time to think * guidelines * two model * based llm * predict next word, based on text training data * instruction tuned llm * tries to follow instructions * fine-tune on instructions and good attempts at following those instructions * RLHF: reinforcement learning with human feedback * principles * 1: write clear and specific instructions * Tactic 1: Use delimiters * Triple quotes: “”” * Triple backticks: ``` ``` ``` * Triple dashes: - - - * Angle brackets: <> * XML tags: > User:") + tokenizer.eos_token, return_tensors='pt') bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id) print("DialoGPT: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True))) code print("www.pirahansiah.com") from transformers import GPT2LMHeadModel, GPT2Tokenizer model_name_or_path = 'microsoft/DialoGPT-medium' tokenizer = GPT2Tokenizer.from_pretrained(model_name_or_path) model = GPT2LMHeadModel.from_pretrained(model_name_or_path, timeout=3000) input_text = "Hi, how are you?" input_ids = tokenizer.encode(input_text, return_tensors='pt') response = model.generate(input_ids) output_text = tokenizer.decode(response[0], skip_special_tokens=True) print(output_text) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/qDZvojKIjUt_BmKSFeFiLcko8E9gUx2UPvpr5lUZqjKQRQ0VpykBihMNMzKE- OOh8UqTvee2oKfKd28QF1FzbWk=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/qDZvojKIjUt_BmKSFeFiLcko8E9gUx2UPvpr5lUZqjKQRQ0VpykBihMNMzKE- OOh8UqTvee2oKfKd28QF1FzbWk=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # ChatGPT ![](https://lh5.googleusercontent.com/W1pJt7EPyHfrb_HwJ7uA5ELTTmGX8w69ieURpOXQ1Bb5Tz3TBUZZc7gtYYa2svJvkH0TMez5TZpCgd2cXRlAR7oXDjirkED6Xw5bQX2N-o1FBCCJ55YEqF3LJOgsziWfSw=w1280) [https://www.pirahansiah.com/topics-and- projects/chatgpt](https://www.pirahansiah.com/topics-and-projects/chatgpt) MindMap of ChatGPT Prompt Engineering for Developers by deeplearning.ai [https://learn.deeplearning.ai/chatgpt-prompt- eng](https://learn.deeplearning.ai/chatgpt-prompt-eng) #ChatGPT #LLM #pirahansiah Download source : [https://github.com/pirahansiah/pirahansiah/blob/main/ChatGPT%20Prompt%20Engineering%20for%20Developers.md](https://github.com/pirahansiah/pirahansiah/blob/main/ChatGPT%20Prompt%20Engineering%20for%20Developers.md) # ChatGPT * introduction * two model * based llm * predict next word, based on text training data * instruction tuned llm * tries to follow instructions * fine-tune on instructions and good attempts at following those instructions * RLHF: reinforcement learning with human feedback * principles * 1: write clear and specific instructions * 2: give the model time to think * guidelines * two model * based llm * predict next word, based on text training data * instruction tuned llm * tries to follow instructions * fine-tune on instructions and good attempts at following those instructions * RLHF: reinforcement learning with human feedback * principles * 1: write clear and specific instructions * Tactic 1: Use delimiters * Triple quotes: “”” * Triple backticks: ``` ``` ``` * Triple dashes: - - - * Angle brackets: <> * XML tags: > User:") + tokenizer.eos_token, return_tensors='pt') bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id) print("DialoGPT: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True))) code print("www.pirahansiah.com") from transformers import GPT2LMHeadModel, GPT2Tokenizer model_name_or_path = 'microsoft/DialoGPT-medium' tokenizer = GPT2Tokenizer.from_pretrained(model_name_or_path) model = GPT2LMHeadModel.from_pretrained(model_name_or_path, timeout=3000) input_text = "Hi, how are you?" input_ids = tokenizer.encode(input_text, return_tensors='pt') response = model.generate(input_ids) output_text = tokenizer.decode(response[0], skip_special_tokens=True) print(output_text) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/HlmNuqyxk5gzYGxXDtUChEUYREK389bSHhqeXOjPaBaXuH- OfSzmgk4xvNu5e2EN7ntuZo418EeXtzqP8ztw9tk=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/HlmNuqyxk5gzYGxXDtUChEUYREK389bSHhqeXOjPaBaXuH- OfSzmgk4xvNu5e2EN7ntuZo418EeXtzqP8ztw9tk=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # ChatGPT ![](https://lh6.googleusercontent.com/NKGc3PsV0Hd2VLGSy4SEH1WVAWbHxCnSw28ZXjsNGBg0Sg8GNDDigoIomyJgE1hOSkfAqnjO9hqQiJtHnJrx2WjF8n9LM2jZI258sEqcBR6FG5LyE5a8ROx4Bb35Gewrfg=w1280) [https://www.pirahansiah.com/topics-and- projects/chatgpt](https://www.pirahansiah.com/topics-and-projects/chatgpt) MindMap of ChatGPT Prompt Engineering for Developers by deeplearning.ai [https://learn.deeplearning.ai/chatgpt-prompt- eng](https://learn.deeplearning.ai/chatgpt-prompt-eng) #ChatGPT #LLM #pirahansiah Download source : [https://github.com/pirahansiah/pirahansiah/blob/main/ChatGPT%20Prompt%20Engineering%20for%20Developers.md](https://github.com/pirahansiah/pirahansiah/blob/main/ChatGPT%20Prompt%20Engineering%20for%20Developers.md) # ChatGPT * introduction * two model * based llm * predict next word, based on text training data * instruction tuned llm * tries to follow instructions * fine-tune on instructions and good attempts at following those instructions * RLHF: reinforcement learning with human feedback * principles * 1: write clear and specific instructions * 2: give the model time to think * guidelines * two model * based llm * predict next word, based on text training data * instruction tuned llm * tries to follow instructions * fine-tune on instructions and good attempts at following those instructions * RLHF: reinforcement learning with human feedback * principles * 1: write clear and specific instructions * Tactic 1: Use delimiters * Triple quotes: “”” * Triple backticks: ``` ``` ``` * Triple dashes: - - - * Angle brackets: <> * XML tags: > User:") + tokenizer.eos_token, return_tensors='pt') bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id) print("DialoGPT: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True))) code print("www.pirahansiah.com") from transformers import GPT2LMHeadModel, GPT2Tokenizer model_name_or_path = 'microsoft/DialoGPT-medium' tokenizer = GPT2Tokenizer.from_pretrained(model_name_or_path) model = GPT2LMHeadModel.from_pretrained(model_name_or_path, timeout=3000) input_text = "Hi, how are you?" input_ids = tokenizer.encode(input_text, return_tensors='pt') response = model.generate(input_ids) output_text = tokenizer.decode(response[0], skip_special_tokens=True) print(output_text) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/qDZvojKIjUt_BmKSFeFiLcko8E9gUx2UPvpr5lUZqjKQRQ0VpykBihMNMzKE- OOh8UqTvee2oKfKd28QF1FzbWk=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/qDZvojKIjUt_BmKSFeFiLcko8E9gUx2UPvpr5lUZqjKQRQ0VpykBihMNMzKE- OOh8UqTvee2oKfKd28QF1FzbWk=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # AI_Hub ## [https://www.pirahansiah.com/topics-and- projects/ai_hub](https://www.pirahansiah.com/topics-and-projects/ai_hub) ## ChatGPT 4; Lamda; Bard; * ChatGPT : [https://chat.openai.com/chat](https://chat.openai.com/chat) * PDF (your inputs): [https://www.humata.ai](https://www.humata.ai) * [www.perplexity.ai](https://www.perplexity.ai/) \- [https://chrome.google.com/webstore/detail/perplexity/hlgbcneanomplepojfcnclggenpcoldo/related](https://chrome.google.com/webstore/detail/perplexity/hlgbcneanomplepojfcnclggenpcoldo/related) * ChatGPT for Google: [https://chrome.google.com/webstore/detail/chatgpt-for-google/jgjaeacdkonaoafenlfkkkmbaopkbilf/related](https://chrome.google.com/webstore/detail/chatgpt-for-google/jgjaeacdkonaoafenlfkkkmbaopkbilf/related) * [https://quickref.me/](https://quickref.me/) * Google Bard: [https://blog.google/technology/ai/bard-google-ai-search-updates/](https://blog.google/technology/ai/bard-google-ai-search-updates/) * [https://www.bing.com/new](https://www.bing.com/new) * Connect ChatGPT to internet and get updated result: [https://github.com/qunash/chatgpt-advanced](https://github.com/qunash/chatgpt-advanced) : [https://chrome.google.com/webstore/detail/webchatgpt-chatgpt-with-i/lpfemeioodjbpieminkklglpmhlngfcn](https://chrome.google.com/webstore/detail/webchatgpt-chatgpt-with-i/lpfemeioodjbpieminkklglpmhlngfcn) * create video: [https://research.runwayml.com/gen1](https://research.runwayml.com/gen1) * [https://you.com/](https://you.com/) * [https://www.descript.com/](https://www.descript.com/) : There’s a new way to make video and podcasts. A good way. * Buildt AI: VS Code Extension [https://marketplace.visualstudio.com/items?itemName=BuildtAI.buildt-vscode](https://marketplace.visualstudio.com/items?itemName=BuildtAI.buildt-vscode) * upload files for ChatGPT [https://knowledgegpt.streamlit.app/](https://knowledgegpt.streamlit.app/) * Build an AI chatbot trained on your data: [https://www.chatbase.co/](https://www.chatbase.co/) * HeyBot can be easily taught to answer questions about any topic in a friendly way. Try it out for yourself by clicking on the avatar. [https://www.heybot.ai/](https://www.heybot.ai/) * VALL-E: Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers: [https://valle-demo.github.io/](https://valle-demo.github.io/) * Creating images with text: [https://imgcreator.ai/](https://imgcreator.ai/) * * Created by [https://app.writesonic.com](https://app.writesonic.com) Creating a cloud-based image processing system with deep learning on the Amazon Web Services (AWS) platform is a complex task, but it is achievable with a combination of OpenCV, C++, Internet of Things (IoT), robotics, augmented reality (AR) and virtual reality (VR), and natural language processing libraries like ChatGPT. OpenCV is a powerful library for computer vision, and it allows you to detect and track objects in images or videos. Using OpenCV, you can build an image processing system that can identify objects in an image, segment them, and apply filters or other transformations. You can also combine OpenCV with C++, a powerful programming language, to make the image processing system even more powerful. The IoT aspect of the system allows it to interact with other devices, like robots, AR and VR systems, or other sensors. By connecting the system to other devices, you can enable it to perform more complex tasks, like controlling robots and interacting with an AR or VR environment. ChatGPT is a natural language processing library that can enable the cloud- based image processing system to generate text descriptions for images. By leveraging deep learning, ChatGPT can accurately recognize objects in an image and generate natural language descriptions, greatly improving the system's accuracy and efficiency. In conclusion, building a cloud-based image processing system with deep learning on the AWS platform is possible with the right combination of tools, including OpenCV, C++, IoT, robotics, AR and VR, and ChatGPT. With these tools, you can create an image processing system capable of accurately recognizing objects and generating natural language descriptions. will come soon: Quora: [https://poe.com/](https://poe.com/) Turing NL G by Nvidia ‏ Sparrow by DeepMind ‏ LaMDA by Google ‏ AlexaTM by Amazon ‏ OPT by Meta ‏ Claude by Anthropic ‏ PaLM by Google Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/qDZvojKIjUt_BmKSFeFiLcko8E9gUx2UPvpr5lUZqjKQRQ0VpykBihMNMzKE- OOh8UqTvee2oKfKd28QF1FzbWk=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/qDZvojKIjUt_BmKSFeFiLcko8E9gUx2UPvpr5lUZqjKQRQ0VpykBihMNMzKE- OOh8UqTvee2oKfKd28QF1FzbWk=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # AI_Hub ## [https://www.pirahansiah.com/topics-and- projects/ai_hub](https://www.pirahansiah.com/topics-and-projects/ai_hub) ## ChatGPT 4; Lamda; Bard; * ChatGPT : [https://chat.openai.com/chat](https://chat.openai.com/chat) * PDF (your inputs): [https://www.humata.ai](https://www.humata.ai) * [www.perplexity.ai](https://www.perplexity.ai/) \- [https://chrome.google.com/webstore/detail/perplexity/hlgbcneanomplepojfcnclggenpcoldo/related](https://chrome.google.com/webstore/detail/perplexity/hlgbcneanomplepojfcnclggenpcoldo/related) * ChatGPT for Google: [https://chrome.google.com/webstore/detail/chatgpt-for-google/jgjaeacdkonaoafenlfkkkmbaopkbilf/related](https://chrome.google.com/webstore/detail/chatgpt-for-google/jgjaeacdkonaoafenlfkkkmbaopkbilf/related) * [https://quickref.me/](https://quickref.me/) * Google Bard: [https://blog.google/technology/ai/bard-google-ai-search-updates/](https://blog.google/technology/ai/bard-google-ai-search-updates/) * [https://www.bing.com/new](https://www.bing.com/new) * Connect ChatGPT to internet and get updated result: [https://github.com/qunash/chatgpt-advanced](https://github.com/qunash/chatgpt-advanced) : [https://chrome.google.com/webstore/detail/webchatgpt-chatgpt-with-i/lpfemeioodjbpieminkklglpmhlngfcn](https://chrome.google.com/webstore/detail/webchatgpt-chatgpt-with-i/lpfemeioodjbpieminkklglpmhlngfcn) * create video: [https://research.runwayml.com/gen1](https://research.runwayml.com/gen1) * [https://you.com/](https://you.com/) * [https://www.descript.com/](https://www.descript.com/) : There’s a new way to make video and podcasts. A good way. * Buildt AI: VS Code Extension [https://marketplace.visualstudio.com/items?itemName=BuildtAI.buildt-vscode](https://marketplace.visualstudio.com/items?itemName=BuildtAI.buildt-vscode) * upload files for ChatGPT [https://knowledgegpt.streamlit.app/](https://knowledgegpt.streamlit.app/) * Build an AI chatbot trained on your data: [https://www.chatbase.co/](https://www.chatbase.co/) * HeyBot can be easily taught to answer questions about any topic in a friendly way. Try it out for yourself by clicking on the avatar. [https://www.heybot.ai/](https://www.heybot.ai/) * VALL-E: Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers: [https://valle-demo.github.io/](https://valle-demo.github.io/) * Creating images with text: [https://imgcreator.ai/](https://imgcreator.ai/) * * Created by [https://app.writesonic.com](https://app.writesonic.com) Creating a cloud-based image processing system with deep learning on the Amazon Web Services (AWS) platform is a complex task, but it is achievable with a combination of OpenCV, C++, Internet of Things (IoT), robotics, augmented reality (AR) and virtual reality (VR), and natural language processing libraries like ChatGPT. OpenCV is a powerful library for computer vision, and it allows you to detect and track objects in images or videos. Using OpenCV, you can build an image processing system that can identify objects in an image, segment them, and apply filters or other transformations. You can also combine OpenCV with C++, a powerful programming language, to make the image processing system even more powerful. The IoT aspect of the system allows it to interact with other devices, like robots, AR and VR systems, or other sensors. By connecting the system to other devices, you can enable it to perform more complex tasks, like controlling robots and interacting with an AR or VR environment. ChatGPT is a natural language processing library that can enable the cloud- based image processing system to generate text descriptions for images. By leveraging deep learning, ChatGPT can accurately recognize objects in an image and generate natural language descriptions, greatly improving the system's accuracy and efficiency. In conclusion, building a cloud-based image processing system with deep learning on the AWS platform is possible with the right combination of tools, including OpenCV, C++, IoT, robotics, AR and VR, and ChatGPT. With these tools, you can create an image processing system capable of accurately recognizing objects and generating natural language descriptions. will come soon: Quora: [https://poe.com/](https://poe.com/) Turing NL G by Nvidia ‏ Sparrow by DeepMind ‏ LaMDA by Google ‏ AlexaTM by Amazon ‏ OPT by Meta ‏ Claude by Anthropic ‏ PaLM by Google Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. 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Lamda; Bard; * ChatGPT : [https://chat.openai.com/chat](https://chat.openai.com/chat) * PDF (your inputs): [https://www.humata.ai](https://www.humata.ai) * [www.perplexity.ai](https://www.perplexity.ai/) \- [https://chrome.google.com/webstore/detail/perplexity/hlgbcneanomplepojfcnclggenpcoldo/related](https://chrome.google.com/webstore/detail/perplexity/hlgbcneanomplepojfcnclggenpcoldo/related) * ChatGPT for Google: [https://chrome.google.com/webstore/detail/chatgpt-for-google/jgjaeacdkonaoafenlfkkkmbaopkbilf/related](https://chrome.google.com/webstore/detail/chatgpt-for-google/jgjaeacdkonaoafenlfkkkmbaopkbilf/related) * [https://quickref.me/](https://quickref.me/) * Google Bard: [https://blog.google/technology/ai/bard-google-ai-search-updates/](https://blog.google/technology/ai/bard-google-ai-search-updates/) * [https://www.bing.com/new](https://www.bing.com/new) * Connect ChatGPT to internet and get updated result: [https://github.com/qunash/chatgpt-advanced](https://github.com/qunash/chatgpt-advanced) : [https://chrome.google.com/webstore/detail/webchatgpt-chatgpt-with-i/lpfemeioodjbpieminkklglpmhlngfcn](https://chrome.google.com/webstore/detail/webchatgpt-chatgpt-with-i/lpfemeioodjbpieminkklglpmhlngfcn) * create video: [https://research.runwayml.com/gen1](https://research.runwayml.com/gen1) * [https://you.com/](https://you.com/) * [https://www.descript.com/](https://www.descript.com/) : There’s a new way to make video and podcasts. A good way. * Buildt AI: VS Code Extension [https://marketplace.visualstudio.com/items?itemName=BuildtAI.buildt-vscode](https://marketplace.visualstudio.com/items?itemName=BuildtAI.buildt-vscode) * upload files for ChatGPT [https://knowledgegpt.streamlit.app/](https://knowledgegpt.streamlit.app/) * Build an AI chatbot trained on your data: [https://www.chatbase.co/](https://www.chatbase.co/) * HeyBot can be easily taught to answer questions about any topic in a friendly way. Try it out for yourself by clicking on the avatar. [https://www.heybot.ai/](https://www.heybot.ai/) * VALL-E: Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers: [https://valle-demo.github.io/](https://valle-demo.github.io/) * Creating images with text: [https://imgcreator.ai/](https://imgcreator.ai/) * * Created by [https://app.writesonic.com](https://app.writesonic.com) Creating a cloud-based image processing system with deep learning on the Amazon Web Services (AWS) platform is a complex task, but it is achievable with a combination of OpenCV, C++, Internet of Things (IoT), robotics, augmented reality (AR) and virtual reality (VR), and natural language processing libraries like ChatGPT. OpenCV is a powerful library for computer vision, and it allows you to detect and track objects in images or videos. Using OpenCV, you can build an image processing system that can identify objects in an image, segment them, and apply filters or other transformations. You can also combine OpenCV with C++, a powerful programming language, to make the image processing system even more powerful. The IoT aspect of the system allows it to interact with other devices, like robots, AR and VR systems, or other sensors. By connecting the system to other devices, you can enable it to perform more complex tasks, like controlling robots and interacting with an AR or VR environment. ChatGPT is a natural language processing library that can enable the cloud- based image processing system to generate text descriptions for images. By leveraging deep learning, ChatGPT can accurately recognize objects in an image and generate natural language descriptions, greatly improving the system's accuracy and efficiency. In conclusion, building a cloud-based image processing system with deep learning on the AWS platform is possible with the right combination of tools, including OpenCV, C++, IoT, robotics, AR and VR, and ChatGPT. With these tools, you can create an image processing system capable of accurately recognizing objects and generating natural language descriptions. will come soon: Quora: [https://poe.com/](https://poe.com/) Turing NL G by Nvidia ‏ Sparrow by DeepMind ‏ LaMDA by Google ‏ AlexaTM by Amazon ‏ OPT by Meta ‏ Claude by Anthropic ‏ PaLM by Google Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/HlmNuqyxk5gzYGxXDtUChEUYREK389bSHhqeXOjPaBaXuH- OfSzmgk4xvNu5e2EN7ntuZo418EeXtzqP8ztw9tk=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/HlmNuqyxk5gzYGxXDtUChEUYREK389bSHhqeXOjPaBaXuH- OfSzmgk4xvNu5e2EN7ntuZo418EeXtzqP8ztw9tk=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/HlmNuqyxk5gzYGxXDtUChEUYREK389bSHhqeXOjPaBaXuH- OfSzmgk4xvNu5e2EN7ntuZo418EeXtzqP8ztw9tk=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/HlmNuqyxk5gzYGxXDtUChEUYREK389bSHhqeXOjPaBaXuH- OfSzmgk4xvNu5e2EN7ntuZo418EeXtzqP8ztw9tk=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # knowledge_management Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/qDZvojKIjUt_BmKSFeFiLcko8E9gUx2UPvpr5lUZqjKQRQ0VpykBihMNMzKE- OOh8UqTvee2oKfKd28QF1FzbWk=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/qDZvojKIjUt_BmKSFeFiLcko8E9gUx2UPvpr5lUZqjKQRQ0VpykBihMNMzKE- OOh8UqTvee2oKfKd28QF1FzbWk=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Commonplace Book Second Brain Why? Reference # Second Brain A commonplace book is a system for writing down and sorting all manner of tidbits: quotes, anecdotes, observations, and information gleaned from books, conversations, movies, song lyrics, social posts, podcasts, life experiences, or anything else that you might want to return to later. * # Why? A commonplace book is a system for writing down and sorting all manner of tidbits: quotes, anecdotes, observations, and information gleaned from books, conversations, movies, song lyrics, social posts, podcasts, life experiences, or anything else that you might want to return to later. Where Is It Located? What Type of Content Is This? Mac=Control–Command–Space bar windows= win+. ✔ Symbols Note * ✅ [] (1) (orange) ToDo * ✳️ * (2) (pink) Important * 🤔 ? (3) (Purple) Question * 😮 ! (4) (yellow) Attention/remember for later * 🥳 ^ (5) (green) New idea or direction * 🧑‍💻 {} (6) (blue) Need research * ⚡︎ || (7) (brown) Management ✅ [] (1) (orange) ToDo * my long term todo ✳️ * (2) (pink) Important * important 🤔 ? (3) (Purple) Question 😮 ! (4) (yellow) Attention/remember for later 🥳 ^ (5) (green) New idea or direction * create datasets for multi-class multi-object tracking based on self collected videos with different camera **s** and resolutions 🧑‍💻 {} (6) (blue) Need research 🧑‍💻 || (7) (brown) Management 1. 6 minutes diary 2. Dran Bleliben 3. 2023 Planer # Reference * [MINIMALIST JOURNAL IDEAS » ft. 6-Minute Diary (productivity, self love, mindfulness) ](https://www.youtube.com/watch?v=s0qGJOP3HaA&ab_channel=SimpleHappyZen) * Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/qDZvojKIjUt_BmKSFeFiLcko8E9gUx2UPvpr5lUZqjKQRQ0VpykBihMNMzKE- OOh8UqTvee2oKfKd28QF1FzbWk=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/qDZvojKIjUt_BmKSFeFiLcko8E9gUx2UPvpr5lUZqjKQRQ0VpykBihMNMzKE- OOh8UqTvee2oKfKd28QF1FzbWk=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Commonplace Book Second Brain Why? Reference # Second Brain A commonplace book is a system for writing down and sorting all manner of tidbits: quotes, anecdotes, observations, and information gleaned from books, conversations, movies, song lyrics, social posts, podcasts, life experiences, or anything else that you might want to return to later. * # Why? A commonplace book is a system for writing down and sorting all manner of tidbits: quotes, anecdotes, observations, and information gleaned from books, conversations, movies, song lyrics, social posts, podcasts, life experiences, or anything else that you might want to return to later. Where Is It Located? What Type of Content Is This? Mac=Control–Command–Space bar windows= win+. ✔ Symbols Note * ✅ [] (1) (orange) ToDo * ✳️ * (2) (pink) Important * 🤔 ? (3) (Purple) Question * 😮 ! (4) (yellow) Attention/remember for later * 🥳 ^ (5) (green) New idea or direction * 🧑‍💻 {} (6) (blue) Need research * ⚡︎ || (7) (brown) Management ✅ [] (1) (orange) ToDo * my long term todo ✳️ * (2) (pink) Important * important 🤔 ? (3) (Purple) Question 😮 ! (4) (yellow) Attention/remember for later 🥳 ^ (5) (green) New idea or direction * create datasets for multi-class multi-object tracking based on self collected videos with different camera **s** and resolutions 🧑‍💻 {} (6) (blue) Need research 🧑‍💻 || (7) (brown) Management 1. 6 minutes diary 2. Dran Bleliben 3. 2023 Planer # Reference * [MINIMALIST JOURNAL IDEAS » ft. 6-Minute Diary (productivity, self love, mindfulness) ](https://www.youtube.com/watch?v=s0qGJOP3HaA&ab_channel=SimpleHappyZen) * Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/qDZvojKIjUt_BmKSFeFiLcko8E9gUx2UPvpr5lUZqjKQRQ0VpykBihMNMzKE- OOh8UqTvee2oKfKd28QF1FzbWk=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/qDZvojKIjUt_BmKSFeFiLcko8E9gUx2UPvpr5lUZqjKQRQ0VpykBihMNMzKE- OOh8UqTvee2oKfKd28QF1FzbWk=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Commonplace Book Second Brain Why? Reference # Second Brain A commonplace book is a system for writing down and sorting all manner of tidbits: quotes, anecdotes, observations, and information gleaned from books, conversations, movies, song lyrics, social posts, podcasts, life experiences, or anything else that you might want to return to later. * # Why? A commonplace book is a system for writing down and sorting all manner of tidbits: quotes, anecdotes, observations, and information gleaned from books, conversations, movies, song lyrics, social posts, podcasts, life experiences, or anything else that you might want to return to later. Where Is It Located? What Type of Content Is This? Mac=Control–Command–Space bar windows= win+. ✔ Symbols Note * ✅ [] (1) (orange) ToDo * ✳️ * (2) (pink) Important * 🤔 ? (3) (Purple) Question * 😮 ! (4) (yellow) Attention/remember for later * 🥳 ^ (5) (green) New idea or direction * 🧑‍💻 {} (6) (blue) Need research * ⚡︎ || (7) (brown) Management ✅ [] (1) (orange) ToDo * my long term todo ✳️ * (2) (pink) Important * important 🤔 ? (3) (Purple) Question 😮 ! (4) (yellow) Attention/remember for later 🥳 ^ (5) (green) New idea or direction * create datasets for multi-class multi-object tracking based on self collected videos with different camera **s** and resolutions 🧑‍💻 {} (6) (blue) Need research 🧑‍💻 || (7) (brown) Management 1. 6 minutes diary 2. Dran Bleliben 3. 2023 Planer # Reference * [MINIMALIST JOURNAL IDEAS » ft. 6-Minute Diary (productivity, self love, mindfulness) ](https://www.youtube.com/watch?v=s0qGJOP3HaA&ab_channel=SimpleHappyZen) * Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/HlmNuqyxk5gzYGxXDtUChEUYREK389bSHhqeXOjPaBaXuH- OfSzmgk4xvNu5e2EN7ntuZo418EeXtzqP8ztw9tk=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/HlmNuqyxk5gzYGxXDtUChEUYREK389bSHhqeXOjPaBaXuH- OfSzmgk4xvNu5e2EN7ntuZo418EeXtzqP8ztw9tk=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Commonplace Book Second Brain Why? Reference # Second Brain A commonplace book is a system for writing down and sorting all manner of tidbits: quotes, anecdotes, observations, and information gleaned from books, conversations, movies, song lyrics, social posts, podcasts, life experiences, or anything else that you might want to return to later. * # Why? A commonplace book is a system for writing down and sorting all manner of tidbits: quotes, anecdotes, observations, and information gleaned from books, conversations, movies, song lyrics, social posts, podcasts, life experiences, or anything else that you might want to return to later. Where Is It Located? What Type of Content Is This? Mac=Control–Command–Space bar windows= win+. ✔ Symbols Note * ✅ [] (1) (orange) ToDo * ✳️ * (2) (pink) Important * 🤔 ? (3) (Purple) Question * 😮 ! (4) (yellow) Attention/remember for later * 🥳 ^ (5) (green) New idea or direction * 🧑‍💻 {} (6) (blue) Need research * ⚡︎ || (7) (brown) Management ✅ [] (1) (orange) ToDo * my long term todo ✳️ * (2) (pink) Important * important 🤔 ? (3) (Purple) Question 😮 ! (4) (yellow) Attention/remember for later 🥳 ^ (5) (green) New idea or direction * create datasets for multi-class multi-object tracking based on self collected videos with different camera **s** and resolutions 🧑‍💻 {} (6) (blue) Need research 🧑‍💻 || (7) (brown) Management 1. 6 minutes diary 2. Dran Bleliben 3. 2023 Planer # Reference * [MINIMALIST JOURNAL IDEAS » ft. 6-Minute Diary (productivity, self love, mindfulness) ](https://www.youtube.com/watch?v=s0qGJOP3HaA&ab_channel=SimpleHappyZen) * Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/3LTMUMvLqIOxMLGRMkavoKhjR0yGpVpsaeb_NnspLybaPexjoQBnvVa2C2973HUQ30RiIqubg5B9F6eLjnxKXlE=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/3LTMUMvLqIOxMLGRMkavoKhjR0yGpVpsaeb_NnspLybaPexjoQBnvVa2C2973HUQ30RiIqubg5B9F6eLjnxKXlE=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Book Summary Books How to take smart Notes Writing Solid Code Shape Up: Stop Running in Circles and Ship Work that Matters Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet List of books I've read: Computer Vision: Algorithms and Applications Multiple View Geometry in Computer Vision Second Edition Complete to download the latest draft of Machine Learning Yearning Multiple view geometry in computer vision Learning OpenCV Book by Adrian Kaehler and Gary Bradski Digital Image Processing, Global Edition by Rafael C. Gonzalez Introduction to Algorithms, 3rd Edition (The MIT Press) The Mythical Man-Month: Essays on Software Engineering OpenCV-4-with-Python-Blueprints-Second-Edition Cracking the coding interview 189 programming questions and solutions Cracking the Tech Career: Insider Advice on Landing a Job at Google, Microsoft, Apple, Or Any Top Tech Company The Art of Startup Fundraising Mathematics for Machine Learning Principles of Economics (6th edition) The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) Reinventing Your Life: The Breakthough Program to End Negative Behavior Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf Magazine Papers Websites and links Tools YouTube - List channels Language: Essential Tips And Tricks For commonplace book knowledge management PKM # Books * ## How to take smart Notes * Introduction 4, The four underlying principles 4, the six steps to successful writing 6, afterword * introduction * write everything/ collect notes not related to any thing, organize notes, revirew, rewrite, * no reorganise no complex system, develop ideas by smart notes * 1 slip-box; 2 repeatable tasks that can become automatic and fit together seamlessly change routinges * overarching workflow "Getting Things Done" GTD * constantly have to ump back and forth between different tasks * 1- bibliographical slip-box: reference * 2- collected and generated ideas slip-box * 3- index: notes serve as entry point / thought/ topic * writing a paper step by step * 1 fleeting notes: it will delete within a day * I always have a slip of paper at hand, on which I note down the ideas of certain pages. on the backside I write down the bibliographic details. after finishing the book I go throught my notes and think how ehse notes might be relevant for already written notes in the slip-box. it means that I always read with an eye towards possible connections in the slip-box * 2 literature notes: contain the necessary information - in reference system or in the slip-box * I make a note with the bibliographic details. on the backside I would write "on page x is this, on page y is that" and then it goes into the bibliographic slib-box where I collect everything I read * read, think about its relevance * theoretical background, methodological approach or perspective of the text we read. * whether something adds to a discussion in the slip-box * we look at our slip-box for already existing lines of thought and think about the questions and problems already on our minds to which a new note might contribute. - assigning keywords is much more than just a bureaucratic act. it is a crucial part of the thinking process, which often leads to a deeper elaboration of the note itself and the connection to other notes. * * 3 permanent notes: develop ideas/new/ - only relevant to one particular project - in project specific folder - can be discarded or archived after project finished * add to slip-box behind the note you dirctyly refer to or behind the last note in the slip-box * add links to other notes or links on other notes to your new note * make sure it can be found from the index (MOC) add an entry in the index if necessary or refer to it from a note that is connected to the index * build a latticework of mental models * 4 behind multiple notes (link backward) - link to related note - link to MOC * make sure it can be found again * **keywords** can easily be added to a note like **tags** and will then show up in the **index** * a day * read and take notes * build connections within the slip-box - new idea * you write on your paper, notice a hole in the argument and have another look in the file system for the missing link. you follow up on a footnote, go back to research and might add a fitting quote to one of your papers in the making. * The four underlying principles * writing * simplicity * not from scratch * let the work carry you forward * the six steps to successful writing * separate and interlocking tasks * we use memo techniques is to bundle items together in a meaningful way and remember the bundles * the brain doesn't distinguish between an actual finished task and one that is postponed by taking a note * the fist step is to break down the amprphous task of "writing" into smaller pieces of different tasks that can be finished in **one go.** the second step is to make sure we always write down the outcome of our thinking, including possible connections to further inquiries. * ego depletion * read for understanding * take smart notes * develp ideas (MOC) * 1 overview of a topic - referred to from the index and used as an entry point into a topic has already develped * 2 keeping tarck of all topic - index - * share your insight * make it a habit * afterword * ## Writing Solid Code Writing Solid Code (Microsoft Programming Series) by Trendelles Store Note from Writing Solid Code Book [https://www.pirahansiah.com/describe/book- summary](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Fdescribe%2Fbook- summary&sa=D&sntz=1&usg=AOvVaw3ACzA6G0MBVN3nw6f-yMQo) #SolidCode #C++ #Python #pirahansiah * **Chapter 1** * Enable all optional compiler warnings. * Use lint to catch bugs that your compiler may miss. * If you have unit tests, use them. * **Chapter 2** * introduction assert page 16; assert.h * assert is nothing more than a repackaged form of the #ifdef code we saw before, but when you use * the macro, it takes one line instead of seven * That's why assert is a macro and not a function; if it were a function, calling * it could cause unexpected memory or code swapping. * Use assertions to validate function arguments . * Strip undefined behavior from your code, or use assertions to catch illegal uses of undefined behavior . * Don't waste people's time. Document unclear assertions . * Either remove implicit assumptions, or assert that they are valid . * Use assertions to detect impossible conditions. * Don't hide bugs when you program defensively. * Use a second algorithm to validate your results. * Don't wait for bugs to happen; use startup checks. * **Chapter 3** * Maintain shipping and debugging versions of your program. Shrink-wrap the shipping version, but use the debugging ver­sion as much as possible to catch bugs quickly. * Assertions are a shorthand way to write debugging checks. Use them to catch illegal conditions that should never arise. Don't confuse such conditions with error conditions, which you must handle in the final product. * Use assertions to validate function arguments and to alert pro­ grammars when they do something undefined. The more rigidly you define your functions, the easier it will be to validate the arguments. * Once you've written a function, review it and ask yourself, 'What am I assuming?" If you find an assumption, either assert that your assumption is always valid, or rewrite the code to re­ move the assumption. Also ask, 'What is most likely to be wrong in this code, and how can I automatically detect the prob­lem?" Strive to implement tests that catch bugs at the earliest possible moment. Textbooks encourage programmers to program defensively, but remember that this coding style hides bugs. When you write de­fensive code, use assertions to alert you if the "can't happen" cases do happen. * Eliminate random behavior. Force bugs to be reproducible . * Destroy your garbage so that it's not misused . * Not only did reallocate have to move the block of memory as it was expanding it, but the old memory had to be reallocated and filled with new data. In the assembler, both happened rarely. * This brings up another guideline for writing bug-free code: You don't want anything to happen rarely. You need to isolate those behaviors in your subsystems that may happen and make sure that they do happen. And of­ ten. If you find rare behavior in your subsystems, be sure to do something­ anything-to stir things up. * The assembler bug could have been found within hours, instead of years, if reallocate hadn't so rarely moved blocks when it expanded them. * If something happens rarely, force it to happen often. * Keep debug information to allow stronger error checking. * There is no question that the debug code will create differences be­ tween the ship and the debug versions of your code, but as long as you're careful not to change the underlying behavior of the code, those differences shouldn't matter. * Create thorough subsystem checks, and use them often. * Design your tests carefully. Nothing should be arbitrary. * Strive to implement transparent integrity checks. * Don't apply ship version constraints to the debug version. Trade size and speed for error detection. * **Chapter 4** * Don't wait until you have a bug to step through your code. * Step through every code path. * What About Sweeping Changes? In the past, programmers have asked, "What if I add a feature that touches code in many places? Stepping through all that new code is going to be time consuming." Let me answer that question with another: Can you make such sweeping changes without introducing any bugs? The habit of stepping through your code creates an interesting negative feedback loop. Programmers who step through their code soon learn to write small, easily testable functions because stepping through large func­tions is so painful. Programmers also spend more time thinking about how they can localize the changes they need to make-again, so that they can more easily test their code. And isn't this exactly what you want? No project lead likes it when programmers touch a lot of code; it's too destabilizing. Nor do leads like large, unmanageable functions; they're often unmaintainable. Try hard to localize your changes. If you find that you must make per­vasive changes, think twice before you decide not to step through all the new code. * Overflow and underflow bugs * Data conversion bugs * Off-by-one bugs * NULL pointer bugs * Bugs using garbage memory (0xA3 bugs) * Assignment bugs in which you've used = instead of == * Precedence bugs * Logic bugs * As you step through code, focus on data flow. Source level debuggers can hide execution details. Step through critical code at the instruction level . * **Chapter 5** * Make it hard to ignore error conditions. Don't bury error codes in return values. * Always look for, and eliminate, flaws in your interfaces. * Don't write multipurpose functions. Write separate functions to allow stronger argument validation. * Don't be wishy-washy. Define explicit function arguments. * Write functions that, given valid inputs, cannot fail. * Make the code intelligible at the point of call. Avoid Boolean arguments. * Write comments that emphasize potential hazards. * **Chapter 6** * Use well-defined data types. * Always ask ,"Can this variable or expression over- or underflow?" * Implement your designs as accurately as possible. Being kind a close is being kind a buggy. * these principles already: Don't accept special purpose arguments such as the NULL pointer, and Implement your design, not something that approximates it. The third principle is new: Strive to make every function perform its task exactly one time. * Implement "the task" just once. * Get rid of extraneous if statements. * Avoid using nested ?: operators . * Handle your special cases just once. * Avoid risky language idioms. * Don't needlessly mix operator types. If you must mix operators, use parentheses to isolate the operations. * Avoid calling functions that return errors. * **Chapter 7** * Don't reference memory that you don't own. * Don't reference memory that you have freed. * Don't use output memory as workspace buffers. * Don't pass data in static (or global) memory. * Don't write parasitic functions. * Don't abuse your programming language. * Tight C does not guarantee efficient machine code. * Write code for the "average" programmer. * **Chapter 8** * Bugs don't just "go away." * Don't fix bugs later; fix them now. * Fix the cause, not the symptom. * Don't clean up code unless the clean­ up is critical to the product's success. * Don't implement nonstrategic features. * There are no free features. * Don't allow unnecessary flexibility. * Don't keep "trying" solutions· until you find one that works. Take the time to find the correct solution. * Write and test code in small chunks. * Always test your code, even if that means your schedule will slip. * Don't rely on the testing group to find your bugs. * Don't blame testers for finding your bugs. * Establish your priorities and stick to them. * Never allow the same bug to bite you twice. * **A: CODING CHECKLISTS** * **B: MEMORY LOGGING ROUTINES** * **To sum up** * Charles Simonyi in the early 1970s. https://en.wikipedia.org/wiki/Hungarian_notation * char ch; * byte b; * flag f;/* flags that are always TRUE or FALSE*/ * symbol sym;/* some sort of symbol structure*/ * char byte flag symbol * *pch:/* character pointer*/ * *pb; * *pf: * *psym: * related link: [https://www.morling.dev/images/code_review_pyramid.svg](https://www.google.com/url?q=https%3A%2F%2Fwww.morling.dev%2Fimages%2Fcode_review_pyramid.svg&sa=D&sntz=1&usg=AOvVaw1y2It_PUzXAtFN7dPLegCa) * ## Shape Up: Stop Running in Circles and Ship Work that Matters * Six-week cycles: Our decisions are based on moving the product forward in the next six weeks, not micromanaging time. We don’t count hours or question how individual days are spent. We don’t have daily meetings. We don’t rethink our roadmap every two weeks. Our focus is at a higher level. We say to ourselves: “If this project ships after six weeks, we’ll be really happy. We’ll feel our time was well spent.” Then we commit the six weeks and leave the team alone to get it done * Shaping the work: key elements of a solution, appetite * **part one shaping:** * two separate tracks: one for shaping, one for building. * Steps to shaping * 1\. Set boundaries. * 2\. Rough out the elements: idea that solves the problem within the appetite but without all the fine details worked out * 3\. Address risks and rabbit holes. * 4\. Write the pitch. * fixed time, variable scope, * Where in the current system does the new thing fit? How do you get to it? What are the key components or interactions? Where does it take you? * five ingredients that we always want to include in a pitch: 1. Problem — The raw idea, a use case, or something we’ve seen that motivates us to work on this 2. Appetite — How much time we want to spend and how that constrains the solution 3. Solution — The core elements we came up with, presented in a form that’s easy for people to immediately understand 4. Rabbit holes — Details about the solution worth calling out to avoid problems 5. No-gos — Anything specifically excluded from the concept: functionality or use cases we intentionally aren’t covering to fit the appetite or make the problem tractable * **part two : Betting** * Some companies use two-week cycles (aka “sprints”). We learned that two weeks is too short to get anything meaningful done. Worse than that, two-week cycles are extremely costly due to the planning overhead. The amount of work you get out of two weeks isn’t worth the collective hours around the table to “sprint plan” or the opportunity cost of breaking everyone’s momentum to re-group. * after each six-week cycle, we schedule two weeks for cool-down. * bugs: Use cool-down. Bring it to the betting table. Schedule a bug smash. * We’ve noticed three phases of work when we build a new product from scratch. R&D mode (bet the time on spiking some key pieces of the new product idea. CEO and designer, we don’t expect to ship anything at the end of an R&D cycle) , Production mode (Shaping is deliberate again. bet multiple teams in parallel , Shipping is the goal) , Cleanup mode (There’s no shaping. There aren’t clear team boundaries. Work is “shipped”) [Cleanup shouldn’t last longer than two cycles] * Example: two years of development, R&D mode for the first year, a year of production mode cycles followed, two cycles of cleanup and significantly cut back the feature set, some overlap between R&D and production mode after that first year, * **Part three: Building** * We don’t start by assigning tasks to anyone. * we define and track the scopes as to-do lists. Each scope corresponds to a list name. Then any tasks for that scope go in that list. * **image 1, 2, 3,** * Mark nice-to-haves with ~ * First there’s the uphill phase of figuring out what our approach is and what we’re going to do. Then, once we can see all the work involved, there’s the downhill phase of execution * image 4, 5, * ## Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet * getting start: idea, technology, passion; What can I do well that I would love to do for an extended period of time? P: 19, 21 * 1 market segmentation: The single necessary and sufficient condition for a business is a paying customer. ; technological enthusiasts=early adopters=early majority ; P: 31, * 2 select a beachhead market: P: 44, * 3 build an end user profile: P: 52, * 4 Calculate the Total Addressable Market (TAM) Size for the Beachhead Market; $200 # [Essential Tips And Tricks For ](/book-summary/commonplace-book)[commonplace book](/book-summary/commonplace-book) # [knowledge management ](/book-summary/knowledge_management) # [PKM](/book-summary/knowledge_management/pkm) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/3LTMUMvLqIOxMLGRMkavoKhjR0yGpVpsaeb_NnspLybaPexjoQBnvVa2C2973HUQ30RiIqubg5B9F6eLjnxKXlE=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/3LTMUMvLqIOxMLGRMkavoKhjR0yGpVpsaeb_NnspLybaPexjoQBnvVa2C2973HUQ30RiIqubg5B9F6eLjnxKXlE=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Book Summary Books How to take smart Notes Writing Solid Code Shape Up: Stop Running in Circles and Ship Work that Matters Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet List of books I've read: Computer Vision: Algorithms and Applications Multiple View Geometry in Computer Vision Second Edition Complete to download the latest draft of Machine Learning Yearning Multiple view geometry in computer vision Learning OpenCV Book by Adrian Kaehler and Gary Bradski Digital Image Processing, Global Edition by Rafael C. Gonzalez Introduction to Algorithms, 3rd Edition (The MIT Press) The Mythical Man-Month: Essays on Software Engineering OpenCV-4-with-Python-Blueprints-Second-Edition Cracking the coding interview 189 programming questions and solutions Cracking the Tech Career: Insider Advice on Landing a Job at Google, Microsoft, Apple, Or Any Top Tech Company The Art of Startup Fundraising Mathematics for Machine Learning Principles of Economics (6th edition) The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) Reinventing Your Life: The Breakthough Program to End Negative Behavior Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf Magazine Papers Websites and links Tools YouTube - List channels Language: Essential Tips And Tricks For commonplace book knowledge management PKM # Books * ## How to take smart Notes * Introduction 4, The four underlying principles 4, the six steps to successful writing 6, afterword * introduction * write everything/ collect notes not related to any thing, organize notes, revirew, rewrite, * no reorganise no complex system, develop ideas by smart notes * 1 slip-box; 2 repeatable tasks that can become automatic and fit together seamlessly change routinges * overarching workflow "Getting Things Done" GTD * constantly have to ump back and forth between different tasks * 1- bibliographical slip-box: reference * 2- collected and generated ideas slip-box * 3- index: notes serve as entry point / thought/ topic * writing a paper step by step * 1 fleeting notes: it will delete within a day * I always have a slip of paper at hand, on which I note down the ideas of certain pages. on the backside I write down the bibliographic details. after finishing the book I go throught my notes and think how ehse notes might be relevant for already written notes in the slip-box. it means that I always read with an eye towards possible connections in the slip-box * 2 literature notes: contain the necessary information - in reference system or in the slip-box * I make a note with the bibliographic details. on the backside I would write "on page x is this, on page y is that" and then it goes into the bibliographic slib-box where I collect everything I read * read, think about its relevance * theoretical background, methodological approach or perspective of the text we read. * whether something adds to a discussion in the slip-box * we look at our slip-box for already existing lines of thought and think about the questions and problems already on our minds to which a new note might contribute. - assigning keywords is much more than just a bureaucratic act. it is a crucial part of the thinking process, which often leads to a deeper elaboration of the note itself and the connection to other notes. * * 3 permanent notes: develop ideas/new/ - only relevant to one particular project - in project specific folder - can be discarded or archived after project finished * add to slip-box behind the note you dirctyly refer to or behind the last note in the slip-box * add links to other notes or links on other notes to your new note * make sure it can be found from the index (MOC) add an entry in the index if necessary or refer to it from a note that is connected to the index * build a latticework of mental models * 4 behind multiple notes (link backward) - link to related note - link to MOC * make sure it can be found again * **keywords** can easily be added to a note like **tags** and will then show up in the **index** * a day * read and take notes * build connections within the slip-box - new idea * you write on your paper, notice a hole in the argument and have another look in the file system for the missing link. you follow up on a footnote, go back to research and might add a fitting quote to one of your papers in the making. * The four underlying principles * writing * simplicity * not from scratch * let the work carry you forward * the six steps to successful writing * separate and interlocking tasks * we use memo techniques is to bundle items together in a meaningful way and remember the bundles * the brain doesn't distinguish between an actual finished task and one that is postponed by taking a note * the fist step is to break down the amprphous task of "writing" into smaller pieces of different tasks that can be finished in **one go.** the second step is to make sure we always write down the outcome of our thinking, including possible connections to further inquiries. * ego depletion * read for understanding * take smart notes * develp ideas (MOC) * 1 overview of a topic - referred to from the index and used as an entry point into a topic has already develped * 2 keeping tarck of all topic - index - * share your insight * make it a habit * afterword * ## Writing Solid Code Writing Solid Code (Microsoft Programming Series) by Trendelles Store Note from Writing Solid Code Book [https://www.pirahansiah.com/describe/book- summary](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Fdescribe%2Fbook- summary&sa=D&sntz=1&usg=AOvVaw3ACzA6G0MBVN3nw6f-yMQo) #SolidCode #C++ #Python #pirahansiah * **Chapter 1** * Enable all optional compiler warnings. * Use lint to catch bugs that your compiler may miss. * If you have unit tests, use them. * **Chapter 2** * introduction assert page 16; assert.h * assert is nothing more than a repackaged form of the #ifdef code we saw before, but when you use * the macro, it takes one line instead of seven * That's why assert is a macro and not a function; if it were a function, calling * it could cause unexpected memory or code swapping. * Use assertions to validate function arguments . * Strip undefined behavior from your code, or use assertions to catch illegal uses of undefined behavior . * Don't waste people's time. Document unclear assertions . * Either remove implicit assumptions, or assert that they are valid . * Use assertions to detect impossible conditions. * Don't hide bugs when you program defensively. * Use a second algorithm to validate your results. * Don't wait for bugs to happen; use startup checks. * **Chapter 3** * Maintain shipping and debugging versions of your program. Shrink-wrap the shipping version, but use the debugging ver­sion as much as possible to catch bugs quickly. * Assertions are a shorthand way to write debugging checks. Use them to catch illegal conditions that should never arise. Don't confuse such conditions with error conditions, which you must handle in the final product. * Use assertions to validate function arguments and to alert pro­ grammars when they do something undefined. The more rigidly you define your functions, the easier it will be to validate the arguments. * Once you've written a function, review it and ask yourself, 'What am I assuming?" If you find an assumption, either assert that your assumption is always valid, or rewrite the code to re­ move the assumption. Also ask, 'What is most likely to be wrong in this code, and how can I automatically detect the prob­lem?" Strive to implement tests that catch bugs at the earliest possible moment. Textbooks encourage programmers to program defensively, but remember that this coding style hides bugs. When you write de­fensive code, use assertions to alert you if the "can't happen" cases do happen. * Eliminate random behavior. Force bugs to be reproducible . * Destroy your garbage so that it's not misused . * Not only did reallocate have to move the block of memory as it was expanding it, but the old memory had to be reallocated and filled with new data. In the assembler, both happened rarely. * This brings up another guideline for writing bug-free code: You don't want anything to happen rarely. You need to isolate those behaviors in your subsystems that may happen and make sure that they do happen. And of­ ten. If you find rare behavior in your subsystems, be sure to do something­ anything-to stir things up. * The assembler bug could have been found within hours, instead of years, if reallocate hadn't so rarely moved blocks when it expanded them. * If something happens rarely, force it to happen often. * Keep debug information to allow stronger error checking. * There is no question that the debug code will create differences be­ tween the ship and the debug versions of your code, but as long as you're careful not to change the underlying behavior of the code, those differences shouldn't matter. * Create thorough subsystem checks, and use them often. * Design your tests carefully. Nothing should be arbitrary. * Strive to implement transparent integrity checks. * Don't apply ship version constraints to the debug version. Trade size and speed for error detection. * **Chapter 4** * Don't wait until you have a bug to step through your code. * Step through every code path. * What About Sweeping Changes? In the past, programmers have asked, "What if I add a feature that touches code in many places? Stepping through all that new code is going to be time consuming." Let me answer that question with another: Can you make such sweeping changes without introducing any bugs? The habit of stepping through your code creates an interesting negative feedback loop. Programmers who step through their code soon learn to write small, easily testable functions because stepping through large func­tions is so painful. Programmers also spend more time thinking about how they can localize the changes they need to make-again, so that they can more easily test their code. And isn't this exactly what you want? No project lead likes it when programmers touch a lot of code; it's too destabilizing. Nor do leads like large, unmanageable functions; they're often unmaintainable. Try hard to localize your changes. If you find that you must make per­vasive changes, think twice before you decide not to step through all the new code. * Overflow and underflow bugs * Data conversion bugs * Off-by-one bugs * NULL pointer bugs * Bugs using garbage memory (0xA3 bugs) * Assignment bugs in which you've used = instead of == * Precedence bugs * Logic bugs * As you step through code, focus on data flow. Source level debuggers can hide execution details. Step through critical code at the instruction level . * **Chapter 5** * Make it hard to ignore error conditions. Don't bury error codes in return values. * Always look for, and eliminate, flaws in your interfaces. * Don't write multipurpose functions. Write separate functions to allow stronger argument validation. * Don't be wishy-washy. Define explicit function arguments. * Write functions that, given valid inputs, cannot fail. * Make the code intelligible at the point of call. Avoid Boolean arguments. * Write comments that emphasize potential hazards. * **Chapter 6** * Use well-defined data types. * Always ask ,"Can this variable or expression over- or underflow?" * Implement your designs as accurately as possible. Being kind a close is being kind a buggy. * these principles already: Don't accept special purpose arguments such as the NULL pointer, and Implement your design, not something that approximates it. The third principle is new: Strive to make every function perform its task exactly one time. * Implement "the task" just once. * Get rid of extraneous if statements. * Avoid using nested ?: operators . * Handle your special cases just once. * Avoid risky language idioms. * Don't needlessly mix operator types. If you must mix operators, use parentheses to isolate the operations. * Avoid calling functions that return errors. * **Chapter 7** * Don't reference memory that you don't own. * Don't reference memory that you have freed. * Don't use output memory as workspace buffers. * Don't pass data in static (or global) memory. * Don't write parasitic functions. * Don't abuse your programming language. * Tight C does not guarantee efficient machine code. * Write code for the "average" programmer. * **Chapter 8** * Bugs don't just "go away." * Don't fix bugs later; fix them now. * Fix the cause, not the symptom. * Don't clean up code unless the clean­ up is critical to the product's success. * Don't implement nonstrategic features. * There are no free features. * Don't allow unnecessary flexibility. * Don't keep "trying" solutions· until you find one that works. Take the time to find the correct solution. * Write and test code in small chunks. * Always test your code, even if that means your schedule will slip. * Don't rely on the testing group to find your bugs. * Don't blame testers for finding your bugs. * Establish your priorities and stick to them. * Never allow the same bug to bite you twice. * **A: CODING CHECKLISTS** * **B: MEMORY LOGGING ROUTINES** * **To sum up** * Charles Simonyi in the early 1970s. https://en.wikipedia.org/wiki/Hungarian_notation * char ch; * byte b; * flag f;/* flags that are always TRUE or FALSE*/ * symbol sym;/* some sort of symbol structure*/ * char byte flag symbol * *pch:/* character pointer*/ * *pb; * *pf: * *psym: * related link: [https://www.morling.dev/images/code_review_pyramid.svg](https://www.google.com/url?q=https%3A%2F%2Fwww.morling.dev%2Fimages%2Fcode_review_pyramid.svg&sa=D&sntz=1&usg=AOvVaw1y2It_PUzXAtFN7dPLegCa) * ## Shape Up: Stop Running in Circles and Ship Work that Matters * Six-week cycles: Our decisions are based on moving the product forward in the next six weeks, not micromanaging time. We don’t count hours or question how individual days are spent. We don’t have daily meetings. We don’t rethink our roadmap every two weeks. Our focus is at a higher level. We say to ourselves: “If this project ships after six weeks, we’ll be really happy. We’ll feel our time was well spent.” Then we commit the six weeks and leave the team alone to get it done * Shaping the work: key elements of a solution, appetite * **part one shaping:** * two separate tracks: one for shaping, one for building. * Steps to shaping * 1\. Set boundaries. * 2\. Rough out the elements: idea that solves the problem within the appetite but without all the fine details worked out * 3\. Address risks and rabbit holes. * 4\. Write the pitch. * fixed time, variable scope, * Where in the current system does the new thing fit? How do you get to it? What are the key components or interactions? Where does it take you? * five ingredients that we always want to include in a pitch: 1. Problem — The raw idea, a use case, or something we’ve seen that motivates us to work on this 2. Appetite — How much time we want to spend and how that constrains the solution 3. Solution — The core elements we came up with, presented in a form that’s easy for people to immediately understand 4. Rabbit holes — Details about the solution worth calling out to avoid problems 5. No-gos — Anything specifically excluded from the concept: functionality or use cases we intentionally aren’t covering to fit the appetite or make the problem tractable * **part two : Betting** * Some companies use two-week cycles (aka “sprints”). We learned that two weeks is too short to get anything meaningful done. Worse than that, two-week cycles are extremely costly due to the planning overhead. The amount of work you get out of two weeks isn’t worth the collective hours around the table to “sprint plan” or the opportunity cost of breaking everyone’s momentum to re-group. * after each six-week cycle, we schedule two weeks for cool-down. * bugs: Use cool-down. Bring it to the betting table. Schedule a bug smash. * We’ve noticed three phases of work when we build a new product from scratch. R&D mode (bet the time on spiking some key pieces of the new product idea. CEO and designer, we don’t expect to ship anything at the end of an R&D cycle) , Production mode (Shaping is deliberate again. bet multiple teams in parallel , Shipping is the goal) , Cleanup mode (There’s no shaping. There aren’t clear team boundaries. Work is “shipped”) [Cleanup shouldn’t last longer than two cycles] * Example: two years of development, R&D mode for the first year, a year of production mode cycles followed, two cycles of cleanup and significantly cut back the feature set, some overlap between R&D and production mode after that first year, * **Part three: Building** * We don’t start by assigning tasks to anyone. * we define and track the scopes as to-do lists. Each scope corresponds to a list name. Then any tasks for that scope go in that list. * **image 1, 2, 3,** * Mark nice-to-haves with ~ * First there’s the uphill phase of figuring out what our approach is and what we’re going to do. Then, once we can see all the work involved, there’s the downhill phase of execution * image 4, 5, * ## Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet * getting start: idea, technology, passion; What can I do well that I would love to do for an extended period of time? P: 19, 21 * 1 market segmentation: The single necessary and sufficient condition for a business is a paying customer. ; technological enthusiasts=early adopters=early majority ; P: 31, * 2 select a beachhead market: P: 44, * 3 build an end user profile: P: 52, * 4 Calculate the Total Addressable Market (TAM) Size for the Beachhead Market; $200 # [Essential Tips And Tricks For ](/book-summary/commonplace-book)[commonplace book](/book-summary/commonplace-book) # [knowledge management ](/book-summary/knowledge_management) # [PKM](/book-summary/knowledge_management/pkm) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/3LTMUMvLqIOxMLGRMkavoKhjR0yGpVpsaeb_NnspLybaPexjoQBnvVa2C2973HUQ30RiIqubg5B9F6eLjnxKXlE=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/3LTMUMvLqIOxMLGRMkavoKhjR0yGpVpsaeb_NnspLybaPexjoQBnvVa2C2973HUQ30RiIqubg5B9F6eLjnxKXlE=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Book Summary Books How to take smart Notes Writing Solid Code Shape Up: Stop Running in Circles and Ship Work that Matters Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet List of books I've read: Computer Vision: Algorithms and Applications Multiple View Geometry in Computer Vision Second Edition Complete to download the latest draft of Machine Learning Yearning Multiple view geometry in computer vision Learning OpenCV Book by Adrian Kaehler and Gary Bradski Digital Image Processing, Global Edition by Rafael C. Gonzalez Introduction to Algorithms, 3rd Edition (The MIT Press) The Mythical Man-Month: Essays on Software Engineering OpenCV-4-with-Python-Blueprints-Second-Edition Cracking the coding interview 189 programming questions and solutions Cracking the Tech Career: Insider Advice on Landing a Job at Google, Microsoft, Apple, Or Any Top Tech Company The Art of Startup Fundraising Mathematics for Machine Learning Principles of Economics (6th edition) The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) Reinventing Your Life: The Breakthough Program to End Negative Behavior Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf Magazine Papers Websites and links Tools YouTube - List channels Language: Essential Tips And Tricks For commonplace book knowledge management PKM # Books * ## How to take smart Notes * Introduction 4, The four underlying principles 4, the six steps to successful writing 6, afterword * introduction * write everything/ collect notes not related to any thing, organize notes, revirew, rewrite, * no reorganise no complex system, develop ideas by smart notes * 1 slip-box; 2 repeatable tasks that can become automatic and fit together seamlessly change routinges * overarching workflow "Getting Things Done" GTD * constantly have to ump back and forth between different tasks * 1- bibliographical slip-box: reference * 2- collected and generated ideas slip-box * 3- index: notes serve as entry point / thought/ topic * writing a paper step by step * 1 fleeting notes: it will delete within a day * I always have a slip of paper at hand, on which I note down the ideas of certain pages. on the backside I write down the bibliographic details. after finishing the book I go throught my notes and think how ehse notes might be relevant for already written notes in the slip-box. it means that I always read with an eye towards possible connections in the slip-box * 2 literature notes: contain the necessary information - in reference system or in the slip-box * I make a note with the bibliographic details. on the backside I would write "on page x is this, on page y is that" and then it goes into the bibliographic slib-box where I collect everything I read * read, think about its relevance * theoretical background, methodological approach or perspective of the text we read. * whether something adds to a discussion in the slip-box * we look at our slip-box for already existing lines of thought and think about the questions and problems already on our minds to which a new note might contribute. - assigning keywords is much more than just a bureaucratic act. it is a crucial part of the thinking process, which often leads to a deeper elaboration of the note itself and the connection to other notes. * * 3 permanent notes: develop ideas/new/ - only relevant to one particular project - in project specific folder - can be discarded or archived after project finished * add to slip-box behind the note you dirctyly refer to or behind the last note in the slip-box * add links to other notes or links on other notes to your new note * make sure it can be found from the index (MOC) add an entry in the index if necessary or refer to it from a note that is connected to the index * build a latticework of mental models * 4 behind multiple notes (link backward) - link to related note - link to MOC * make sure it can be found again * **keywords** can easily be added to a note like **tags** and will then show up in the **index** * a day * read and take notes * build connections within the slip-box - new idea * you write on your paper, notice a hole in the argument and have another look in the file system for the missing link. you follow up on a footnote, go back to research and might add a fitting quote to one of your papers in the making. * The four underlying principles * writing * simplicity * not from scratch * let the work carry you forward * the six steps to successful writing * separate and interlocking tasks * we use memo techniques is to bundle items together in a meaningful way and remember the bundles * the brain doesn't distinguish between an actual finished task and one that is postponed by taking a note * the fist step is to break down the amprphous task of "writing" into smaller pieces of different tasks that can be finished in **one go.** the second step is to make sure we always write down the outcome of our thinking, including possible connections to further inquiries. * ego depletion * read for understanding * take smart notes * develp ideas (MOC) * 1 overview of a topic - referred to from the index and used as an entry point into a topic has already develped * 2 keeping tarck of all topic - index - * share your insight * make it a habit * afterword * ## Writing Solid Code Writing Solid Code (Microsoft Programming Series) by Trendelles Store Note from Writing Solid Code Book [https://www.pirahansiah.com/describe/book- summary](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Fdescribe%2Fbook- summary&sa=D&sntz=1&usg=AOvVaw3ACzA6G0MBVN3nw6f-yMQo) #SolidCode #C++ #Python #pirahansiah * **Chapter 1** * Enable all optional compiler warnings. * Use lint to catch bugs that your compiler may miss. * If you have unit tests, use them. * **Chapter 2** * introduction assert page 16; assert.h * assert is nothing more than a repackaged form of the #ifdef code we saw before, but when you use * the macro, it takes one line instead of seven * That's why assert is a macro and not a function; if it were a function, calling * it could cause unexpected memory or code swapping. * Use assertions to validate function arguments . * Strip undefined behavior from your code, or use assertions to catch illegal uses of undefined behavior . * Don't waste people's time. Document unclear assertions . * Either remove implicit assumptions, or assert that they are valid . * Use assertions to detect impossible conditions. * Don't hide bugs when you program defensively. * Use a second algorithm to validate your results. * Don't wait for bugs to happen; use startup checks. * **Chapter 3** * Maintain shipping and debugging versions of your program. Shrink-wrap the shipping version, but use the debugging ver­sion as much as possible to catch bugs quickly. * Assertions are a shorthand way to write debugging checks. Use them to catch illegal conditions that should never arise. Don't confuse such conditions with error conditions, which you must handle in the final product. * Use assertions to validate function arguments and to alert pro­ grammars when they do something undefined. The more rigidly you define your functions, the easier it will be to validate the arguments. * Once you've written a function, review it and ask yourself, 'What am I assuming?" If you find an assumption, either assert that your assumption is always valid, or rewrite the code to re­ move the assumption. Also ask, 'What is most likely to be wrong in this code, and how can I automatically detect the prob­lem?" Strive to implement tests that catch bugs at the earliest possible moment. Textbooks encourage programmers to program defensively, but remember that this coding style hides bugs. When you write de­fensive code, use assertions to alert you if the "can't happen" cases do happen. * Eliminate random behavior. Force bugs to be reproducible . * Destroy your garbage so that it's not misused . * Not only did reallocate have to move the block of memory as it was expanding it, but the old memory had to be reallocated and filled with new data. In the assembler, both happened rarely. * This brings up another guideline for writing bug-free code: You don't want anything to happen rarely. You need to isolate those behaviors in your subsystems that may happen and make sure that they do happen. And of­ ten. If you find rare behavior in your subsystems, be sure to do something­ anything-to stir things up. * The assembler bug could have been found within hours, instead of years, if reallocate hadn't so rarely moved blocks when it expanded them. * If something happens rarely, force it to happen often. * Keep debug information to allow stronger error checking. * There is no question that the debug code will create differences be­ tween the ship and the debug versions of your code, but as long as you're careful not to change the underlying behavior of the code, those differences shouldn't matter. * Create thorough subsystem checks, and use them often. * Design your tests carefully. Nothing should be arbitrary. * Strive to implement transparent integrity checks. * Don't apply ship version constraints to the debug version. Trade size and speed for error detection. * **Chapter 4** * Don't wait until you have a bug to step through your code. * Step through every code path. * What About Sweeping Changes? In the past, programmers have asked, "What if I add a feature that touches code in many places? Stepping through all that new code is going to be time consuming." Let me answer that question with another: Can you make such sweeping changes without introducing any bugs? The habit of stepping through your code creates an interesting negative feedback loop. Programmers who step through their code soon learn to write small, easily testable functions because stepping through large func­tions is so painful. Programmers also spend more time thinking about how they can localize the changes they need to make-again, so that they can more easily test their code. And isn't this exactly what you want? No project lead likes it when programmers touch a lot of code; it's too destabilizing. Nor do leads like large, unmanageable functions; they're often unmaintainable. Try hard to localize your changes. If you find that you must make per­vasive changes, think twice before you decide not to step through all the new code. * Overflow and underflow bugs * Data conversion bugs * Off-by-one bugs * NULL pointer bugs * Bugs using garbage memory (0xA3 bugs) * Assignment bugs in which you've used = instead of == * Precedence bugs * Logic bugs * As you step through code, focus on data flow. Source level debuggers can hide execution details. Step through critical code at the instruction level . * **Chapter 5** * Make it hard to ignore error conditions. Don't bury error codes in return values. * Always look for, and eliminate, flaws in your interfaces. * Don't write multipurpose functions. Write separate functions to allow stronger argument validation. * Don't be wishy-washy. Define explicit function arguments. * Write functions that, given valid inputs, cannot fail. * Make the code intelligible at the point of call. Avoid Boolean arguments. * Write comments that emphasize potential hazards. * **Chapter 6** * Use well-defined data types. * Always ask ,"Can this variable or expression over- or underflow?" * Implement your designs as accurately as possible. Being kind a close is being kind a buggy. * these principles already: Don't accept special purpose arguments such as the NULL pointer, and Implement your design, not something that approximates it. The third principle is new: Strive to make every function perform its task exactly one time. * Implement "the task" just once. * Get rid of extraneous if statements. * Avoid using nested ?: operators . * Handle your special cases just once. * Avoid risky language idioms. * Don't needlessly mix operator types. If you must mix operators, use parentheses to isolate the operations. * Avoid calling functions that return errors. * **Chapter 7** * Don't reference memory that you don't own. * Don't reference memory that you have freed. * Don't use output memory as workspace buffers. * Don't pass data in static (or global) memory. * Don't write parasitic functions. * Don't abuse your programming language. * Tight C does not guarantee efficient machine code. * Write code for the "average" programmer. * **Chapter 8** * Bugs don't just "go away." * Don't fix bugs later; fix them now. * Fix the cause, not the symptom. * Don't clean up code unless the clean­ up is critical to the product's success. * Don't implement nonstrategic features. * There are no free features. * Don't allow unnecessary flexibility. * Don't keep "trying" solutions· until you find one that works. Take the time to find the correct solution. * Write and test code in small chunks. * Always test your code, even if that means your schedule will slip. * Don't rely on the testing group to find your bugs. * Don't blame testers for finding your bugs. * Establish your priorities and stick to them. * Never allow the same bug to bite you twice. * **A: CODING CHECKLISTS** * **B: MEMORY LOGGING ROUTINES** * **To sum up** * Charles Simonyi in the early 1970s. https://en.wikipedia.org/wiki/Hungarian_notation * char ch; * byte b; * flag f;/* flags that are always TRUE or FALSE*/ * symbol sym;/* some sort of symbol structure*/ * char byte flag symbol * *pch:/* character pointer*/ * *pb; * *pf: * *psym: * related link: [https://www.morling.dev/images/code_review_pyramid.svg](https://www.google.com/url?q=https%3A%2F%2Fwww.morling.dev%2Fimages%2Fcode_review_pyramid.svg&sa=D&sntz=1&usg=AOvVaw1y2It_PUzXAtFN7dPLegCa) * ## Shape Up: Stop Running in Circles and Ship Work that Matters * Six-week cycles: Our decisions are based on moving the product forward in the next six weeks, not micromanaging time. We don’t count hours or question how individual days are spent. We don’t have daily meetings. We don’t rethink our roadmap every two weeks. Our focus is at a higher level. We say to ourselves: “If this project ships after six weeks, we’ll be really happy. We’ll feel our time was well spent.” Then we commit the six weeks and leave the team alone to get it done * Shaping the work: key elements of a solution, appetite * **part one shaping:** * two separate tracks: one for shaping, one for building. * Steps to shaping * 1\. Set boundaries. * 2\. Rough out the elements: idea that solves the problem within the appetite but without all the fine details worked out * 3\. Address risks and rabbit holes. * 4\. Write the pitch. * fixed time, variable scope, * Where in the current system does the new thing fit? How do you get to it? What are the key components or interactions? Where does it take you? * five ingredients that we always want to include in a pitch: 1. Problem — The raw idea, a use case, or something we’ve seen that motivates us to work on this 2. Appetite — How much time we want to spend and how that constrains the solution 3. Solution — The core elements we came up with, presented in a form that’s easy for people to immediately understand 4. Rabbit holes — Details about the solution worth calling out to avoid problems 5. No-gos — Anything specifically excluded from the concept: functionality or use cases we intentionally aren’t covering to fit the appetite or make the problem tractable * **part two : Betting** * Some companies use two-week cycles (aka “sprints”). We learned that two weeks is too short to get anything meaningful done. Worse than that, two-week cycles are extremely costly due to the planning overhead. The amount of work you get out of two weeks isn’t worth the collective hours around the table to “sprint plan” or the opportunity cost of breaking everyone’s momentum to re-group. * after each six-week cycle, we schedule two weeks for cool-down. * bugs: Use cool-down. Bring it to the betting table. Schedule a bug smash. * We’ve noticed three phases of work when we build a new product from scratch. R&D mode (bet the time on spiking some key pieces of the new product idea. CEO and designer, we don’t expect to ship anything at the end of an R&D cycle) , Production mode (Shaping is deliberate again. bet multiple teams in parallel , Shipping is the goal) , Cleanup mode (There’s no shaping. There aren’t clear team boundaries. Work is “shipped”) [Cleanup shouldn’t last longer than two cycles] * Example: two years of development, R&D mode for the first year, a year of production mode cycles followed, two cycles of cleanup and significantly cut back the feature set, some overlap between R&D and production mode after that first year, * **Part three: Building** * We don’t start by assigning tasks to anyone. * we define and track the scopes as to-do lists. Each scope corresponds to a list name. Then any tasks for that scope go in that list. * **image 1, 2, 3,** * Mark nice-to-haves with ~ * First there’s the uphill phase of figuring out what our approach is and what we’re going to do. Then, once we can see all the work involved, there’s the downhill phase of execution * image 4, 5, * ## Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet * getting start: idea, technology, passion; What can I do well that I would love to do for an extended period of time? P: 19, 21 * 1 market segmentation: The single necessary and sufficient condition for a business is a paying customer. ; technological enthusiasts=early adopters=early majority ; P: 31, * 2 select a beachhead market: P: 44, * 3 build an end user profile: P: 52, * 4 Calculate the Total Addressable Market (TAM) Size for the Beachhead Market; $200 # [Essential Tips And Tricks For ](/book-summary/commonplace-book)[commonplace book](/book-summary/commonplace-book) # [knowledge management ](/book-summary/knowledge_management) # [PKM](/book-summary/knowledge_management/pkm) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/3LTMUMvLqIOxMLGRMkavoKhjR0yGpVpsaeb_NnspLybaPexjoQBnvVa2C2973HUQ30RiIqubg5B9F6eLjnxKXlE=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/3LTMUMvLqIOxMLGRMkavoKhjR0yGpVpsaeb_NnspLybaPexjoQBnvVa2C2973HUQ30RiIqubg5B9F6eLjnxKXlE=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Book Summary Books How to take smart Notes Writing Solid Code Shape Up: Stop Running in Circles and Ship Work that Matters Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet List of books I've read: Computer Vision: Algorithms and Applications Multiple View Geometry in Computer Vision Second Edition Complete to download the latest draft of Machine Learning Yearning Multiple view geometry in computer vision Learning OpenCV Book by Adrian Kaehler and Gary Bradski Digital Image Processing, Global Edition by Rafael C. Gonzalez Introduction to Algorithms, 3rd Edition (The MIT Press) The Mythical Man-Month: Essays on Software Engineering OpenCV-4-with-Python-Blueprints-Second-Edition Cracking the coding interview 189 programming questions and solutions Cracking the Tech Career: Insider Advice on Landing a Job at Google, Microsoft, Apple, Or Any Top Tech Company The Art of Startup Fundraising Mathematics for Machine Learning Principles of Economics (6th edition) The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) Reinventing Your Life: The Breakthough Program to End Negative Behavior Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf Magazine Papers Websites and links Tools YouTube - List channels Language: Essential Tips And Tricks For commonplace book knowledge management PKM # Books * ## How to take smart Notes * Introduction 4, The four underlying principles 4, the six steps to successful writing 6, afterword * introduction * write everything/ collect notes not related to any thing, organize notes, revirew, rewrite, * no reorganise no complex system, develop ideas by smart notes * 1 slip-box; 2 repeatable tasks that can become automatic and fit together seamlessly change routinges * overarching workflow "Getting Things Done" GTD * constantly have to ump back and forth between different tasks * 1- bibliographical slip-box: reference * 2- collected and generated ideas slip-box * 3- index: notes serve as entry point / thought/ topic * writing a paper step by step * 1 fleeting notes: it will delete within a day * I always have a slip of paper at hand, on which I note down the ideas of certain pages. on the backside I write down the bibliographic details. after finishing the book I go throught my notes and think how ehse notes might be relevant for already written notes in the slip-box. it means that I always read with an eye towards possible connections in the slip-box * 2 literature notes: contain the necessary information - in reference system or in the slip-box * I make a note with the bibliographic details. on the backside I would write "on page x is this, on page y is that" and then it goes into the bibliographic slib-box where I collect everything I read * read, think about its relevance * theoretical background, methodological approach or perspective of the text we read. * whether something adds to a discussion in the slip-box * we look at our slip-box for already existing lines of thought and think about the questions and problems already on our minds to which a new note might contribute. - assigning keywords is much more than just a bureaucratic act. it is a crucial part of the thinking process, which often leads to a deeper elaboration of the note itself and the connection to other notes. * * 3 permanent notes: develop ideas/new/ - only relevant to one particular project - in project specific folder - can be discarded or archived after project finished * add to slip-box behind the note you dirctyly refer to or behind the last note in the slip-box * add links to other notes or links on other notes to your new note * make sure it can be found from the index (MOC) add an entry in the index if necessary or refer to it from a note that is connected to the index * build a latticework of mental models * 4 behind multiple notes (link backward) - link to related note - link to MOC * make sure it can be found again * **keywords** can easily be added to a note like **tags** and will then show up in the **index** * a day * read and take notes * build connections within the slip-box - new idea * you write on your paper, notice a hole in the argument and have another look in the file system for the missing link. you follow up on a footnote, go back to research and might add a fitting quote to one of your papers in the making. * The four underlying principles * writing * simplicity * not from scratch * let the work carry you forward * the six steps to successful writing * separate and interlocking tasks * we use memo techniques is to bundle items together in a meaningful way and remember the bundles * the brain doesn't distinguish between an actual finished task and one that is postponed by taking a note * the fist step is to break down the amprphous task of "writing" into smaller pieces of different tasks that can be finished in **one go.** the second step is to make sure we always write down the outcome of our thinking, including possible connections to further inquiries. * ego depletion * read for understanding * take smart notes * develp ideas (MOC) * 1 overview of a topic - referred to from the index and used as an entry point into a topic has already develped * 2 keeping tarck of all topic - index - * share your insight * make it a habit * afterword * ## Writing Solid Code Writing Solid Code (Microsoft Programming Series) by Trendelles Store Note from Writing Solid Code Book [https://www.pirahansiah.com/describe/book- summary](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Fdescribe%2Fbook- summary&sa=D&sntz=1&usg=AOvVaw3ACzA6G0MBVN3nw6f-yMQo) #SolidCode #C++ #Python #pirahansiah * **Chapter 1** * Enable all optional compiler warnings. * Use lint to catch bugs that your compiler may miss. * If you have unit tests, use them. * **Chapter 2** * introduction assert page 16; assert.h * assert is nothing more than a repackaged form of the #ifdef code we saw before, but when you use * the macro, it takes one line instead of seven * That's why assert is a macro and not a function; if it were a function, calling * it could cause unexpected memory or code swapping. * Use assertions to validate function arguments . * Strip undefined behavior from your code, or use assertions to catch illegal uses of undefined behavior . * Don't waste people's time. Document unclear assertions . * Either remove implicit assumptions, or assert that they are valid . * Use assertions to detect impossible conditions. * Don't hide bugs when you program defensively. * Use a second algorithm to validate your results. * Don't wait for bugs to happen; use startup checks. * **Chapter 3** * Maintain shipping and debugging versions of your program. Shrink-wrap the shipping version, but use the debugging ver­sion as much as possible to catch bugs quickly. * Assertions are a shorthand way to write debugging checks. Use them to catch illegal conditions that should never arise. Don't confuse such conditions with error conditions, which you must handle in the final product. * Use assertions to validate function arguments and to alert pro­ grammars when they do something undefined. The more rigidly you define your functions, the easier it will be to validate the arguments. * Once you've written a function, review it and ask yourself, 'What am I assuming?" If you find an assumption, either assert that your assumption is always valid, or rewrite the code to re­ move the assumption. Also ask, 'What is most likely to be wrong in this code, and how can I automatically detect the prob­lem?" Strive to implement tests that catch bugs at the earliest possible moment. Textbooks encourage programmers to program defensively, but remember that this coding style hides bugs. When you write de­fensive code, use assertions to alert you if the "can't happen" cases do happen. * Eliminate random behavior. Force bugs to be reproducible . * Destroy your garbage so that it's not misused . * Not only did reallocate have to move the block of memory as it was expanding it, but the old memory had to be reallocated and filled with new data. In the assembler, both happened rarely. * This brings up another guideline for writing bug-free code: You don't want anything to happen rarely. You need to isolate those behaviors in your subsystems that may happen and make sure that they do happen. And of­ ten. If you find rare behavior in your subsystems, be sure to do something­ anything-to stir things up. * The assembler bug could have been found within hours, instead of years, if reallocate hadn't so rarely moved blocks when it expanded them. * If something happens rarely, force it to happen often. * Keep debug information to allow stronger error checking. * There is no question that the debug code will create differences be­ tween the ship and the debug versions of your code, but as long as you're careful not to change the underlying behavior of the code, those differences shouldn't matter. * Create thorough subsystem checks, and use them often. * Design your tests carefully. Nothing should be arbitrary. * Strive to implement transparent integrity checks. * Don't apply ship version constraints to the debug version. Trade size and speed for error detection. * **Chapter 4** * Don't wait until you have a bug to step through your code. * Step through every code path. * What About Sweeping Changes? In the past, programmers have asked, "What if I add a feature that touches code in many places? Stepping through all that new code is going to be time consuming." Let me answer that question with another: Can you make such sweeping changes without introducing any bugs? The habit of stepping through your code creates an interesting negative feedback loop. Programmers who step through their code soon learn to write small, easily testable functions because stepping through large func­tions is so painful. Programmers also spend more time thinking about how they can localize the changes they need to make-again, so that they can more easily test their code. And isn't this exactly what you want? No project lead likes it when programmers touch a lot of code; it's too destabilizing. Nor do leads like large, unmanageable functions; they're often unmaintainable. Try hard to localize your changes. If you find that you must make per­vasive changes, think twice before you decide not to step through all the new code. * Overflow and underflow bugs * Data conversion bugs * Off-by-one bugs * NULL pointer bugs * Bugs using garbage memory (0xA3 bugs) * Assignment bugs in which you've used = instead of == * Precedence bugs * Logic bugs * As you step through code, focus on data flow. Source level debuggers can hide execution details. Step through critical code at the instruction level . * **Chapter 5** * Make it hard to ignore error conditions. Don't bury error codes in return values. * Always look for, and eliminate, flaws in your interfaces. * Don't write multipurpose functions. Write separate functions to allow stronger argument validation. * Don't be wishy-washy. Define explicit function arguments. * Write functions that, given valid inputs, cannot fail. * Make the code intelligible at the point of call. Avoid Boolean arguments. * Write comments that emphasize potential hazards. * **Chapter 6** * Use well-defined data types. * Always ask ,"Can this variable or expression over- or underflow?" * Implement your designs as accurately as possible. Being kind a close is being kind a buggy. * these principles already: Don't accept special purpose arguments such as the NULL pointer, and Implement your design, not something that approximates it. The third principle is new: Strive to make every function perform its task exactly one time. * Implement "the task" just once. * Get rid of extraneous if statements. * Avoid using nested ?: operators . * Handle your special cases just once. * Avoid risky language idioms. * Don't needlessly mix operator types. If you must mix operators, use parentheses to isolate the operations. * Avoid calling functions that return errors. * **Chapter 7** * Don't reference memory that you don't own. * Don't reference memory that you have freed. * Don't use output memory as workspace buffers. * Don't pass data in static (or global) memory. * Don't write parasitic functions. * Don't abuse your programming language. * Tight C does not guarantee efficient machine code. * Write code for the "average" programmer. * **Chapter 8** * Bugs don't just "go away." * Don't fix bugs later; fix them now. * Fix the cause, not the symptom. * Don't clean up code unless the clean­ up is critical to the product's success. * Don't implement nonstrategic features. * There are no free features. * Don't allow unnecessary flexibility. * Don't keep "trying" solutions· until you find one that works. Take the time to find the correct solution. * Write and test code in small chunks. * Always test your code, even if that means your schedule will slip. * Don't rely on the testing group to find your bugs. * Don't blame testers for finding your bugs. * Establish your priorities and stick to them. * Never allow the same bug to bite you twice. * **A: CODING CHECKLISTS** * **B: MEMORY LOGGING ROUTINES** * **To sum up** * Charles Simonyi in the early 1970s. https://en.wikipedia.org/wiki/Hungarian_notation * char ch; * byte b; * flag f;/* flags that are always TRUE or FALSE*/ * symbol sym;/* some sort of symbol structure*/ * char byte flag symbol * *pch:/* character pointer*/ * *pb; * *pf: * *psym: * related link: [https://www.morling.dev/images/code_review_pyramid.svg](https://www.google.com/url?q=https%3A%2F%2Fwww.morling.dev%2Fimages%2Fcode_review_pyramid.svg&sa=D&sntz=1&usg=AOvVaw1y2It_PUzXAtFN7dPLegCa) * ## Shape Up: Stop Running in Circles and Ship Work that Matters * Six-week cycles: Our decisions are based on moving the product forward in the next six weeks, not micromanaging time. We don’t count hours or question how individual days are spent. We don’t have daily meetings. We don’t rethink our roadmap every two weeks. Our focus is at a higher level. We say to ourselves: “If this project ships after six weeks, we’ll be really happy. We’ll feel our time was well spent.” Then we commit the six weeks and leave the team alone to get it done * Shaping the work: key elements of a solution, appetite * **part one shaping:** * two separate tracks: one for shaping, one for building. * Steps to shaping * 1\. Set boundaries. * 2\. Rough out the elements: idea that solves the problem within the appetite but without all the fine details worked out * 3\. Address risks and rabbit holes. * 4\. Write the pitch. * fixed time, variable scope, * Where in the current system does the new thing fit? How do you get to it? What are the key components or interactions? Where does it take you? * five ingredients that we always want to include in a pitch: 1. Problem — The raw idea, a use case, or something we’ve seen that motivates us to work on this 2. Appetite — How much time we want to spend and how that constrains the solution 3. Solution — The core elements we came up with, presented in a form that’s easy for people to immediately understand 4. Rabbit holes — Details about the solution worth calling out to avoid problems 5. No-gos — Anything specifically excluded from the concept: functionality or use cases we intentionally aren’t covering to fit the appetite or make the problem tractable * **part two : Betting** * Some companies use two-week cycles (aka “sprints”). We learned that two weeks is too short to get anything meaningful done. Worse than that, two-week cycles are extremely costly due to the planning overhead. The amount of work you get out of two weeks isn’t worth the collective hours around the table to “sprint plan” or the opportunity cost of breaking everyone’s momentum to re-group. * after each six-week cycle, we schedule two weeks for cool-down. * bugs: Use cool-down. Bring it to the betting table. Schedule a bug smash. * We’ve noticed three phases of work when we build a new product from scratch. R&D mode (bet the time on spiking some key pieces of the new product idea. CEO and designer, we don’t expect to ship anything at the end of an R&D cycle) , Production mode (Shaping is deliberate again. bet multiple teams in parallel , Shipping is the goal) , Cleanup mode (There’s no shaping. There aren’t clear team boundaries. Work is “shipped”) [Cleanup shouldn’t last longer than two cycles] * Example: two years of development, R&D mode for the first year, a year of production mode cycles followed, two cycles of cleanup and significantly cut back the feature set, some overlap between R&D and production mode after that first year, * **Part three: Building** * We don’t start by assigning tasks to anyone. * we define and track the scopes as to-do lists. Each scope corresponds to a list name. Then any tasks for that scope go in that list. * **image 1, 2, 3,** * Mark nice-to-haves with ~ * First there’s the uphill phase of figuring out what our approach is and what we’re going to do. Then, once we can see all the work involved, there’s the downhill phase of execution * image 4, 5, * ## Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet * getting start: idea, technology, passion; What can I do well that I would love to do for an extended period of time? P: 19, 21 * 1 market segmentation: The single necessary and sufficient condition for a business is a paying customer. ; technological enthusiasts=early adopters=early majority ; P: 31, * 2 select a beachhead market: P: 44, * 3 build an end user profile: P: 52, * 4 Calculate the Total Addressable Market (TAM) Size for the Beachhead Market; $200 # [Essential Tips And Tricks For ](/book-summary/commonplace-book)[commonplace book](/book-summary/commonplace-book) # [knowledge management ](/book-summary/knowledge_management) # [PKM](/book-summary/knowledge_management/pkm) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. 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Gonzalez Introduction to Algorithms, 3rd Edition (The MIT Press) The Mythical Man-Month: Essays on Software Engineering OpenCV-4-with-Python-Blueprints-Second-Edition Cracking the coding interview 189 programming questions and solutions Cracking the Tech Career: Insider Advice on Landing a Job at Google, Microsoft, Apple, Or Any Top Tech Company The Art of Startup Fundraising Mathematics for Machine Learning Principles of Economics (6th edition) The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) Reinventing Your Life: The Breakthough Program to End Negative Behavior Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf Magazine Papers Websites and links Tools YouTube - List channels Language: Essential Tips And Tricks For commonplace book knowledge management PKM # Books * ## How to take smart Notes * Introduction 4, The four underlying principles 4, the six steps to successful writing 6, afterword * introduction * write everything/ collect notes not related to any thing, organize notes, revirew, rewrite, * no reorganise no complex system, develop ideas by smart notes * 1 slip-box; 2 repeatable tasks that can become automatic and fit together seamlessly change routinges * overarching workflow "Getting Things Done" GTD * constantly have to ump back and forth between different tasks * 1- bibliographical slip-box: reference * 2- collected and generated ideas slip-box * 3- index: notes serve as entry point / thought/ topic * writing a paper step by step * 1 fleeting notes: it will delete within a day * I always have a slip of paper at hand, on which I note down the ideas of certain pages. on the backside I write down the bibliographic details. after finishing the book I go throught my notes and think how ehse notes might be relevant for already written notes in the slip-box. it means that I always read with an eye towards possible connections in the slip-box * 2 literature notes: contain the necessary information - in reference system or in the slip-box * I make a note with the bibliographic details. on the backside I would write "on page x is this, on page y is that" and then it goes into the bibliographic slib-box where I collect everything I read * read, think about its relevance * theoretical background, methodological approach or perspective of the text we read. * whether something adds to a discussion in the slip-box * we look at our slip-box for already existing lines of thought and think about the questions and problems already on our minds to which a new note might contribute. - assigning keywords is much more than just a bureaucratic act. it is a crucial part of the thinking process, which often leads to a deeper elaboration of the note itself and the connection to other notes. * * 3 permanent notes: develop ideas/new/ - only relevant to one particular project - in project specific folder - can be discarded or archived after project finished * add to slip-box behind the note you dirctyly refer to or behind the last note in the slip-box * add links to other notes or links on other notes to your new note * make sure it can be found from the index (MOC) add an entry in the index if necessary or refer to it from a note that is connected to the index * build a latticework of mental models * 4 behind multiple notes (link backward) - link to related note - link to MOC * make sure it can be found again * **keywords** can easily be added to a note like **tags** and will then show up in the **index** * a day * read and take notes * build connections within the slip-box - new idea * you write on your paper, notice a hole in the argument and have another look in the file system for the missing link. you follow up on a footnote, go back to research and might add a fitting quote to one of your papers in the making. * The four underlying principles * writing * simplicity * not from scratch * let the work carry you forward * the six steps to successful writing * separate and interlocking tasks * we use memo techniques is to bundle items together in a meaningful way and remember the bundles * the brain doesn't distinguish between an actual finished task and one that is postponed by taking a note * the fist step is to break down the amprphous task of "writing" into smaller pieces of different tasks that can be finished in **one go.** the second step is to make sure we always write down the outcome of our thinking, including possible connections to further inquiries. * ego depletion * read for understanding * take smart notes * develp ideas (MOC) * 1 overview of a topic - referred to from the index and used as an entry point into a topic has already develped * 2 keeping tarck of all topic - index - * share your insight * make it a habit * afterword * ## Writing Solid Code Writing Solid Code (Microsoft Programming Series) by Trendelles Store Note from Writing Solid Code Book [https://www.pirahansiah.com/describe/book- summary](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Fdescribe%2Fbook- summary&sa=D&sntz=1&usg=AOvVaw3ACzA6G0MBVN3nw6f-yMQo) #SolidCode #C++ #Python #pirahansiah * **Chapter 1** * Enable all optional compiler warnings. * Use lint to catch bugs that your compiler may miss. * If you have unit tests, use them. * **Chapter 2** * introduction assert page 16; assert.h * assert is nothing more than a repackaged form of the #ifdef code we saw before, but when you use * the macro, it takes one line instead of seven * That's why assert is a macro and not a function; if it were a function, calling * it could cause unexpected memory or code swapping. * Use assertions to validate function arguments . * Strip undefined behavior from your code, or use assertions to catch illegal uses of undefined behavior . * Don't waste people's time. Document unclear assertions . * Either remove implicit assumptions, or assert that they are valid . * Use assertions to detect impossible conditions. * Don't hide bugs when you program defensively. * Use a second algorithm to validate your results. * Don't wait for bugs to happen; use startup checks. * **Chapter 3** * Maintain shipping and debugging versions of your program. Shrink-wrap the shipping version, but use the debugging ver­sion as much as possible to catch bugs quickly. * Assertions are a shorthand way to write debugging checks. Use them to catch illegal conditions that should never arise. Don't confuse such conditions with error conditions, which you must handle in the final product. * Use assertions to validate function arguments and to alert pro­ grammars when they do something undefined. The more rigidly you define your functions, the easier it will be to validate the arguments. * Once you've written a function, review it and ask yourself, 'What am I assuming?" If you find an assumption, either assert that your assumption is always valid, or rewrite the code to re­ move the assumption. Also ask, 'What is most likely to be wrong in this code, and how can I automatically detect the prob­lem?" Strive to implement tests that catch bugs at the earliest possible moment. Textbooks encourage programmers to program defensively, but remember that this coding style hides bugs. When you write de­fensive code, use assertions to alert you if the "can't happen" cases do happen. * Eliminate random behavior. Force bugs to be reproducible . * Destroy your garbage so that it's not misused . * Not only did reallocate have to move the block of memory as it was expanding it, but the old memory had to be reallocated and filled with new data. In the assembler, both happened rarely. * This brings up another guideline for writing bug-free code: You don't want anything to happen rarely. You need to isolate those behaviors in your subsystems that may happen and make sure that they do happen. And of­ ten. If you find rare behavior in your subsystems, be sure to do something­ anything-to stir things up. * The assembler bug could have been found within hours, instead of years, if reallocate hadn't so rarely moved blocks when it expanded them. * If something happens rarely, force it to happen often. * Keep debug information to allow stronger error checking. * There is no question that the debug code will create differences be­ tween the ship and the debug versions of your code, but as long as you're careful not to change the underlying behavior of the code, those differences shouldn't matter. * Create thorough subsystem checks, and use them often. * Design your tests carefully. Nothing should be arbitrary. * Strive to implement transparent integrity checks. * Don't apply ship version constraints to the debug version. Trade size and speed for error detection. * **Chapter 4** * Don't wait until you have a bug to step through your code. * Step through every code path. * What About Sweeping Changes? In the past, programmers have asked, "What if I add a feature that touches code in many places? Stepping through all that new code is going to be time consuming." Let me answer that question with another: Can you make such sweeping changes without introducing any bugs? The habit of stepping through your code creates an interesting negative feedback loop. Programmers who step through their code soon learn to write small, easily testable functions because stepping through large func­tions is so painful. Programmers also spend more time thinking about how they can localize the changes they need to make-again, so that they can more easily test their code. And isn't this exactly what you want? No project lead likes it when programmers touch a lot of code; it's too destabilizing. Nor do leads like large, unmanageable functions; they're often unmaintainable. Try hard to localize your changes. If you find that you must make per­vasive changes, think twice before you decide not to step through all the new code. * Overflow and underflow bugs * Data conversion bugs * Off-by-one bugs * NULL pointer bugs * Bugs using garbage memory (0xA3 bugs) * Assignment bugs in which you've used = instead of == * Precedence bugs * Logic bugs * As you step through code, focus on data flow. Source level debuggers can hide execution details. Step through critical code at the instruction level . * **Chapter 5** * Make it hard to ignore error conditions. Don't bury error codes in return values. * Always look for, and eliminate, flaws in your interfaces. * Don't write multipurpose functions. Write separate functions to allow stronger argument validation. * Don't be wishy-washy. Define explicit function arguments. * Write functions that, given valid inputs, cannot fail. * Make the code intelligible at the point of call. Avoid Boolean arguments. * Write comments that emphasize potential hazards. * **Chapter 6** * Use well-defined data types. * Always ask ,"Can this variable or expression over- or underflow?" * Implement your designs as accurately as possible. Being kind a close is being kind a buggy. * these principles already: Don't accept special purpose arguments such as the NULL pointer, and Implement your design, not something that approximates it. The third principle is new: Strive to make every function perform its task exactly one time. * Implement "the task" just once. * Get rid of extraneous if statements. * Avoid using nested ?: operators . * Handle your special cases just once. * Avoid risky language idioms. * Don't needlessly mix operator types. If you must mix operators, use parentheses to isolate the operations. * Avoid calling functions that return errors. * **Chapter 7** * Don't reference memory that you don't own. * Don't reference memory that you have freed. * Don't use output memory as workspace buffers. * Don't pass data in static (or global) memory. * Don't write parasitic functions. * Don't abuse your programming language. * Tight C does not guarantee efficient machine code. * Write code for the "average" programmer. * **Chapter 8** * Bugs don't just "go away." * Don't fix bugs later; fix them now. * Fix the cause, not the symptom. * Don't clean up code unless the clean­ up is critical to the product's success. * Don't implement nonstrategic features. * There are no free features. * Don't allow unnecessary flexibility. * Don't keep "trying" solutions· until you find one that works. Take the time to find the correct solution. * Write and test code in small chunks. * Always test your code, even if that means your schedule will slip. * Don't rely on the testing group to find your bugs. * Don't blame testers for finding your bugs. * Establish your priorities and stick to them. * Never allow the same bug to bite you twice. * **A: CODING CHECKLISTS** * **B: MEMORY LOGGING ROUTINES** * **To sum up** * Charles Simonyi in the early 1970s. https://en.wikipedia.org/wiki/Hungarian_notation * char ch; * byte b; * flag f;/* flags that are always TRUE or FALSE*/ * symbol sym;/* some sort of symbol structure*/ * char byte flag symbol * *pch:/* character pointer*/ * *pb; * *pf: * *psym: * related link: [https://www.morling.dev/images/code_review_pyramid.svg](https://www.google.com/url?q=https%3A%2F%2Fwww.morling.dev%2Fimages%2Fcode_review_pyramid.svg&sa=D&sntz=1&usg=AOvVaw1y2It_PUzXAtFN7dPLegCa) * ## Shape Up: Stop Running in Circles and Ship Work that Matters * Six-week cycles: Our decisions are based on moving the product forward in the next six weeks, not micromanaging time. We don’t count hours or question how individual days are spent. We don’t have daily meetings. We don’t rethink our roadmap every two weeks. Our focus is at a higher level. We say to ourselves: “If this project ships after six weeks, we’ll be really happy. We’ll feel our time was well spent.” Then we commit the six weeks and leave the team alone to get it done * Shaping the work: key elements of a solution, appetite * **part one shaping:** * two separate tracks: one for shaping, one for building. * Steps to shaping * 1\. Set boundaries. * 2\. Rough out the elements: idea that solves the problem within the appetite but without all the fine details worked out * 3\. Address risks and rabbit holes. * 4\. Write the pitch. * fixed time, variable scope, * Where in the current system does the new thing fit? How do you get to it? What are the key components or interactions? Where does it take you? * five ingredients that we always want to include in a pitch: 1. Problem — The raw idea, a use case, or something we’ve seen that motivates us to work on this 2. Appetite — How much time we want to spend and how that constrains the solution 3. Solution — The core elements we came up with, presented in a form that’s easy for people to immediately understand 4. Rabbit holes — Details about the solution worth calling out to avoid problems 5. No-gos — Anything specifically excluded from the concept: functionality or use cases we intentionally aren’t covering to fit the appetite or make the problem tractable * **part two : Betting** * Some companies use two-week cycles (aka “sprints”). We learned that two weeks is too short to get anything meaningful done. Worse than that, two-week cycles are extremely costly due to the planning overhead. The amount of work you get out of two weeks isn’t worth the collective hours around the table to “sprint plan” or the opportunity cost of breaking everyone’s momentum to re-group. * after each six-week cycle, we schedule two weeks for cool-down. * bugs: Use cool-down. Bring it to the betting table. Schedule a bug smash. * We’ve noticed three phases of work when we build a new product from scratch. R&D mode (bet the time on spiking some key pieces of the new product idea. CEO and designer, we don’t expect to ship anything at the end of an R&D cycle) , Production mode (Shaping is deliberate again. bet multiple teams in parallel , Shipping is the goal) , Cleanup mode (There’s no shaping. There aren’t clear team boundaries. Work is “shipped”) [Cleanup shouldn’t last longer than two cycles] * Example: two years of development, R&D mode for the first year, a year of production mode cycles followed, two cycles of cleanup and significantly cut back the feature set, some overlap between R&D and production mode after that first year, * **Part three: Building** * We don’t start by assigning tasks to anyone. * we define and track the scopes as to-do lists. Each scope corresponds to a list name. Then any tasks for that scope go in that list. * **image 1, 2, 3,** * Mark nice-to-haves with ~ * First there’s the uphill phase of figuring out what our approach is and what we’re going to do. Then, once we can see all the work involved, there’s the downhill phase of execution * image 4, 5, * ## Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet * getting start: idea, technology, passion; What can I do well that I would love to do for an extended period of time? P: 19, 21 * 1 market segmentation: The single necessary and sufficient condition for a business is a paying customer. ; technological enthusiasts=early adopters=early majority ; P: 31, * 2 select a beachhead market: P: 44, * 3 build an end user profile: P: 52, * 4 Calculate the Total Addressable Market (TAM) Size for the Beachhead Market; $200 # [Essential Tips And Tricks For ](/book-summary/commonplace-book)[commonplace book](/book-summary/commonplace-book) # [knowledge management ](/book-summary/knowledge_management) # [PKM](/book-summary/knowledge_management/pkm) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/3LTMUMvLqIOxMLGRMkavoKhjR0yGpVpsaeb_NnspLybaPexjoQBnvVa2C2973HUQ30RiIqubg5B9F6eLjnxKXlE=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/3LTMUMvLqIOxMLGRMkavoKhjR0yGpVpsaeb_NnspLybaPexjoQBnvVa2C2973HUQ30RiIqubg5B9F6eLjnxKXlE=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Book Summary Books How to take smart Notes Writing Solid Code Shape Up: Stop Running in Circles and Ship Work that Matters Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet List of books I've read: Computer Vision: Algorithms and Applications Multiple View Geometry in Computer Vision Second Edition Complete to download the latest draft of Machine Learning Yearning Multiple view geometry in computer vision Learning OpenCV Book by Adrian Kaehler and Gary Bradski Digital Image Processing, Global Edition by Rafael C. Gonzalez Introduction to Algorithms, 3rd Edition (The MIT Press) The Mythical Man-Month: Essays on Software Engineering OpenCV-4-with-Python-Blueprints-Second-Edition Cracking the coding interview 189 programming questions and solutions Cracking the Tech Career: Insider Advice on Landing a Job at Google, Microsoft, Apple, Or Any Top Tech Company The Art of Startup Fundraising Mathematics for Machine Learning Principles of Economics (6th edition) The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) Reinventing Your Life: The Breakthough Program to End Negative Behavior Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf Magazine Papers Websites and links Tools YouTube - List channels Language: Essential Tips And Tricks For commonplace book knowledge management PKM # Books * ## How to take smart Notes * Introduction 4, The four underlying principles 4, the six steps to successful writing 6, afterword * introduction * write everything/ collect notes not related to any thing, organize notes, revirew, rewrite, * no reorganise no complex system, develop ideas by smart notes * 1 slip-box; 2 repeatable tasks that can become automatic and fit together seamlessly change routinges * overarching workflow "Getting Things Done" GTD * constantly have to ump back and forth between different tasks * 1- bibliographical slip-box: reference * 2- collected and generated ideas slip-box * 3- index: notes serve as entry point / thought/ topic * writing a paper step by step * 1 fleeting notes: it will delete within a day * I always have a slip of paper at hand, on which I note down the ideas of certain pages. on the backside I write down the bibliographic details. after finishing the book I go throught my notes and think how ehse notes might be relevant for already written notes in the slip-box. it means that I always read with an eye towards possible connections in the slip-box * 2 literature notes: contain the necessary information - in reference system or in the slip-box * I make a note with the bibliographic details. on the backside I would write "on page x is this, on page y is that" and then it goes into the bibliographic slib-box where I collect everything I read * read, think about its relevance * theoretical background, methodological approach or perspective of the text we read. * whether something adds to a discussion in the slip-box * we look at our slip-box for already existing lines of thought and think about the questions and problems already on our minds to which a new note might contribute. - assigning keywords is much more than just a bureaucratic act. it is a crucial part of the thinking process, which often leads to a deeper elaboration of the note itself and the connection to other notes. * * 3 permanent notes: develop ideas/new/ - only relevant to one particular project - in project specific folder - can be discarded or archived after project finished * add to slip-box behind the note you dirctyly refer to or behind the last note in the slip-box * add links to other notes or links on other notes to your new note * make sure it can be found from the index (MOC) add an entry in the index if necessary or refer to it from a note that is connected to the index * build a latticework of mental models * 4 behind multiple notes (link backward) - link to related note - link to MOC * make sure it can be found again * **keywords** can easily be added to a note like **tags** and will then show up in the **index** * a day * read and take notes * build connections within the slip-box - new idea * you write on your paper, notice a hole in the argument and have another look in the file system for the missing link. you follow up on a footnote, go back to research and might add a fitting quote to one of your papers in the making. * The four underlying principles * writing * simplicity * not from scratch * let the work carry you forward * the six steps to successful writing * separate and interlocking tasks * we use memo techniques is to bundle items together in a meaningful way and remember the bundles * the brain doesn't distinguish between an actual finished task and one that is postponed by taking a note * the fist step is to break down the amprphous task of "writing" into smaller pieces of different tasks that can be finished in **one go.** the second step is to make sure we always write down the outcome of our thinking, including possible connections to further inquiries. * ego depletion * read for understanding * take smart notes * develp ideas (MOC) * 1 overview of a topic - referred to from the index and used as an entry point into a topic has already develped * 2 keeping tarck of all topic - index - * share your insight * make it a habit * afterword * ## Writing Solid Code Writing Solid Code (Microsoft Programming Series) by Trendelles Store Note from Writing Solid Code Book [https://www.pirahansiah.com/describe/book- summary](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Fdescribe%2Fbook- summary&sa=D&sntz=1&usg=AOvVaw3ACzA6G0MBVN3nw6f-yMQo) #SolidCode #C++ #Python #pirahansiah * **Chapter 1** * Enable all optional compiler warnings. * Use lint to catch bugs that your compiler may miss. * If you have unit tests, use them. * **Chapter 2** * introduction assert page 16; assert.h * assert is nothing more than a repackaged form of the #ifdef code we saw before, but when you use * the macro, it takes one line instead of seven * That's why assert is a macro and not a function; if it were a function, calling * it could cause unexpected memory or code swapping. * Use assertions to validate function arguments . * Strip undefined behavior from your code, or use assertions to catch illegal uses of undefined behavior . * Don't waste people's time. Document unclear assertions . * Either remove implicit assumptions, or assert that they are valid . * Use assertions to detect impossible conditions. * Don't hide bugs when you program defensively. * Use a second algorithm to validate your results. * Don't wait for bugs to happen; use startup checks. * **Chapter 3** * Maintain shipping and debugging versions of your program. Shrink-wrap the shipping version, but use the debugging ver­sion as much as possible to catch bugs quickly. * Assertions are a shorthand way to write debugging checks. Use them to catch illegal conditions that should never arise. Don't confuse such conditions with error conditions, which you must handle in the final product. * Use assertions to validate function arguments and to alert pro­ grammars when they do something undefined. The more rigidly you define your functions, the easier it will be to validate the arguments. * Once you've written a function, review it and ask yourself, 'What am I assuming?" If you find an assumption, either assert that your assumption is always valid, or rewrite the code to re­ move the assumption. Also ask, 'What is most likely to be wrong in this code, and how can I automatically detect the prob­lem?" Strive to implement tests that catch bugs at the earliest possible moment. Textbooks encourage programmers to program defensively, but remember that this coding style hides bugs. When you write de­fensive code, use assertions to alert you if the "can't happen" cases do happen. * Eliminate random behavior. Force bugs to be reproducible . * Destroy your garbage so that it's not misused . * Not only did reallocate have to move the block of memory as it was expanding it, but the old memory had to be reallocated and filled with new data. In the assembler, both happened rarely. * This brings up another guideline for writing bug-free code: You don't want anything to happen rarely. You need to isolate those behaviors in your subsystems that may happen and make sure that they do happen. And of­ ten. If you find rare behavior in your subsystems, be sure to do something­ anything-to stir things up. * The assembler bug could have been found within hours, instead of years, if reallocate hadn't so rarely moved blocks when it expanded them. * If something happens rarely, force it to happen often. * Keep debug information to allow stronger error checking. * There is no question that the debug code will create differences be­ tween the ship and the debug versions of your code, but as long as you're careful not to change the underlying behavior of the code, those differences shouldn't matter. * Create thorough subsystem checks, and use them often. * Design your tests carefully. Nothing should be arbitrary. * Strive to implement transparent integrity checks. * Don't apply ship version constraints to the debug version. Trade size and speed for error detection. * **Chapter 4** * Don't wait until you have a bug to step through your code. * Step through every code path. * What About Sweeping Changes? In the past, programmers have asked, "What if I add a feature that touches code in many places? Stepping through all that new code is going to be time consuming." Let me answer that question with another: Can you make such sweeping changes without introducing any bugs? The habit of stepping through your code creates an interesting negative feedback loop. Programmers who step through their code soon learn to write small, easily testable functions because stepping through large func­tions is so painful. Programmers also spend more time thinking about how they can localize the changes they need to make-again, so that they can more easily test their code. And isn't this exactly what you want? No project lead likes it when programmers touch a lot of code; it's too destabilizing. Nor do leads like large, unmanageable functions; they're often unmaintainable. Try hard to localize your changes. If you find that you must make per­vasive changes, think twice before you decide not to step through all the new code. * Overflow and underflow bugs * Data conversion bugs * Off-by-one bugs * NULL pointer bugs * Bugs using garbage memory (0xA3 bugs) * Assignment bugs in which you've used = instead of == * Precedence bugs * Logic bugs * As you step through code, focus on data flow. Source level debuggers can hide execution details. Step through critical code at the instruction level . * **Chapter 5** * Make it hard to ignore error conditions. Don't bury error codes in return values. * Always look for, and eliminate, flaws in your interfaces. * Don't write multipurpose functions. Write separate functions to allow stronger argument validation. * Don't be wishy-washy. Define explicit function arguments. * Write functions that, given valid inputs, cannot fail. * Make the code intelligible at the point of call. Avoid Boolean arguments. * Write comments that emphasize potential hazards. * **Chapter 6** * Use well-defined data types. * Always ask ,"Can this variable or expression over- or underflow?" * Implement your designs as accurately as possible. Being kind a close is being kind a buggy. * these principles already: Don't accept special purpose arguments such as the NULL pointer, and Implement your design, not something that approximates it. The third principle is new: Strive to make every function perform its task exactly one time. * Implement "the task" just once. * Get rid of extraneous if statements. * Avoid using nested ?: operators . * Handle your special cases just once. * Avoid risky language idioms. * Don't needlessly mix operator types. If you must mix operators, use parentheses to isolate the operations. * Avoid calling functions that return errors. * **Chapter 7** * Don't reference memory that you don't own. * Don't reference memory that you have freed. * Don't use output memory as workspace buffers. * Don't pass data in static (or global) memory. * Don't write parasitic functions. * Don't abuse your programming language. * Tight C does not guarantee efficient machine code. * Write code for the "average" programmer. * **Chapter 8** * Bugs don't just "go away." * Don't fix bugs later; fix them now. * Fix the cause, not the symptom. * Don't clean up code unless the clean­ up is critical to the product's success. * Don't implement nonstrategic features. * There are no free features. * Don't allow unnecessary flexibility. * Don't keep "trying" solutions· until you find one that works. Take the time to find the correct solution. * Write and test code in small chunks. * Always test your code, even if that means your schedule will slip. * Don't rely on the testing group to find your bugs. * Don't blame testers for finding your bugs. * Establish your priorities and stick to them. * Never allow the same bug to bite you twice. * **A: CODING CHECKLISTS** * **B: MEMORY LOGGING ROUTINES** * **To sum up** * Charles Simonyi in the early 1970s. https://en.wikipedia.org/wiki/Hungarian_notation * char ch; * byte b; * flag f;/* flags that are always TRUE or FALSE*/ * symbol sym;/* some sort of symbol structure*/ * char byte flag symbol * *pch:/* character pointer*/ * *pb; * *pf: * *psym: * related link: [https://www.morling.dev/images/code_review_pyramid.svg](https://www.google.com/url?q=https%3A%2F%2Fwww.morling.dev%2Fimages%2Fcode_review_pyramid.svg&sa=D&sntz=1&usg=AOvVaw1y2It_PUzXAtFN7dPLegCa) * ## Shape Up: Stop Running in Circles and Ship Work that Matters * Six-week cycles: Our decisions are based on moving the product forward in the next six weeks, not micromanaging time. We don’t count hours or question how individual days are spent. We don’t have daily meetings. We don’t rethink our roadmap every two weeks. Our focus is at a higher level. We say to ourselves: “If this project ships after six weeks, we’ll be really happy. We’ll feel our time was well spent.” Then we commit the six weeks and leave the team alone to get it done * Shaping the work: key elements of a solution, appetite * **part one shaping:** * two separate tracks: one for shaping, one for building. * Steps to shaping * 1\. Set boundaries. * 2\. Rough out the elements: idea that solves the problem within the appetite but without all the fine details worked out * 3\. Address risks and rabbit holes. * 4\. Write the pitch. * fixed time, variable scope, * Where in the current system does the new thing fit? How do you get to it? What are the key components or interactions? Where does it take you? * five ingredients that we always want to include in a pitch: 1. Problem — The raw idea, a use case, or something we’ve seen that motivates us to work on this 2. Appetite — How much time we want to spend and how that constrains the solution 3. Solution — The core elements we came up with, presented in a form that’s easy for people to immediately understand 4. Rabbit holes — Details about the solution worth calling out to avoid problems 5. No-gos — Anything specifically excluded from the concept: functionality or use cases we intentionally aren’t covering to fit the appetite or make the problem tractable * **part two : Betting** * Some companies use two-week cycles (aka “sprints”). We learned that two weeks is too short to get anything meaningful done. Worse than that, two-week cycles are extremely costly due to the planning overhead. The amount of work you get out of two weeks isn’t worth the collective hours around the table to “sprint plan” or the opportunity cost of breaking everyone’s momentum to re-group. * after each six-week cycle, we schedule two weeks for cool-down. * bugs: Use cool-down. Bring it to the betting table. Schedule a bug smash. * We’ve noticed three phases of work when we build a new product from scratch. R&D mode (bet the time on spiking some key pieces of the new product idea. CEO and designer, we don’t expect to ship anything at the end of an R&D cycle) , Production mode (Shaping is deliberate again. bet multiple teams in parallel , Shipping is the goal) , Cleanup mode (There’s no shaping. There aren’t clear team boundaries. Work is “shipped”) [Cleanup shouldn’t last longer than two cycles] * Example: two years of development, R&D mode for the first year, a year of production mode cycles followed, two cycles of cleanup and significantly cut back the feature set, some overlap between R&D and production mode after that first year, * **Part three: Building** * We don’t start by assigning tasks to anyone. * we define and track the scopes as to-do lists. Each scope corresponds to a list name. Then any tasks for that scope go in that list. * **image 1, 2, 3,** * Mark nice-to-haves with ~ * First there’s the uphill phase of figuring out what our approach is and what we’re going to do. Then, once we can see all the work involved, there’s the downhill phase of execution * image 4, 5, * ## Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet * getting start: idea, technology, passion; What can I do well that I would love to do for an extended period of time? P: 19, 21 * 1 market segmentation: The single necessary and sufficient condition for a business is a paying customer. ; technological enthusiasts=early adopters=early majority ; P: 31, * 2 select a beachhead market: P: 44, * 3 build an end user profile: P: 52, * 4 Calculate the Total Addressable Market (TAM) Size for the Beachhead Market; $200 # [Essential Tips And Tricks For ](/book-summary/commonplace-book)[commonplace book](/book-summary/commonplace-book) # [knowledge management ](/book-summary/knowledge_management) # [PKM](/book-summary/knowledge_management/pkm) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/LMErpRo3SemuSDj3pLjSSh1xcteMOPvF9yzhoEb8_AB5vppySOpESQgqlBlCy2nymRjHv3i3BNkjBtED1LpjpWk=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/LMErpRo3SemuSDj3pLjSSh1xcteMOPvF9yzhoEb8_AB5vppySOpESQgqlBlCy2nymRjHv3i3BNkjBtED1LpjpWk=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Book Summary Books How to take smart Notes Writing Solid Code Shape Up: Stop Running in Circles and Ship Work that Matters Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet List of books I've read: Computer Vision: Algorithms and Applications Multiple View Geometry in Computer Vision Second Edition Complete to download the latest draft of Machine Learning Yearning Multiple view geometry in computer vision Learning OpenCV Book by Adrian Kaehler and Gary Bradski Digital Image Processing, Global Edition by Rafael C. Gonzalez Introduction to Algorithms, 3rd Edition (The MIT Press) The Mythical Man-Month: Essays on Software Engineering OpenCV-4-with-Python-Blueprints-Second-Edition Cracking the coding interview 189 programming questions and solutions Cracking the Tech Career: Insider Advice on Landing a Job at Google, Microsoft, Apple, Or Any Top Tech Company The Art of Startup Fundraising Mathematics for Machine Learning Principles of Economics (6th edition) The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) Reinventing Your Life: The Breakthough Program to End Negative Behavior Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf Magazine Papers Websites and links Tools YouTube - List channels Language: Essential Tips And Tricks For commonplace book knowledge management PKM # Books * ## How to take smart Notes * Introduction 4, The four underlying principles 4, the six steps to successful writing 6, afterword * introduction * write everything/ collect notes not related to any thing, organize notes, revirew, rewrite, * no reorganise no complex system, develop ideas by smart notes * 1 slip-box; 2 repeatable tasks that can become automatic and fit together seamlessly change routinges * overarching workflow "Getting Things Done" GTD * constantly have to ump back and forth between different tasks * 1- bibliographical slip-box: reference * 2- collected and generated ideas slip-box * 3- index: notes serve as entry point / thought/ topic * writing a paper step by step * 1 fleeting notes: it will delete within a day * I always have a slip of paper at hand, on which I note down the ideas of certain pages. on the backside I write down the bibliographic details. after finishing the book I go throught my notes and think how ehse notes might be relevant for already written notes in the slip-box. it means that I always read with an eye towards possible connections in the slip-box * 2 literature notes: contain the necessary information - in reference system or in the slip-box * I make a note with the bibliographic details. on the backside I would write "on page x is this, on page y is that" and then it goes into the bibliographic slib-box where I collect everything I read * read, think about its relevance * theoretical background, methodological approach or perspective of the text we read. * whether something adds to a discussion in the slip-box * we look at our slip-box for already existing lines of thought and think about the questions and problems already on our minds to which a new note might contribute. - assigning keywords is much more than just a bureaucratic act. it is a crucial part of the thinking process, which often leads to a deeper elaboration of the note itself and the connection to other notes. * * 3 permanent notes: develop ideas/new/ - only relevant to one particular project - in project specific folder - can be discarded or archived after project finished * add to slip-box behind the note you dirctyly refer to or behind the last note in the slip-box * add links to other notes or links on other notes to your new note * make sure it can be found from the index (MOC) add an entry in the index if necessary or refer to it from a note that is connected to the index * build a latticework of mental models * 4 behind multiple notes (link backward) - link to related note - link to MOC * make sure it can be found again * **keywords** can easily be added to a note like **tags** and will then show up in the **index** * a day * read and take notes * build connections within the slip-box - new idea * you write on your paper, notice a hole in the argument and have another look in the file system for the missing link. you follow up on a footnote, go back to research and might add a fitting quote to one of your papers in the making. * The four underlying principles * writing * simplicity * not from scratch * let the work carry you forward * the six steps to successful writing * separate and interlocking tasks * we use memo techniques is to bundle items together in a meaningful way and remember the bundles * the brain doesn't distinguish between an actual finished task and one that is postponed by taking a note * the fist step is to break down the amprphous task of "writing" into smaller pieces of different tasks that can be finished in **one go.** the second step is to make sure we always write down the outcome of our thinking, including possible connections to further inquiries. * ego depletion * read for understanding * take smart notes * develp ideas (MOC) * 1 overview of a topic - referred to from the index and used as an entry point into a topic has already develped * 2 keeping tarck of all topic - index - * share your insight * make it a habit * afterword * ## Writing Solid Code Writing Solid Code (Microsoft Programming Series) by Trendelles Store Note from Writing Solid Code Book [https://www.pirahansiah.com/describe/book- summary](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Fdescribe%2Fbook- summary&sa=D&sntz=1&usg=AOvVaw3ACzA6G0MBVN3nw6f-yMQo) #SolidCode #C++ #Python #pirahansiah * **Chapter 1** * Enable all optional compiler warnings. * Use lint to catch bugs that your compiler may miss. * If you have unit tests, use them. * **Chapter 2** * introduction assert page 16; assert.h * assert is nothing more than a repackaged form of the #ifdef code we saw before, but when you use * the macro, it takes one line instead of seven * That's why assert is a macro and not a function; if it were a function, calling * it could cause unexpected memory or code swapping. * Use assertions to validate function arguments . * Strip undefined behavior from your code, or use assertions to catch illegal uses of undefined behavior . * Don't waste people's time. Document unclear assertions . * Either remove implicit assumptions, or assert that they are valid . * Use assertions to detect impossible conditions. * Don't hide bugs when you program defensively. * Use a second algorithm to validate your results. * Don't wait for bugs to happen; use startup checks. * **Chapter 3** * Maintain shipping and debugging versions of your program. Shrink-wrap the shipping version, but use the debugging ver­sion as much as possible to catch bugs quickly. * Assertions are a shorthand way to write debugging checks. Use them to catch illegal conditions that should never arise. Don't confuse such conditions with error conditions, which you must handle in the final product. * Use assertions to validate function arguments and to alert pro­ grammars when they do something undefined. The more rigidly you define your functions, the easier it will be to validate the arguments. * Once you've written a function, review it and ask yourself, 'What am I assuming?" If you find an assumption, either assert that your assumption is always valid, or rewrite the code to re­ move the assumption. Also ask, 'What is most likely to be wrong in this code, and how can I automatically detect the prob­lem?" Strive to implement tests that catch bugs at the earliest possible moment. Textbooks encourage programmers to program defensively, but remember that this coding style hides bugs. When you write de­fensive code, use assertions to alert you if the "can't happen" cases do happen. * Eliminate random behavior. Force bugs to be reproducible . * Destroy your garbage so that it's not misused . * Not only did reallocate have to move the block of memory as it was expanding it, but the old memory had to be reallocated and filled with new data. In the assembler, both happened rarely. * This brings up another guideline for writing bug-free code: You don't want anything to happen rarely. You need to isolate those behaviors in your subsystems that may happen and make sure that they do happen. And of­ ten. If you find rare behavior in your subsystems, be sure to do something­ anything-to stir things up. * The assembler bug could have been found within hours, instead of years, if reallocate hadn't so rarely moved blocks when it expanded them. * If something happens rarely, force it to happen often. * Keep debug information to allow stronger error checking. * There is no question that the debug code will create differences be­ tween the ship and the debug versions of your code, but as long as you're careful not to change the underlying behavior of the code, those differences shouldn't matter. * Create thorough subsystem checks, and use them often. * Design your tests carefully. Nothing should be arbitrary. * Strive to implement transparent integrity checks. * Don't apply ship version constraints to the debug version. Trade size and speed for error detection. * **Chapter 4** * Don't wait until you have a bug to step through your code. * Step through every code path. * What About Sweeping Changes? In the past, programmers have asked, "What if I add a feature that touches code in many places? Stepping through all that new code is going to be time consuming." Let me answer that question with another: Can you make such sweeping changes without introducing any bugs? The habit of stepping through your code creates an interesting negative feedback loop. Programmers who step through their code soon learn to write small, easily testable functions because stepping through large func­tions is so painful. Programmers also spend more time thinking about how they can localize the changes they need to make-again, so that they can more easily test their code. And isn't this exactly what you want? No project lead likes it when programmers touch a lot of code; it's too destabilizing. Nor do leads like large, unmanageable functions; they're often unmaintainable. Try hard to localize your changes. If you find that you must make per­vasive changes, think twice before you decide not to step through all the new code. * Overflow and underflow bugs * Data conversion bugs * Off-by-one bugs * NULL pointer bugs * Bugs using garbage memory (0xA3 bugs) * Assignment bugs in which you've used = instead of == * Precedence bugs * Logic bugs * As you step through code, focus on data flow. Source level debuggers can hide execution details. Step through critical code at the instruction level . * **Chapter 5** * Make it hard to ignore error conditions. Don't bury error codes in return values. * Always look for, and eliminate, flaws in your interfaces. * Don't write multipurpose functions. Write separate functions to allow stronger argument validation. * Don't be wishy-washy. Define explicit function arguments. * Write functions that, given valid inputs, cannot fail. * Make the code intelligible at the point of call. Avoid Boolean arguments. * Write comments that emphasize potential hazards. * **Chapter 6** * Use well-defined data types. * Always ask ,"Can this variable or expression over- or underflow?" * Implement your designs as accurately as possible. Being kind a close is being kind a buggy. * these principles already: Don't accept special purpose arguments such as the NULL pointer, and Implement your design, not something that approximates it. The third principle is new: Strive to make every function perform its task exactly one time. * Implement "the task" just once. * Get rid of extraneous if statements. * Avoid using nested ?: operators . * Handle your special cases just once. * Avoid risky language idioms. * Don't needlessly mix operator types. If you must mix operators, use parentheses to isolate the operations. * Avoid calling functions that return errors. * **Chapter 7** * Don't reference memory that you don't own. * Don't reference memory that you have freed. * Don't use output memory as workspace buffers. * Don't pass data in static (or global) memory. * Don't write parasitic functions. * Don't abuse your programming language. * Tight C does not guarantee efficient machine code. * Write code for the "average" programmer. * **Chapter 8** * Bugs don't just "go away." * Don't fix bugs later; fix them now. * Fix the cause, not the symptom. * Don't clean up code unless the clean­ up is critical to the product's success. * Don't implement nonstrategic features. * There are no free features. * Don't allow unnecessary flexibility. * Don't keep "trying" solutions· until you find one that works. Take the time to find the correct solution. * Write and test code in small chunks. * Always test your code, even if that means your schedule will slip. * Don't rely on the testing group to find your bugs. * Don't blame testers for finding your bugs. * Establish your priorities and stick to them. * Never allow the same bug to bite you twice. * **A: CODING CHECKLISTS** * **B: MEMORY LOGGING ROUTINES** * **To sum up** * Charles Simonyi in the early 1970s. https://en.wikipedia.org/wiki/Hungarian_notation * char ch; * byte b; * flag f;/* flags that are always TRUE or FALSE*/ * symbol sym;/* some sort of symbol structure*/ * char byte flag symbol * *pch:/* character pointer*/ * *pb; * *pf: * *psym: * related link: [https://www.morling.dev/images/code_review_pyramid.svg](https://www.google.com/url?q=https%3A%2F%2Fwww.morling.dev%2Fimages%2Fcode_review_pyramid.svg&sa=D&sntz=1&usg=AOvVaw1y2It_PUzXAtFN7dPLegCa) * ## Shape Up: Stop Running in Circles and Ship Work that Matters * Six-week cycles: Our decisions are based on moving the product forward in the next six weeks, not micromanaging time. We don’t count hours or question how individual days are spent. We don’t have daily meetings. We don’t rethink our roadmap every two weeks. Our focus is at a higher level. We say to ourselves: “If this project ships after six weeks, we’ll be really happy. We’ll feel our time was well spent.” Then we commit the six weeks and leave the team alone to get it done * Shaping the work: key elements of a solution, appetite * **part one shaping:** * two separate tracks: one for shaping, one for building. * Steps to shaping * 1\. Set boundaries. * 2\. Rough out the elements: idea that solves the problem within the appetite but without all the fine details worked out * 3\. Address risks and rabbit holes. * 4\. Write the pitch. * fixed time, variable scope, * Where in the current system does the new thing fit? How do you get to it? What are the key components or interactions? Where does it take you? * five ingredients that we always want to include in a pitch: 1. Problem — The raw idea, a use case, or something we’ve seen that motivates us to work on this 2. Appetite — How much time we want to spend and how that constrains the solution 3. Solution — The core elements we came up with, presented in a form that’s easy for people to immediately understand 4. Rabbit holes — Details about the solution worth calling out to avoid problems 5. No-gos — Anything specifically excluded from the concept: functionality or use cases we intentionally aren’t covering to fit the appetite or make the problem tractable * **part two : Betting** * Some companies use two-week cycles (aka “sprints”). We learned that two weeks is too short to get anything meaningful done. Worse than that, two-week cycles are extremely costly due to the planning overhead. The amount of work you get out of two weeks isn’t worth the collective hours around the table to “sprint plan” or the opportunity cost of breaking everyone’s momentum to re-group. * after each six-week cycle, we schedule two weeks for cool-down. * bugs: Use cool-down. Bring it to the betting table. Schedule a bug smash. * We’ve noticed three phases of work when we build a new product from scratch. R&D mode (bet the time on spiking some key pieces of the new product idea. CEO and designer, we don’t expect to ship anything at the end of an R&D cycle) , Production mode (Shaping is deliberate again. bet multiple teams in parallel , Shipping is the goal) , Cleanup mode (There’s no shaping. There aren’t clear team boundaries. Work is “shipped”) [Cleanup shouldn’t last longer than two cycles] * Example: two years of development, R&D mode for the first year, a year of production mode cycles followed, two cycles of cleanup and significantly cut back the feature set, some overlap between R&D and production mode after that first year, * **Part three: Building** * We don’t start by assigning tasks to anyone. * we define and track the scopes as to-do lists. Each scope corresponds to a list name. Then any tasks for that scope go in that list. * **image 1, 2, 3,** * Mark nice-to-haves with ~ * First there’s the uphill phase of figuring out what our approach is and what we’re going to do. Then, once we can see all the work involved, there’s the downhill phase of execution * image 4, 5, * ## Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet * getting start: idea, technology, passion; What can I do well that I would love to do for an extended period of time? P: 19, 21 * 1 market segmentation: The single necessary and sufficient condition for a business is a paying customer. ; technological enthusiasts=early adopters=early majority ; P: 31, * 2 select a beachhead market: P: 44, * 3 build an end user profile: P: 52, * 4 Calculate the Total Addressable Market (TAM) Size for the Beachhead Market; $200 # [Essential Tips And Tricks For ](/book-summary/commonplace-book)[commonplace book](/book-summary/commonplace-book) # [knowledge management ](/book-summary/knowledge_management) # [PKM](/book-summary/knowledge_management/pkm) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/LMErpRo3SemuSDj3pLjSSh1xcteMOPvF9yzhoEb8_AB5vppySOpESQgqlBlCy2nymRjHv3i3BNkjBtED1LpjpWk=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/LMErpRo3SemuSDj3pLjSSh1xcteMOPvF9yzhoEb8_AB5vppySOpESQgqlBlCy2nymRjHv3i3BNkjBtED1LpjpWk=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Book Summary Books How to take smart Notes Writing Solid Code Shape Up: Stop Running in Circles and Ship Work that Matters Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet List of books I've read: Computer Vision: Algorithms and Applications Multiple View Geometry in Computer Vision Second Edition Complete to download the latest draft of Machine Learning Yearning Multiple view geometry in computer vision Learning OpenCV Book by Adrian Kaehler and Gary Bradski Digital Image Processing, Global Edition by Rafael C. Gonzalez Introduction to Algorithms, 3rd Edition (The MIT Press) The Mythical Man-Month: Essays on Software Engineering OpenCV-4-with-Python-Blueprints-Second-Edition Cracking the coding interview 189 programming questions and solutions Cracking the Tech Career: Insider Advice on Landing a Job at Google, Microsoft, Apple, Or Any Top Tech Company The Art of Startup Fundraising Mathematics for Machine Learning Principles of Economics (6th edition) The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) Reinventing Your Life: The Breakthough Program to End Negative Behavior Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf Magazine Papers Websites and links Tools YouTube - List channels Language: Essential Tips And Tricks For commonplace book knowledge management PKM # Books * ## How to take smart Notes * Introduction 4, The four underlying principles 4, the six steps to successful writing 6, afterword * introduction * write everything/ collect notes not related to any thing, organize notes, revirew, rewrite, * no reorganise no complex system, develop ideas by smart notes * 1 slip-box; 2 repeatable tasks that can become automatic and fit together seamlessly change routinges * overarching workflow "Getting Things Done" GTD * constantly have to ump back and forth between different tasks * 1- bibliographical slip-box: reference * 2- collected and generated ideas slip-box * 3- index: notes serve as entry point / thought/ topic * writing a paper step by step * 1 fleeting notes: it will delete within a day * I always have a slip of paper at hand, on which I note down the ideas of certain pages. on the backside I write down the bibliographic details. after finishing the book I go throught my notes and think how ehse notes might be relevant for already written notes in the slip-box. it means that I always read with an eye towards possible connections in the slip-box * 2 literature notes: contain the necessary information - in reference system or in the slip-box * I make a note with the bibliographic details. on the backside I would write "on page x is this, on page y is that" and then it goes into the bibliographic slib-box where I collect everything I read * read, think about its relevance * theoretical background, methodological approach or perspective of the text we read. * whether something adds to a discussion in the slip-box * we look at our slip-box for already existing lines of thought and think about the questions and problems already on our minds to which a new note might contribute. - assigning keywords is much more than just a bureaucratic act. it is a crucial part of the thinking process, which often leads to a deeper elaboration of the note itself and the connection to other notes. * * 3 permanent notes: develop ideas/new/ - only relevant to one particular project - in project specific folder - can be discarded or archived after project finished * add to slip-box behind the note you dirctyly refer to or behind the last note in the slip-box * add links to other notes or links on other notes to your new note * make sure it can be found from the index (MOC) add an entry in the index if necessary or refer to it from a note that is connected to the index * build a latticework of mental models * 4 behind multiple notes (link backward) - link to related note - link to MOC * make sure it can be found again * **keywords** can easily be added to a note like **tags** and will then show up in the **index** * a day * read and take notes * build connections within the slip-box - new idea * you write on your paper, notice a hole in the argument and have another look in the file system for the missing link. you follow up on a footnote, go back to research and might add a fitting quote to one of your papers in the making. * The four underlying principles * writing * simplicity * not from scratch * let the work carry you forward * the six steps to successful writing * separate and interlocking tasks * we use memo techniques is to bundle items together in a meaningful way and remember the bundles * the brain doesn't distinguish between an actual finished task and one that is postponed by taking a note * the fist step is to break down the amprphous task of "writing" into smaller pieces of different tasks that can be finished in **one go.** the second step is to make sure we always write down the outcome of our thinking, including possible connections to further inquiries. * ego depletion * read for understanding * take smart notes * develp ideas (MOC) * 1 overview of a topic - referred to from the index and used as an entry point into a topic has already develped * 2 keeping tarck of all topic - index - * share your insight * make it a habit * afterword * ## Writing Solid Code Writing Solid Code (Microsoft Programming Series) by Trendelles Store Note from Writing Solid Code Book [https://www.pirahansiah.com/describe/book- summary](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Fdescribe%2Fbook- summary&sa=D&sntz=1&usg=AOvVaw3ACzA6G0MBVN3nw6f-yMQo) #SolidCode #C++ #Python #pirahansiah * **Chapter 1** * Enable all optional compiler warnings. * Use lint to catch bugs that your compiler may miss. * If you have unit tests, use them. * **Chapter 2** * introduction assert page 16; assert.h * assert is nothing more than a repackaged form of the #ifdef code we saw before, but when you use * the macro, it takes one line instead of seven * That's why assert is a macro and not a function; if it were a function, calling * it could cause unexpected memory or code swapping. * Use assertions to validate function arguments . * Strip undefined behavior from your code, or use assertions to catch illegal uses of undefined behavior . * Don't waste people's time. Document unclear assertions . * Either remove implicit assumptions, or assert that they are valid . * Use assertions to detect impossible conditions. * Don't hide bugs when you program defensively. * Use a second algorithm to validate your results. * Don't wait for bugs to happen; use startup checks. * **Chapter 3** * Maintain shipping and debugging versions of your program. Shrink-wrap the shipping version, but use the debugging ver­sion as much as possible to catch bugs quickly. * Assertions are a shorthand way to write debugging checks. Use them to catch illegal conditions that should never arise. Don't confuse such conditions with error conditions, which you must handle in the final product. * Use assertions to validate function arguments and to alert pro­ grammars when they do something undefined. The more rigidly you define your functions, the easier it will be to validate the arguments. * Once you've written a function, review it and ask yourself, 'What am I assuming?" If you find an assumption, either assert that your assumption is always valid, or rewrite the code to re­ move the assumption. Also ask, 'What is most likely to be wrong in this code, and how can I automatically detect the prob­lem?" Strive to implement tests that catch bugs at the earliest possible moment. Textbooks encourage programmers to program defensively, but remember that this coding style hides bugs. When you write de­fensive code, use assertions to alert you if the "can't happen" cases do happen. * Eliminate random behavior. Force bugs to be reproducible . * Destroy your garbage so that it's not misused . * Not only did reallocate have to move the block of memory as it was expanding it, but the old memory had to be reallocated and filled with new data. In the assembler, both happened rarely. * This brings up another guideline for writing bug-free code: You don't want anything to happen rarely. You need to isolate those behaviors in your subsystems that may happen and make sure that they do happen. And of­ ten. If you find rare behavior in your subsystems, be sure to do something­ anything-to stir things up. * The assembler bug could have been found within hours, instead of years, if reallocate hadn't so rarely moved blocks when it expanded them. * If something happens rarely, force it to happen often. * Keep debug information to allow stronger error checking. * There is no question that the debug code will create differences be­ tween the ship and the debug versions of your code, but as long as you're careful not to change the underlying behavior of the code, those differences shouldn't matter. * Create thorough subsystem checks, and use them often. * Design your tests carefully. Nothing should be arbitrary. * Strive to implement transparent integrity checks. * Don't apply ship version constraints to the debug version. Trade size and speed for error detection. * **Chapter 4** * Don't wait until you have a bug to step through your code. * Step through every code path. * What About Sweeping Changes? In the past, programmers have asked, "What if I add a feature that touches code in many places? Stepping through all that new code is going to be time consuming." Let me answer that question with another: Can you make such sweeping changes without introducing any bugs? The habit of stepping through your code creates an interesting negative feedback loop. Programmers who step through their code soon learn to write small, easily testable functions because stepping through large func­tions is so painful. Programmers also spend more time thinking about how they can localize the changes they need to make-again, so that they can more easily test their code. And isn't this exactly what you want? No project lead likes it when programmers touch a lot of code; it's too destabilizing. Nor do leads like large, unmanageable functions; they're often unmaintainable. Try hard to localize your changes. If you find that you must make per­vasive changes, think twice before you decide not to step through all the new code. * Overflow and underflow bugs * Data conversion bugs * Off-by-one bugs * NULL pointer bugs * Bugs using garbage memory (0xA3 bugs) * Assignment bugs in which you've used = instead of == * Precedence bugs * Logic bugs * As you step through code, focus on data flow. Source level debuggers can hide execution details. Step through critical code at the instruction level . * **Chapter 5** * Make it hard to ignore error conditions. Don't bury error codes in return values. * Always look for, and eliminate, flaws in your interfaces. * Don't write multipurpose functions. Write separate functions to allow stronger argument validation. * Don't be wishy-washy. Define explicit function arguments. * Write functions that, given valid inputs, cannot fail. * Make the code intelligible at the point of call. Avoid Boolean arguments. * Write comments that emphasize potential hazards. * **Chapter 6** * Use well-defined data types. * Always ask ,"Can this variable or expression over- or underflow?" * Implement your designs as accurately as possible. Being kind a close is being kind a buggy. * these principles already: Don't accept special purpose arguments such as the NULL pointer, and Implement your design, not something that approximates it. The third principle is new: Strive to make every function perform its task exactly one time. * Implement "the task" just once. * Get rid of extraneous if statements. * Avoid using nested ?: operators . * Handle your special cases just once. * Avoid risky language idioms. * Don't needlessly mix operator types. If you must mix operators, use parentheses to isolate the operations. * Avoid calling functions that return errors. * **Chapter 7** * Don't reference memory that you don't own. * Don't reference memory that you have freed. * Don't use output memory as workspace buffers. * Don't pass data in static (or global) memory. * Don't write parasitic functions. * Don't abuse your programming language. * Tight C does not guarantee efficient machine code. * Write code for the "average" programmer. * **Chapter 8** * Bugs don't just "go away." * Don't fix bugs later; fix them now. * Fix the cause, not the symptom. * Don't clean up code unless the clean­ up is critical to the product's success. * Don't implement nonstrategic features. * There are no free features. * Don't allow unnecessary flexibility. * Don't keep "trying" solutions· until you find one that works. Take the time to find the correct solution. * Write and test code in small chunks. * Always test your code, even if that means your schedule will slip. * Don't rely on the testing group to find your bugs. * Don't blame testers for finding your bugs. * Establish your priorities and stick to them. * Never allow the same bug to bite you twice. * **A: CODING CHECKLISTS** * **B: MEMORY LOGGING ROUTINES** * **To sum up** * Charles Simonyi in the early 1970s. https://en.wikipedia.org/wiki/Hungarian_notation * char ch; * byte b; * flag f;/* flags that are always TRUE or FALSE*/ * symbol sym;/* some sort of symbol structure*/ * char byte flag symbol * *pch:/* character pointer*/ * *pb; * *pf: * *psym: * related link: [https://www.morling.dev/images/code_review_pyramid.svg](https://www.google.com/url?q=https%3A%2F%2Fwww.morling.dev%2Fimages%2Fcode_review_pyramid.svg&sa=D&sntz=1&usg=AOvVaw1y2It_PUzXAtFN7dPLegCa) * ## Shape Up: Stop Running in Circles and Ship Work that Matters * Six-week cycles: Our decisions are based on moving the product forward in the next six weeks, not micromanaging time. We don’t count hours or question how individual days are spent. We don’t have daily meetings. We don’t rethink our roadmap every two weeks. Our focus is at a higher level. We say to ourselves: “If this project ships after six weeks, we’ll be really happy. We’ll feel our time was well spent.” Then we commit the six weeks and leave the team alone to get it done * Shaping the work: key elements of a solution, appetite * **part one shaping:** * two separate tracks: one for shaping, one for building. * Steps to shaping * 1\. Set boundaries. * 2\. Rough out the elements: idea that solves the problem within the appetite but without all the fine details worked out * 3\. Address risks and rabbit holes. * 4\. Write the pitch. * fixed time, variable scope, * Where in the current system does the new thing fit? How do you get to it? What are the key components or interactions? Where does it take you? * five ingredients that we always want to include in a pitch: 1. Problem — The raw idea, a use case, or something we’ve seen that motivates us to work on this 2. Appetite — How much time we want to spend and how that constrains the solution 3. Solution — The core elements we came up with, presented in a form that’s easy for people to immediately understand 4. Rabbit holes — Details about the solution worth calling out to avoid problems 5. No-gos — Anything specifically excluded from the concept: functionality or use cases we intentionally aren’t covering to fit the appetite or make the problem tractable * **part two : Betting** * Some companies use two-week cycles (aka “sprints”). We learned that two weeks is too short to get anything meaningful done. Worse than that, two-week cycles are extremely costly due to the planning overhead. The amount of work you get out of two weeks isn’t worth the collective hours around the table to “sprint plan” or the opportunity cost of breaking everyone’s momentum to re-group. * after each six-week cycle, we schedule two weeks for cool-down. * bugs: Use cool-down. Bring it to the betting table. Schedule a bug smash. * We’ve noticed three phases of work when we build a new product from scratch. R&D mode (bet the time on spiking some key pieces of the new product idea. CEO and designer, we don’t expect to ship anything at the end of an R&D cycle) , Production mode (Shaping is deliberate again. bet multiple teams in parallel , Shipping is the goal) , Cleanup mode (There’s no shaping. There aren’t clear team boundaries. Work is “shipped”) [Cleanup shouldn’t last longer than two cycles] * Example: two years of development, R&D mode for the first year, a year of production mode cycles followed, two cycles of cleanup and significantly cut back the feature set, some overlap between R&D and production mode after that first year, * **Part three: Building** * We don’t start by assigning tasks to anyone. * we define and track the scopes as to-do lists. Each scope corresponds to a list name. Then any tasks for that scope go in that list. * **image 1, 2, 3,** * Mark nice-to-haves with ~ * First there’s the uphill phase of figuring out what our approach is and what we’re going to do. Then, once we can see all the work involved, there’s the downhill phase of execution * image 4, 5, * ## Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet * getting start: idea, technology, passion; What can I do well that I would love to do for an extended period of time? P: 19, 21 * 1 market segmentation: The single necessary and sufficient condition for a business is a paying customer. ; technological enthusiasts=early adopters=early majority ; P: 31, * 2 select a beachhead market: P: 44, * 3 build an end user profile: P: 52, * 4 Calculate the Total Addressable Market (TAM) Size for the Beachhead Market; $200 # [Essential Tips And Tricks For ](/book-summary/commonplace-book)[commonplace book](/book-summary/commonplace-book) # [knowledge management ](/book-summary/knowledge_management) # [PKM](/book-summary/knowledge_management/pkm) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/LMErpRo3SemuSDj3pLjSSh1xcteMOPvF9yzhoEb8_AB5vppySOpESQgqlBlCy2nymRjHv3i3BNkjBtED1LpjpWk=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/LMErpRo3SemuSDj3pLjSSh1xcteMOPvF9yzhoEb8_AB5vppySOpESQgqlBlCy2nymRjHv3i3BNkjBtED1LpjpWk=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Book Summary Books How to take smart Notes Writing Solid Code Shape Up: Stop Running in Circles and Ship Work that Matters Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet List of books I've read: Computer Vision: Algorithms and Applications Multiple View Geometry in Computer Vision Second Edition Complete to download the latest draft of Machine Learning Yearning Multiple view geometry in computer vision Learning OpenCV Book by Adrian Kaehler and Gary Bradski Digital Image Processing, Global Edition by Rafael C. Gonzalez Introduction to Algorithms, 3rd Edition (The MIT Press) The Mythical Man-Month: Essays on Software Engineering OpenCV-4-with-Python-Blueprints-Second-Edition Cracking the coding interview 189 programming questions and solutions Cracking the Tech Career: Insider Advice on Landing a Job at Google, Microsoft, Apple, Or Any Top Tech Company The Art of Startup Fundraising Mathematics for Machine Learning Principles of Economics (6th edition) The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) Reinventing Your Life: The Breakthough Program to End Negative Behavior Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf Magazine Papers Websites and links Tools YouTube - List channels Language: Essential Tips And Tricks For commonplace book knowledge management PKM # Books * ## How to take smart Notes * Introduction 4, The four underlying principles 4, the six steps to successful writing 6, afterword * introduction * write everything/ collect notes not related to any thing, organize notes, revirew, rewrite, * no reorganise no complex system, develop ideas by smart notes * 1 slip-box; 2 repeatable tasks that can become automatic and fit together seamlessly change routinges * overarching workflow "Getting Things Done" GTD * constantly have to ump back and forth between different tasks * 1- bibliographical slip-box: reference * 2- collected and generated ideas slip-box * 3- index: notes serve as entry point / thought/ topic * writing a paper step by step * 1 fleeting notes: it will delete within a day * I always have a slip of paper at hand, on which I note down the ideas of certain pages. on the backside I write down the bibliographic details. after finishing the book I go throught my notes and think how ehse notes might be relevant for already written notes in the slip-box. it means that I always read with an eye towards possible connections in the slip-box * 2 literature notes: contain the necessary information - in reference system or in the slip-box * I make a note with the bibliographic details. on the backside I would write "on page x is this, on page y is that" and then it goes into the bibliographic slib-box where I collect everything I read * read, think about its relevance * theoretical background, methodological approach or perspective of the text we read. * whether something adds to a discussion in the slip-box * we look at our slip-box for already existing lines of thought and think about the questions and problems already on our minds to which a new note might contribute. - assigning keywords is much more than just a bureaucratic act. it is a crucial part of the thinking process, which often leads to a deeper elaboration of the note itself and the connection to other notes. * * 3 permanent notes: develop ideas/new/ - only relevant to one particular project - in project specific folder - can be discarded or archived after project finished * add to slip-box behind the note you dirctyly refer to or behind the last note in the slip-box * add links to other notes or links on other notes to your new note * make sure it can be found from the index (MOC) add an entry in the index if necessary or refer to it from a note that is connected to the index * build a latticework of mental models * 4 behind multiple notes (link backward) - link to related note - link to MOC * make sure it can be found again * **keywords** can easily be added to a note like **tags** and will then show up in the **index** * a day * read and take notes * build connections within the slip-box - new idea * you write on your paper, notice a hole in the argument and have another look in the file system for the missing link. you follow up on a footnote, go back to research and might add a fitting quote to one of your papers in the making. * The four underlying principles * writing * simplicity * not from scratch * let the work carry you forward * the six steps to successful writing * separate and interlocking tasks * we use memo techniques is to bundle items together in a meaningful way and remember the bundles * the brain doesn't distinguish between an actual finished task and one that is postponed by taking a note * the fist step is to break down the amprphous task of "writing" into smaller pieces of different tasks that can be finished in **one go.** the second step is to make sure we always write down the outcome of our thinking, including possible connections to further inquiries. * ego depletion * read for understanding * take smart notes * develp ideas (MOC) * 1 overview of a topic - referred to from the index and used as an entry point into a topic has already develped * 2 keeping tarck of all topic - index - * share your insight * make it a habit * afterword * ## Writing Solid Code Writing Solid Code (Microsoft Programming Series) by Trendelles Store Note from Writing Solid Code Book [https://www.pirahansiah.com/describe/book- summary](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Fdescribe%2Fbook- summary&sa=D&sntz=1&usg=AOvVaw3ACzA6G0MBVN3nw6f-yMQo) #SolidCode #C++ #Python #pirahansiah * **Chapter 1** * Enable all optional compiler warnings. * Use lint to catch bugs that your compiler may miss. * If you have unit tests, use them. * **Chapter 2** * introduction assert page 16; assert.h * assert is nothing more than a repackaged form of the #ifdef code we saw before, but when you use * the macro, it takes one line instead of seven * That's why assert is a macro and not a function; if it were a function, calling * it could cause unexpected memory or code swapping. * Use assertions to validate function arguments . * Strip undefined behavior from your code, or use assertions to catch illegal uses of undefined behavior . * Don't waste people's time. Document unclear assertions . * Either remove implicit assumptions, or assert that they are valid . * Use assertions to detect impossible conditions. * Don't hide bugs when you program defensively. * Use a second algorithm to validate your results. * Don't wait for bugs to happen; use startup checks. * **Chapter 3** * Maintain shipping and debugging versions of your program. Shrink-wrap the shipping version, but use the debugging ver­sion as much as possible to catch bugs quickly. * Assertions are a shorthand way to write debugging checks. Use them to catch illegal conditions that should never arise. Don't confuse such conditions with error conditions, which you must handle in the final product. * Use assertions to validate function arguments and to alert pro­ grammars when they do something undefined. The more rigidly you define your functions, the easier it will be to validate the arguments. * Once you've written a function, review it and ask yourself, 'What am I assuming?" If you find an assumption, either assert that your assumption is always valid, or rewrite the code to re­ move the assumption. Also ask, 'What is most likely to be wrong in this code, and how can I automatically detect the prob­lem?" Strive to implement tests that catch bugs at the earliest possible moment. Textbooks encourage programmers to program defensively, but remember that this coding style hides bugs. When you write de­fensive code, use assertions to alert you if the "can't happen" cases do happen. * Eliminate random behavior. Force bugs to be reproducible . * Destroy your garbage so that it's not misused . * Not only did reallocate have to move the block of memory as it was expanding it, but the old memory had to be reallocated and filled with new data. In the assembler, both happened rarely. * This brings up another guideline for writing bug-free code: You don't want anything to happen rarely. You need to isolate those behaviors in your subsystems that may happen and make sure that they do happen. And of­ ten. If you find rare behavior in your subsystems, be sure to do something­ anything-to stir things up. * The assembler bug could have been found within hours, instead of years, if reallocate hadn't so rarely moved blocks when it expanded them. * If something happens rarely, force it to happen often. * Keep debug information to allow stronger error checking. * There is no question that the debug code will create differences be­ tween the ship and the debug versions of your code, but as long as you're careful not to change the underlying behavior of the code, those differences shouldn't matter. * Create thorough subsystem checks, and use them often. * Design your tests carefully. Nothing should be arbitrary. * Strive to implement transparent integrity checks. * Don't apply ship version constraints to the debug version. Trade size and speed for error detection. * **Chapter 4** * Don't wait until you have a bug to step through your code. * Step through every code path. * What About Sweeping Changes? In the past, programmers have asked, "What if I add a feature that touches code in many places? Stepping through all that new code is going to be time consuming." Let me answer that question with another: Can you make such sweeping changes without introducing any bugs? The habit of stepping through your code creates an interesting negative feedback loop. Programmers who step through their code soon learn to write small, easily testable functions because stepping through large func­tions is so painful. Programmers also spend more time thinking about how they can localize the changes they need to make-again, so that they can more easily test their code. And isn't this exactly what you want? No project lead likes it when programmers touch a lot of code; it's too destabilizing. Nor do leads like large, unmanageable functions; they're often unmaintainable. Try hard to localize your changes. If you find that you must make per­vasive changes, think twice before you decide not to step through all the new code. * Overflow and underflow bugs * Data conversion bugs * Off-by-one bugs * NULL pointer bugs * Bugs using garbage memory (0xA3 bugs) * Assignment bugs in which you've used = instead of == * Precedence bugs * Logic bugs * As you step through code, focus on data flow. Source level debuggers can hide execution details. Step through critical code at the instruction level . * **Chapter 5** * Make it hard to ignore error conditions. Don't bury error codes in return values. * Always look for, and eliminate, flaws in your interfaces. * Don't write multipurpose functions. Write separate functions to allow stronger argument validation. * Don't be wishy-washy. Define explicit function arguments. * Write functions that, given valid inputs, cannot fail. * Make the code intelligible at the point of call. Avoid Boolean arguments. * Write comments that emphasize potential hazards. * **Chapter 6** * Use well-defined data types. * Always ask ,"Can this variable or expression over- or underflow?" * Implement your designs as accurately as possible. Being kind a close is being kind a buggy. * these principles already: Don't accept special purpose arguments such as the NULL pointer, and Implement your design, not something that approximates it. The third principle is new: Strive to make every function perform its task exactly one time. * Implement "the task" just once. * Get rid of extraneous if statements. * Avoid using nested ?: operators . * Handle your special cases just once. * Avoid risky language idioms. * Don't needlessly mix operator types. If you must mix operators, use parentheses to isolate the operations. * Avoid calling functions that return errors. * **Chapter 7** * Don't reference memory that you don't own. * Don't reference memory that you have freed. * Don't use output memory as workspace buffers. * Don't pass data in static (or global) memory. * Don't write parasitic functions. * Don't abuse your programming language. * Tight C does not guarantee efficient machine code. * Write code for the "average" programmer. * **Chapter 8** * Bugs don't just "go away." * Don't fix bugs later; fix them now. * Fix the cause, not the symptom. * Don't clean up code unless the clean­ up is critical to the product's success. * Don't implement nonstrategic features. * There are no free features. * Don't allow unnecessary flexibility. * Don't keep "trying" solutions· until you find one that works. Take the time to find the correct solution. * Write and test code in small chunks. * Always test your code, even if that means your schedule will slip. * Don't rely on the testing group to find your bugs. * Don't blame testers for finding your bugs. * Establish your priorities and stick to them. * Never allow the same bug to bite you twice. * **A: CODING CHECKLISTS** * **B: MEMORY LOGGING ROUTINES** * **To sum up** * Charles Simonyi in the early 1970s. https://en.wikipedia.org/wiki/Hungarian_notation * char ch; * byte b; * flag f;/* flags that are always TRUE or FALSE*/ * symbol sym;/* some sort of symbol structure*/ * char byte flag symbol * *pch:/* character pointer*/ * *pb; * *pf: * *psym: * related link: [https://www.morling.dev/images/code_review_pyramid.svg](https://www.google.com/url?q=https%3A%2F%2Fwww.morling.dev%2Fimages%2Fcode_review_pyramid.svg&sa=D&sntz=1&usg=AOvVaw1y2It_PUzXAtFN7dPLegCa) * ## Shape Up: Stop Running in Circles and Ship Work that Matters * Six-week cycles: Our decisions are based on moving the product forward in the next six weeks, not micromanaging time. We don’t count hours or question how individual days are spent. We don’t have daily meetings. We don’t rethink our roadmap every two weeks. Our focus is at a higher level. We say to ourselves: “If this project ships after six weeks, we’ll be really happy. We’ll feel our time was well spent.” Then we commit the six weeks and leave the team alone to get it done * Shaping the work: key elements of a solution, appetite * **part one shaping:** * two separate tracks: one for shaping, one for building. * Steps to shaping * 1\. Set boundaries. * 2\. Rough out the elements: idea that solves the problem within the appetite but without all the fine details worked out * 3\. Address risks and rabbit holes. * 4\. Write the pitch. * fixed time, variable scope, * Where in the current system does the new thing fit? How do you get to it? What are the key components or interactions? Where does it take you? * five ingredients that we always want to include in a pitch: 1. Problem — The raw idea, a use case, or something we’ve seen that motivates us to work on this 2. Appetite — How much time we want to spend and how that constrains the solution 3. Solution — The core elements we came up with, presented in a form that’s easy for people to immediately understand 4. Rabbit holes — Details about the solution worth calling out to avoid problems 5. No-gos — Anything specifically excluded from the concept: functionality or use cases we intentionally aren’t covering to fit the appetite or make the problem tractable * **part two : Betting** * Some companies use two-week cycles (aka “sprints”). We learned that two weeks is too short to get anything meaningful done. Worse than that, two-week cycles are extremely costly due to the planning overhead. The amount of work you get out of two weeks isn’t worth the collective hours around the table to “sprint plan” or the opportunity cost of breaking everyone’s momentum to re-group. * after each six-week cycle, we schedule two weeks for cool-down. * bugs: Use cool-down. Bring it to the betting table. Schedule a bug smash. * We’ve noticed three phases of work when we build a new product from scratch. R&D mode (bet the time on spiking some key pieces of the new product idea. CEO and designer, we don’t expect to ship anything at the end of an R&D cycle) , Production mode (Shaping is deliberate again. bet multiple teams in parallel , Shipping is the goal) , Cleanup mode (There’s no shaping. There aren’t clear team boundaries. Work is “shipped”) [Cleanup shouldn’t last longer than two cycles] * Example: two years of development, R&D mode for the first year, a year of production mode cycles followed, two cycles of cleanup and significantly cut back the feature set, some overlap between R&D and production mode after that first year, * **Part three: Building** * We don’t start by assigning tasks to anyone. * we define and track the scopes as to-do lists. Each scope corresponds to a list name. Then any tasks for that scope go in that list. * **image 1, 2, 3,** * Mark nice-to-haves with ~ * First there’s the uphill phase of figuring out what our approach is and what we’re going to do. Then, once we can see all the work involved, there’s the downhill phase of execution * image 4, 5, * ## Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet * getting start: idea, technology, passion; What can I do well that I would love to do for an extended period of time? P: 19, 21 * 1 market segmentation: The single necessary and sufficient condition for a business is a paying customer. ; technological enthusiasts=early adopters=early majority ; P: 31, * 2 select a beachhead market: P: 44, * 3 build an end user profile: P: 52, * 4 Calculate the Total Addressable Market (TAM) Size for the Beachhead Market; $200 # [Essential Tips And Tricks For ](/book-summary/commonplace-book)[commonplace book](/book-summary/commonplace-book) # [knowledge management ](/book-summary/knowledge_management) # [PKM](/book-summary/knowledge_management/pkm) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. 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Gonzalez Introduction to Algorithms, 3rd Edition (The MIT Press) The Mythical Man-Month: Essays on Software Engineering OpenCV-4-with-Python-Blueprints-Second-Edition Cracking the coding interview 189 programming questions and solutions Cracking the Tech Career: Insider Advice on Landing a Job at Google, Microsoft, Apple, Or Any Top Tech Company The Art of Startup Fundraising Mathematics for Machine Learning Principles of Economics (6th edition) The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) Reinventing Your Life: The Breakthough Program to End Negative Behavior Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf Magazine Papers Websites and links Tools YouTube - List channels Language: Essential Tips And Tricks For commonplace book knowledge management PKM # Books * ## How to take smart Notes * Introduction 4, The four underlying principles 4, the six steps to successful writing 6, afterword * introduction * write everything/ collect notes not related to any thing, organize notes, revirew, rewrite, * no reorganise no complex system, develop ideas by smart notes * 1 slip-box; 2 repeatable tasks that can become automatic and fit together seamlessly change routinges * overarching workflow "Getting Things Done" GTD * constantly have to ump back and forth between different tasks * 1- bibliographical slip-box: reference * 2- collected and generated ideas slip-box * 3- index: notes serve as entry point / thought/ topic * writing a paper step by step * 1 fleeting notes: it will delete within a day * I always have a slip of paper at hand, on which I note down the ideas of certain pages. on the backside I write down the bibliographic details. after finishing the book I go throught my notes and think how ehse notes might be relevant for already written notes in the slip-box. it means that I always read with an eye towards possible connections in the slip-box * 2 literature notes: contain the necessary information - in reference system or in the slip-box * I make a note with the bibliographic details. on the backside I would write "on page x is this, on page y is that" and then it goes into the bibliographic slib-box where I collect everything I read * read, think about its relevance * theoretical background, methodological approach or perspective of the text we read. * whether something adds to a discussion in the slip-box * we look at our slip-box for already existing lines of thought and think about the questions and problems already on our minds to which a new note might contribute. - assigning keywords is much more than just a bureaucratic act. it is a crucial part of the thinking process, which often leads to a deeper elaboration of the note itself and the connection to other notes. * * 3 permanent notes: develop ideas/new/ - only relevant to one particular project - in project specific folder - can be discarded or archived after project finished * add to slip-box behind the note you dirctyly refer to or behind the last note in the slip-box * add links to other notes or links on other notes to your new note * make sure it can be found from the index (MOC) add an entry in the index if necessary or refer to it from a note that is connected to the index * build a latticework of mental models * 4 behind multiple notes (link backward) - link to related note - link to MOC * make sure it can be found again * **keywords** can easily be added to a note like **tags** and will then show up in the **index** * a day * read and take notes * build connections within the slip-box - new idea * you write on your paper, notice a hole in the argument and have another look in the file system for the missing link. you follow up on a footnote, go back to research and might add a fitting quote to one of your papers in the making. * The four underlying principles * writing * simplicity * not from scratch * let the work carry you forward * the six steps to successful writing * separate and interlocking tasks * we use memo techniques is to bundle items together in a meaningful way and remember the bundles * the brain doesn't distinguish between an actual finished task and one that is postponed by taking a note * the fist step is to break down the amprphous task of "writing" into smaller pieces of different tasks that can be finished in **one go.** the second step is to make sure we always write down the outcome of our thinking, including possible connections to further inquiries. * ego depletion * read for understanding * take smart notes * develp ideas (MOC) * 1 overview of a topic - referred to from the index and used as an entry point into a topic has already develped * 2 keeping tarck of all topic - index - * share your insight * make it a habit * afterword * ## Writing Solid Code Writing Solid Code (Microsoft Programming Series) by Trendelles Store Note from Writing Solid Code Book [https://www.pirahansiah.com/describe/book- summary](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Fdescribe%2Fbook- summary&sa=D&sntz=1&usg=AOvVaw3ACzA6G0MBVN3nw6f-yMQo) #SolidCode #C++ #Python #pirahansiah * **Chapter 1** * Enable all optional compiler warnings. * Use lint to catch bugs that your compiler may miss. * If you have unit tests, use them. * **Chapter 2** * introduction assert page 16; assert.h * assert is nothing more than a repackaged form of the #ifdef code we saw before, but when you use * the macro, it takes one line instead of seven * That's why assert is a macro and not a function; if it were a function, calling * it could cause unexpected memory or code swapping. * Use assertions to validate function arguments . * Strip undefined behavior from your code, or use assertions to catch illegal uses of undefined behavior . * Don't waste people's time. Document unclear assertions . * Either remove implicit assumptions, or assert that they are valid . * Use assertions to detect impossible conditions. * Don't hide bugs when you program defensively. * Use a second algorithm to validate your results. * Don't wait for bugs to happen; use startup checks. * **Chapter 3** * Maintain shipping and debugging versions of your program. Shrink-wrap the shipping version, but use the debugging ver­sion as much as possible to catch bugs quickly. * Assertions are a shorthand way to write debugging checks. Use them to catch illegal conditions that should never arise. Don't confuse such conditions with error conditions, which you must handle in the final product. * Use assertions to validate function arguments and to alert pro­ grammars when they do something undefined. The more rigidly you define your functions, the easier it will be to validate the arguments. * Once you've written a function, review it and ask yourself, 'What am I assuming?" If you find an assumption, either assert that your assumption is always valid, or rewrite the code to re­ move the assumption. Also ask, 'What is most likely to be wrong in this code, and how can I automatically detect the prob­lem?" Strive to implement tests that catch bugs at the earliest possible moment. Textbooks encourage programmers to program defensively, but remember that this coding style hides bugs. When you write de­fensive code, use assertions to alert you if the "can't happen" cases do happen. * Eliminate random behavior. Force bugs to be reproducible . * Destroy your garbage so that it's not misused . * Not only did reallocate have to move the block of memory as it was expanding it, but the old memory had to be reallocated and filled with new data. In the assembler, both happened rarely. * This brings up another guideline for writing bug-free code: You don't want anything to happen rarely. You need to isolate those behaviors in your subsystems that may happen and make sure that they do happen. And of­ ten. If you find rare behavior in your subsystems, be sure to do something­ anything-to stir things up. * The assembler bug could have been found within hours, instead of years, if reallocate hadn't so rarely moved blocks when it expanded them. * If something happens rarely, force it to happen often. * Keep debug information to allow stronger error checking. * There is no question that the debug code will create differences be­ tween the ship and the debug versions of your code, but as long as you're careful not to change the underlying behavior of the code, those differences shouldn't matter. * Create thorough subsystem checks, and use them often. * Design your tests carefully. Nothing should be arbitrary. * Strive to implement transparent integrity checks. * Don't apply ship version constraints to the debug version. Trade size and speed for error detection. * **Chapter 4** * Don't wait until you have a bug to step through your code. * Step through every code path. * What About Sweeping Changes? In the past, programmers have asked, "What if I add a feature that touches code in many places? Stepping through all that new code is going to be time consuming." Let me answer that question with another: Can you make such sweeping changes without introducing any bugs? The habit of stepping through your code creates an interesting negative feedback loop. Programmers who step through their code soon learn to write small, easily testable functions because stepping through large func­tions is so painful. Programmers also spend more time thinking about how they can localize the changes they need to make-again, so that they can more easily test their code. And isn't this exactly what you want? No project lead likes it when programmers touch a lot of code; it's too destabilizing. Nor do leads like large, unmanageable functions; they're often unmaintainable. Try hard to localize your changes. If you find that you must make per­vasive changes, think twice before you decide not to step through all the new code. * Overflow and underflow bugs * Data conversion bugs * Off-by-one bugs * NULL pointer bugs * Bugs using garbage memory (0xA3 bugs) * Assignment bugs in which you've used = instead of == * Precedence bugs * Logic bugs * As you step through code, focus on data flow. Source level debuggers can hide execution details. Step through critical code at the instruction level . * **Chapter 5** * Make it hard to ignore error conditions. Don't bury error codes in return values. * Always look for, and eliminate, flaws in your interfaces. * Don't write multipurpose functions. Write separate functions to allow stronger argument validation. * Don't be wishy-washy. Define explicit function arguments. * Write functions that, given valid inputs, cannot fail. * Make the code intelligible at the point of call. Avoid Boolean arguments. * Write comments that emphasize potential hazards. * **Chapter 6** * Use well-defined data types. * Always ask ,"Can this variable or expression over- or underflow?" * Implement your designs as accurately as possible. Being kind a close is being kind a buggy. * these principles already: Don't accept special purpose arguments such as the NULL pointer, and Implement your design, not something that approximates it. The third principle is new: Strive to make every function perform its task exactly one time. * Implement "the task" just once. * Get rid of extraneous if statements. * Avoid using nested ?: operators . * Handle your special cases just once. * Avoid risky language idioms. * Don't needlessly mix operator types. If you must mix operators, use parentheses to isolate the operations. * Avoid calling functions that return errors. * **Chapter 7** * Don't reference memory that you don't own. * Don't reference memory that you have freed. * Don't use output memory as workspace buffers. * Don't pass data in static (or global) memory. * Don't write parasitic functions. * Don't abuse your programming language. * Tight C does not guarantee efficient machine code. * Write code for the "average" programmer. * **Chapter 8** * Bugs don't just "go away." * Don't fix bugs later; fix them now. * Fix the cause, not the symptom. * Don't clean up code unless the clean­ up is critical to the product's success. * Don't implement nonstrategic features. * There are no free features. * Don't allow unnecessary flexibility. * Don't keep "trying" solutions· until you find one that works. Take the time to find the correct solution. * Write and test code in small chunks. * Always test your code, even if that means your schedule will slip. * Don't rely on the testing group to find your bugs. * Don't blame testers for finding your bugs. * Establish your priorities and stick to them. * Never allow the same bug to bite you twice. * **A: CODING CHECKLISTS** * **B: MEMORY LOGGING ROUTINES** * **To sum up** * Charles Simonyi in the early 1970s. https://en.wikipedia.org/wiki/Hungarian_notation * char ch; * byte b; * flag f;/* flags that are always TRUE or FALSE*/ * symbol sym;/* some sort of symbol structure*/ * char byte flag symbol * *pch:/* character pointer*/ * *pb; * *pf: * *psym: * related link: [https://www.morling.dev/images/code_review_pyramid.svg](https://www.google.com/url?q=https%3A%2F%2Fwww.morling.dev%2Fimages%2Fcode_review_pyramid.svg&sa=D&sntz=1&usg=AOvVaw1y2It_PUzXAtFN7dPLegCa) * ## Shape Up: Stop Running in Circles and Ship Work that Matters * Six-week cycles: Our decisions are based on moving the product forward in the next six weeks, not micromanaging time. We don’t count hours or question how individual days are spent. We don’t have daily meetings. We don’t rethink our roadmap every two weeks. Our focus is at a higher level. We say to ourselves: “If this project ships after six weeks, we’ll be really happy. We’ll feel our time was well spent.” Then we commit the six weeks and leave the team alone to get it done * Shaping the work: key elements of a solution, appetite * **part one shaping:** * two separate tracks: one for shaping, one for building. * Steps to shaping * 1\. Set boundaries. * 2\. Rough out the elements: idea that solves the problem within the appetite but without all the fine details worked out * 3\. Address risks and rabbit holes. * 4\. Write the pitch. * fixed time, variable scope, * Where in the current system does the new thing fit? How do you get to it? What are the key components or interactions? Where does it take you? * five ingredients that we always want to include in a pitch: 1. Problem — The raw idea, a use case, or something we’ve seen that motivates us to work on this 2. Appetite — How much time we want to spend and how that constrains the solution 3. Solution — The core elements we came up with, presented in a form that’s easy for people to immediately understand 4. Rabbit holes — Details about the solution worth calling out to avoid problems 5. No-gos — Anything specifically excluded from the concept: functionality or use cases we intentionally aren’t covering to fit the appetite or make the problem tractable * **part two : Betting** * Some companies use two-week cycles (aka “sprints”). We learned that two weeks is too short to get anything meaningful done. Worse than that, two-week cycles are extremely costly due to the planning overhead. The amount of work you get out of two weeks isn’t worth the collective hours around the table to “sprint plan” or the opportunity cost of breaking everyone’s momentum to re-group. * after each six-week cycle, we schedule two weeks for cool-down. * bugs: Use cool-down. Bring it to the betting table. Schedule a bug smash. * We’ve noticed three phases of work when we build a new product from scratch. R&D mode (bet the time on spiking some key pieces of the new product idea. CEO and designer, we don’t expect to ship anything at the end of an R&D cycle) , Production mode (Shaping is deliberate again. bet multiple teams in parallel , Shipping is the goal) , Cleanup mode (There’s no shaping. There aren’t clear team boundaries. Work is “shipped”) [Cleanup shouldn’t last longer than two cycles] * Example: two years of development, R&D mode for the first year, a year of production mode cycles followed, two cycles of cleanup and significantly cut back the feature set, some overlap between R&D and production mode after that first year, * **Part three: Building** * We don’t start by assigning tasks to anyone. * we define and track the scopes as to-do lists. Each scope corresponds to a list name. Then any tasks for that scope go in that list. * **image 1, 2, 3,** * Mark nice-to-haves with ~ * First there’s the uphill phase of figuring out what our approach is and what we’re going to do. Then, once we can see all the work involved, there’s the downhill phase of execution * image 4, 5, * ## Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet * getting start: idea, technology, passion; What can I do well that I would love to do for an extended period of time? P: 19, 21 * 1 market segmentation: The single necessary and sufficient condition for a business is a paying customer. ; technological enthusiasts=early adopters=early majority ; P: 31, * 2 select a beachhead market: P: 44, * 3 build an end user profile: P: 52, * 4 Calculate the Total Addressable Market (TAM) Size for the Beachhead Market; $200 # [Essential Tips And Tricks For ](/book-summary/commonplace-book)[commonplace book](/book-summary/commonplace-book) # [knowledge management ](/book-summary/knowledge_management) # [PKM](/book-summary/knowledge_management/pkm) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/LMErpRo3SemuSDj3pLjSSh1xcteMOPvF9yzhoEb8_AB5vppySOpESQgqlBlCy2nymRjHv3i3BNkjBtED1LpjpWk=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/LMErpRo3SemuSDj3pLjSSh1xcteMOPvF9yzhoEb8_AB5vppySOpESQgqlBlCy2nymRjHv3i3BNkjBtED1LpjpWk=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Book Summary Books How to take smart Notes Writing Solid Code Shape Up: Stop Running in Circles and Ship Work that Matters Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet List of books I've read: Computer Vision: Algorithms and Applications Multiple View Geometry in Computer Vision Second Edition Complete to download the latest draft of Machine Learning Yearning Multiple view geometry in computer vision Learning OpenCV Book by Adrian Kaehler and Gary Bradski Digital Image Processing, Global Edition by Rafael C. Gonzalez Introduction to Algorithms, 3rd Edition (The MIT Press) The Mythical Man-Month: Essays on Software Engineering OpenCV-4-with-Python-Blueprints-Second-Edition Cracking the coding interview 189 programming questions and solutions Cracking the Tech Career: Insider Advice on Landing a Job at Google, Microsoft, Apple, Or Any Top Tech Company The Art of Startup Fundraising Mathematics for Machine Learning Principles of Economics (6th edition) The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) Reinventing Your Life: The Breakthough Program to End Negative Behavior Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf Magazine Papers Websites and links Tools YouTube - List channels Language: Essential Tips And Tricks For commonplace book knowledge management PKM # Books * ## How to take smart Notes * Introduction 4, The four underlying principles 4, the six steps to successful writing 6, afterword * introduction * write everything/ collect notes not related to any thing, organize notes, revirew, rewrite, * no reorganise no complex system, develop ideas by smart notes * 1 slip-box; 2 repeatable tasks that can become automatic and fit together seamlessly change routinges * overarching workflow "Getting Things Done" GTD * constantly have to ump back and forth between different tasks * 1- bibliographical slip-box: reference * 2- collected and generated ideas slip-box * 3- index: notes serve as entry point / thought/ topic * writing a paper step by step * 1 fleeting notes: it will delete within a day * I always have a slip of paper at hand, on which I note down the ideas of certain pages. on the backside I write down the bibliographic details. after finishing the book I go throught my notes and think how ehse notes might be relevant for already written notes in the slip-box. it means that I always read with an eye towards possible connections in the slip-box * 2 literature notes: contain the necessary information - in reference system or in the slip-box * I make a note with the bibliographic details. on the backside I would write "on page x is this, on page y is that" and then it goes into the bibliographic slib-box where I collect everything I read * read, think about its relevance * theoretical background, methodological approach or perspective of the text we read. * whether something adds to a discussion in the slip-box * we look at our slip-box for already existing lines of thought and think about the questions and problems already on our minds to which a new note might contribute. - assigning keywords is much more than just a bureaucratic act. it is a crucial part of the thinking process, which often leads to a deeper elaboration of the note itself and the connection to other notes. * * 3 permanent notes: develop ideas/new/ - only relevant to one particular project - in project specific folder - can be discarded or archived after project finished * add to slip-box behind the note you dirctyly refer to or behind the last note in the slip-box * add links to other notes or links on other notes to your new note * make sure it can be found from the index (MOC) add an entry in the index if necessary or refer to it from a note that is connected to the index * build a latticework of mental models * 4 behind multiple notes (link backward) - link to related note - link to MOC * make sure it can be found again * **keywords** can easily be added to a note like **tags** and will then show up in the **index** * a day * read and take notes * build connections within the slip-box - new idea * you write on your paper, notice a hole in the argument and have another look in the file system for the missing link. you follow up on a footnote, go back to research and might add a fitting quote to one of your papers in the making. * The four underlying principles * writing * simplicity * not from scratch * let the work carry you forward * the six steps to successful writing * separate and interlocking tasks * we use memo techniques is to bundle items together in a meaningful way and remember the bundles * the brain doesn't distinguish between an actual finished task and one that is postponed by taking a note * the fist step is to break down the amprphous task of "writing" into smaller pieces of different tasks that can be finished in **one go.** the second step is to make sure we always write down the outcome of our thinking, including possible connections to further inquiries. * ego depletion * read for understanding * take smart notes * develp ideas (MOC) * 1 overview of a topic - referred to from the index and used as an entry point into a topic has already develped * 2 keeping tarck of all topic - index - * share your insight * make it a habit * afterword * ## Writing Solid Code Writing Solid Code (Microsoft Programming Series) by Trendelles Store Note from Writing Solid Code Book [https://www.pirahansiah.com/describe/book- summary](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Fdescribe%2Fbook- summary&sa=D&sntz=1&usg=AOvVaw3ACzA6G0MBVN3nw6f-yMQo) #SolidCode #C++ #Python #pirahansiah * **Chapter 1** * Enable all optional compiler warnings. * Use lint to catch bugs that your compiler may miss. * If you have unit tests, use them. * **Chapter 2** * introduction assert page 16; assert.h * assert is nothing more than a repackaged form of the #ifdef code we saw before, but when you use * the macro, it takes one line instead of seven * That's why assert is a macro and not a function; if it were a function, calling * it could cause unexpected memory or code swapping. * Use assertions to validate function arguments . * Strip undefined behavior from your code, or use assertions to catch illegal uses of undefined behavior . * Don't waste people's time. Document unclear assertions . * Either remove implicit assumptions, or assert that they are valid . * Use assertions to detect impossible conditions. * Don't hide bugs when you program defensively. * Use a second algorithm to validate your results. * Don't wait for bugs to happen; use startup checks. * **Chapter 3** * Maintain shipping and debugging versions of your program. Shrink-wrap the shipping version, but use the debugging ver­sion as much as possible to catch bugs quickly. * Assertions are a shorthand way to write debugging checks. Use them to catch illegal conditions that should never arise. Don't confuse such conditions with error conditions, which you must handle in the final product. * Use assertions to validate function arguments and to alert pro­ grammars when they do something undefined. The more rigidly you define your functions, the easier it will be to validate the arguments. * Once you've written a function, review it and ask yourself, 'What am I assuming?" If you find an assumption, either assert that your assumption is always valid, or rewrite the code to re­ move the assumption. Also ask, 'What is most likely to be wrong in this code, and how can I automatically detect the prob­lem?" Strive to implement tests that catch bugs at the earliest possible moment. Textbooks encourage programmers to program defensively, but remember that this coding style hides bugs. When you write de­fensive code, use assertions to alert you if the "can't happen" cases do happen. * Eliminate random behavior. Force bugs to be reproducible . * Destroy your garbage so that it's not misused . * Not only did reallocate have to move the block of memory as it was expanding it, but the old memory had to be reallocated and filled with new data. In the assembler, both happened rarely. * This brings up another guideline for writing bug-free code: You don't want anything to happen rarely. You need to isolate those behaviors in your subsystems that may happen and make sure that they do happen. And of­ ten. If you find rare behavior in your subsystems, be sure to do something­ anything-to stir things up. * The assembler bug could have been found within hours, instead of years, if reallocate hadn't so rarely moved blocks when it expanded them. * If something happens rarely, force it to happen often. * Keep debug information to allow stronger error checking. * There is no question that the debug code will create differences be­ tween the ship and the debug versions of your code, but as long as you're careful not to change the underlying behavior of the code, those differences shouldn't matter. * Create thorough subsystem checks, and use them often. * Design your tests carefully. Nothing should be arbitrary. * Strive to implement transparent integrity checks. * Don't apply ship version constraints to the debug version. Trade size and speed for error detection. * **Chapter 4** * Don't wait until you have a bug to step through your code. * Step through every code path. * What About Sweeping Changes? In the past, programmers have asked, "What if I add a feature that touches code in many places? Stepping through all that new code is going to be time consuming." Let me answer that question with another: Can you make such sweeping changes without introducing any bugs? The habit of stepping through your code creates an interesting negative feedback loop. Programmers who step through their code soon learn to write small, easily testable functions because stepping through large func­tions is so painful. Programmers also spend more time thinking about how they can localize the changes they need to make-again, so that they can more easily test their code. And isn't this exactly what you want? No project lead likes it when programmers touch a lot of code; it's too destabilizing. Nor do leads like large, unmanageable functions; they're often unmaintainable. Try hard to localize your changes. If you find that you must make per­vasive changes, think twice before you decide not to step through all the new code. * Overflow and underflow bugs * Data conversion bugs * Off-by-one bugs * NULL pointer bugs * Bugs using garbage memory (0xA3 bugs) * Assignment bugs in which you've used = instead of == * Precedence bugs * Logic bugs * As you step through code, focus on data flow. Source level debuggers can hide execution details. Step through critical code at the instruction level . * **Chapter 5** * Make it hard to ignore error conditions. Don't bury error codes in return values. * Always look for, and eliminate, flaws in your interfaces. * Don't write multipurpose functions. Write separate functions to allow stronger argument validation. * Don't be wishy-washy. Define explicit function arguments. * Write functions that, given valid inputs, cannot fail. * Make the code intelligible at the point of call. Avoid Boolean arguments. * Write comments that emphasize potential hazards. * **Chapter 6** * Use well-defined data types. * Always ask ,"Can this variable or expression over- or underflow?" * Implement your designs as accurately as possible. Being kind a close is being kind a buggy. * these principles already: Don't accept special purpose arguments such as the NULL pointer, and Implement your design, not something that approximates it. The third principle is new: Strive to make every function perform its task exactly one time. * Implement "the task" just once. * Get rid of extraneous if statements. * Avoid using nested ?: operators . * Handle your special cases just once. * Avoid risky language idioms. * Don't needlessly mix operator types. If you must mix operators, use parentheses to isolate the operations. * Avoid calling functions that return errors. * **Chapter 7** * Don't reference memory that you don't own. * Don't reference memory that you have freed. * Don't use output memory as workspace buffers. * Don't pass data in static (or global) memory. * Don't write parasitic functions. * Don't abuse your programming language. * Tight C does not guarantee efficient machine code. * Write code for the "average" programmer. * **Chapter 8** * Bugs don't just "go away." * Don't fix bugs later; fix them now. * Fix the cause, not the symptom. * Don't clean up code unless the clean­ up is critical to the product's success. * Don't implement nonstrategic features. * There are no free features. * Don't allow unnecessary flexibility. * Don't keep "trying" solutions· until you find one that works. Take the time to find the correct solution. * Write and test code in small chunks. * Always test your code, even if that means your schedule will slip. * Don't rely on the testing group to find your bugs. * Don't blame testers for finding your bugs. * Establish your priorities and stick to them. * Never allow the same bug to bite you twice. * **A: CODING CHECKLISTS** * **B: MEMORY LOGGING ROUTINES** * **To sum up** * Charles Simonyi in the early 1970s. https://en.wikipedia.org/wiki/Hungarian_notation * char ch; * byte b; * flag f;/* flags that are always TRUE or FALSE*/ * symbol sym;/* some sort of symbol structure*/ * char byte flag symbol * *pch:/* character pointer*/ * *pb; * *pf: * *psym: * related link: [https://www.morling.dev/images/code_review_pyramid.svg](https://www.google.com/url?q=https%3A%2F%2Fwww.morling.dev%2Fimages%2Fcode_review_pyramid.svg&sa=D&sntz=1&usg=AOvVaw1y2It_PUzXAtFN7dPLegCa) * ## Shape Up: Stop Running in Circles and Ship Work that Matters * Six-week cycles: Our decisions are based on moving the product forward in the next six weeks, not micromanaging time. We don’t count hours or question how individual days are spent. We don’t have daily meetings. We don’t rethink our roadmap every two weeks. Our focus is at a higher level. We say to ourselves: “If this project ships after six weeks, we’ll be really happy. We’ll feel our time was well spent.” Then we commit the six weeks and leave the team alone to get it done * Shaping the work: key elements of a solution, appetite * **part one shaping:** * two separate tracks: one for shaping, one for building. * Steps to shaping * 1\. Set boundaries. * 2\. Rough out the elements: idea that solves the problem within the appetite but without all the fine details worked out * 3\. Address risks and rabbit holes. * 4\. Write the pitch. * fixed time, variable scope, * Where in the current system does the new thing fit? How do you get to it? What are the key components or interactions? Where does it take you? * five ingredients that we always want to include in a pitch: 1. Problem — The raw idea, a use case, or something we’ve seen that motivates us to work on this 2. Appetite — How much time we want to spend and how that constrains the solution 3. Solution — The core elements we came up with, presented in a form that’s easy for people to immediately understand 4. Rabbit holes — Details about the solution worth calling out to avoid problems 5. No-gos — Anything specifically excluded from the concept: functionality or use cases we intentionally aren’t covering to fit the appetite or make the problem tractable * **part two : Betting** * Some companies use two-week cycles (aka “sprints”). We learned that two weeks is too short to get anything meaningful done. Worse than that, two-week cycles are extremely costly due to the planning overhead. The amount of work you get out of two weeks isn’t worth the collective hours around the table to “sprint plan” or the opportunity cost of breaking everyone’s momentum to re-group. * after each six-week cycle, we schedule two weeks for cool-down. * bugs: Use cool-down. Bring it to the betting table. Schedule a bug smash. * We’ve noticed three phases of work when we build a new product from scratch. R&D mode (bet the time on spiking some key pieces of the new product idea. CEO and designer, we don’t expect to ship anything at the end of an R&D cycle) , Production mode (Shaping is deliberate again. bet multiple teams in parallel , Shipping is the goal) , Cleanup mode (There’s no shaping. There aren’t clear team boundaries. Work is “shipped”) [Cleanup shouldn’t last longer than two cycles] * Example: two years of development, R&D mode for the first year, a year of production mode cycles followed, two cycles of cleanup and significantly cut back the feature set, some overlap between R&D and production mode after that first year, * **Part three: Building** * We don’t start by assigning tasks to anyone. * we define and track the scopes as to-do lists. Each scope corresponds to a list name. Then any tasks for that scope go in that list. * **image 1, 2, 3,** * Mark nice-to-haves with ~ * First there’s the uphill phase of figuring out what our approach is and what we’re going to do. Then, once we can see all the work involved, there’s the downhill phase of execution * image 4, 5, * ## Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet * getting start: idea, technology, passion; What can I do well that I would love to do for an extended period of time? P: 19, 21 * 1 market segmentation: The single necessary and sufficient condition for a business is a paying customer. ; technological enthusiasts=early adopters=early majority ; P: 31, * 2 select a beachhead market: P: 44, * 3 build an end user profile: P: 52, * 4 Calculate the Total Addressable Market (TAM) Size for the Beachhead Market; $200 # [Essential Tips And Tricks For ](/book-summary/commonplace-book)[commonplace book](/book-summary/commonplace-book) # [knowledge management ](/book-summary/knowledge_management) # [PKM](/book-summary/knowledge_management/pkm) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/LMErpRo3SemuSDj3pLjSSh1xcteMOPvF9yzhoEb8_AB5vppySOpESQgqlBlCy2nymRjHv3i3BNkjBtED1LpjpWk=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/LMErpRo3SemuSDj3pLjSSh1xcteMOPvF9yzhoEb8_AB5vppySOpESQgqlBlCy2nymRjHv3i3BNkjBtED1LpjpWk=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Book Summary Books How to take smart Notes Writing Solid Code Shape Up: Stop Running in Circles and Ship Work that Matters Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet List of books I've read: Computer Vision: Algorithms and Applications Multiple View Geometry in Computer Vision Second Edition Complete to download the latest draft of Machine Learning Yearning Multiple view geometry in computer vision Learning OpenCV Book by Adrian Kaehler and Gary Bradski Digital Image Processing, Global Edition by Rafael C. Gonzalez Introduction to Algorithms, 3rd Edition (The MIT Press) The Mythical Man-Month: Essays on Software Engineering OpenCV-4-with-Python-Blueprints-Second-Edition Cracking the coding interview 189 programming questions and solutions Cracking the Tech Career: Insider Advice on Landing a Job at Google, Microsoft, Apple, Or Any Top Tech Company The Art of Startup Fundraising Mathematics for Machine Learning Principles of Economics (6th edition) The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) Reinventing Your Life: The Breakthough Program to End Negative Behavior Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf Magazine Papers Websites and links Tools YouTube - List channels Language: Essential Tips And Tricks For commonplace book knowledge management PKM # Books * ## How to take smart Notes * Introduction 4, The four underlying principles 4, the six steps to successful writing 6, afterword * introduction * write everything/ collect notes not related to any thing, organize notes, revirew, rewrite, * no reorganise no complex system, develop ideas by smart notes * 1 slip-box; 2 repeatable tasks that can become automatic and fit together seamlessly change routinges * overarching workflow "Getting Things Done" GTD * constantly have to ump back and forth between different tasks * 1- bibliographical slip-box: reference * 2- collected and generated ideas slip-box * 3- index: notes serve as entry point / thought/ topic * writing a paper step by step * 1 fleeting notes: it will delete within a day * I always have a slip of paper at hand, on which I note down the ideas of certain pages. on the backside I write down the bibliographic details. after finishing the book I go throught my notes and think how ehse notes might be relevant for already written notes in the slip-box. it means that I always read with an eye towards possible connections in the slip-box * 2 literature notes: contain the necessary information - in reference system or in the slip-box * I make a note with the bibliographic details. on the backside I would write "on page x is this, on page y is that" and then it goes into the bibliographic slib-box where I collect everything I read * read, think about its relevance * theoretical background, methodological approach or perspective of the text we read. * whether something adds to a discussion in the slip-box * we look at our slip-box for already existing lines of thought and think about the questions and problems already on our minds to which a new note might contribute. - assigning keywords is much more than just a bureaucratic act. it is a crucial part of the thinking process, which often leads to a deeper elaboration of the note itself and the connection to other notes. * * 3 permanent notes: develop ideas/new/ - only relevant to one particular project - in project specific folder - can be discarded or archived after project finished * add to slip-box behind the note you dirctyly refer to or behind the last note in the slip-box * add links to other notes or links on other notes to your new note * make sure it can be found from the index (MOC) add an entry in the index if necessary or refer to it from a note that is connected to the index * build a latticework of mental models * 4 behind multiple notes (link backward) - link to related note - link to MOC * make sure it can be found again * **keywords** can easily be added to a note like **tags** and will then show up in the **index** * a day * read and take notes * build connections within the slip-box - new idea * you write on your paper, notice a hole in the argument and have another look in the file system for the missing link. you follow up on a footnote, go back to research and might add a fitting quote to one of your papers in the making. * The four underlying principles * writing * simplicity * not from scratch * let the work carry you forward * the six steps to successful writing * separate and interlocking tasks * we use memo techniques is to bundle items together in a meaningful way and remember the bundles * the brain doesn't distinguish between an actual finished task and one that is postponed by taking a note * the fist step is to break down the amprphous task of "writing" into smaller pieces of different tasks that can be finished in **one go.** the second step is to make sure we always write down the outcome of our thinking, including possible connections to further inquiries. * ego depletion * read for understanding * take smart notes * develp ideas (MOC) * 1 overview of a topic - referred to from the index and used as an entry point into a topic has already develped * 2 keeping tarck of all topic - index - * share your insight * make it a habit * afterword * ## Writing Solid Code Writing Solid Code (Microsoft Programming Series) by Trendelles Store Note from Writing Solid Code Book [https://www.pirahansiah.com/describe/book- summary](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Fdescribe%2Fbook- summary&sa=D&sntz=1&usg=AOvVaw3ACzA6G0MBVN3nw6f-yMQo) #SolidCode #C++ #Python #pirahansiah * **Chapter 1** * Enable all optional compiler warnings. * Use lint to catch bugs that your compiler may miss. * If you have unit tests, use them. * **Chapter 2** * introduction assert page 16; assert.h * assert is nothing more than a repackaged form of the #ifdef code we saw before, but when you use * the macro, it takes one line instead of seven * That's why assert is a macro and not a function; if it were a function, calling * it could cause unexpected memory or code swapping. * Use assertions to validate function arguments . * Strip undefined behavior from your code, or use assertions to catch illegal uses of undefined behavior . * Don't waste people's time. Document unclear assertions . * Either remove implicit assumptions, or assert that they are valid . * Use assertions to detect impossible conditions. * Don't hide bugs when you program defensively. * Use a second algorithm to validate your results. * Don't wait for bugs to happen; use startup checks. * **Chapter 3** * Maintain shipping and debugging versions of your program. Shrink-wrap the shipping version, but use the debugging ver­sion as much as possible to catch bugs quickly. * Assertions are a shorthand way to write debugging checks. Use them to catch illegal conditions that should never arise. Don't confuse such conditions with error conditions, which you must handle in the final product. * Use assertions to validate function arguments and to alert pro­ grammars when they do something undefined. The more rigidly you define your functions, the easier it will be to validate the arguments. * Once you've written a function, review it and ask yourself, 'What am I assuming?" If you find an assumption, either assert that your assumption is always valid, or rewrite the code to re­ move the assumption. Also ask, 'What is most likely to be wrong in this code, and how can I automatically detect the prob­lem?" Strive to implement tests that catch bugs at the earliest possible moment. Textbooks encourage programmers to program defensively, but remember that this coding style hides bugs. When you write de­fensive code, use assertions to alert you if the "can't happen" cases do happen. * Eliminate random behavior. Force bugs to be reproducible . * Destroy your garbage so that it's not misused . * Not only did reallocate have to move the block of memory as it was expanding it, but the old memory had to be reallocated and filled with new data. In the assembler, both happened rarely. * This brings up another guideline for writing bug-free code: You don't want anything to happen rarely. You need to isolate those behaviors in your subsystems that may happen and make sure that they do happen. And of­ ten. If you find rare behavior in your subsystems, be sure to do something­ anything-to stir things up. * The assembler bug could have been found within hours, instead of years, if reallocate hadn't so rarely moved blocks when it expanded them. * If something happens rarely, force it to happen often. * Keep debug information to allow stronger error checking. * There is no question that the debug code will create differences be­ tween the ship and the debug versions of your code, but as long as you're careful not to change the underlying behavior of the code, those differences shouldn't matter. * Create thorough subsystem checks, and use them often. * Design your tests carefully. Nothing should be arbitrary. * Strive to implement transparent integrity checks. * Don't apply ship version constraints to the debug version. Trade size and speed for error detection. * **Chapter 4** * Don't wait until you have a bug to step through your code. * Step through every code path. * What About Sweeping Changes? In the past, programmers have asked, "What if I add a feature that touches code in many places? Stepping through all that new code is going to be time consuming." Let me answer that question with another: Can you make such sweeping changes without introducing any bugs? The habit of stepping through your code creates an interesting negative feedback loop. Programmers who step through their code soon learn to write small, easily testable functions because stepping through large func­tions is so painful. Programmers also spend more time thinking about how they can localize the changes they need to make-again, so that they can more easily test their code. And isn't this exactly what you want? No project lead likes it when programmers touch a lot of code; it's too destabilizing. Nor do leads like large, unmanageable functions; they're often unmaintainable. Try hard to localize your changes. If you find that you must make per­vasive changes, think twice before you decide not to step through all the new code. * Overflow and underflow bugs * Data conversion bugs * Off-by-one bugs * NULL pointer bugs * Bugs using garbage memory (0xA3 bugs) * Assignment bugs in which you've used = instead of == * Precedence bugs * Logic bugs * As you step through code, focus on data flow. Source level debuggers can hide execution details. Step through critical code at the instruction level . * **Chapter 5** * Make it hard to ignore error conditions. Don't bury error codes in return values. * Always look for, and eliminate, flaws in your interfaces. * Don't write multipurpose functions. Write separate functions to allow stronger argument validation. * Don't be wishy-washy. Define explicit function arguments. * Write functions that, given valid inputs, cannot fail. * Make the code intelligible at the point of call. Avoid Boolean arguments. * Write comments that emphasize potential hazards. * **Chapter 6** * Use well-defined data types. * Always ask ,"Can this variable or expression over- or underflow?" * Implement your designs as accurately as possible. Being kind a close is being kind a buggy. * these principles already: Don't accept special purpose arguments such as the NULL pointer, and Implement your design, not something that approximates it. The third principle is new: Strive to make every function perform its task exactly one time. * Implement "the task" just once. * Get rid of extraneous if statements. * Avoid using nested ?: operators . * Handle your special cases just once. * Avoid risky language idioms. * Don't needlessly mix operator types. If you must mix operators, use parentheses to isolate the operations. * Avoid calling functions that return errors. * **Chapter 7** * Don't reference memory that you don't own. * Don't reference memory that you have freed. * Don't use output memory as workspace buffers. * Don't pass data in static (or global) memory. * Don't write parasitic functions. * Don't abuse your programming language. * Tight C does not guarantee efficient machine code. * Write code for the "average" programmer. * **Chapter 8** * Bugs don't just "go away." * Don't fix bugs later; fix them now. * Fix the cause, not the symptom. * Don't clean up code unless the clean­ up is critical to the product's success. * Don't implement nonstrategic features. * There are no free features. * Don't allow unnecessary flexibility. * Don't keep "trying" solutions· until you find one that works. Take the time to find the correct solution. * Write and test code in small chunks. * Always test your code, even if that means your schedule will slip. * Don't rely on the testing group to find your bugs. * Don't blame testers for finding your bugs. * Establish your priorities and stick to them. * Never allow the same bug to bite you twice. * **A: CODING CHECKLISTS** * **B: MEMORY LOGGING ROUTINES** * **To sum up** * Charles Simonyi in the early 1970s. https://en.wikipedia.org/wiki/Hungarian_notation * char ch; * byte b; * flag f;/* flags that are always TRUE or FALSE*/ * symbol sym;/* some sort of symbol structure*/ * char byte flag symbol * *pch:/* character pointer*/ * *pb; * *pf: * *psym: * related link: [https://www.morling.dev/images/code_review_pyramid.svg](https://www.google.com/url?q=https%3A%2F%2Fwww.morling.dev%2Fimages%2Fcode_review_pyramid.svg&sa=D&sntz=1&usg=AOvVaw1y2It_PUzXAtFN7dPLegCa) * ## Shape Up: Stop Running in Circles and Ship Work that Matters * Six-week cycles: Our decisions are based on moving the product forward in the next six weeks, not micromanaging time. We don’t count hours or question how individual days are spent. We don’t have daily meetings. We don’t rethink our roadmap every two weeks. Our focus is at a higher level. We say to ourselves: “If this project ships after six weeks, we’ll be really happy. We’ll feel our time was well spent.” Then we commit the six weeks and leave the team alone to get it done * Shaping the work: key elements of a solution, appetite * **part one shaping:** * two separate tracks: one for shaping, one for building. * Steps to shaping * 1\. Set boundaries. * 2\. Rough out the elements: idea that solves the problem within the appetite but without all the fine details worked out * 3\. Address risks and rabbit holes. * 4\. Write the pitch. * fixed time, variable scope, * Where in the current system does the new thing fit? How do you get to it? What are the key components or interactions? Where does it take you? * five ingredients that we always want to include in a pitch: 1. Problem — The raw idea, a use case, or something we’ve seen that motivates us to work on this 2. Appetite — How much time we want to spend and how that constrains the solution 3. Solution — The core elements we came up with, presented in a form that’s easy for people to immediately understand 4. Rabbit holes — Details about the solution worth calling out to avoid problems 5. No-gos — Anything specifically excluded from the concept: functionality or use cases we intentionally aren’t covering to fit the appetite or make the problem tractable * **part two : Betting** * Some companies use two-week cycles (aka “sprints”). We learned that two weeks is too short to get anything meaningful done. Worse than that, two-week cycles are extremely costly due to the planning overhead. The amount of work you get out of two weeks isn’t worth the collective hours around the table to “sprint plan” or the opportunity cost of breaking everyone’s momentum to re-group. * after each six-week cycle, we schedule two weeks for cool-down. * bugs: Use cool-down. Bring it to the betting table. Schedule a bug smash. * We’ve noticed three phases of work when we build a new product from scratch. R&D mode (bet the time on spiking some key pieces of the new product idea. CEO and designer, we don’t expect to ship anything at the end of an R&D cycle) , Production mode (Shaping is deliberate again. bet multiple teams in parallel , Shipping is the goal) , Cleanup mode (There’s no shaping. There aren’t clear team boundaries. Work is “shipped”) [Cleanup shouldn’t last longer than two cycles] * Example: two years of development, R&D mode for the first year, a year of production mode cycles followed, two cycles of cleanup and significantly cut back the feature set, some overlap between R&D and production mode after that first year, * **Part three: Building** * We don’t start by assigning tasks to anyone. * we define and track the scopes as to-do lists. Each scope corresponds to a list name. Then any tasks for that scope go in that list. * **image 1, 2, 3,** * Mark nice-to-haves with ~ * First there’s the uphill phase of figuring out what our approach is and what we’re going to do. Then, once we can see all the work involved, there’s the downhill phase of execution * image 4, 5, * ## Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet * getting start: idea, technology, passion; What can I do well that I would love to do for an extended period of time? P: 19, 21 * 1 market segmentation: The single necessary and sufficient condition for a business is a paying customer. ; technological enthusiasts=early adopters=early majority ; P: 31, * 2 select a beachhead market: P: 44, * 3 build an end user profile: P: 52, * 4 Calculate the Total Addressable Market (TAM) Size for the Beachhead Market; $200 # [Essential Tips And Tricks For ](/book-summary/commonplace-book)[commonplace book](/book-summary/commonplace-book) # [knowledge management ](/book-summary/knowledge_management) # [PKM](/book-summary/knowledge_management/pkm) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/LMErpRo3SemuSDj3pLjSSh1xcteMOPvF9yzhoEb8_AB5vppySOpESQgqlBlCy2nymRjHv3i3BNkjBtED1LpjpWk=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/LMErpRo3SemuSDj3pLjSSh1xcteMOPvF9yzhoEb8_AB5vppySOpESQgqlBlCy2nymRjHv3i3BNkjBtED1LpjpWk=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Book Summary Books How to take smart Notes Writing Solid Code Shape Up: Stop Running in Circles and Ship Work that Matters Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet List of books I've read: Computer Vision: Algorithms and Applications Multiple View Geometry in Computer Vision Second Edition Complete to download the latest draft of Machine Learning Yearning Multiple view geometry in computer vision Learning OpenCV Book by Adrian Kaehler and Gary Bradski Digital Image Processing, Global Edition by Rafael C. Gonzalez Introduction to Algorithms, 3rd Edition (The MIT Press) The Mythical Man-Month: Essays on Software Engineering OpenCV-4-with-Python-Blueprints-Second-Edition Cracking the coding interview 189 programming questions and solutions Cracking the Tech Career: Insider Advice on Landing a Job at Google, Microsoft, Apple, Or Any Top Tech Company The Art of Startup Fundraising Mathematics for Machine Learning Principles of Economics (6th edition) The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) Reinventing Your Life: The Breakthough Program to End Negative Behavior Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf Magazine Papers Websites and links Tools YouTube - List channels Language: Essential Tips And Tricks For commonplace book knowledge management PKM # Books * ## How to take smart Notes * Introduction 4, The four underlying principles 4, the six steps to successful writing 6, afterword * introduction * write everything/ collect notes not related to any thing, organize notes, revirew, rewrite, * no reorganise no complex system, develop ideas by smart notes * 1 slip-box; 2 repeatable tasks that can become automatic and fit together seamlessly change routinges * overarching workflow "Getting Things Done" GTD * constantly have to ump back and forth between different tasks * 1- bibliographical slip-box: reference * 2- collected and generated ideas slip-box * 3- index: notes serve as entry point / thought/ topic * writing a paper step by step * 1 fleeting notes: it will delete within a day * I always have a slip of paper at hand, on which I note down the ideas of certain pages. on the backside I write down the bibliographic details. after finishing the book I go throught my notes and think how ehse notes might be relevant for already written notes in the slip-box. it means that I always read with an eye towards possible connections in the slip-box * 2 literature notes: contain the necessary information - in reference system or in the slip-box * I make a note with the bibliographic details. on the backside I would write "on page x is this, on page y is that" and then it goes into the bibliographic slib-box where I collect everything I read * read, think about its relevance * theoretical background, methodological approach or perspective of the text we read. * whether something adds to a discussion in the slip-box * we look at our slip-box for already existing lines of thought and think about the questions and problems already on our minds to which a new note might contribute. - assigning keywords is much more than just a bureaucratic act. it is a crucial part of the thinking process, which often leads to a deeper elaboration of the note itself and the connection to other notes. * * 3 permanent notes: develop ideas/new/ - only relevant to one particular project - in project specific folder - can be discarded or archived after project finished * add to slip-box behind the note you dirctyly refer to or behind the last note in the slip-box * add links to other notes or links on other notes to your new note * make sure it can be found from the index (MOC) add an entry in the index if necessary or refer to it from a note that is connected to the index * build a latticework of mental models * 4 behind multiple notes (link backward) - link to related note - link to MOC * make sure it can be found again * **keywords** can easily be added to a note like **tags** and will then show up in the **index** * a day * read and take notes * build connections within the slip-box - new idea * you write on your paper, notice a hole in the argument and have another look in the file system for the missing link. you follow up on a footnote, go back to research and might add a fitting quote to one of your papers in the making. * The four underlying principles * writing * simplicity * not from scratch * let the work carry you forward * the six steps to successful writing * separate and interlocking tasks * we use memo techniques is to bundle items together in a meaningful way and remember the bundles * the brain doesn't distinguish between an actual finished task and one that is postponed by taking a note * the fist step is to break down the amprphous task of "writing" into smaller pieces of different tasks that can be finished in **one go.** the second step is to make sure we always write down the outcome of our thinking, including possible connections to further inquiries. * ego depletion * read for understanding * take smart notes * develp ideas (MOC) * 1 overview of a topic - referred to from the index and used as an entry point into a topic has already develped * 2 keeping tarck of all topic - index - * share your insight * make it a habit * afterword * ## Writing Solid Code Writing Solid Code (Microsoft Programming Series) by Trendelles Store Note from Writing Solid Code Book [https://www.pirahansiah.com/describe/book- summary](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Fdescribe%2Fbook- summary&sa=D&sntz=1&usg=AOvVaw3ACzA6G0MBVN3nw6f-yMQo) #SolidCode #C++ #Python #pirahansiah * **Chapter 1** * Enable all optional compiler warnings. * Use lint to catch bugs that your compiler may miss. * If you have unit tests, use them. * **Chapter 2** * introduction assert page 16; assert.h * assert is nothing more than a repackaged form of the #ifdef code we saw before, but when you use * the macro, it takes one line instead of seven * That's why assert is a macro and not a function; if it were a function, calling * it could cause unexpected memory or code swapping. * Use assertions to validate function arguments . * Strip undefined behavior from your code, or use assertions to catch illegal uses of undefined behavior . * Don't waste people's time. Document unclear assertions . * Either remove implicit assumptions, or assert that they are valid . * Use assertions to detect impossible conditions. * Don't hide bugs when you program defensively. * Use a second algorithm to validate your results. * Don't wait for bugs to happen; use startup checks. * **Chapter 3** * Maintain shipping and debugging versions of your program. Shrink-wrap the shipping version, but use the debugging ver­sion as much as possible to catch bugs quickly. * Assertions are a shorthand way to write debugging checks. Use them to catch illegal conditions that should never arise. Don't confuse such conditions with error conditions, which you must handle in the final product. * Use assertions to validate function arguments and to alert pro­ grammars when they do something undefined. The more rigidly you define your functions, the easier it will be to validate the arguments. * Once you've written a function, review it and ask yourself, 'What am I assuming?" If you find an assumption, either assert that your assumption is always valid, or rewrite the code to re­ move the assumption. Also ask, 'What is most likely to be wrong in this code, and how can I automatically detect the prob­lem?" Strive to implement tests that catch bugs at the earliest possible moment. Textbooks encourage programmers to program defensively, but remember that this coding style hides bugs. When you write de­fensive code, use assertions to alert you if the "can't happen" cases do happen. * Eliminate random behavior. Force bugs to be reproducible . * Destroy your garbage so that it's not misused . * Not only did reallocate have to move the block of memory as it was expanding it, but the old memory had to be reallocated and filled with new data. In the assembler, both happened rarely. * This brings up another guideline for writing bug-free code: You don't want anything to happen rarely. You need to isolate those behaviors in your subsystems that may happen and make sure that they do happen. And of­ ten. If you find rare behavior in your subsystems, be sure to do something­ anything-to stir things up. * The assembler bug could have been found within hours, instead of years, if reallocate hadn't so rarely moved blocks when it expanded them. * If something happens rarely, force it to happen often. * Keep debug information to allow stronger error checking. * There is no question that the debug code will create differences be­ tween the ship and the debug versions of your code, but as long as you're careful not to change the underlying behavior of the code, those differences shouldn't matter. * Create thorough subsystem checks, and use them often. * Design your tests carefully. Nothing should be arbitrary. * Strive to implement transparent integrity checks. * Don't apply ship version constraints to the debug version. Trade size and speed for error detection. * **Chapter 4** * Don't wait until you have a bug to step through your code. * Step through every code path. * What About Sweeping Changes? In the past, programmers have asked, "What if I add a feature that touches code in many places? Stepping through all that new code is going to be time consuming." Let me answer that question with another: Can you make such sweeping changes without introducing any bugs? The habit of stepping through your code creates an interesting negative feedback loop. Programmers who step through their code soon learn to write small, easily testable functions because stepping through large func­tions is so painful. Programmers also spend more time thinking about how they can localize the changes they need to make-again, so that they can more easily test their code. And isn't this exactly what you want? No project lead likes it when programmers touch a lot of code; it's too destabilizing. Nor do leads like large, unmanageable functions; they're often unmaintainable. Try hard to localize your changes. If you find that you must make per­vasive changes, think twice before you decide not to step through all the new code. * Overflow and underflow bugs * Data conversion bugs * Off-by-one bugs * NULL pointer bugs * Bugs using garbage memory (0xA3 bugs) * Assignment bugs in which you've used = instead of == * Precedence bugs * Logic bugs * As you step through code, focus on data flow. Source level debuggers can hide execution details. Step through critical code at the instruction level . * **Chapter 5** * Make it hard to ignore error conditions. Don't bury error codes in return values. * Always look for, and eliminate, flaws in your interfaces. * Don't write multipurpose functions. Write separate functions to allow stronger argument validation. * Don't be wishy-washy. Define explicit function arguments. * Write functions that, given valid inputs, cannot fail. * Make the code intelligible at the point of call. Avoid Boolean arguments. * Write comments that emphasize potential hazards. * **Chapter 6** * Use well-defined data types. * Always ask ,"Can this variable or expression over- or underflow?" * Implement your designs as accurately as possible. Being kind a close is being kind a buggy. * these principles already: Don't accept special purpose arguments such as the NULL pointer, and Implement your design, not something that approximates it. The third principle is new: Strive to make every function perform its task exactly one time. * Implement "the task" just once. * Get rid of extraneous if statements. * Avoid using nested ?: operators . * Handle your special cases just once. * Avoid risky language idioms. * Don't needlessly mix operator types. If you must mix operators, use parentheses to isolate the operations. * Avoid calling functions that return errors. * **Chapter 7** * Don't reference memory that you don't own. * Don't reference memory that you have freed. * Don't use output memory as workspace buffers. * Don't pass data in static (or global) memory. * Don't write parasitic functions. * Don't abuse your programming language. * Tight C does not guarantee efficient machine code. * Write code for the "average" programmer. * **Chapter 8** * Bugs don't just "go away." * Don't fix bugs later; fix them now. * Fix the cause, not the symptom. * Don't clean up code unless the clean­ up is critical to the product's success. * Don't implement nonstrategic features. * There are no free features. * Don't allow unnecessary flexibility. * Don't keep "trying" solutions· until you find one that works. Take the time to find the correct solution. * Write and test code in small chunks. * Always test your code, even if that means your schedule will slip. * Don't rely on the testing group to find your bugs. * Don't blame testers for finding your bugs. * Establish your priorities and stick to them. * Never allow the same bug to bite you twice. * **A: CODING CHECKLISTS** * **B: MEMORY LOGGING ROUTINES** * **To sum up** * Charles Simonyi in the early 1970s. https://en.wikipedia.org/wiki/Hungarian_notation * char ch; * byte b; * flag f;/* flags that are always TRUE or FALSE*/ * symbol sym;/* some sort of symbol structure*/ * char byte flag symbol * *pch:/* character pointer*/ * *pb; * *pf: * *psym: * related link: [https://www.morling.dev/images/code_review_pyramid.svg](https://www.google.com/url?q=https%3A%2F%2Fwww.morling.dev%2Fimages%2Fcode_review_pyramid.svg&sa=D&sntz=1&usg=AOvVaw1y2It_PUzXAtFN7dPLegCa) * ## Shape Up: Stop Running in Circles and Ship Work that Matters * Six-week cycles: Our decisions are based on moving the product forward in the next six weeks, not micromanaging time. We don’t count hours or question how individual days are spent. We don’t have daily meetings. We don’t rethink our roadmap every two weeks. Our focus is at a higher level. We say to ourselves: “If this project ships after six weeks, we’ll be really happy. We’ll feel our time was well spent.” Then we commit the six weeks and leave the team alone to get it done * Shaping the work: key elements of a solution, appetite * **part one shaping:** * two separate tracks: one for shaping, one for building. * Steps to shaping * 1\. Set boundaries. * 2\. Rough out the elements: idea that solves the problem within the appetite but without all the fine details worked out * 3\. Address risks and rabbit holes. * 4\. Write the pitch. * fixed time, variable scope, * Where in the current system does the new thing fit? How do you get to it? What are the key components or interactions? Where does it take you? * five ingredients that we always want to include in a pitch: 1. Problem — The raw idea, a use case, or something we’ve seen that motivates us to work on this 2. Appetite — How much time we want to spend and how that constrains the solution 3. Solution — The core elements we came up with, presented in a form that’s easy for people to immediately understand 4. Rabbit holes — Details about the solution worth calling out to avoid problems 5. No-gos — Anything specifically excluded from the concept: functionality or use cases we intentionally aren’t covering to fit the appetite or make the problem tractable * **part two : Betting** * Some companies use two-week cycles (aka “sprints”). We learned that two weeks is too short to get anything meaningful done. Worse than that, two-week cycles are extremely costly due to the planning overhead. The amount of work you get out of two weeks isn’t worth the collective hours around the table to “sprint plan” or the opportunity cost of breaking everyone’s momentum to re-group. * after each six-week cycle, we schedule two weeks for cool-down. * bugs: Use cool-down. Bring it to the betting table. Schedule a bug smash. * We’ve noticed three phases of work when we build a new product from scratch. R&D mode (bet the time on spiking some key pieces of the new product idea. CEO and designer, we don’t expect to ship anything at the end of an R&D cycle) , Production mode (Shaping is deliberate again. bet multiple teams in parallel , Shipping is the goal) , Cleanup mode (There’s no shaping. There aren’t clear team boundaries. Work is “shipped”) [Cleanup shouldn’t last longer than two cycles] * Example: two years of development, R&D mode for the first year, a year of production mode cycles followed, two cycles of cleanup and significantly cut back the feature set, some overlap between R&D and production mode after that first year, * **Part three: Building** * We don’t start by assigning tasks to anyone. * we define and track the scopes as to-do lists. Each scope corresponds to a list name. Then any tasks for that scope go in that list. * **image 1, 2, 3,** * Mark nice-to-haves with ~ * First there’s the uphill phase of figuring out what our approach is and what we’re going to do. Then, once we can see all the work involved, there’s the downhill phase of execution * image 4, 5, * ## Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet * getting start: idea, technology, passion; What can I do well that I would love to do for an extended period of time? P: 19, 21 * 1 market segmentation: The single necessary and sufficient condition for a business is a paying customer. ; technological enthusiasts=early adopters=early majority ; P: 31, * 2 select a beachhead market: P: 44, * 3 build an end user profile: P: 52, * 4 Calculate the Total Addressable Market (TAM) Size for the Beachhead Market; $200 # [Essential Tips And Tricks For ](/book-summary/commonplace-book)[commonplace book](/book-summary/commonplace-book) # [knowledge management ](/book-summary/knowledge_management) # [PKM](/book-summary/knowledge_management/pkm) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/LMErpRo3SemuSDj3pLjSSh1xcteMOPvF9yzhoEb8_AB5vppySOpESQgqlBlCy2nymRjHv3i3BNkjBtED1LpjpWk=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/LMErpRo3SemuSDj3pLjSSh1xcteMOPvF9yzhoEb8_AB5vppySOpESQgqlBlCy2nymRjHv3i3BNkjBtED1LpjpWk=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Book Summary Books How to take smart Notes Writing Solid Code Shape Up: Stop Running in Circles and Ship Work that Matters Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet List of books I've read: Computer Vision: Algorithms and Applications Multiple View Geometry in Computer Vision Second Edition Complete to download the latest draft of Machine Learning Yearning Multiple view geometry in computer vision Learning OpenCV Book by Adrian Kaehler and Gary Bradski Digital Image Processing, Global Edition by Rafael C. Gonzalez Introduction to Algorithms, 3rd Edition (The MIT Press) The Mythical Man-Month: Essays on Software Engineering OpenCV-4-with-Python-Blueprints-Second-Edition Cracking the coding interview 189 programming questions and solutions Cracking the Tech Career: Insider Advice on Landing a Job at Google, Microsoft, Apple, Or Any Top Tech Company The Art of Startup Fundraising Mathematics for Machine Learning Principles of Economics (6th edition) The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) Reinventing Your Life: The Breakthough Program to End Negative Behavior Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf Magazine Papers Websites and links Tools YouTube - List channels Language: Essential Tips And Tricks For commonplace book knowledge management PKM # Books * ## How to take smart Notes * Introduction 4, The four underlying principles 4, the six steps to successful writing 6, afterword * introduction * write everything/ collect notes not related to any thing, organize notes, revirew, rewrite, * no reorganise no complex system, develop ideas by smart notes * 1 slip-box; 2 repeatable tasks that can become automatic and fit together seamlessly change routinges * overarching workflow "Getting Things Done" GTD * constantly have to ump back and forth between different tasks * 1- bibliographical slip-box: reference * 2- collected and generated ideas slip-box * 3- index: notes serve as entry point / thought/ topic * writing a paper step by step * 1 fleeting notes: it will delete within a day * I always have a slip of paper at hand, on which I note down the ideas of certain pages. on the backside I write down the bibliographic details. after finishing the book I go throught my notes and think how ehse notes might be relevant for already written notes in the slip-box. it means that I always read with an eye towards possible connections in the slip-box * 2 literature notes: contain the necessary information - in reference system or in the slip-box * I make a note with the bibliographic details. on the backside I would write "on page x is this, on page y is that" and then it goes into the bibliographic slib-box where I collect everything I read * read, think about its relevance * theoretical background, methodological approach or perspective of the text we read. * whether something adds to a discussion in the slip-box * we look at our slip-box for already existing lines of thought and think about the questions and problems already on our minds to which a new note might contribute. - assigning keywords is much more than just a bureaucratic act. it is a crucial part of the thinking process, which often leads to a deeper elaboration of the note itself and the connection to other notes. * * 3 permanent notes: develop ideas/new/ - only relevant to one particular project - in project specific folder - can be discarded or archived after project finished * add to slip-box behind the note you dirctyly refer to or behind the last note in the slip-box * add links to other notes or links on other notes to your new note * make sure it can be found from the index (MOC) add an entry in the index if necessary or refer to it from a note that is connected to the index * build a latticework of mental models * 4 behind multiple notes (link backward) - link to related note - link to MOC * make sure it can be found again * **keywords** can easily be added to a note like **tags** and will then show up in the **index** * a day * read and take notes * build connections within the slip-box - new idea * you write on your paper, notice a hole in the argument and have another look in the file system for the missing link. you follow up on a footnote, go back to research and might add a fitting quote to one of your papers in the making. * The four underlying principles * writing * simplicity * not from scratch * let the work carry you forward * the six steps to successful writing * separate and interlocking tasks * we use memo techniques is to bundle items together in a meaningful way and remember the bundles * the brain doesn't distinguish between an actual finished task and one that is postponed by taking a note * the fist step is to break down the amprphous task of "writing" into smaller pieces of different tasks that can be finished in **one go.** the second step is to make sure we always write down the outcome of our thinking, including possible connections to further inquiries. * ego depletion * read for understanding * take smart notes * develp ideas (MOC) * 1 overview of a topic - referred to from the index and used as an entry point into a topic has already develped * 2 keeping tarck of all topic - index - * share your insight * make it a habit * afterword * ## Writing Solid Code Writing Solid Code (Microsoft Programming Series) by Trendelles Store Note from Writing Solid Code Book [https://www.pirahansiah.com/describe/book- summary](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Fdescribe%2Fbook- summary&sa=D&sntz=1&usg=AOvVaw3ACzA6G0MBVN3nw6f-yMQo) #SolidCode #C++ #Python #pirahansiah * **Chapter 1** * Enable all optional compiler warnings. * Use lint to catch bugs that your compiler may miss. * If you have unit tests, use them. * **Chapter 2** * introduction assert page 16; assert.h * assert is nothing more than a repackaged form of the #ifdef code we saw before, but when you use * the macro, it takes one line instead of seven * That's why assert is a macro and not a function; if it were a function, calling * it could cause unexpected memory or code swapping. * Use assertions to validate function arguments . * Strip undefined behavior from your code, or use assertions to catch illegal uses of undefined behavior . * Don't waste people's time. Document unclear assertions . * Either remove implicit assumptions, or assert that they are valid . * Use assertions to detect impossible conditions. * Don't hide bugs when you program defensively. * Use a second algorithm to validate your results. * Don't wait for bugs to happen; use startup checks. * **Chapter 3** * Maintain shipping and debugging versions of your program. Shrink-wrap the shipping version, but use the debugging ver­sion as much as possible to catch bugs quickly. * Assertions are a shorthand way to write debugging checks. Use them to catch illegal conditions that should never arise. Don't confuse such conditions with error conditions, which you must handle in the final product. * Use assertions to validate function arguments and to alert pro­ grammars when they do something undefined. The more rigidly you define your functions, the easier it will be to validate the arguments. * Once you've written a function, review it and ask yourself, 'What am I assuming?" If you find an assumption, either assert that your assumption is always valid, or rewrite the code to re­ move the assumption. Also ask, 'What is most likely to be wrong in this code, and how can I automatically detect the prob­lem?" Strive to implement tests that catch bugs at the earliest possible moment. Textbooks encourage programmers to program defensively, but remember that this coding style hides bugs. When you write de­fensive code, use assertions to alert you if the "can't happen" cases do happen. * Eliminate random behavior. Force bugs to be reproducible . * Destroy your garbage so that it's not misused . * Not only did reallocate have to move the block of memory as it was expanding it, but the old memory had to be reallocated and filled with new data. In the assembler, both happened rarely. * This brings up another guideline for writing bug-free code: You don't want anything to happen rarely. You need to isolate those behaviors in your subsystems that may happen and make sure that they do happen. And of­ ten. If you find rare behavior in your subsystems, be sure to do something­ anything-to stir things up. * The assembler bug could have been found within hours, instead of years, if reallocate hadn't so rarely moved blocks when it expanded them. * If something happens rarely, force it to happen often. * Keep debug information to allow stronger error checking. * There is no question that the debug code will create differences be­ tween the ship and the debug versions of your code, but as long as you're careful not to change the underlying behavior of the code, those differences shouldn't matter. * Create thorough subsystem checks, and use them often. * Design your tests carefully. Nothing should be arbitrary. * Strive to implement transparent integrity checks. * Don't apply ship version constraints to the debug version. Trade size and speed for error detection. * **Chapter 4** * Don't wait until you have a bug to step through your code. * Step through every code path. * What About Sweeping Changes? In the past, programmers have asked, "What if I add a feature that touches code in many places? Stepping through all that new code is going to be time consuming." Let me answer that question with another: Can you make such sweeping changes without introducing any bugs? The habit of stepping through your code creates an interesting negative feedback loop. Programmers who step through their code soon learn to write small, easily testable functions because stepping through large func­tions is so painful. Programmers also spend more time thinking about how they can localize the changes they need to make-again, so that they can more easily test their code. And isn't this exactly what you want? No project lead likes it when programmers touch a lot of code; it's too destabilizing. Nor do leads like large, unmanageable functions; they're often unmaintainable. Try hard to localize your changes. If you find that you must make per­vasive changes, think twice before you decide not to step through all the new code. * Overflow and underflow bugs * Data conversion bugs * Off-by-one bugs * NULL pointer bugs * Bugs using garbage memory (0xA3 bugs) * Assignment bugs in which you've used = instead of == * Precedence bugs * Logic bugs * As you step through code, focus on data flow. Source level debuggers can hide execution details. Step through critical code at the instruction level . * **Chapter 5** * Make it hard to ignore error conditions. Don't bury error codes in return values. * Always look for, and eliminate, flaws in your interfaces. * Don't write multipurpose functions. Write separate functions to allow stronger argument validation. * Don't be wishy-washy. Define explicit function arguments. * Write functions that, given valid inputs, cannot fail. * Make the code intelligible at the point of call. Avoid Boolean arguments. * Write comments that emphasize potential hazards. * **Chapter 6** * Use well-defined data types. * Always ask ,"Can this variable or expression over- or underflow?" * Implement your designs as accurately as possible. Being kind a close is being kind a buggy. * these principles already: Don't accept special purpose arguments such as the NULL pointer, and Implement your design, not something that approximates it. The third principle is new: Strive to make every function perform its task exactly one time. * Implement "the task" just once. * Get rid of extraneous if statements. * Avoid using nested ?: operators . * Handle your special cases just once. * Avoid risky language idioms. * Don't needlessly mix operator types. If you must mix operators, use parentheses to isolate the operations. * Avoid calling functions that return errors. * **Chapter 7** * Don't reference memory that you don't own. * Don't reference memory that you have freed. * Don't use output memory as workspace buffers. * Don't pass data in static (or global) memory. * Don't write parasitic functions. * Don't abuse your programming language. * Tight C does not guarantee efficient machine code. * Write code for the "average" programmer. * **Chapter 8** * Bugs don't just "go away." * Don't fix bugs later; fix them now. * Fix the cause, not the symptom. * Don't clean up code unless the clean­ up is critical to the product's success. * Don't implement nonstrategic features. * There are no free features. * Don't allow unnecessary flexibility. * Don't keep "trying" solutions· until you find one that works. Take the time to find the correct solution. * Write and test code in small chunks. * Always test your code, even if that means your schedule will slip. * Don't rely on the testing group to find your bugs. * Don't blame testers for finding your bugs. * Establish your priorities and stick to them. * Never allow the same bug to bite you twice. * **A: CODING CHECKLISTS** * **B: MEMORY LOGGING ROUTINES** * **To sum up** * Charles Simonyi in the early 1970s. https://en.wikipedia.org/wiki/Hungarian_notation * char ch; * byte b; * flag f;/* flags that are always TRUE or FALSE*/ * symbol sym;/* some sort of symbol structure*/ * char byte flag symbol * *pch:/* character pointer*/ * *pb; * *pf: * *psym: * related link: [https://www.morling.dev/images/code_review_pyramid.svg](https://www.google.com/url?q=https%3A%2F%2Fwww.morling.dev%2Fimages%2Fcode_review_pyramid.svg&sa=D&sntz=1&usg=AOvVaw1y2It_PUzXAtFN7dPLegCa) * ## Shape Up: Stop Running in Circles and Ship Work that Matters * Six-week cycles: Our decisions are based on moving the product forward in the next six weeks, not micromanaging time. We don’t count hours or question how individual days are spent. We don’t have daily meetings. We don’t rethink our roadmap every two weeks. Our focus is at a higher level. We say to ourselves: “If this project ships after six weeks, we’ll be really happy. We’ll feel our time was well spent.” Then we commit the six weeks and leave the team alone to get it done * Shaping the work: key elements of a solution, appetite * **part one shaping:** * two separate tracks: one for shaping, one for building. * Steps to shaping * 1\. Set boundaries. * 2\. Rough out the elements: idea that solves the problem within the appetite but without all the fine details worked out * 3\. Address risks and rabbit holes. * 4\. Write the pitch. * fixed time, variable scope, * Where in the current system does the new thing fit? How do you get to it? What are the key components or interactions? Where does it take you? * five ingredients that we always want to include in a pitch: 1. Problem — The raw idea, a use case, or something we’ve seen that motivates us to work on this 2. Appetite — How much time we want to spend and how that constrains the solution 3. Solution — The core elements we came up with, presented in a form that’s easy for people to immediately understand 4. Rabbit holes — Details about the solution worth calling out to avoid problems 5. No-gos — Anything specifically excluded from the concept: functionality or use cases we intentionally aren’t covering to fit the appetite or make the problem tractable * **part two : Betting** * Some companies use two-week cycles (aka “sprints”). We learned that two weeks is too short to get anything meaningful done. Worse than that, two-week cycles are extremely costly due to the planning overhead. The amount of work you get out of two weeks isn’t worth the collective hours around the table to “sprint plan” or the opportunity cost of breaking everyone’s momentum to re-group. * after each six-week cycle, we schedule two weeks for cool-down. * bugs: Use cool-down. Bring it to the betting table. Schedule a bug smash. * We’ve noticed three phases of work when we build a new product from scratch. R&D mode (bet the time on spiking some key pieces of the new product idea. CEO and designer, we don’t expect to ship anything at the end of an R&D cycle) , Production mode (Shaping is deliberate again. bet multiple teams in parallel , Shipping is the goal) , Cleanup mode (There’s no shaping. There aren’t clear team boundaries. Work is “shipped”) [Cleanup shouldn’t last longer than two cycles] * Example: two years of development, R&D mode for the first year, a year of production mode cycles followed, two cycles of cleanup and significantly cut back the feature set, some overlap between R&D and production mode after that first year, * **Part three: Building** * We don’t start by assigning tasks to anyone. * we define and track the scopes as to-do lists. Each scope corresponds to a list name. Then any tasks for that scope go in that list. * **image 1, 2, 3,** * Mark nice-to-haves with ~ * First there’s the uphill phase of figuring out what our approach is and what we’re going to do. Then, once we can see all the work involved, there’s the downhill phase of execution * image 4, 5, * ## Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet * getting start: idea, technology, passion; What can I do well that I would love to do for an extended period of time? P: 19, 21 * 1 market segmentation: The single necessary and sufficient condition for a business is a paying customer. ; technological enthusiasts=early adopters=early majority ; P: 31, * 2 select a beachhead market: P: 44, * 3 build an end user profile: P: 52, * 4 Calculate the Total Addressable Market (TAM) Size for the Beachhead Market; $200 # [Essential Tips And Tricks For ](/book-summary/commonplace-book)[commonplace book](/book-summary/commonplace-book) # [knowledge management ](/book-summary/knowledge_management) # [PKM](/book-summary/knowledge_management/pkm) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. 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Gonzalez Introduction to Algorithms, 3rd Edition (The MIT Press) The Mythical Man-Month: Essays on Software Engineering OpenCV-4-with-Python-Blueprints-Second-Edition Cracking the coding interview 189 programming questions and solutions Cracking the Tech Career: Insider Advice on Landing a Job at Google, Microsoft, Apple, Or Any Top Tech Company The Art of Startup Fundraising Mathematics for Machine Learning Principles of Economics (6th edition) The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) Reinventing Your Life: The Breakthough Program to End Negative Behavior Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf Magazine Papers Websites and links Tools YouTube - List channels Language: Essential Tips And Tricks For commonplace book knowledge management PKM # Books * ## How to take smart Notes * Introduction 4, The four underlying principles 4, the six steps to successful writing 6, afterword * introduction * write everything/ collect notes not related to any thing, organize notes, revirew, rewrite, * no reorganise no complex system, develop ideas by smart notes * 1 slip-box; 2 repeatable tasks that can become automatic and fit together seamlessly change routinges * overarching workflow "Getting Things Done" GTD * constantly have to ump back and forth between different tasks * 1- bibliographical slip-box: reference * 2- collected and generated ideas slip-box * 3- index: notes serve as entry point / thought/ topic * writing a paper step by step * 1 fleeting notes: it will delete within a day * I always have a slip of paper at hand, on which I note down the ideas of certain pages. on the backside I write down the bibliographic details. after finishing the book I go throught my notes and think how ehse notes might be relevant for already written notes in the slip-box. it means that I always read with an eye towards possible connections in the slip-box * 2 literature notes: contain the necessary information - in reference system or in the slip-box * I make a note with the bibliographic details. on the backside I would write "on page x is this, on page y is that" and then it goes into the bibliographic slib-box where I collect everything I read * read, think about its relevance * theoretical background, methodological approach or perspective of the text we read. * whether something adds to a discussion in the slip-box * we look at our slip-box for already existing lines of thought and think about the questions and problems already on our minds to which a new note might contribute. - assigning keywords is much more than just a bureaucratic act. it is a crucial part of the thinking process, which often leads to a deeper elaboration of the note itself and the connection to other notes. * * 3 permanent notes: develop ideas/new/ - only relevant to one particular project - in project specific folder - can be discarded or archived after project finished * add to slip-box behind the note you dirctyly refer to or behind the last note in the slip-box * add links to other notes or links on other notes to your new note * make sure it can be found from the index (MOC) add an entry in the index if necessary or refer to it from a note that is connected to the index * build a latticework of mental models * 4 behind multiple notes (link backward) - link to related note - link to MOC * make sure it can be found again * **keywords** can easily be added to a note like **tags** and will then show up in the **index** * a day * read and take notes * build connections within the slip-box - new idea * you write on your paper, notice a hole in the argument and have another look in the file system for the missing link. you follow up on a footnote, go back to research and might add a fitting quote to one of your papers in the making. * The four underlying principles * writing * simplicity * not from scratch * let the work carry you forward * the six steps to successful writing * separate and interlocking tasks * we use memo techniques is to bundle items together in a meaningful way and remember the bundles * the brain doesn't distinguish between an actual finished task and one that is postponed by taking a note * the fist step is to break down the amprphous task of "writing" into smaller pieces of different tasks that can be finished in **one go.** the second step is to make sure we always write down the outcome of our thinking, including possible connections to further inquiries. * ego depletion * read for understanding * take smart notes * develp ideas (MOC) * 1 overview of a topic - referred to from the index and used as an entry point into a topic has already develped * 2 keeping tarck of all topic - index - * share your insight * make it a habit * afterword * ## Writing Solid Code Writing Solid Code (Microsoft Programming Series) by Trendelles Store Note from Writing Solid Code Book [https://www.pirahansiah.com/describe/book- summary](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Fdescribe%2Fbook- summary&sa=D&sntz=1&usg=AOvVaw3ACzA6G0MBVN3nw6f-yMQo) #SolidCode #C++ #Python #pirahansiah * **Chapter 1** * Enable all optional compiler warnings. * Use lint to catch bugs that your compiler may miss. * If you have unit tests, use them. * **Chapter 2** * introduction assert page 16; assert.h * assert is nothing more than a repackaged form of the #ifdef code we saw before, but when you use * the macro, it takes one line instead of seven * That's why assert is a macro and not a function; if it were a function, calling * it could cause unexpected memory or code swapping. * Use assertions to validate function arguments . * Strip undefined behavior from your code, or use assertions to catch illegal uses of undefined behavior . * Don't waste people's time. Document unclear assertions . * Either remove implicit assumptions, or assert that they are valid . * Use assertions to detect impossible conditions. * Don't hide bugs when you program defensively. * Use a second algorithm to validate your results. * Don't wait for bugs to happen; use startup checks. * **Chapter 3** * Maintain shipping and debugging versions of your program. Shrink-wrap the shipping version, but use the debugging ver­sion as much as possible to catch bugs quickly. * Assertions are a shorthand way to write debugging checks. Use them to catch illegal conditions that should never arise. Don't confuse such conditions with error conditions, which you must handle in the final product. * Use assertions to validate function arguments and to alert pro­ grammars when they do something undefined. The more rigidly you define your functions, the easier it will be to validate the arguments. * Once you've written a function, review it and ask yourself, 'What am I assuming?" If you find an assumption, either assert that your assumption is always valid, or rewrite the code to re­ move the assumption. Also ask, 'What is most likely to be wrong in this code, and how can I automatically detect the prob­lem?" Strive to implement tests that catch bugs at the earliest possible moment. Textbooks encourage programmers to program defensively, but remember that this coding style hides bugs. When you write de­fensive code, use assertions to alert you if the "can't happen" cases do happen. * Eliminate random behavior. Force bugs to be reproducible . * Destroy your garbage so that it's not misused . * Not only did reallocate have to move the block of memory as it was expanding it, but the old memory had to be reallocated and filled with new data. In the assembler, both happened rarely. * This brings up another guideline for writing bug-free code: You don't want anything to happen rarely. You need to isolate those behaviors in your subsystems that may happen and make sure that they do happen. And of­ ten. If you find rare behavior in your subsystems, be sure to do something­ anything-to stir things up. * The assembler bug could have been found within hours, instead of years, if reallocate hadn't so rarely moved blocks when it expanded them. * If something happens rarely, force it to happen often. * Keep debug information to allow stronger error checking. * There is no question that the debug code will create differences be­ tween the ship and the debug versions of your code, but as long as you're careful not to change the underlying behavior of the code, those differences shouldn't matter. * Create thorough subsystem checks, and use them often. * Design your tests carefully. Nothing should be arbitrary. * Strive to implement transparent integrity checks. * Don't apply ship version constraints to the debug version. Trade size and speed for error detection. * **Chapter 4** * Don't wait until you have a bug to step through your code. * Step through every code path. * What About Sweeping Changes? In the past, programmers have asked, "What if I add a feature that touches code in many places? Stepping through all that new code is going to be time consuming." Let me answer that question with another: Can you make such sweeping changes without introducing any bugs? The habit of stepping through your code creates an interesting negative feedback loop. Programmers who step through their code soon learn to write small, easily testable functions because stepping through large func­tions is so painful. Programmers also spend more time thinking about how they can localize the changes they need to make-again, so that they can more easily test their code. And isn't this exactly what you want? No project lead likes it when programmers touch a lot of code; it's too destabilizing. Nor do leads like large, unmanageable functions; they're often unmaintainable. Try hard to localize your changes. If you find that you must make per­vasive changes, think twice before you decide not to step through all the new code. * Overflow and underflow bugs * Data conversion bugs * Off-by-one bugs * NULL pointer bugs * Bugs using garbage memory (0xA3 bugs) * Assignment bugs in which you've used = instead of == * Precedence bugs * Logic bugs * As you step through code, focus on data flow. Source level debuggers can hide execution details. Step through critical code at the instruction level . * **Chapter 5** * Make it hard to ignore error conditions. Don't bury error codes in return values. * Always look for, and eliminate, flaws in your interfaces. * Don't write multipurpose functions. Write separate functions to allow stronger argument validation. * Don't be wishy-washy. Define explicit function arguments. * Write functions that, given valid inputs, cannot fail. * Make the code intelligible at the point of call. Avoid Boolean arguments. * Write comments that emphasize potential hazards. * **Chapter 6** * Use well-defined data types. * Always ask ,"Can this variable or expression over- or underflow?" * Implement your designs as accurately as possible. Being kind a close is being kind a buggy. * these principles already: Don't accept special purpose arguments such as the NULL pointer, and Implement your design, not something that approximates it. The third principle is new: Strive to make every function perform its task exactly one time. * Implement "the task" just once. * Get rid of extraneous if statements. * Avoid using nested ?: operators . * Handle your special cases just once. * Avoid risky language idioms. * Don't needlessly mix operator types. If you must mix operators, use parentheses to isolate the operations. * Avoid calling functions that return errors. * **Chapter 7** * Don't reference memory that you don't own. * Don't reference memory that you have freed. * Don't use output memory as workspace buffers. * Don't pass data in static (or global) memory. * Don't write parasitic functions. * Don't abuse your programming language. * Tight C does not guarantee efficient machine code. * Write code for the "average" programmer. * **Chapter 8** * Bugs don't just "go away." * Don't fix bugs later; fix them now. * Fix the cause, not the symptom. * Don't clean up code unless the clean­ up is critical to the product's success. * Don't implement nonstrategic features. * There are no free features. * Don't allow unnecessary flexibility. * Don't keep "trying" solutions· until you find one that works. Take the time to find the correct solution. * Write and test code in small chunks. * Always test your code, even if that means your schedule will slip. * Don't rely on the testing group to find your bugs. * Don't blame testers for finding your bugs. * Establish your priorities and stick to them. * Never allow the same bug to bite you twice. * **A: CODING CHECKLISTS** * **B: MEMORY LOGGING ROUTINES** * **To sum up** * Charles Simonyi in the early 1970s. https://en.wikipedia.org/wiki/Hungarian_notation * char ch; * byte b; * flag f;/* flags that are always TRUE or FALSE*/ * symbol sym;/* some sort of symbol structure*/ * char byte flag symbol * *pch:/* character pointer*/ * *pb; * *pf: * *psym: * related link: [https://www.morling.dev/images/code_review_pyramid.svg](https://www.google.com/url?q=https%3A%2F%2Fwww.morling.dev%2Fimages%2Fcode_review_pyramid.svg&sa=D&sntz=1&usg=AOvVaw1y2It_PUzXAtFN7dPLegCa) * ## Shape Up: Stop Running in Circles and Ship Work that Matters * Six-week cycles: Our decisions are based on moving the product forward in the next six weeks, not micromanaging time. We don’t count hours or question how individual days are spent. We don’t have daily meetings. We don’t rethink our roadmap every two weeks. Our focus is at a higher level. We say to ourselves: “If this project ships after six weeks, we’ll be really happy. We’ll feel our time was well spent.” Then we commit the six weeks and leave the team alone to get it done * Shaping the work: key elements of a solution, appetite * **part one shaping:** * two separate tracks: one for shaping, one for building. * Steps to shaping * 1\. Set boundaries. * 2\. Rough out the elements: idea that solves the problem within the appetite but without all the fine details worked out * 3\. Address risks and rabbit holes. * 4\. Write the pitch. * fixed time, variable scope, * Where in the current system does the new thing fit? How do you get to it? What are the key components or interactions? Where does it take you? * five ingredients that we always want to include in a pitch: 1. Problem — The raw idea, a use case, or something we’ve seen that motivates us to work on this 2. Appetite — How much time we want to spend and how that constrains the solution 3. Solution — The core elements we came up with, presented in a form that’s easy for people to immediately understand 4. Rabbit holes — Details about the solution worth calling out to avoid problems 5. No-gos — Anything specifically excluded from the concept: functionality or use cases we intentionally aren’t covering to fit the appetite or make the problem tractable * **part two : Betting** * Some companies use two-week cycles (aka “sprints”). We learned that two weeks is too short to get anything meaningful done. Worse than that, two-week cycles are extremely costly due to the planning overhead. The amount of work you get out of two weeks isn’t worth the collective hours around the table to “sprint plan” or the opportunity cost of breaking everyone’s momentum to re-group. * after each six-week cycle, we schedule two weeks for cool-down. * bugs: Use cool-down. Bring it to the betting table. Schedule a bug smash. * We’ve noticed three phases of work when we build a new product from scratch. R&D mode (bet the time on spiking some key pieces of the new product idea. CEO and designer, we don’t expect to ship anything at the end of an R&D cycle) , Production mode (Shaping is deliberate again. bet multiple teams in parallel , Shipping is the goal) , Cleanup mode (There’s no shaping. There aren’t clear team boundaries. Work is “shipped”) [Cleanup shouldn’t last longer than two cycles] * Example: two years of development, R&D mode for the first year, a year of production mode cycles followed, two cycles of cleanup and significantly cut back the feature set, some overlap between R&D and production mode after that first year, * **Part three: Building** * We don’t start by assigning tasks to anyone. * we define and track the scopes as to-do lists. Each scope corresponds to a list name. Then any tasks for that scope go in that list. * **image 1, 2, 3,** * Mark nice-to-haves with ~ * First there’s the uphill phase of figuring out what our approach is and what we’re going to do. Then, once we can see all the work involved, there’s the downhill phase of execution * image 4, 5, * ## Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet * getting start: idea, technology, passion; What can I do well that I would love to do for an extended period of time? P: 19, 21 * 1 market segmentation: The single necessary and sufficient condition for a business is a paying customer. ; technological enthusiasts=early adopters=early majority ; P: 31, * 2 select a beachhead market: P: 44, * 3 build an end user profile: P: 52, * 4 Calculate the Total Addressable Market (TAM) Size for the Beachhead Market; $200 # [Essential Tips And Tricks For ](/book-summary/commonplace-book)[commonplace book](/book-summary/commonplace-book) # [knowledge management ](/book-summary/knowledge_management) # [PKM](/book-summary/knowledge_management/pkm) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/LMErpRo3SemuSDj3pLjSSh1xcteMOPvF9yzhoEb8_AB5vppySOpESQgqlBlCy2nymRjHv3i3BNkjBtED1LpjpWk=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/LMErpRo3SemuSDj3pLjSSh1xcteMOPvF9yzhoEb8_AB5vppySOpESQgqlBlCy2nymRjHv3i3BNkjBtED1LpjpWk=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Book Summary Books How to take smart Notes Writing Solid Code Shape Up: Stop Running in Circles and Ship Work that Matters Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet List of books I've read: Computer Vision: Algorithms and Applications Multiple View Geometry in Computer Vision Second Edition Complete to download the latest draft of Machine Learning Yearning Multiple view geometry in computer vision Learning OpenCV Book by Adrian Kaehler and Gary Bradski Digital Image Processing, Global Edition by Rafael C. Gonzalez Introduction to Algorithms, 3rd Edition (The MIT Press) The Mythical Man-Month: Essays on Software Engineering OpenCV-4-with-Python-Blueprints-Second-Edition Cracking the coding interview 189 programming questions and solutions Cracking the Tech Career: Insider Advice on Landing a Job at Google, Microsoft, Apple, Or Any Top Tech Company The Art of Startup Fundraising Mathematics for Machine Learning Principles of Economics (6th edition) The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) Reinventing Your Life: The Breakthough Program to End Negative Behavior Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf Magazine Papers Websites and links Tools YouTube - List channels Language: Essential Tips And Tricks For commonplace book knowledge management PKM # Books * ## How to take smart Notes * Introduction 4, The four underlying principles 4, the six steps to successful writing 6, afterword * introduction * write everything/ collect notes not related to any thing, organize notes, revirew, rewrite, * no reorganise no complex system, develop ideas by smart notes * 1 slip-box; 2 repeatable tasks that can become automatic and fit together seamlessly change routinges * overarching workflow "Getting Things Done" GTD * constantly have to ump back and forth between different tasks * 1- bibliographical slip-box: reference * 2- collected and generated ideas slip-box * 3- index: notes serve as entry point / thought/ topic * writing a paper step by step * 1 fleeting notes: it will delete within a day * I always have a slip of paper at hand, on which I note down the ideas of certain pages. on the backside I write down the bibliographic details. after finishing the book I go throught my notes and think how ehse notes might be relevant for already written notes in the slip-box. it means that I always read with an eye towards possible connections in the slip-box * 2 literature notes: contain the necessary information - in reference system or in the slip-box * I make a note with the bibliographic details. on the backside I would write "on page x is this, on page y is that" and then it goes into the bibliographic slib-box where I collect everything I read * read, think about its relevance * theoretical background, methodological approach or perspective of the text we read. * whether something adds to a discussion in the slip-box * we look at our slip-box for already existing lines of thought and think about the questions and problems already on our minds to which a new note might contribute. - assigning keywords is much more than just a bureaucratic act. it is a crucial part of the thinking process, which often leads to a deeper elaboration of the note itself and the connection to other notes. * * 3 permanent notes: develop ideas/new/ - only relevant to one particular project - in project specific folder - can be discarded or archived after project finished * add to slip-box behind the note you dirctyly refer to or behind the last note in the slip-box * add links to other notes or links on other notes to your new note * make sure it can be found from the index (MOC) add an entry in the index if necessary or refer to it from a note that is connected to the index * build a latticework of mental models * 4 behind multiple notes (link backward) - link to related note - link to MOC * make sure it can be found again * **keywords** can easily be added to a note like **tags** and will then show up in the **index** * a day * read and take notes * build connections within the slip-box - new idea * you write on your paper, notice a hole in the argument and have another look in the file system for the missing link. you follow up on a footnote, go back to research and might add a fitting quote to one of your papers in the making. * The four underlying principles * writing * simplicity * not from scratch * let the work carry you forward * the six steps to successful writing * separate and interlocking tasks * we use memo techniques is to bundle items together in a meaningful way and remember the bundles * the brain doesn't distinguish between an actual finished task and one that is postponed by taking a note * the fist step is to break down the amprphous task of "writing" into smaller pieces of different tasks that can be finished in **one go.** the second step is to make sure we always write down the outcome of our thinking, including possible connections to further inquiries. * ego depletion * read for understanding * take smart notes * develp ideas (MOC) * 1 overview of a topic - referred to from the index and used as an entry point into a topic has already develped * 2 keeping tarck of all topic - index - * share your insight * make it a habit * afterword * ## Writing Solid Code Writing Solid Code (Microsoft Programming Series) by Trendelles Store Note from Writing Solid Code Book [https://www.pirahansiah.com/describe/book- summary](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Fdescribe%2Fbook- summary&sa=D&sntz=1&usg=AOvVaw3ACzA6G0MBVN3nw6f-yMQo) #SolidCode #C++ #Python #pirahansiah * **Chapter 1** * Enable all optional compiler warnings. * Use lint to catch bugs that your compiler may miss. * If you have unit tests, use them. * **Chapter 2** * introduction assert page 16; assert.h * assert is nothing more than a repackaged form of the #ifdef code we saw before, but when you use * the macro, it takes one line instead of seven * That's why assert is a macro and not a function; if it were a function, calling * it could cause unexpected memory or code swapping. * Use assertions to validate function arguments . * Strip undefined behavior from your code, or use assertions to catch illegal uses of undefined behavior . * Don't waste people's time. Document unclear assertions . * Either remove implicit assumptions, or assert that they are valid . * Use assertions to detect impossible conditions. * Don't hide bugs when you program defensively. * Use a second algorithm to validate your results. * Don't wait for bugs to happen; use startup checks. * **Chapter 3** * Maintain shipping and debugging versions of your program. Shrink-wrap the shipping version, but use the debugging ver­sion as much as possible to catch bugs quickly. * Assertions are a shorthand way to write debugging checks. Use them to catch illegal conditions that should never arise. Don't confuse such conditions with error conditions, which you must handle in the final product. * Use assertions to validate function arguments and to alert pro­ grammars when they do something undefined. The more rigidly you define your functions, the easier it will be to validate the arguments. * Once you've written a function, review it and ask yourself, 'What am I assuming?" If you find an assumption, either assert that your assumption is always valid, or rewrite the code to re­ move the assumption. Also ask, 'What is most likely to be wrong in this code, and how can I automatically detect the prob­lem?" Strive to implement tests that catch bugs at the earliest possible moment. Textbooks encourage programmers to program defensively, but remember that this coding style hides bugs. When you write de­fensive code, use assertions to alert you if the "can't happen" cases do happen. * Eliminate random behavior. Force bugs to be reproducible . * Destroy your garbage so that it's not misused . * Not only did reallocate have to move the block of memory as it was expanding it, but the old memory had to be reallocated and filled with new data. In the assembler, both happened rarely. * This brings up another guideline for writing bug-free code: You don't want anything to happen rarely. You need to isolate those behaviors in your subsystems that may happen and make sure that they do happen. And of­ ten. If you find rare behavior in your subsystems, be sure to do something­ anything-to stir things up. * The assembler bug could have been found within hours, instead of years, if reallocate hadn't so rarely moved blocks when it expanded them. * If something happens rarely, force it to happen often. * Keep debug information to allow stronger error checking. * There is no question that the debug code will create differences be­ tween the ship and the debug versions of your code, but as long as you're careful not to change the underlying behavior of the code, those differences shouldn't matter. * Create thorough subsystem checks, and use them often. * Design your tests carefully. Nothing should be arbitrary. * Strive to implement transparent integrity checks. * Don't apply ship version constraints to the debug version. Trade size and speed for error detection. * **Chapter 4** * Don't wait until you have a bug to step through your code. * Step through every code path. * What About Sweeping Changes? In the past, programmers have asked, "What if I add a feature that touches code in many places? Stepping through all that new code is going to be time consuming." Let me answer that question with another: Can you make such sweeping changes without introducing any bugs? The habit of stepping through your code creates an interesting negative feedback loop. Programmers who step through their code soon learn to write small, easily testable functions because stepping through large func­tions is so painful. Programmers also spend more time thinking about how they can localize the changes they need to make-again, so that they can more easily test their code. And isn't this exactly what you want? No project lead likes it when programmers touch a lot of code; it's too destabilizing. Nor do leads like large, unmanageable functions; they're often unmaintainable. Try hard to localize your changes. If you find that you must make per­vasive changes, think twice before you decide not to step through all the new code. * Overflow and underflow bugs * Data conversion bugs * Off-by-one bugs * NULL pointer bugs * Bugs using garbage memory (0xA3 bugs) * Assignment bugs in which you've used = instead of == * Precedence bugs * Logic bugs * As you step through code, focus on data flow. Source level debuggers can hide execution details. Step through critical code at the instruction level . * **Chapter 5** * Make it hard to ignore error conditions. Don't bury error codes in return values. * Always look for, and eliminate, flaws in your interfaces. * Don't write multipurpose functions. Write separate functions to allow stronger argument validation. * Don't be wishy-washy. Define explicit function arguments. * Write functions that, given valid inputs, cannot fail. * Make the code intelligible at the point of call. Avoid Boolean arguments. * Write comments that emphasize potential hazards. * **Chapter 6** * Use well-defined data types. * Always ask ,"Can this variable or expression over- or underflow?" * Implement your designs as accurately as possible. Being kind a close is being kind a buggy. * these principles already: Don't accept special purpose arguments such as the NULL pointer, and Implement your design, not something that approximates it. The third principle is new: Strive to make every function perform its task exactly one time. * Implement "the task" just once. * Get rid of extraneous if statements. * Avoid using nested ?: operators . * Handle your special cases just once. * Avoid risky language idioms. * Don't needlessly mix operator types. If you must mix operators, use parentheses to isolate the operations. * Avoid calling functions that return errors. * **Chapter 7** * Don't reference memory that you don't own. * Don't reference memory that you have freed. * Don't use output memory as workspace buffers. * Don't pass data in static (or global) memory. * Don't write parasitic functions. * Don't abuse your programming language. * Tight C does not guarantee efficient machine code. * Write code for the "average" programmer. * **Chapter 8** * Bugs don't just "go away." * Don't fix bugs later; fix them now. * Fix the cause, not the symptom. * Don't clean up code unless the clean­ up is critical to the product's success. * Don't implement nonstrategic features. * There are no free features. * Don't allow unnecessary flexibility. * Don't keep "trying" solutions· until you find one that works. Take the time to find the correct solution. * Write and test code in small chunks. * Always test your code, even if that means your schedule will slip. * Don't rely on the testing group to find your bugs. * Don't blame testers for finding your bugs. * Establish your priorities and stick to them. * Never allow the same bug to bite you twice. * **A: CODING CHECKLISTS** * **B: MEMORY LOGGING ROUTINES** * **To sum up** * Charles Simonyi in the early 1970s. https://en.wikipedia.org/wiki/Hungarian_notation * char ch; * byte b; * flag f;/* flags that are always TRUE or FALSE*/ * symbol sym;/* some sort of symbol structure*/ * char byte flag symbol * *pch:/* character pointer*/ * *pb; * *pf: * *psym: * related link: [https://www.morling.dev/images/code_review_pyramid.svg](https://www.google.com/url?q=https%3A%2F%2Fwww.morling.dev%2Fimages%2Fcode_review_pyramid.svg&sa=D&sntz=1&usg=AOvVaw1y2It_PUzXAtFN7dPLegCa) * ## Shape Up: Stop Running in Circles and Ship Work that Matters * Six-week cycles: Our decisions are based on moving the product forward in the next six weeks, not micromanaging time. We don’t count hours or question how individual days are spent. We don’t have daily meetings. We don’t rethink our roadmap every two weeks. Our focus is at a higher level. We say to ourselves: “If this project ships after six weeks, we’ll be really happy. We’ll feel our time was well spent.” Then we commit the six weeks and leave the team alone to get it done * Shaping the work: key elements of a solution, appetite * **part one shaping:** * two separate tracks: one for shaping, one for building. * Steps to shaping * 1\. Set boundaries. * 2\. Rough out the elements: idea that solves the problem within the appetite but without all the fine details worked out * 3\. Address risks and rabbit holes. * 4\. Write the pitch. * fixed time, variable scope, * Where in the current system does the new thing fit? How do you get to it? What are the key components or interactions? Where does it take you? * five ingredients that we always want to include in a pitch: 1. Problem — The raw idea, a use case, or something we’ve seen that motivates us to work on this 2. Appetite — How much time we want to spend and how that constrains the solution 3. Solution — The core elements we came up with, presented in a form that’s easy for people to immediately understand 4. Rabbit holes — Details about the solution worth calling out to avoid problems 5. No-gos — Anything specifically excluded from the concept: functionality or use cases we intentionally aren’t covering to fit the appetite or make the problem tractable * **part two : Betting** * Some companies use two-week cycles (aka “sprints”). We learned that two weeks is too short to get anything meaningful done. Worse than that, two-week cycles are extremely costly due to the planning overhead. The amount of work you get out of two weeks isn’t worth the collective hours around the table to “sprint plan” or the opportunity cost of breaking everyone’s momentum to re-group. * after each six-week cycle, we schedule two weeks for cool-down. * bugs: Use cool-down. Bring it to the betting table. Schedule a bug smash. * We’ve noticed three phases of work when we build a new product from scratch. R&D mode (bet the time on spiking some key pieces of the new product idea. CEO and designer, we don’t expect to ship anything at the end of an R&D cycle) , Production mode (Shaping is deliberate again. bet multiple teams in parallel , Shipping is the goal) , Cleanup mode (There’s no shaping. There aren’t clear team boundaries. Work is “shipped”) [Cleanup shouldn’t last longer than two cycles] * Example: two years of development, R&D mode for the first year, a year of production mode cycles followed, two cycles of cleanup and significantly cut back the feature set, some overlap between R&D and production mode after that first year, * **Part three: Building** * We don’t start by assigning tasks to anyone. * we define and track the scopes as to-do lists. Each scope corresponds to a list name. Then any tasks for that scope go in that list. * **image 1, 2, 3,** * Mark nice-to-haves with ~ * First there’s the uphill phase of figuring out what our approach is and what we’re going to do. Then, once we can see all the work involved, there’s the downhill phase of execution * image 4, 5, * ## Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet * getting start: idea, technology, passion; What can I do well that I would love to do for an extended period of time? P: 19, 21 * 1 market segmentation: The single necessary and sufficient condition for a business is a paying customer. ; technological enthusiasts=early adopters=early majority ; P: 31, * 2 select a beachhead market: P: 44, * 3 build an end user profile: P: 52, * 4 Calculate the Total Addressable Market (TAM) Size for the Beachhead Market; $200 # [Essential Tips And Tricks For ](/book-summary/commonplace-book)[commonplace book](/book-summary/commonplace-book) # [knowledge management ](/book-summary/knowledge_management) # [PKM](/book-summary/knowledge_management/pkm) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/LMErpRo3SemuSDj3pLjSSh1xcteMOPvF9yzhoEb8_AB5vppySOpESQgqlBlCy2nymRjHv3i3BNkjBtED1LpjpWk=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/LMErpRo3SemuSDj3pLjSSh1xcteMOPvF9yzhoEb8_AB5vppySOpESQgqlBlCy2nymRjHv3i3BNkjBtED1LpjpWk=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Book Summary Books How to take smart Notes Writing Solid Code Shape Up: Stop Running in Circles and Ship Work that Matters Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet List of books I've read: Computer Vision: Algorithms and Applications Multiple View Geometry in Computer Vision Second Edition Complete to download the latest draft of Machine Learning Yearning Multiple view geometry in computer vision Learning OpenCV Book by Adrian Kaehler and Gary Bradski Digital Image Processing, Global Edition by Rafael C. Gonzalez Introduction to Algorithms, 3rd Edition (The MIT Press) The Mythical Man-Month: Essays on Software Engineering OpenCV-4-with-Python-Blueprints-Second-Edition Cracking the coding interview 189 programming questions and solutions Cracking the Tech Career: Insider Advice on Landing a Job at Google, Microsoft, Apple, Or Any Top Tech Company The Art of Startup Fundraising Mathematics for Machine Learning Principles of Economics (6th edition) The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) Reinventing Your Life: The Breakthough Program to End Negative Behavior Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf Magazine Papers Websites and links Tools YouTube - List channels Language: Essential Tips And Tricks For commonplace book knowledge management PKM # Books * ## How to take smart Notes * Introduction 4, The four underlying principles 4, the six steps to successful writing 6, afterword * introduction * write everything/ collect notes not related to any thing, organize notes, revirew, rewrite, * no reorganise no complex system, develop ideas by smart notes * 1 slip-box; 2 repeatable tasks that can become automatic and fit together seamlessly change routinges * overarching workflow "Getting Things Done" GTD * constantly have to ump back and forth between different tasks * 1- bibliographical slip-box: reference * 2- collected and generated ideas slip-box * 3- index: notes serve as entry point / thought/ topic * writing a paper step by step * 1 fleeting notes: it will delete within a day * I always have a slip of paper at hand, on which I note down the ideas of certain pages. on the backside I write down the bibliographic details. after finishing the book I go throught my notes and think how ehse notes might be relevant for already written notes in the slip-box. it means that I always read with an eye towards possible connections in the slip-box * 2 literature notes: contain the necessary information - in reference system or in the slip-box * I make a note with the bibliographic details. on the backside I would write "on page x is this, on page y is that" and then it goes into the bibliographic slib-box where I collect everything I read * read, think about its relevance * theoretical background, methodological approach or perspective of the text we read. * whether something adds to a discussion in the slip-box * we look at our slip-box for already existing lines of thought and think about the questions and problems already on our minds to which a new note might contribute. - assigning keywords is much more than just a bureaucratic act. it is a crucial part of the thinking process, which often leads to a deeper elaboration of the note itself and the connection to other notes. * * 3 permanent notes: develop ideas/new/ - only relevant to one particular project - in project specific folder - can be discarded or archived after project finished * add to slip-box behind the note you dirctyly refer to or behind the last note in the slip-box * add links to other notes or links on other notes to your new note * make sure it can be found from the index (MOC) add an entry in the index if necessary or refer to it from a note that is connected to the index * build a latticework of mental models * 4 behind multiple notes (link backward) - link to related note - link to MOC * make sure it can be found again * **keywords** can easily be added to a note like **tags** and will then show up in the **index** * a day * read and take notes * build connections within the slip-box - new idea * you write on your paper, notice a hole in the argument and have another look in the file system for the missing link. you follow up on a footnote, go back to research and might add a fitting quote to one of your papers in the making. * The four underlying principles * writing * simplicity * not from scratch * let the work carry you forward * the six steps to successful writing * separate and interlocking tasks * we use memo techniques is to bundle items together in a meaningful way and remember the bundles * the brain doesn't distinguish between an actual finished task and one that is postponed by taking a note * the fist step is to break down the amprphous task of "writing" into smaller pieces of different tasks that can be finished in **one go.** the second step is to make sure we always write down the outcome of our thinking, including possible connections to further inquiries. * ego depletion * read for understanding * take smart notes * develp ideas (MOC) * 1 overview of a topic - referred to from the index and used as an entry point into a topic has already develped * 2 keeping tarck of all topic - index - * share your insight * make it a habit * afterword * ## Writing Solid Code Writing Solid Code (Microsoft Programming Series) by Trendelles Store Note from Writing Solid Code Book [https://www.pirahansiah.com/describe/book- summary](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Fdescribe%2Fbook- summary&sa=D&sntz=1&usg=AOvVaw3ACzA6G0MBVN3nw6f-yMQo) #SolidCode #C++ #Python #pirahansiah * **Chapter 1** * Enable all optional compiler warnings. * Use lint to catch bugs that your compiler may miss. * If you have unit tests, use them. * **Chapter 2** * introduction assert page 16; assert.h * assert is nothing more than a repackaged form of the #ifdef code we saw before, but when you use * the macro, it takes one line instead of seven * That's why assert is a macro and not a function; if it were a function, calling * it could cause unexpected memory or code swapping. * Use assertions to validate function arguments . * Strip undefined behavior from your code, or use assertions to catch illegal uses of undefined behavior . * Don't waste people's time. Document unclear assertions . * Either remove implicit assumptions, or assert that they are valid . * Use assertions to detect impossible conditions. * Don't hide bugs when you program defensively. * Use a second algorithm to validate your results. * Don't wait for bugs to happen; use startup checks. * **Chapter 3** * Maintain shipping and debugging versions of your program. Shrink-wrap the shipping version, but use the debugging ver­sion as much as possible to catch bugs quickly. * Assertions are a shorthand way to write debugging checks. Use them to catch illegal conditions that should never arise. Don't confuse such conditions with error conditions, which you must handle in the final product. * Use assertions to validate function arguments and to alert pro­ grammars when they do something undefined. The more rigidly you define your functions, the easier it will be to validate the arguments. * Once you've written a function, review it and ask yourself, 'What am I assuming?" If you find an assumption, either assert that your assumption is always valid, or rewrite the code to re­ move the assumption. Also ask, 'What is most likely to be wrong in this code, and how can I automatically detect the prob­lem?" Strive to implement tests that catch bugs at the earliest possible moment. Textbooks encourage programmers to program defensively, but remember that this coding style hides bugs. When you write de­fensive code, use assertions to alert you if the "can't happen" cases do happen. * Eliminate random behavior. Force bugs to be reproducible . * Destroy your garbage so that it's not misused . * Not only did reallocate have to move the block of memory as it was expanding it, but the old memory had to be reallocated and filled with new data. In the assembler, both happened rarely. * This brings up another guideline for writing bug-free code: You don't want anything to happen rarely. You need to isolate those behaviors in your subsystems that may happen and make sure that they do happen. And of­ ten. If you find rare behavior in your subsystems, be sure to do something­ anything-to stir things up. * The assembler bug could have been found within hours, instead of years, if reallocate hadn't so rarely moved blocks when it expanded them. * If something happens rarely, force it to happen often. * Keep debug information to allow stronger error checking. * There is no question that the debug code will create differences be­ tween the ship and the debug versions of your code, but as long as you're careful not to change the underlying behavior of the code, those differences shouldn't matter. * Create thorough subsystem checks, and use them often. * Design your tests carefully. Nothing should be arbitrary. * Strive to implement transparent integrity checks. * Don't apply ship version constraints to the debug version. Trade size and speed for error detection. * **Chapter 4** * Don't wait until you have a bug to step through your code. * Step through every code path. * What About Sweeping Changes? In the past, programmers have asked, "What if I add a feature that touches code in many places? Stepping through all that new code is going to be time consuming." Let me answer that question with another: Can you make such sweeping changes without introducing any bugs? The habit of stepping through your code creates an interesting negative feedback loop. Programmers who step through their code soon learn to write small, easily testable functions because stepping through large func­tions is so painful. Programmers also spend more time thinking about how they can localize the changes they need to make-again, so that they can more easily test their code. And isn't this exactly what you want? No project lead likes it when programmers touch a lot of code; it's too destabilizing. Nor do leads like large, unmanageable functions; they're often unmaintainable. Try hard to localize your changes. If you find that you must make per­vasive changes, think twice before you decide not to step through all the new code. * Overflow and underflow bugs * Data conversion bugs * Off-by-one bugs * NULL pointer bugs * Bugs using garbage memory (0xA3 bugs) * Assignment bugs in which you've used = instead of == * Precedence bugs * Logic bugs * As you step through code, focus on data flow. Source level debuggers can hide execution details. Step through critical code at the instruction level . * **Chapter 5** * Make it hard to ignore error conditions. Don't bury error codes in return values. * Always look for, and eliminate, flaws in your interfaces. * Don't write multipurpose functions. Write separate functions to allow stronger argument validation. * Don't be wishy-washy. Define explicit function arguments. * Write functions that, given valid inputs, cannot fail. * Make the code intelligible at the point of call. Avoid Boolean arguments. * Write comments that emphasize potential hazards. * **Chapter 6** * Use well-defined data types. * Always ask ,"Can this variable or expression over- or underflow?" * Implement your designs as accurately as possible. Being kind a close is being kind a buggy. * these principles already: Don't accept special purpose arguments such as the NULL pointer, and Implement your design, not something that approximates it. The third principle is new: Strive to make every function perform its task exactly one time. * Implement "the task" just once. * Get rid of extraneous if statements. * Avoid using nested ?: operators . * Handle your special cases just once. * Avoid risky language idioms. * Don't needlessly mix operator types. If you must mix operators, use parentheses to isolate the operations. * Avoid calling functions that return errors. * **Chapter 7** * Don't reference memory that you don't own. * Don't reference memory that you have freed. * Don't use output memory as workspace buffers. * Don't pass data in static (or global) memory. * Don't write parasitic functions. * Don't abuse your programming language. * Tight C does not guarantee efficient machine code. * Write code for the "average" programmer. * **Chapter 8** * Bugs don't just "go away." * Don't fix bugs later; fix them now. * Fix the cause, not the symptom. * Don't clean up code unless the clean­ up is critical to the product's success. * Don't implement nonstrategic features. * There are no free features. * Don't allow unnecessary flexibility. * Don't keep "trying" solutions· until you find one that works. Take the time to find the correct solution. * Write and test code in small chunks. * Always test your code, even if that means your schedule will slip. * Don't rely on the testing group to find your bugs. * Don't blame testers for finding your bugs. * Establish your priorities and stick to them. * Never allow the same bug to bite you twice. * **A: CODING CHECKLISTS** * **B: MEMORY LOGGING ROUTINES** * **To sum up** * Charles Simonyi in the early 1970s. https://en.wikipedia.org/wiki/Hungarian_notation * char ch; * byte b; * flag f;/* flags that are always TRUE or FALSE*/ * symbol sym;/* some sort of symbol structure*/ * char byte flag symbol * *pch:/* character pointer*/ * *pb; * *pf: * *psym: * related link: [https://www.morling.dev/images/code_review_pyramid.svg](https://www.google.com/url?q=https%3A%2F%2Fwww.morling.dev%2Fimages%2Fcode_review_pyramid.svg&sa=D&sntz=1&usg=AOvVaw1y2It_PUzXAtFN7dPLegCa) * ## Shape Up: Stop Running in Circles and Ship Work that Matters * Six-week cycles: Our decisions are based on moving the product forward in the next six weeks, not micromanaging time. We don’t count hours or question how individual days are spent. We don’t have daily meetings. We don’t rethink our roadmap every two weeks. Our focus is at a higher level. We say to ourselves: “If this project ships after six weeks, we’ll be really happy. We’ll feel our time was well spent.” Then we commit the six weeks and leave the team alone to get it done * Shaping the work: key elements of a solution, appetite * **part one shaping:** * two separate tracks: one for shaping, one for building. * Steps to shaping * 1\. Set boundaries. * 2\. Rough out the elements: idea that solves the problem within the appetite but without all the fine details worked out * 3\. Address risks and rabbit holes. * 4\. Write the pitch. * fixed time, variable scope, * Where in the current system does the new thing fit? How do you get to it? What are the key components or interactions? Where does it take you? * five ingredients that we always want to include in a pitch: 1. Problem — The raw idea, a use case, or something we’ve seen that motivates us to work on this 2. Appetite — How much time we want to spend and how that constrains the solution 3. Solution — The core elements we came up with, presented in a form that’s easy for people to immediately understand 4. Rabbit holes — Details about the solution worth calling out to avoid problems 5. No-gos — Anything specifically excluded from the concept: functionality or use cases we intentionally aren’t covering to fit the appetite or make the problem tractable * **part two : Betting** * Some companies use two-week cycles (aka “sprints”). We learned that two weeks is too short to get anything meaningful done. Worse than that, two-week cycles are extremely costly due to the planning overhead. The amount of work you get out of two weeks isn’t worth the collective hours around the table to “sprint plan” or the opportunity cost of breaking everyone’s momentum to re-group. * after each six-week cycle, we schedule two weeks for cool-down. * bugs: Use cool-down. Bring it to the betting table. Schedule a bug smash. * We’ve noticed three phases of work when we build a new product from scratch. R&D mode (bet the time on spiking some key pieces of the new product idea. CEO and designer, we don’t expect to ship anything at the end of an R&D cycle) , Production mode (Shaping is deliberate again. bet multiple teams in parallel , Shipping is the goal) , Cleanup mode (There’s no shaping. There aren’t clear team boundaries. Work is “shipped”) [Cleanup shouldn’t last longer than two cycles] * Example: two years of development, R&D mode for the first year, a year of production mode cycles followed, two cycles of cleanup and significantly cut back the feature set, some overlap between R&D and production mode after that first year, * **Part three: Building** * We don’t start by assigning tasks to anyone. * we define and track the scopes as to-do lists. Each scope corresponds to a list name. Then any tasks for that scope go in that list. * **image 1, 2, 3,** * Mark nice-to-haves with ~ * First there’s the uphill phase of figuring out what our approach is and what we’re going to do. Then, once we can see all the work involved, there’s the downhill phase of execution * image 4, 5, * ## Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet * getting start: idea, technology, passion; What can I do well that I would love to do for an extended period of time? P: 19, 21 * 1 market segmentation: The single necessary and sufficient condition for a business is a paying customer. ; technological enthusiasts=early adopters=early majority ; P: 31, * 2 select a beachhead market: P: 44, * 3 build an end user profile: P: 52, * 4 Calculate the Total Addressable Market (TAM) Size for the Beachhead Market; $200 # [Essential Tips And Tricks For ](/book-summary/commonplace-book)[commonplace book](/book-summary/commonplace-book) # [knowledge management ](/book-summary/knowledge_management) # [PKM](/book-summary/knowledge_management/pkm) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/LMErpRo3SemuSDj3pLjSSh1xcteMOPvF9yzhoEb8_AB5vppySOpESQgqlBlCy2nymRjHv3i3BNkjBtED1LpjpWk=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/LMErpRo3SemuSDj3pLjSSh1xcteMOPvF9yzhoEb8_AB5vppySOpESQgqlBlCy2nymRjHv3i3BNkjBtED1LpjpWk=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Book Summary Books How to take smart Notes Writing Solid Code Shape Up: Stop Running in Circles and Ship Work that Matters Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet List of books I've read: Computer Vision: Algorithms and Applications Multiple View Geometry in Computer Vision Second Edition Complete to download the latest draft of Machine Learning Yearning Multiple view geometry in computer vision Learning OpenCV Book by Adrian Kaehler and Gary Bradski Digital Image Processing, Global Edition by Rafael C. Gonzalez Introduction to Algorithms, 3rd Edition (The MIT Press) The Mythical Man-Month: Essays on Software Engineering OpenCV-4-with-Python-Blueprints-Second-Edition Cracking the coding interview 189 programming questions and solutions Cracking the Tech Career: Insider Advice on Landing a Job at Google, Microsoft, Apple, Or Any Top Tech Company The Art of Startup Fundraising Mathematics for Machine Learning Principles of Economics (6th edition) The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) Reinventing Your Life: The Breakthough Program to End Negative Behavior Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf Magazine Papers Websites and links Tools YouTube - List channels Language: Essential Tips And Tricks For commonplace book knowledge management PKM # Books * ## How to take smart Notes * Introduction 4, The four underlying principles 4, the six steps to successful writing 6, afterword * introduction * write everything/ collect notes not related to any thing, organize notes, revirew, rewrite, * no reorganise no complex system, develop ideas by smart notes * 1 slip-box; 2 repeatable tasks that can become automatic and fit together seamlessly change routinges * overarching workflow "Getting Things Done" GTD * constantly have to ump back and forth between different tasks * 1- bibliographical slip-box: reference * 2- collected and generated ideas slip-box * 3- index: notes serve as entry point / thought/ topic * writing a paper step by step * 1 fleeting notes: it will delete within a day * I always have a slip of paper at hand, on which I note down the ideas of certain pages. on the backside I write down the bibliographic details. after finishing the book I go throught my notes and think how ehse notes might be relevant for already written notes in the slip-box. it means that I always read with an eye towards possible connections in the slip-box * 2 literature notes: contain the necessary information - in reference system or in the slip-box * I make a note with the bibliographic details. on the backside I would write "on page x is this, on page y is that" and then it goes into the bibliographic slib-box where I collect everything I read * read, think about its relevance * theoretical background, methodological approach or perspective of the text we read. * whether something adds to a discussion in the slip-box * we look at our slip-box for already existing lines of thought and think about the questions and problems already on our minds to which a new note might contribute. - assigning keywords is much more than just a bureaucratic act. it is a crucial part of the thinking process, which often leads to a deeper elaboration of the note itself and the connection to other notes. * * 3 permanent notes: develop ideas/new/ - only relevant to one particular project - in project specific folder - can be discarded or archived after project finished * add to slip-box behind the note you dirctyly refer to or behind the last note in the slip-box * add links to other notes or links on other notes to your new note * make sure it can be found from the index (MOC) add an entry in the index if necessary or refer to it from a note that is connected to the index * build a latticework of mental models * 4 behind multiple notes (link backward) - link to related note - link to MOC * make sure it can be found again * **keywords** can easily be added to a note like **tags** and will then show up in the **index** * a day * read and take notes * build connections within the slip-box - new idea * you write on your paper, notice a hole in the argument and have another look in the file system for the missing link. you follow up on a footnote, go back to research and might add a fitting quote to one of your papers in the making. * The four underlying principles * writing * simplicity * not from scratch * let the work carry you forward * the six steps to successful writing * separate and interlocking tasks * we use memo techniques is to bundle items together in a meaningful way and remember the bundles * the brain doesn't distinguish between an actual finished task and one that is postponed by taking a note * the fist step is to break down the amprphous task of "writing" into smaller pieces of different tasks that can be finished in **one go.** the second step is to make sure we always write down the outcome of our thinking, including possible connections to further inquiries. * ego depletion * read for understanding * take smart notes * develp ideas (MOC) * 1 overview of a topic - referred to from the index and used as an entry point into a topic has already develped * 2 keeping tarck of all topic - index - * share your insight * make it a habit * afterword * ## Writing Solid Code Writing Solid Code (Microsoft Programming Series) by Trendelles Store Note from Writing Solid Code Book [https://www.pirahansiah.com/describe/book- summary](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Fdescribe%2Fbook- summary&sa=D&sntz=1&usg=AOvVaw3ACzA6G0MBVN3nw6f-yMQo) #SolidCode #C++ #Python #pirahansiah * **Chapter 1** * Enable all optional compiler warnings. * Use lint to catch bugs that your compiler may miss. * If you have unit tests, use them. * **Chapter 2** * introduction assert page 16; assert.h * assert is nothing more than a repackaged form of the #ifdef code we saw before, but when you use * the macro, it takes one line instead of seven * That's why assert is a macro and not a function; if it were a function, calling * it could cause unexpected memory or code swapping. * Use assertions to validate function arguments . * Strip undefined behavior from your code, or use assertions to catch illegal uses of undefined behavior . * Don't waste people's time. Document unclear assertions . * Either remove implicit assumptions, or assert that they are valid . * Use assertions to detect impossible conditions. * Don't hide bugs when you program defensively. * Use a second algorithm to validate your results. * Don't wait for bugs to happen; use startup checks. * **Chapter 3** * Maintain shipping and debugging versions of your program. Shrink-wrap the shipping version, but use the debugging ver­sion as much as possible to catch bugs quickly. * Assertions are a shorthand way to write debugging checks. Use them to catch illegal conditions that should never arise. Don't confuse such conditions with error conditions, which you must handle in the final product. * Use assertions to validate function arguments and to alert pro­ grammars when they do something undefined. The more rigidly you define your functions, the easier it will be to validate the arguments. * Once you've written a function, review it and ask yourself, 'What am I assuming?" If you find an assumption, either assert that your assumption is always valid, or rewrite the code to re­ move the assumption. Also ask, 'What is most likely to be wrong in this code, and how can I automatically detect the prob­lem?" Strive to implement tests that catch bugs at the earliest possible moment. Textbooks encourage programmers to program defensively, but remember that this coding style hides bugs. When you write de­fensive code, use assertions to alert you if the "can't happen" cases do happen. * Eliminate random behavior. Force bugs to be reproducible . * Destroy your garbage so that it's not misused . * Not only did reallocate have to move the block of memory as it was expanding it, but the old memory had to be reallocated and filled with new data. In the assembler, both happened rarely. * This brings up another guideline for writing bug-free code: You don't want anything to happen rarely. You need to isolate those behaviors in your subsystems that may happen and make sure that they do happen. And of­ ten. If you find rare behavior in your subsystems, be sure to do something­ anything-to stir things up. * The assembler bug could have been found within hours, instead of years, if reallocate hadn't so rarely moved blocks when it expanded them. * If something happens rarely, force it to happen often. * Keep debug information to allow stronger error checking. * There is no question that the debug code will create differences be­ tween the ship and the debug versions of your code, but as long as you're careful not to change the underlying behavior of the code, those differences shouldn't matter. * Create thorough subsystem checks, and use them often. * Design your tests carefully. Nothing should be arbitrary. * Strive to implement transparent integrity checks. * Don't apply ship version constraints to the debug version. Trade size and speed for error detection. * **Chapter 4** * Don't wait until you have a bug to step through your code. * Step through every code path. * What About Sweeping Changes? In the past, programmers have asked, "What if I add a feature that touches code in many places? Stepping through all that new code is going to be time consuming." Let me answer that question with another: Can you make such sweeping changes without introducing any bugs? The habit of stepping through your code creates an interesting negative feedback loop. Programmers who step through their code soon learn to write small, easily testable functions because stepping through large func­tions is so painful. Programmers also spend more time thinking about how they can localize the changes they need to make-again, so that they can more easily test their code. And isn't this exactly what you want? No project lead likes it when programmers touch a lot of code; it's too destabilizing. Nor do leads like large, unmanageable functions; they're often unmaintainable. Try hard to localize your changes. If you find that you must make per­vasive changes, think twice before you decide not to step through all the new code. * Overflow and underflow bugs * Data conversion bugs * Off-by-one bugs * NULL pointer bugs * Bugs using garbage memory (0xA3 bugs) * Assignment bugs in which you've used = instead of == * Precedence bugs * Logic bugs * As you step through code, focus on data flow. Source level debuggers can hide execution details. Step through critical code at the instruction level . * **Chapter 5** * Make it hard to ignore error conditions. Don't bury error codes in return values. * Always look for, and eliminate, flaws in your interfaces. * Don't write multipurpose functions. Write separate functions to allow stronger argument validation. * Don't be wishy-washy. Define explicit function arguments. * Write functions that, given valid inputs, cannot fail. * Make the code intelligible at the point of call. Avoid Boolean arguments. * Write comments that emphasize potential hazards. * **Chapter 6** * Use well-defined data types. * Always ask ,"Can this variable or expression over- or underflow?" * Implement your designs as accurately as possible. Being kind a close is being kind a buggy. * these principles already: Don't accept special purpose arguments such as the NULL pointer, and Implement your design, not something that approximates it. The third principle is new: Strive to make every function perform its task exactly one time. * Implement "the task" just once. * Get rid of extraneous if statements. * Avoid using nested ?: operators . * Handle your special cases just once. * Avoid risky language idioms. * Don't needlessly mix operator types. If you must mix operators, use parentheses to isolate the operations. * Avoid calling functions that return errors. * **Chapter 7** * Don't reference memory that you don't own. * Don't reference memory that you have freed. * Don't use output memory as workspace buffers. * Don't pass data in static (or global) memory. * Don't write parasitic functions. * Don't abuse your programming language. * Tight C does not guarantee efficient machine code. * Write code for the "average" programmer. * **Chapter 8** * Bugs don't just "go away." * Don't fix bugs later; fix them now. * Fix the cause, not the symptom. * Don't clean up code unless the clean­ up is critical to the product's success. * Don't implement nonstrategic features. * There are no free features. * Don't allow unnecessary flexibility. * Don't keep "trying" solutions· until you find one that works. Take the time to find the correct solution. * Write and test code in small chunks. * Always test your code, even if that means your schedule will slip. * Don't rely on the testing group to find your bugs. * Don't blame testers for finding your bugs. * Establish your priorities and stick to them. * Never allow the same bug to bite you twice. * **A: CODING CHECKLISTS** * **B: MEMORY LOGGING ROUTINES** * **To sum up** * Charles Simonyi in the early 1970s. https://en.wikipedia.org/wiki/Hungarian_notation * char ch; * byte b; * flag f;/* flags that are always TRUE or FALSE*/ * symbol sym;/* some sort of symbol structure*/ * char byte flag symbol * *pch:/* character pointer*/ * *pb; * *pf: * *psym: * related link: [https://www.morling.dev/images/code_review_pyramid.svg](https://www.google.com/url?q=https%3A%2F%2Fwww.morling.dev%2Fimages%2Fcode_review_pyramid.svg&sa=D&sntz=1&usg=AOvVaw1y2It_PUzXAtFN7dPLegCa) * ## Shape Up: Stop Running in Circles and Ship Work that Matters * Six-week cycles: Our decisions are based on moving the product forward in the next six weeks, not micromanaging time. We don’t count hours or question how individual days are spent. We don’t have daily meetings. We don’t rethink our roadmap every two weeks. Our focus is at a higher level. We say to ourselves: “If this project ships after six weeks, we’ll be really happy. We’ll feel our time was well spent.” Then we commit the six weeks and leave the team alone to get it done * Shaping the work: key elements of a solution, appetite * **part one shaping:** * two separate tracks: one for shaping, one for building. * Steps to shaping * 1\. Set boundaries. * 2\. Rough out the elements: idea that solves the problem within the appetite but without all the fine details worked out * 3\. Address risks and rabbit holes. * 4\. Write the pitch. * fixed time, variable scope, * Where in the current system does the new thing fit? How do you get to it? What are the key components or interactions? Where does it take you? * five ingredients that we always want to include in a pitch: 1. Problem — The raw idea, a use case, or something we’ve seen that motivates us to work on this 2. Appetite — How much time we want to spend and how that constrains the solution 3. Solution — The core elements we came up with, presented in a form that’s easy for people to immediately understand 4. Rabbit holes — Details about the solution worth calling out to avoid problems 5. No-gos — Anything specifically excluded from the concept: functionality or use cases we intentionally aren’t covering to fit the appetite or make the problem tractable * **part two : Betting** * Some companies use two-week cycles (aka “sprints”). We learned that two weeks is too short to get anything meaningful done. Worse than that, two-week cycles are extremely costly due to the planning overhead. The amount of work you get out of two weeks isn’t worth the collective hours around the table to “sprint plan” or the opportunity cost of breaking everyone’s momentum to re-group. * after each six-week cycle, we schedule two weeks for cool-down. * bugs: Use cool-down. Bring it to the betting table. Schedule a bug smash. * We’ve noticed three phases of work when we build a new product from scratch. R&D mode (bet the time on spiking some key pieces of the new product idea. CEO and designer, we don’t expect to ship anything at the end of an R&D cycle) , Production mode (Shaping is deliberate again. bet multiple teams in parallel , Shipping is the goal) , Cleanup mode (There’s no shaping. There aren’t clear team boundaries. Work is “shipped”) [Cleanup shouldn’t last longer than two cycles] * Example: two years of development, R&D mode for the first year, a year of production mode cycles followed, two cycles of cleanup and significantly cut back the feature set, some overlap between R&D and production mode after that first year, * **Part three: Building** * We don’t start by assigning tasks to anyone. * we define and track the scopes as to-do lists. Each scope corresponds to a list name. Then any tasks for that scope go in that list. * **image 1, 2, 3,** * Mark nice-to-haves with ~ * First there’s the uphill phase of figuring out what our approach is and what we’re going to do. Then, once we can see all the work involved, there’s the downhill phase of execution * image 4, 5, * ## Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet * getting start: idea, technology, passion; What can I do well that I would love to do for an extended period of time? P: 19, 21 * 1 market segmentation: The single necessary and sufficient condition for a business is a paying customer. ; technological enthusiasts=early adopters=early majority ; P: 31, * 2 select a beachhead market: P: 44, * 3 build an end user profile: P: 52, * 4 Calculate the Total Addressable Market (TAM) Size for the Beachhead Market; $200 # [Essential Tips And Tricks For ](/book-summary/commonplace-book)[commonplace book](/book-summary/commonplace-book) # [knowledge management ](/book-summary/knowledge_management) # [PKM](/book-summary/knowledge_management/pkm) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/LMErpRo3SemuSDj3pLjSSh1xcteMOPvF9yzhoEb8_AB5vppySOpESQgqlBlCy2nymRjHv3i3BNkjBtED1LpjpWk=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/LMErpRo3SemuSDj3pLjSSh1xcteMOPvF9yzhoEb8_AB5vppySOpESQgqlBlCy2nymRjHv3i3BNkjBtED1LpjpWk=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Book Summary Books How to take smart Notes Writing Solid Code Shape Up: Stop Running in Circles and Ship Work that Matters Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet List of books I've read: Computer Vision: Algorithms and Applications Multiple View Geometry in Computer Vision Second Edition Complete to download the latest draft of Machine Learning Yearning Multiple view geometry in computer vision Learning OpenCV Book by Adrian Kaehler and Gary Bradski Digital Image Processing, Global Edition by Rafael C. Gonzalez Introduction to Algorithms, 3rd Edition (The MIT Press) The Mythical Man-Month: Essays on Software Engineering OpenCV-4-with-Python-Blueprints-Second-Edition Cracking the coding interview 189 programming questions and solutions Cracking the Tech Career: Insider Advice on Landing a Job at Google, Microsoft, Apple, Or Any Top Tech Company The Art of Startup Fundraising Mathematics for Machine Learning Principles of Economics (6th edition) The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) Reinventing Your Life: The Breakthough Program to End Negative Behavior Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf Magazine Papers Websites and links Tools YouTube - List channels Language: Essential Tips And Tricks For commonplace book knowledge management PKM # Books * ## How to take smart Notes * Introduction 4, The four underlying principles 4, the six steps to successful writing 6, afterword * introduction * write everything/ collect notes not related to any thing, organize notes, revirew, rewrite, * no reorganise no complex system, develop ideas by smart notes * 1 slip-box; 2 repeatable tasks that can become automatic and fit together seamlessly change routinges * overarching workflow "Getting Things Done" GTD * constantly have to ump back and forth between different tasks * 1- bibliographical slip-box: reference * 2- collected and generated ideas slip-box * 3- index: notes serve as entry point / thought/ topic * writing a paper step by step * 1 fleeting notes: it will delete within a day * I always have a slip of paper at hand, on which I note down the ideas of certain pages. on the backside I write down the bibliographic details. after finishing the book I go throught my notes and think how ehse notes might be relevant for already written notes in the slip-box. it means that I always read with an eye towards possible connections in the slip-box * 2 literature notes: contain the necessary information - in reference system or in the slip-box * I make a note with the bibliographic details. on the backside I would write "on page x is this, on page y is that" and then it goes into the bibliographic slib-box where I collect everything I read * read, think about its relevance * theoretical background, methodological approach or perspective of the text we read. * whether something adds to a discussion in the slip-box * we look at our slip-box for already existing lines of thought and think about the questions and problems already on our minds to which a new note might contribute. - assigning keywords is much more than just a bureaucratic act. it is a crucial part of the thinking process, which often leads to a deeper elaboration of the note itself and the connection to other notes. * * 3 permanent notes: develop ideas/new/ - only relevant to one particular project - in project specific folder - can be discarded or archived after project finished * add to slip-box behind the note you dirctyly refer to or behind the last note in the slip-box * add links to other notes or links on other notes to your new note * make sure it can be found from the index (MOC) add an entry in the index if necessary or refer to it from a note that is connected to the index * build a latticework of mental models * 4 behind multiple notes (link backward) - link to related note - link to MOC * make sure it can be found again * **keywords** can easily be added to a note like **tags** and will then show up in the **index** * a day * read and take notes * build connections within the slip-box - new idea * you write on your paper, notice a hole in the argument and have another look in the file system for the missing link. you follow up on a footnote, go back to research and might add a fitting quote to one of your papers in the making. * The four underlying principles * writing * simplicity * not from scratch * let the work carry you forward * the six steps to successful writing * separate and interlocking tasks * we use memo techniques is to bundle items together in a meaningful way and remember the bundles * the brain doesn't distinguish between an actual finished task and one that is postponed by taking a note * the fist step is to break down the amprphous task of "writing" into smaller pieces of different tasks that can be finished in **one go.** the second step is to make sure we always write down the outcome of our thinking, including possible connections to further inquiries. * ego depletion * read for understanding * take smart notes * develp ideas (MOC) * 1 overview of a topic - referred to from the index and used as an entry point into a topic has already develped * 2 keeping tarck of all topic - index - * share your insight * make it a habit * afterword * ## Writing Solid Code Writing Solid Code (Microsoft Programming Series) by Trendelles Store Note from Writing Solid Code Book [https://www.pirahansiah.com/describe/book- summary](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Fdescribe%2Fbook- summary&sa=D&sntz=1&usg=AOvVaw3ACzA6G0MBVN3nw6f-yMQo) #SolidCode #C++ #Python #pirahansiah * **Chapter 1** * Enable all optional compiler warnings. * Use lint to catch bugs that your compiler may miss. * If you have unit tests, use them. * **Chapter 2** * introduction assert page 16; assert.h * assert is nothing more than a repackaged form of the #ifdef code we saw before, but when you use * the macro, it takes one line instead of seven * That's why assert is a macro and not a function; if it were a function, calling * it could cause unexpected memory or code swapping. * Use assertions to validate function arguments . * Strip undefined behavior from your code, or use assertions to catch illegal uses of undefined behavior . * Don't waste people's time. Document unclear assertions . * Either remove implicit assumptions, or assert that they are valid . * Use assertions to detect impossible conditions. * Don't hide bugs when you program defensively. * Use a second algorithm to validate your results. * Don't wait for bugs to happen; use startup checks. * **Chapter 3** * Maintain shipping and debugging versions of your program. Shrink-wrap the shipping version, but use the debugging ver­sion as much as possible to catch bugs quickly. * Assertions are a shorthand way to write debugging checks. Use them to catch illegal conditions that should never arise. Don't confuse such conditions with error conditions, which you must handle in the final product. * Use assertions to validate function arguments and to alert pro­ grammars when they do something undefined. The more rigidly you define your functions, the easier it will be to validate the arguments. * Once you've written a function, review it and ask yourself, 'What am I assuming?" If you find an assumption, either assert that your assumption is always valid, or rewrite the code to re­ move the assumption. Also ask, 'What is most likely to be wrong in this code, and how can I automatically detect the prob­lem?" Strive to implement tests that catch bugs at the earliest possible moment. Textbooks encourage programmers to program defensively, but remember that this coding style hides bugs. When you write de­fensive code, use assertions to alert you if the "can't happen" cases do happen. * Eliminate random behavior. Force bugs to be reproducible . * Destroy your garbage so that it's not misused . * Not only did reallocate have to move the block of memory as it was expanding it, but the old memory had to be reallocated and filled with new data. In the assembler, both happened rarely. * This brings up another guideline for writing bug-free code: You don't want anything to happen rarely. You need to isolate those behaviors in your subsystems that may happen and make sure that they do happen. And of­ ten. If you find rare behavior in your subsystems, be sure to do something­ anything-to stir things up. * The assembler bug could have been found within hours, instead of years, if reallocate hadn't so rarely moved blocks when it expanded them. * If something happens rarely, force it to happen often. * Keep debug information to allow stronger error checking. * There is no question that the debug code will create differences be­ tween the ship and the debug versions of your code, but as long as you're careful not to change the underlying behavior of the code, those differences shouldn't matter. * Create thorough subsystem checks, and use them often. * Design your tests carefully. Nothing should be arbitrary. * Strive to implement transparent integrity checks. * Don't apply ship version constraints to the debug version. Trade size and speed for error detection. * **Chapter 4** * Don't wait until you have a bug to step through your code. * Step through every code path. * What About Sweeping Changes? In the past, programmers have asked, "What if I add a feature that touches code in many places? Stepping through all that new code is going to be time consuming." Let me answer that question with another: Can you make such sweeping changes without introducing any bugs? The habit of stepping through your code creates an interesting negative feedback loop. Programmers who step through their code soon learn to write small, easily testable functions because stepping through large func­tions is so painful. Programmers also spend more time thinking about how they can localize the changes they need to make-again, so that they can more easily test their code. And isn't this exactly what you want? No project lead likes it when programmers touch a lot of code; it's too destabilizing. Nor do leads like large, unmanageable functions; they're often unmaintainable. Try hard to localize your changes. If you find that you must make per­vasive changes, think twice before you decide not to step through all the new code. * Overflow and underflow bugs * Data conversion bugs * Off-by-one bugs * NULL pointer bugs * Bugs using garbage memory (0xA3 bugs) * Assignment bugs in which you've used = instead of == * Precedence bugs * Logic bugs * As you step through code, focus on data flow. Source level debuggers can hide execution details. Step through critical code at the instruction level . * **Chapter 5** * Make it hard to ignore error conditions. Don't bury error codes in return values. * Always look for, and eliminate, flaws in your interfaces. * Don't write multipurpose functions. Write separate functions to allow stronger argument validation. * Don't be wishy-washy. Define explicit function arguments. * Write functions that, given valid inputs, cannot fail. * Make the code intelligible at the point of call. Avoid Boolean arguments. * Write comments that emphasize potential hazards. * **Chapter 6** * Use well-defined data types. * Always ask ,"Can this variable or expression over- or underflow?" * Implement your designs as accurately as possible. Being kind a close is being kind a buggy. * these principles already: Don't accept special purpose arguments such as the NULL pointer, and Implement your design, not something that approximates it. The third principle is new: Strive to make every function perform its task exactly one time. * Implement "the task" just once. * Get rid of extraneous if statements. * Avoid using nested ?: operators . * Handle your special cases just once. * Avoid risky language idioms. * Don't needlessly mix operator types. If you must mix operators, use parentheses to isolate the operations. * Avoid calling functions that return errors. * **Chapter 7** * Don't reference memory that you don't own. * Don't reference memory that you have freed. * Don't use output memory as workspace buffers. * Don't pass data in static (or global) memory. * Don't write parasitic functions. * Don't abuse your programming language. * Tight C does not guarantee efficient machine code. * Write code for the "average" programmer. * **Chapter 8** * Bugs don't just "go away." * Don't fix bugs later; fix them now. * Fix the cause, not the symptom. * Don't clean up code unless the clean­ up is critical to the product's success. * Don't implement nonstrategic features. * There are no free features. * Don't allow unnecessary flexibility. * Don't keep "trying" solutions· until you find one that works. Take the time to find the correct solution. * Write and test code in small chunks. * Always test your code, even if that means your schedule will slip. * Don't rely on the testing group to find your bugs. * Don't blame testers for finding your bugs. * Establish your priorities and stick to them. * Never allow the same bug to bite you twice. * **A: CODING CHECKLISTS** * **B: MEMORY LOGGING ROUTINES** * **To sum up** * Charles Simonyi in the early 1970s. https://en.wikipedia.org/wiki/Hungarian_notation * char ch; * byte b; * flag f;/* flags that are always TRUE or FALSE*/ * symbol sym;/* some sort of symbol structure*/ * char byte flag symbol * *pch:/* character pointer*/ * *pb; * *pf: * *psym: * related link: [https://www.morling.dev/images/code_review_pyramid.svg](https://www.google.com/url?q=https%3A%2F%2Fwww.morling.dev%2Fimages%2Fcode_review_pyramid.svg&sa=D&sntz=1&usg=AOvVaw1y2It_PUzXAtFN7dPLegCa) * ## Shape Up: Stop Running in Circles and Ship Work that Matters * Six-week cycles: Our decisions are based on moving the product forward in the next six weeks, not micromanaging time. We don’t count hours or question how individual days are spent. We don’t have daily meetings. We don’t rethink our roadmap every two weeks. Our focus is at a higher level. We say to ourselves: “If this project ships after six weeks, we’ll be really happy. We’ll feel our time was well spent.” Then we commit the six weeks and leave the team alone to get it done * Shaping the work: key elements of a solution, appetite * **part one shaping:** * two separate tracks: one for shaping, one for building. * Steps to shaping * 1\. Set boundaries. * 2\. Rough out the elements: idea that solves the problem within the appetite but without all the fine details worked out * 3\. Address risks and rabbit holes. * 4\. Write the pitch. * fixed time, variable scope, * Where in the current system does the new thing fit? How do you get to it? What are the key components or interactions? Where does it take you? * five ingredients that we always want to include in a pitch: 1. Problem — The raw idea, a use case, or something we’ve seen that motivates us to work on this 2. Appetite — How much time we want to spend and how that constrains the solution 3. Solution — The core elements we came up with, presented in a form that’s easy for people to immediately understand 4. Rabbit holes — Details about the solution worth calling out to avoid problems 5. No-gos — Anything specifically excluded from the concept: functionality or use cases we intentionally aren’t covering to fit the appetite or make the problem tractable * **part two : Betting** * Some companies use two-week cycles (aka “sprints”). We learned that two weeks is too short to get anything meaningful done. Worse than that, two-week cycles are extremely costly due to the planning overhead. The amount of work you get out of two weeks isn’t worth the collective hours around the table to “sprint plan” or the opportunity cost of breaking everyone’s momentum to re-group. * after each six-week cycle, we schedule two weeks for cool-down. * bugs: Use cool-down. Bring it to the betting table. Schedule a bug smash. * We’ve noticed three phases of work when we build a new product from scratch. R&D mode (bet the time on spiking some key pieces of the new product idea. CEO and designer, we don’t expect to ship anything at the end of an R&D cycle) , Production mode (Shaping is deliberate again. bet multiple teams in parallel , Shipping is the goal) , Cleanup mode (There’s no shaping. There aren’t clear team boundaries. Work is “shipped”) [Cleanup shouldn’t last longer than two cycles] * Example: two years of development, R&D mode for the first year, a year of production mode cycles followed, two cycles of cleanup and significantly cut back the feature set, some overlap between R&D and production mode after that first year, * **Part three: Building** * We don’t start by assigning tasks to anyone. * we define and track the scopes as to-do lists. Each scope corresponds to a list name. Then any tasks for that scope go in that list. * **image 1, 2, 3,** * Mark nice-to-haves with ~ * First there’s the uphill phase of figuring out what our approach is and what we’re going to do. Then, once we can see all the work involved, there’s the downhill phase of execution * image 4, 5, * ## Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet * getting start: idea, technology, passion; What can I do well that I would love to do for an extended period of time? P: 19, 21 * 1 market segmentation: The single necessary and sufficient condition for a business is a paying customer. ; technological enthusiasts=early adopters=early majority ; P: 31, * 2 select a beachhead market: P: 44, * 3 build an end user profile: P: 52, * 4 Calculate the Total Addressable Market (TAM) Size for the Beachhead Market; $200 # [Essential Tips And Tricks For ](/book-summary/commonplace-book)[commonplace book](/book-summary/commonplace-book) # [knowledge management ](/book-summary/knowledge_management) # [PKM](/book-summary/knowledge_management/pkm) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. 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Gonzalez Introduction to Algorithms, 3rd Edition (The MIT Press) The Mythical Man-Month: Essays on Software Engineering OpenCV-4-with-Python-Blueprints-Second-Edition Cracking the coding interview 189 programming questions and solutions Cracking the Tech Career: Insider Advice on Landing a Job at Google, Microsoft, Apple, Or Any Top Tech Company The Art of Startup Fundraising Mathematics for Machine Learning Principles of Economics (6th edition) The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) Reinventing Your Life: The Breakthough Program to End Negative Behavior Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf Magazine Papers Websites and links Tools YouTube - List channels Language: Essential Tips And Tricks For commonplace book knowledge management PKM # Books * ## How to take smart Notes * Introduction 4, The four underlying principles 4, the six steps to successful writing 6, afterword * introduction * write everything/ collect notes not related to any thing, organize notes, revirew, rewrite, * no reorganise no complex system, develop ideas by smart notes * 1 slip-box; 2 repeatable tasks that can become automatic and fit together seamlessly change routinges * overarching workflow "Getting Things Done" GTD * constantly have to ump back and forth between different tasks * 1- bibliographical slip-box: reference * 2- collected and generated ideas slip-box * 3- index: notes serve as entry point / thought/ topic * writing a paper step by step * 1 fleeting notes: it will delete within a day * I always have a slip of paper at hand, on which I note down the ideas of certain pages. on the backside I write down the bibliographic details. after finishing the book I go throught my notes and think how ehse notes might be relevant for already written notes in the slip-box. it means that I always read with an eye towards possible connections in the slip-box * 2 literature notes: contain the necessary information - in reference system or in the slip-box * I make a note with the bibliographic details. on the backside I would write "on page x is this, on page y is that" and then it goes into the bibliographic slib-box where I collect everything I read * read, think about its relevance * theoretical background, methodological approach or perspective of the text we read. * whether something adds to a discussion in the slip-box * we look at our slip-box for already existing lines of thought and think about the questions and problems already on our minds to which a new note might contribute. - assigning keywords is much more than just a bureaucratic act. it is a crucial part of the thinking process, which often leads to a deeper elaboration of the note itself and the connection to other notes. * * 3 permanent notes: develop ideas/new/ - only relevant to one particular project - in project specific folder - can be discarded or archived after project finished * add to slip-box behind the note you dirctyly refer to or behind the last note in the slip-box * add links to other notes or links on other notes to your new note * make sure it can be found from the index (MOC) add an entry in the index if necessary or refer to it from a note that is connected to the index * build a latticework of mental models * 4 behind multiple notes (link backward) - link to related note - link to MOC * make sure it can be found again * **keywords** can easily be added to a note like **tags** and will then show up in the **index** * a day * read and take notes * build connections within the slip-box - new idea * you write on your paper, notice a hole in the argument and have another look in the file system for the missing link. you follow up on a footnote, go back to research and might add a fitting quote to one of your papers in the making. * The four underlying principles * writing * simplicity * not from scratch * let the work carry you forward * the six steps to successful writing * separate and interlocking tasks * we use memo techniques is to bundle items together in a meaningful way and remember the bundles * the brain doesn't distinguish between an actual finished task and one that is postponed by taking a note * the fist step is to break down the amprphous task of "writing" into smaller pieces of different tasks that can be finished in **one go.** the second step is to make sure we always write down the outcome of our thinking, including possible connections to further inquiries. * ego depletion * read for understanding * take smart notes * develp ideas (MOC) * 1 overview of a topic - referred to from the index and used as an entry point into a topic has already develped * 2 keeping tarck of all topic - index - * share your insight * make it a habit * afterword * ## Writing Solid Code Writing Solid Code (Microsoft Programming Series) by Trendelles Store Note from Writing Solid Code Book [https://www.pirahansiah.com/describe/book- summary](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Fdescribe%2Fbook- summary&sa=D&sntz=1&usg=AOvVaw3ACzA6G0MBVN3nw6f-yMQo) #SolidCode #C++ #Python #pirahansiah * **Chapter 1** * Enable all optional compiler warnings. * Use lint to catch bugs that your compiler may miss. * If you have unit tests, use them. * **Chapter 2** * introduction assert page 16; assert.h * assert is nothing more than a repackaged form of the #ifdef code we saw before, but when you use * the macro, it takes one line instead of seven * That's why assert is a macro and not a function; if it were a function, calling * it could cause unexpected memory or code swapping. * Use assertions to validate function arguments . * Strip undefined behavior from your code, or use assertions to catch illegal uses of undefined behavior . * Don't waste people's time. Document unclear assertions . * Either remove implicit assumptions, or assert that they are valid . * Use assertions to detect impossible conditions. * Don't hide bugs when you program defensively. * Use a second algorithm to validate your results. * Don't wait for bugs to happen; use startup checks. * **Chapter 3** * Maintain shipping and debugging versions of your program. Shrink-wrap the shipping version, but use the debugging ver­sion as much as possible to catch bugs quickly. * Assertions are a shorthand way to write debugging checks. Use them to catch illegal conditions that should never arise. Don't confuse such conditions with error conditions, which you must handle in the final product. * Use assertions to validate function arguments and to alert pro­ grammars when they do something undefined. The more rigidly you define your functions, the easier it will be to validate the arguments. * Once you've written a function, review it and ask yourself, 'What am I assuming?" If you find an assumption, either assert that your assumption is always valid, or rewrite the code to re­ move the assumption. Also ask, 'What is most likely to be wrong in this code, and how can I automatically detect the prob­lem?" Strive to implement tests that catch bugs at the earliest possible moment. Textbooks encourage programmers to program defensively, but remember that this coding style hides bugs. When you write de­fensive code, use assertions to alert you if the "can't happen" cases do happen. * Eliminate random behavior. Force bugs to be reproducible . * Destroy your garbage so that it's not misused . * Not only did reallocate have to move the block of memory as it was expanding it, but the old memory had to be reallocated and filled with new data. In the assembler, both happened rarely. * This brings up another guideline for writing bug-free code: You don't want anything to happen rarely. You need to isolate those behaviors in your subsystems that may happen and make sure that they do happen. And of­ ten. If you find rare behavior in your subsystems, be sure to do something­ anything-to stir things up. * The assembler bug could have been found within hours, instead of years, if reallocate hadn't so rarely moved blocks when it expanded them. * If something happens rarely, force it to happen often. * Keep debug information to allow stronger error checking. * There is no question that the debug code will create differences be­ tween the ship and the debug versions of your code, but as long as you're careful not to change the underlying behavior of the code, those differences shouldn't matter. * Create thorough subsystem checks, and use them often. * Design your tests carefully. Nothing should be arbitrary. * Strive to implement transparent integrity checks. * Don't apply ship version constraints to the debug version. Trade size and speed for error detection. * **Chapter 4** * Don't wait until you have a bug to step through your code. * Step through every code path. * What About Sweeping Changes? In the past, programmers have asked, "What if I add a feature that touches code in many places? Stepping through all that new code is going to be time consuming." Let me answer that question with another: Can you make such sweeping changes without introducing any bugs? The habit of stepping through your code creates an interesting negative feedback loop. Programmers who step through their code soon learn to write small, easily testable functions because stepping through large func­tions is so painful. Programmers also spend more time thinking about how they can localize the changes they need to make-again, so that they can more easily test their code. And isn't this exactly what you want? No project lead likes it when programmers touch a lot of code; it's too destabilizing. Nor do leads like large, unmanageable functions; they're often unmaintainable. Try hard to localize your changes. If you find that you must make per­vasive changes, think twice before you decide not to step through all the new code. * Overflow and underflow bugs * Data conversion bugs * Off-by-one bugs * NULL pointer bugs * Bugs using garbage memory (0xA3 bugs) * Assignment bugs in which you've used = instead of == * Precedence bugs * Logic bugs * As you step through code, focus on data flow. Source level debuggers can hide execution details. Step through critical code at the instruction level . * **Chapter 5** * Make it hard to ignore error conditions. Don't bury error codes in return values. * Always look for, and eliminate, flaws in your interfaces. * Don't write multipurpose functions. Write separate functions to allow stronger argument validation. * Don't be wishy-washy. Define explicit function arguments. * Write functions that, given valid inputs, cannot fail. * Make the code intelligible at the point of call. Avoid Boolean arguments. * Write comments that emphasize potential hazards. * **Chapter 6** * Use well-defined data types. * Always ask ,"Can this variable or expression over- or underflow?" * Implement your designs as accurately as possible. Being kind a close is being kind a buggy. * these principles already: Don't accept special purpose arguments such as the NULL pointer, and Implement your design, not something that approximates it. The third principle is new: Strive to make every function perform its task exactly one time. * Implement "the task" just once. * Get rid of extraneous if statements. * Avoid using nested ?: operators . * Handle your special cases just once. * Avoid risky language idioms. * Don't needlessly mix operator types. If you must mix operators, use parentheses to isolate the operations. * Avoid calling functions that return errors. * **Chapter 7** * Don't reference memory that you don't own. * Don't reference memory that you have freed. * Don't use output memory as workspace buffers. * Don't pass data in static (or global) memory. * Don't write parasitic functions. * Don't abuse your programming language. * Tight C does not guarantee efficient machine code. * Write code for the "average" programmer. * **Chapter 8** * Bugs don't just "go away." * Don't fix bugs later; fix them now. * Fix the cause, not the symptom. * Don't clean up code unless the clean­ up is critical to the product's success. * Don't implement nonstrategic features. * There are no free features. * Don't allow unnecessary flexibility. * Don't keep "trying" solutions· until you find one that works. Take the time to find the correct solution. * Write and test code in small chunks. * Always test your code, even if that means your schedule will slip. * Don't rely on the testing group to find your bugs. * Don't blame testers for finding your bugs. * Establish your priorities and stick to them. * Never allow the same bug to bite you twice. * **A: CODING CHECKLISTS** * **B: MEMORY LOGGING ROUTINES** * **To sum up** * Charles Simonyi in the early 1970s. https://en.wikipedia.org/wiki/Hungarian_notation * char ch; * byte b; * flag f;/* flags that are always TRUE or FALSE*/ * symbol sym;/* some sort of symbol structure*/ * char byte flag symbol * *pch:/* character pointer*/ * *pb; * *pf: * *psym: * related link: [https://www.morling.dev/images/code_review_pyramid.svg](https://www.google.com/url?q=https%3A%2F%2Fwww.morling.dev%2Fimages%2Fcode_review_pyramid.svg&sa=D&sntz=1&usg=AOvVaw1y2It_PUzXAtFN7dPLegCa) * ## Shape Up: Stop Running in Circles and Ship Work that Matters * Six-week cycles: Our decisions are based on moving the product forward in the next six weeks, not micromanaging time. We don’t count hours or question how individual days are spent. We don’t have daily meetings. We don’t rethink our roadmap every two weeks. Our focus is at a higher level. We say to ourselves: “If this project ships after six weeks, we’ll be really happy. We’ll feel our time was well spent.” Then we commit the six weeks and leave the team alone to get it done * Shaping the work: key elements of a solution, appetite * **part one shaping:** * two separate tracks: one for shaping, one for building. * Steps to shaping * 1\. Set boundaries. * 2\. Rough out the elements: idea that solves the problem within the appetite but without all the fine details worked out * 3\. Address risks and rabbit holes. * 4\. Write the pitch. * fixed time, variable scope, * Where in the current system does the new thing fit? How do you get to it? What are the key components or interactions? Where does it take you? * five ingredients that we always want to include in a pitch: 1. Problem — The raw idea, a use case, or something we’ve seen that motivates us to work on this 2. Appetite — How much time we want to spend and how that constrains the solution 3. Solution — The core elements we came up with, presented in a form that’s easy for people to immediately understand 4. Rabbit holes — Details about the solution worth calling out to avoid problems 5. No-gos — Anything specifically excluded from the concept: functionality or use cases we intentionally aren’t covering to fit the appetite or make the problem tractable * **part two : Betting** * Some companies use two-week cycles (aka “sprints”). We learned that two weeks is too short to get anything meaningful done. Worse than that, two-week cycles are extremely costly due to the planning overhead. The amount of work you get out of two weeks isn’t worth the collective hours around the table to “sprint plan” or the opportunity cost of breaking everyone’s momentum to re-group. * after each six-week cycle, we schedule two weeks for cool-down. * bugs: Use cool-down. Bring it to the betting table. Schedule a bug smash. * We’ve noticed three phases of work when we build a new product from scratch. R&D mode (bet the time on spiking some key pieces of the new product idea. CEO and designer, we don’t expect to ship anything at the end of an R&D cycle) , Production mode (Shaping is deliberate again. bet multiple teams in parallel , Shipping is the goal) , Cleanup mode (There’s no shaping. There aren’t clear team boundaries. Work is “shipped”) [Cleanup shouldn’t last longer than two cycles] * Example: two years of development, R&D mode for the first year, a year of production mode cycles followed, two cycles of cleanup and significantly cut back the feature set, some overlap between R&D and production mode after that first year, * **Part three: Building** * We don’t start by assigning tasks to anyone. * we define and track the scopes as to-do lists. Each scope corresponds to a list name. Then any tasks for that scope go in that list. * **image 1, 2, 3,** * Mark nice-to-haves with ~ * First there’s the uphill phase of figuring out what our approach is and what we’re going to do. Then, once we can see all the work involved, there’s the downhill phase of execution * image 4, 5, * ## Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet * getting start: idea, technology, passion; What can I do well that I would love to do for an extended period of time? P: 19, 21 * 1 market segmentation: The single necessary and sufficient condition for a business is a paying customer. ; technological enthusiasts=early adopters=early majority ; P: 31, * 2 select a beachhead market: P: 44, * 3 build an end user profile: P: 52, * 4 Calculate the Total Addressable Market (TAM) Size for the Beachhead Market; $200 # [Essential Tips And Tricks For ](/book-summary/commonplace-book)[commonplace book](/book-summary/commonplace-book) # [knowledge management ](/book-summary/knowledge_management) # [PKM](/book-summary/knowledge_management/pkm) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/LMErpRo3SemuSDj3pLjSSh1xcteMOPvF9yzhoEb8_AB5vppySOpESQgqlBlCy2nymRjHv3i3BNkjBtED1LpjpWk=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/LMErpRo3SemuSDj3pLjSSh1xcteMOPvF9yzhoEb8_AB5vppySOpESQgqlBlCy2nymRjHv3i3BNkjBtED1LpjpWk=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Book Summary Books How to take smart Notes Writing Solid Code Shape Up: Stop Running in Circles and Ship Work that Matters Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet List of books I've read: Computer Vision: Algorithms and Applications Multiple View Geometry in Computer Vision Second Edition Complete to download the latest draft of Machine Learning Yearning Multiple view geometry in computer vision Learning OpenCV Book by Adrian Kaehler and Gary Bradski Digital Image Processing, Global Edition by Rafael C. Gonzalez Introduction to Algorithms, 3rd Edition (The MIT Press) The Mythical Man-Month: Essays on Software Engineering OpenCV-4-with-Python-Blueprints-Second-Edition Cracking the coding interview 189 programming questions and solutions Cracking the Tech Career: Insider Advice on Landing a Job at Google, Microsoft, Apple, Or Any Top Tech Company The Art of Startup Fundraising Mathematics for Machine Learning Principles of Economics (6th edition) The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) Reinventing Your Life: The Breakthough Program to End Negative Behavior Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf Magazine Papers Websites and links Tools YouTube - List channels Language: Essential Tips And Tricks For commonplace book knowledge management PKM # Books * ## How to take smart Notes * Introduction 4, The four underlying principles 4, the six steps to successful writing 6, afterword * introduction * write everything/ collect notes not related to any thing, organize notes, revirew, rewrite, * no reorganise no complex system, develop ideas by smart notes * 1 slip-box; 2 repeatable tasks that can become automatic and fit together seamlessly change routinges * overarching workflow "Getting Things Done" GTD * constantly have to ump back and forth between different tasks * 1- bibliographical slip-box: reference * 2- collected and generated ideas slip-box * 3- index: notes serve as entry point / thought/ topic * writing a paper step by step * 1 fleeting notes: it will delete within a day * I always have a slip of paper at hand, on which I note down the ideas of certain pages. on the backside I write down the bibliographic details. after finishing the book I go throught my notes and think how ehse notes might be relevant for already written notes in the slip-box. it means that I always read with an eye towards possible connections in the slip-box * 2 literature notes: contain the necessary information - in reference system or in the slip-box * I make a note with the bibliographic details. on the backside I would write "on page x is this, on page y is that" and then it goes into the bibliographic slib-box where I collect everything I read * read, think about its relevance * theoretical background, methodological approach or perspective of the text we read. * whether something adds to a discussion in the slip-box * we look at our slip-box for already existing lines of thought and think about the questions and problems already on our minds to which a new note might contribute. - assigning keywords is much more than just a bureaucratic act. it is a crucial part of the thinking process, which often leads to a deeper elaboration of the note itself and the connection to other notes. * * 3 permanent notes: develop ideas/new/ - only relevant to one particular project - in project specific folder - can be discarded or archived after project finished * add to slip-box behind the note you dirctyly refer to or behind the last note in the slip-box * add links to other notes or links on other notes to your new note * make sure it can be found from the index (MOC) add an entry in the index if necessary or refer to it from a note that is connected to the index * build a latticework of mental models * 4 behind multiple notes (link backward) - link to related note - link to MOC * make sure it can be found again * **keywords** can easily be added to a note like **tags** and will then show up in the **index** * a day * read and take notes * build connections within the slip-box - new idea * you write on your paper, notice a hole in the argument and have another look in the file system for the missing link. you follow up on a footnote, go back to research and might add a fitting quote to one of your papers in the making. * The four underlying principles * writing * simplicity * not from scratch * let the work carry you forward * the six steps to successful writing * separate and interlocking tasks * we use memo techniques is to bundle items together in a meaningful way and remember the bundles * the brain doesn't distinguish between an actual finished task and one that is postponed by taking a note * the fist step is to break down the amprphous task of "writing" into smaller pieces of different tasks that can be finished in **one go.** the second step is to make sure we always write down the outcome of our thinking, including possible connections to further inquiries. * ego depletion * read for understanding * take smart notes * develp ideas (MOC) * 1 overview of a topic - referred to from the index and used as an entry point into a topic has already develped * 2 keeping tarck of all topic - index - * share your insight * make it a habit * afterword * ## Writing Solid Code Writing Solid Code (Microsoft Programming Series) by Trendelles Store Note from Writing Solid Code Book [https://www.pirahansiah.com/describe/book- summary](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Fdescribe%2Fbook- summary&sa=D&sntz=1&usg=AOvVaw3ACzA6G0MBVN3nw6f-yMQo) #SolidCode #C++ #Python #pirahansiah * **Chapter 1** * Enable all optional compiler warnings. * Use lint to catch bugs that your compiler may miss. * If you have unit tests, use them. * **Chapter 2** * introduction assert page 16; assert.h * assert is nothing more than a repackaged form of the #ifdef code we saw before, but when you use * the macro, it takes one line instead of seven * That's why assert is a macro and not a function; if it were a function, calling * it could cause unexpected memory or code swapping. * Use assertions to validate function arguments . * Strip undefined behavior from your code, or use assertions to catch illegal uses of undefined behavior . * Don't waste people's time. Document unclear assertions . * Either remove implicit assumptions, or assert that they are valid . * Use assertions to detect impossible conditions. * Don't hide bugs when you program defensively. * Use a second algorithm to validate your results. * Don't wait for bugs to happen; use startup checks. * **Chapter 3** * Maintain shipping and debugging versions of your program. Shrink-wrap the shipping version, but use the debugging ver­sion as much as possible to catch bugs quickly. * Assertions are a shorthand way to write debugging checks. Use them to catch illegal conditions that should never arise. Don't confuse such conditions with error conditions, which you must handle in the final product. * Use assertions to validate function arguments and to alert pro­ grammars when they do something undefined. The more rigidly you define your functions, the easier it will be to validate the arguments. * Once you've written a function, review it and ask yourself, 'What am I assuming?" If you find an assumption, either assert that your assumption is always valid, or rewrite the code to re­ move the assumption. Also ask, 'What is most likely to be wrong in this code, and how can I automatically detect the prob­lem?" Strive to implement tests that catch bugs at the earliest possible moment. Textbooks encourage programmers to program defensively, but remember that this coding style hides bugs. When you write de­fensive code, use assertions to alert you if the "can't happen" cases do happen. * Eliminate random behavior. Force bugs to be reproducible . * Destroy your garbage so that it's not misused . * Not only did reallocate have to move the block of memory as it was expanding it, but the old memory had to be reallocated and filled with new data. In the assembler, both happened rarely. * This brings up another guideline for writing bug-free code: You don't want anything to happen rarely. You need to isolate those behaviors in your subsystems that may happen and make sure that they do happen. And of­ ten. If you find rare behavior in your subsystems, be sure to do something­ anything-to stir things up. * The assembler bug could have been found within hours, instead of years, if reallocate hadn't so rarely moved blocks when it expanded them. * If something happens rarely, force it to happen often. * Keep debug information to allow stronger error checking. * There is no question that the debug code will create differences be­ tween the ship and the debug versions of your code, but as long as you're careful not to change the underlying behavior of the code, those differences shouldn't matter. * Create thorough subsystem checks, and use them often. * Design your tests carefully. Nothing should be arbitrary. * Strive to implement transparent integrity checks. * Don't apply ship version constraints to the debug version. Trade size and speed for error detection. * **Chapter 4** * Don't wait until you have a bug to step through your code. * Step through every code path. * What About Sweeping Changes? In the past, programmers have asked, "What if I add a feature that touches code in many places? Stepping through all that new code is going to be time consuming." Let me answer that question with another: Can you make such sweeping changes without introducing any bugs? The habit of stepping through your code creates an interesting negative feedback loop. Programmers who step through their code soon learn to write small, easily testable functions because stepping through large func­tions is so painful. Programmers also spend more time thinking about how they can localize the changes they need to make-again, so that they can more easily test their code. And isn't this exactly what you want? No project lead likes it when programmers touch a lot of code; it's too destabilizing. Nor do leads like large, unmanageable functions; they're often unmaintainable. Try hard to localize your changes. If you find that you must make per­vasive changes, think twice before you decide not to step through all the new code. * Overflow and underflow bugs * Data conversion bugs * Off-by-one bugs * NULL pointer bugs * Bugs using garbage memory (0xA3 bugs) * Assignment bugs in which you've used = instead of == * Precedence bugs * Logic bugs * As you step through code, focus on data flow. Source level debuggers can hide execution details. Step through critical code at the instruction level . * **Chapter 5** * Make it hard to ignore error conditions. Don't bury error codes in return values. * Always look for, and eliminate, flaws in your interfaces. * Don't write multipurpose functions. Write separate functions to allow stronger argument validation. * Don't be wishy-washy. Define explicit function arguments. * Write functions that, given valid inputs, cannot fail. * Make the code intelligible at the point of call. Avoid Boolean arguments. * Write comments that emphasize potential hazards. * **Chapter 6** * Use well-defined data types. * Always ask ,"Can this variable or expression over- or underflow?" * Implement your designs as accurately as possible. Being kind a close is being kind a buggy. * these principles already: Don't accept special purpose arguments such as the NULL pointer, and Implement your design, not something that approximates it. The third principle is new: Strive to make every function perform its task exactly one time. * Implement "the task" just once. * Get rid of extraneous if statements. * Avoid using nested ?: operators . * Handle your special cases just once. * Avoid risky language idioms. * Don't needlessly mix operator types. If you must mix operators, use parentheses to isolate the operations. * Avoid calling functions that return errors. * **Chapter 7** * Don't reference memory that you don't own. * Don't reference memory that you have freed. * Don't use output memory as workspace buffers. * Don't pass data in static (or global) memory. * Don't write parasitic functions. * Don't abuse your programming language. * Tight C does not guarantee efficient machine code. * Write code for the "average" programmer. * **Chapter 8** * Bugs don't just "go away." * Don't fix bugs later; fix them now. * Fix the cause, not the symptom. * Don't clean up code unless the clean­ up is critical to the product's success. * Don't implement nonstrategic features. * There are no free features. * Don't allow unnecessary flexibility. * Don't keep "trying" solutions· until you find one that works. Take the time to find the correct solution. * Write and test code in small chunks. * Always test your code, even if that means your schedule will slip. * Don't rely on the testing group to find your bugs. * Don't blame testers for finding your bugs. * Establish your priorities and stick to them. * Never allow the same bug to bite you twice. * **A: CODING CHECKLISTS** * **B: MEMORY LOGGING ROUTINES** * **To sum up** * Charles Simonyi in the early 1970s. https://en.wikipedia.org/wiki/Hungarian_notation * char ch; * byte b; * flag f;/* flags that are always TRUE or FALSE*/ * symbol sym;/* some sort of symbol structure*/ * char byte flag symbol * *pch:/* character pointer*/ * *pb; * *pf: * *psym: * related link: [https://www.morling.dev/images/code_review_pyramid.svg](https://www.google.com/url?q=https%3A%2F%2Fwww.morling.dev%2Fimages%2Fcode_review_pyramid.svg&sa=D&sntz=1&usg=AOvVaw1y2It_PUzXAtFN7dPLegCa) * ## Shape Up: Stop Running in Circles and Ship Work that Matters * Six-week cycles: Our decisions are based on moving the product forward in the next six weeks, not micromanaging time. We don’t count hours or question how individual days are spent. We don’t have daily meetings. We don’t rethink our roadmap every two weeks. Our focus is at a higher level. We say to ourselves: “If this project ships after six weeks, we’ll be really happy. We’ll feel our time was well spent.” Then we commit the six weeks and leave the team alone to get it done * Shaping the work: key elements of a solution, appetite * **part one shaping:** * two separate tracks: one for shaping, one for building. * Steps to shaping * 1\. Set boundaries. * 2\. Rough out the elements: idea that solves the problem within the appetite but without all the fine details worked out * 3\. Address risks and rabbit holes. * 4\. Write the pitch. * fixed time, variable scope, * Where in the current system does the new thing fit? How do you get to it? What are the key components or interactions? Where does it take you? * five ingredients that we always want to include in a pitch: 1. Problem — The raw idea, a use case, or something we’ve seen that motivates us to work on this 2. Appetite — How much time we want to spend and how that constrains the solution 3. Solution — The core elements we came up with, presented in a form that’s easy for people to immediately understand 4. Rabbit holes — Details about the solution worth calling out to avoid problems 5. No-gos — Anything specifically excluded from the concept: functionality or use cases we intentionally aren’t covering to fit the appetite or make the problem tractable * **part two : Betting** * Some companies use two-week cycles (aka “sprints”). We learned that two weeks is too short to get anything meaningful done. Worse than that, two-week cycles are extremely costly due to the planning overhead. The amount of work you get out of two weeks isn’t worth the collective hours around the table to “sprint plan” or the opportunity cost of breaking everyone’s momentum to re-group. * after each six-week cycle, we schedule two weeks for cool-down. * bugs: Use cool-down. Bring it to the betting table. Schedule a bug smash. * We’ve noticed three phases of work when we build a new product from scratch. R&D mode (bet the time on spiking some key pieces of the new product idea. CEO and designer, we don’t expect to ship anything at the end of an R&D cycle) , Production mode (Shaping is deliberate again. bet multiple teams in parallel , Shipping is the goal) , Cleanup mode (There’s no shaping. There aren’t clear team boundaries. Work is “shipped”) [Cleanup shouldn’t last longer than two cycles] * Example: two years of development, R&D mode for the first year, a year of production mode cycles followed, two cycles of cleanup and significantly cut back the feature set, some overlap between R&D and production mode after that first year, * **Part three: Building** * We don’t start by assigning tasks to anyone. * we define and track the scopes as to-do lists. Each scope corresponds to a list name. Then any tasks for that scope go in that list. * **image 1, 2, 3,** * Mark nice-to-haves with ~ * First there’s the uphill phase of figuring out what our approach is and what we’re going to do. Then, once we can see all the work involved, there’s the downhill phase of execution * image 4, 5, * ## Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet * getting start: idea, technology, passion; What can I do well that I would love to do for an extended period of time? P: 19, 21 * 1 market segmentation: The single necessary and sufficient condition for a business is a paying customer. ; technological enthusiasts=early adopters=early majority ; P: 31, * 2 select a beachhead market: P: 44, * 3 build an end user profile: P: 52, * 4 Calculate the Total Addressable Market (TAM) Size for the Beachhead Market; $200 # [Essential Tips And Tricks For ](/book-summary/commonplace-book)[commonplace book](/book-summary/commonplace-book) # [knowledge management ](/book-summary/knowledge_management) # [PKM](/book-summary/knowledge_management/pkm) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/LMErpRo3SemuSDj3pLjSSh1xcteMOPvF9yzhoEb8_AB5vppySOpESQgqlBlCy2nymRjHv3i3BNkjBtED1LpjpWk=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/LMErpRo3SemuSDj3pLjSSh1xcteMOPvF9yzhoEb8_AB5vppySOpESQgqlBlCy2nymRjHv3i3BNkjBtED1LpjpWk=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Book Summary Books How to take smart Notes Writing Solid Code Shape Up: Stop Running in Circles and Ship Work that Matters Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet List of books I've read: Computer Vision: Algorithms and Applications Multiple View Geometry in Computer Vision Second Edition Complete to download the latest draft of Machine Learning Yearning Multiple view geometry in computer vision Learning OpenCV Book by Adrian Kaehler and Gary Bradski Digital Image Processing, Global Edition by Rafael C. Gonzalez Introduction to Algorithms, 3rd Edition (The MIT Press) The Mythical Man-Month: Essays on Software Engineering OpenCV-4-with-Python-Blueprints-Second-Edition Cracking the coding interview 189 programming questions and solutions Cracking the Tech Career: Insider Advice on Landing a Job at Google, Microsoft, Apple, Or Any Top Tech Company The Art of Startup Fundraising Mathematics for Machine Learning Principles of Economics (6th edition) The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) Reinventing Your Life: The Breakthough Program to End Negative Behavior Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf Magazine Papers Websites and links Tools YouTube - List channels Language: Essential Tips And Tricks For commonplace book knowledge management PKM # Books * ## How to take smart Notes * Introduction 4, The four underlying principles 4, the six steps to successful writing 6, afterword * introduction * write everything/ collect notes not related to any thing, organize notes, revirew, rewrite, * no reorganise no complex system, develop ideas by smart notes * 1 slip-box; 2 repeatable tasks that can become automatic and fit together seamlessly change routinges * overarching workflow "Getting Things Done" GTD * constantly have to ump back and forth between different tasks * 1- bibliographical slip-box: reference * 2- collected and generated ideas slip-box * 3- index: notes serve as entry point / thought/ topic * writing a paper step by step * 1 fleeting notes: it will delete within a day * I always have a slip of paper at hand, on which I note down the ideas of certain pages. on the backside I write down the bibliographic details. after finishing the book I go throught my notes and think how ehse notes might be relevant for already written notes in the slip-box. it means that I always read with an eye towards possible connections in the slip-box * 2 literature notes: contain the necessary information - in reference system or in the slip-box * I make a note with the bibliographic details. on the backside I would write "on page x is this, on page y is that" and then it goes into the bibliographic slib-box where I collect everything I read * read, think about its relevance * theoretical background, methodological approach or perspective of the text we read. * whether something adds to a discussion in the slip-box * we look at our slip-box for already existing lines of thought and think about the questions and problems already on our minds to which a new note might contribute. - assigning keywords is much more than just a bureaucratic act. it is a crucial part of the thinking process, which often leads to a deeper elaboration of the note itself and the connection to other notes. * * 3 permanent notes: develop ideas/new/ - only relevant to one particular project - in project specific folder - can be discarded or archived after project finished * add to slip-box behind the note you dirctyly refer to or behind the last note in the slip-box * add links to other notes or links on other notes to your new note * make sure it can be found from the index (MOC) add an entry in the index if necessary or refer to it from a note that is connected to the index * build a latticework of mental models * 4 behind multiple notes (link backward) - link to related note - link to MOC * make sure it can be found again * **keywords** can easily be added to a note like **tags** and will then show up in the **index** * a day * read and take notes * build connections within the slip-box - new idea * you write on your paper, notice a hole in the argument and have another look in the file system for the missing link. you follow up on a footnote, go back to research and might add a fitting quote to one of your papers in the making. * The four underlying principles * writing * simplicity * not from scratch * let the work carry you forward * the six steps to successful writing * separate and interlocking tasks * we use memo techniques is to bundle items together in a meaningful way and remember the bundles * the brain doesn't distinguish between an actual finished task and one that is postponed by taking a note * the fist step is to break down the amprphous task of "writing" into smaller pieces of different tasks that can be finished in **one go.** the second step is to make sure we always write down the outcome of our thinking, including possible connections to further inquiries. * ego depletion * read for understanding * take smart notes * develp ideas (MOC) * 1 overview of a topic - referred to from the index and used as an entry point into a topic has already develped * 2 keeping tarck of all topic - index - * share your insight * make it a habit * afterword * ## Writing Solid Code Writing Solid Code (Microsoft Programming Series) by Trendelles Store Note from Writing Solid Code Book [https://www.pirahansiah.com/describe/book- summary](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Fdescribe%2Fbook- summary&sa=D&sntz=1&usg=AOvVaw3ACzA6G0MBVN3nw6f-yMQo) #SolidCode #C++ #Python #pirahansiah * **Chapter 1** * Enable all optional compiler warnings. * Use lint to catch bugs that your compiler may miss. * If you have unit tests, use them. * **Chapter 2** * introduction assert page 16; assert.h * assert is nothing more than a repackaged form of the #ifdef code we saw before, but when you use * the macro, it takes one line instead of seven * That's why assert is a macro and not a function; if it were a function, calling * it could cause unexpected memory or code swapping. * Use assertions to validate function arguments . * Strip undefined behavior from your code, or use assertions to catch illegal uses of undefined behavior . * Don't waste people's time. Document unclear assertions . * Either remove implicit assumptions, or assert that they are valid . * Use assertions to detect impossible conditions. * Don't hide bugs when you program defensively. * Use a second algorithm to validate your results. * Don't wait for bugs to happen; use startup checks. * **Chapter 3** * Maintain shipping and debugging versions of your program. Shrink-wrap the shipping version, but use the debugging ver­sion as much as possible to catch bugs quickly. * Assertions are a shorthand way to write debugging checks. Use them to catch illegal conditions that should never arise. Don't confuse such conditions with error conditions, which you must handle in the final product. * Use assertions to validate function arguments and to alert pro­ grammars when they do something undefined. The more rigidly you define your functions, the easier it will be to validate the arguments. * Once you've written a function, review it and ask yourself, 'What am I assuming?" If you find an assumption, either assert that your assumption is always valid, or rewrite the code to re­ move the assumption. Also ask, 'What is most likely to be wrong in this code, and how can I automatically detect the prob­lem?" Strive to implement tests that catch bugs at the earliest possible moment. Textbooks encourage programmers to program defensively, but remember that this coding style hides bugs. When you write de­fensive code, use assertions to alert you if the "can't happen" cases do happen. * Eliminate random behavior. Force bugs to be reproducible . * Destroy your garbage so that it's not misused . * Not only did reallocate have to move the block of memory as it was expanding it, but the old memory had to be reallocated and filled with new data. In the assembler, both happened rarely. * This brings up another guideline for writing bug-free code: You don't want anything to happen rarely. You need to isolate those behaviors in your subsystems that may happen and make sure that they do happen. And of­ ten. If you find rare behavior in your subsystems, be sure to do something­ anything-to stir things up. * The assembler bug could have been found within hours, instead of years, if reallocate hadn't so rarely moved blocks when it expanded them. * If something happens rarely, force it to happen often. * Keep debug information to allow stronger error checking. * There is no question that the debug code will create differences be­ tween the ship and the debug versions of your code, but as long as you're careful not to change the underlying behavior of the code, those differences shouldn't matter. * Create thorough subsystem checks, and use them often. * Design your tests carefully. Nothing should be arbitrary. * Strive to implement transparent integrity checks. * Don't apply ship version constraints to the debug version. Trade size and speed for error detection. * **Chapter 4** * Don't wait until you have a bug to step through your code. * Step through every code path. * What About Sweeping Changes? In the past, programmers have asked, "What if I add a feature that touches code in many places? Stepping through all that new code is going to be time consuming." Let me answer that question with another: Can you make such sweeping changes without introducing any bugs? The habit of stepping through your code creates an interesting negative feedback loop. Programmers who step through their code soon learn to write small, easily testable functions because stepping through large func­tions is so painful. Programmers also spend more time thinking about how they can localize the changes they need to make-again, so that they can more easily test their code. And isn't this exactly what you want? No project lead likes it when programmers touch a lot of code; it's too destabilizing. Nor do leads like large, unmanageable functions; they're often unmaintainable. Try hard to localize your changes. If you find that you must make per­vasive changes, think twice before you decide not to step through all the new code. * Overflow and underflow bugs * Data conversion bugs * Off-by-one bugs * NULL pointer bugs * Bugs using garbage memory (0xA3 bugs) * Assignment bugs in which you've used = instead of == * Precedence bugs * Logic bugs * As you step through code, focus on data flow. Source level debuggers can hide execution details. Step through critical code at the instruction level . * **Chapter 5** * Make it hard to ignore error conditions. Don't bury error codes in return values. * Always look for, and eliminate, flaws in your interfaces. * Don't write multipurpose functions. Write separate functions to allow stronger argument validation. * Don't be wishy-washy. Define explicit function arguments. * Write functions that, given valid inputs, cannot fail. * Make the code intelligible at the point of call. Avoid Boolean arguments. * Write comments that emphasize potential hazards. * **Chapter 6** * Use well-defined data types. * Always ask ,"Can this variable or expression over- or underflow?" * Implement your designs as accurately as possible. Being kind a close is being kind a buggy. * these principles already: Don't accept special purpose arguments such as the NULL pointer, and Implement your design, not something that approximates it. The third principle is new: Strive to make every function perform its task exactly one time. * Implement "the task" just once. * Get rid of extraneous if statements. * Avoid using nested ?: operators . * Handle your special cases just once. * Avoid risky language idioms. * Don't needlessly mix operator types. If you must mix operators, use parentheses to isolate the operations. * Avoid calling functions that return errors. * **Chapter 7** * Don't reference memory that you don't own. * Don't reference memory that you have freed. * Don't use output memory as workspace buffers. * Don't pass data in static (or global) memory. * Don't write parasitic functions. * Don't abuse your programming language. * Tight C does not guarantee efficient machine code. * Write code for the "average" programmer. * **Chapter 8** * Bugs don't just "go away." * Don't fix bugs later; fix them now. * Fix the cause, not the symptom. * Don't clean up code unless the clean­ up is critical to the product's success. * Don't implement nonstrategic features. * There are no free features. * Don't allow unnecessary flexibility. * Don't keep "trying" solutions· until you find one that works. Take the time to find the correct solution. * Write and test code in small chunks. * Always test your code, even if that means your schedule will slip. * Don't rely on the testing group to find your bugs. * Don't blame testers for finding your bugs. * Establish your priorities and stick to them. * Never allow the same bug to bite you twice. * **A: CODING CHECKLISTS** * **B: MEMORY LOGGING ROUTINES** * **To sum up** * Charles Simonyi in the early 1970s. https://en.wikipedia.org/wiki/Hungarian_notation * char ch; * byte b; * flag f;/* flags that are always TRUE or FALSE*/ * symbol sym;/* some sort of symbol structure*/ * char byte flag symbol * *pch:/* character pointer*/ * *pb; * *pf: * *psym: * related link: [https://www.morling.dev/images/code_review_pyramid.svg](https://www.google.com/url?q=https%3A%2F%2Fwww.morling.dev%2Fimages%2Fcode_review_pyramid.svg&sa=D&sntz=1&usg=AOvVaw1y2It_PUzXAtFN7dPLegCa) * ## Shape Up: Stop Running in Circles and Ship Work that Matters * Six-week cycles: Our decisions are based on moving the product forward in the next six weeks, not micromanaging time. We don’t count hours or question how individual days are spent. We don’t have daily meetings. We don’t rethink our roadmap every two weeks. Our focus is at a higher level. We say to ourselves: “If this project ships after six weeks, we’ll be really happy. We’ll feel our time was well spent.” Then we commit the six weeks and leave the team alone to get it done * Shaping the work: key elements of a solution, appetite * **part one shaping:** * two separate tracks: one for shaping, one for building. * Steps to shaping * 1\. Set boundaries. * 2\. Rough out the elements: idea that solves the problem within the appetite but without all the fine details worked out * 3\. Address risks and rabbit holes. * 4\. Write the pitch. * fixed time, variable scope, * Where in the current system does the new thing fit? How do you get to it? What are the key components or interactions? Where does it take you? * five ingredients that we always want to include in a pitch: 1. Problem — The raw idea, a use case, or something we’ve seen that motivates us to work on this 2. Appetite — How much time we want to spend and how that constrains the solution 3. Solution — The core elements we came up with, presented in a form that’s easy for people to immediately understand 4. Rabbit holes — Details about the solution worth calling out to avoid problems 5. No-gos — Anything specifically excluded from the concept: functionality or use cases we intentionally aren’t covering to fit the appetite or make the problem tractable * **part two : Betting** * Some companies use two-week cycles (aka “sprints”). We learned that two weeks is too short to get anything meaningful done. Worse than that, two-week cycles are extremely costly due to the planning overhead. The amount of work you get out of two weeks isn’t worth the collective hours around the table to “sprint plan” or the opportunity cost of breaking everyone’s momentum to re-group. * after each six-week cycle, we schedule two weeks for cool-down. * bugs: Use cool-down. Bring it to the betting table. Schedule a bug smash. * We’ve noticed three phases of work when we build a new product from scratch. R&D mode (bet the time on spiking some key pieces of the new product idea. CEO and designer, we don’t expect to ship anything at the end of an R&D cycle) , Production mode (Shaping is deliberate again. bet multiple teams in parallel , Shipping is the goal) , Cleanup mode (There’s no shaping. There aren’t clear team boundaries. Work is “shipped”) [Cleanup shouldn’t last longer than two cycles] * Example: two years of development, R&D mode for the first year, a year of production mode cycles followed, two cycles of cleanup and significantly cut back the feature set, some overlap between R&D and production mode after that first year, * **Part three: Building** * We don’t start by assigning tasks to anyone. * we define and track the scopes as to-do lists. Each scope corresponds to a list name. Then any tasks for that scope go in that list. * **image 1, 2, 3,** * Mark nice-to-haves with ~ * First there’s the uphill phase of figuring out what our approach is and what we’re going to do. Then, once we can see all the work involved, there’s the downhill phase of execution * image 4, 5, * ## Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet * getting start: idea, technology, passion; What can I do well that I would love to do for an extended period of time? P: 19, 21 * 1 market segmentation: The single necessary and sufficient condition for a business is a paying customer. ; technological enthusiasts=early adopters=early majority ; P: 31, * 2 select a beachhead market: P: 44, * 3 build an end user profile: P: 52, * 4 Calculate the Total Addressable Market (TAM) Size for the Beachhead Market; $200 # [Essential Tips And Tricks For ](/book-summary/commonplace-book)[commonplace book](/book-summary/commonplace-book) # [knowledge management ](/book-summary/knowledge_management) # [PKM](/book-summary/knowledge_management/pkm) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/LMErpRo3SemuSDj3pLjSSh1xcteMOPvF9yzhoEb8_AB5vppySOpESQgqlBlCy2nymRjHv3i3BNkjBtED1LpjpWk=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/LMErpRo3SemuSDj3pLjSSh1xcteMOPvF9yzhoEb8_AB5vppySOpESQgqlBlCy2nymRjHv3i3BNkjBtED1LpjpWk=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Book Summary Books How to take smart Notes Writing Solid Code Shape Up: Stop Running in Circles and Ship Work that Matters Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet List of books I've read: Computer Vision: Algorithms and Applications Multiple View Geometry in Computer Vision Second Edition Complete to download the latest draft of Machine Learning Yearning Multiple view geometry in computer vision Learning OpenCV Book by Adrian Kaehler and Gary Bradski Digital Image Processing, Global Edition by Rafael C. Gonzalez Introduction to Algorithms, 3rd Edition (The MIT Press) The Mythical Man-Month: Essays on Software Engineering OpenCV-4-with-Python-Blueprints-Second-Edition Cracking the coding interview 189 programming questions and solutions Cracking the Tech Career: Insider Advice on Landing a Job at Google, Microsoft, Apple, Or Any Top Tech Company The Art of Startup Fundraising Mathematics for Machine Learning Principles of Economics (6th edition) The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) Reinventing Your Life: The Breakthough Program to End Negative Behavior Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf Magazine Papers Websites and links Tools YouTube - List channels Language: Essential Tips And Tricks For commonplace book knowledge management PKM # Books * ## How to take smart Notes * Introduction 4, The four underlying principles 4, the six steps to successful writing 6, afterword * introduction * write everything/ collect notes not related to any thing, organize notes, revirew, rewrite, * no reorganise no complex system, develop ideas by smart notes * 1 slip-box; 2 repeatable tasks that can become automatic and fit together seamlessly change routinges * overarching workflow "Getting Things Done" GTD * constantly have to ump back and forth between different tasks * 1- bibliographical slip-box: reference * 2- collected and generated ideas slip-box * 3- index: notes serve as entry point / thought/ topic * writing a paper step by step * 1 fleeting notes: it will delete within a day * I always have a slip of paper at hand, on which I note down the ideas of certain pages. on the backside I write down the bibliographic details. after finishing the book I go throught my notes and think how ehse notes might be relevant for already written notes in the slip-box. it means that I always read with an eye towards possible connections in the slip-box * 2 literature notes: contain the necessary information - in reference system or in the slip-box * I make a note with the bibliographic details. on the backside I would write "on page x is this, on page y is that" and then it goes into the bibliographic slib-box where I collect everything I read * read, think about its relevance * theoretical background, methodological approach or perspective of the text we read. * whether something adds to a discussion in the slip-box * we look at our slip-box for already existing lines of thought and think about the questions and problems already on our minds to which a new note might contribute. - assigning keywords is much more than just a bureaucratic act. it is a crucial part of the thinking process, which often leads to a deeper elaboration of the note itself and the connection to other notes. * * 3 permanent notes: develop ideas/new/ - only relevant to one particular project - in project specific folder - can be discarded or archived after project finished * add to slip-box behind the note you dirctyly refer to or behind the last note in the slip-box * add links to other notes or links on other notes to your new note * make sure it can be found from the index (MOC) add an entry in the index if necessary or refer to it from a note that is connected to the index * build a latticework of mental models * 4 behind multiple notes (link backward) - link to related note - link to MOC * make sure it can be found again * **keywords** can easily be added to a note like **tags** and will then show up in the **index** * a day * read and take notes * build connections within the slip-box - new idea * you write on your paper, notice a hole in the argument and have another look in the file system for the missing link. you follow up on a footnote, go back to research and might add a fitting quote to one of your papers in the making. * The four underlying principles * writing * simplicity * not from scratch * let the work carry you forward * the six steps to successful writing * separate and interlocking tasks * we use memo techniques is to bundle items together in a meaningful way and remember the bundles * the brain doesn't distinguish between an actual finished task and one that is postponed by taking a note * the fist step is to break down the amprphous task of "writing" into smaller pieces of different tasks that can be finished in **one go.** the second step is to make sure we always write down the outcome of our thinking, including possible connections to further inquiries. * ego depletion * read for understanding * take smart notes * develp ideas (MOC) * 1 overview of a topic - referred to from the index and used as an entry point into a topic has already develped * 2 keeping tarck of all topic - index - * share your insight * make it a habit * afterword * ## Writing Solid Code Writing Solid Code (Microsoft Programming Series) by Trendelles Store Note from Writing Solid Code Book [https://www.pirahansiah.com/describe/book- summary](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Fdescribe%2Fbook- summary&sa=D&sntz=1&usg=AOvVaw3ACzA6G0MBVN3nw6f-yMQo) #SolidCode #C++ #Python #pirahansiah * **Chapter 1** * Enable all optional compiler warnings. * Use lint to catch bugs that your compiler may miss. * If you have unit tests, use them. * **Chapter 2** * introduction assert page 16; assert.h * assert is nothing more than a repackaged form of the #ifdef code we saw before, but when you use * the macro, it takes one line instead of seven * That's why assert is a macro and not a function; if it were a function, calling * it could cause unexpected memory or code swapping. * Use assertions to validate function arguments . * Strip undefined behavior from your code, or use assertions to catch illegal uses of undefined behavior . * Don't waste people's time. Document unclear assertions . * Either remove implicit assumptions, or assert that they are valid . * Use assertions to detect impossible conditions. * Don't hide bugs when you program defensively. * Use a second algorithm to validate your results. * Don't wait for bugs to happen; use startup checks. * **Chapter 3** * Maintain shipping and debugging versions of your program. Shrink-wrap the shipping version, but use the debugging ver­sion as much as possible to catch bugs quickly. * Assertions are a shorthand way to write debugging checks. Use them to catch illegal conditions that should never arise. Don't confuse such conditions with error conditions, which you must handle in the final product. * Use assertions to validate function arguments and to alert pro­ grammars when they do something undefined. The more rigidly you define your functions, the easier it will be to validate the arguments. * Once you've written a function, review it and ask yourself, 'What am I assuming?" If you find an assumption, either assert that your assumption is always valid, or rewrite the code to re­ move the assumption. Also ask, 'What is most likely to be wrong in this code, and how can I automatically detect the prob­lem?" Strive to implement tests that catch bugs at the earliest possible moment. Textbooks encourage programmers to program defensively, but remember that this coding style hides bugs. When you write de­fensive code, use assertions to alert you if the "can't happen" cases do happen. * Eliminate random behavior. Force bugs to be reproducible . * Destroy your garbage so that it's not misused . * Not only did reallocate have to move the block of memory as it was expanding it, but the old memory had to be reallocated and filled with new data. In the assembler, both happened rarely. * This brings up another guideline for writing bug-free code: You don't want anything to happen rarely. You need to isolate those behaviors in your subsystems that may happen and make sure that they do happen. And of­ ten. If you find rare behavior in your subsystems, be sure to do something­ anything-to stir things up. * The assembler bug could have been found within hours, instead of years, if reallocate hadn't so rarely moved blocks when it expanded them. * If something happens rarely, force it to happen often. * Keep debug information to allow stronger error checking. * There is no question that the debug code will create differences be­ tween the ship and the debug versions of your code, but as long as you're careful not to change the underlying behavior of the code, those differences shouldn't matter. * Create thorough subsystem checks, and use them often. * Design your tests carefully. Nothing should be arbitrary. * Strive to implement transparent integrity checks. * Don't apply ship version constraints to the debug version. Trade size and speed for error detection. * **Chapter 4** * Don't wait until you have a bug to step through your code. * Step through every code path. * What About Sweeping Changes? In the past, programmers have asked, "What if I add a feature that touches code in many places? Stepping through all that new code is going to be time consuming." Let me answer that question with another: Can you make such sweeping changes without introducing any bugs? The habit of stepping through your code creates an interesting negative feedback loop. Programmers who step through their code soon learn to write small, easily testable functions because stepping through large func­tions is so painful. Programmers also spend more time thinking about how they can localize the changes they need to make-again, so that they can more easily test their code. And isn't this exactly what you want? No project lead likes it when programmers touch a lot of code; it's too destabilizing. Nor do leads like large, unmanageable functions; they're often unmaintainable. Try hard to localize your changes. If you find that you must make per­vasive changes, think twice before you decide not to step through all the new code. * Overflow and underflow bugs * Data conversion bugs * Off-by-one bugs * NULL pointer bugs * Bugs using garbage memory (0xA3 bugs) * Assignment bugs in which you've used = instead of == * Precedence bugs * Logic bugs * As you step through code, focus on data flow. Source level debuggers can hide execution details. Step through critical code at the instruction level . * **Chapter 5** * Make it hard to ignore error conditions. Don't bury error codes in return values. * Always look for, and eliminate, flaws in your interfaces. * Don't write multipurpose functions. Write separate functions to allow stronger argument validation. * Don't be wishy-washy. Define explicit function arguments. * Write functions that, given valid inputs, cannot fail. * Make the code intelligible at the point of call. Avoid Boolean arguments. * Write comments that emphasize potential hazards. * **Chapter 6** * Use well-defined data types. * Always ask ,"Can this variable or expression over- or underflow?" * Implement your designs as accurately as possible. Being kind a close is being kind a buggy. * these principles already: Don't accept special purpose arguments such as the NULL pointer, and Implement your design, not something that approximates it. The third principle is new: Strive to make every function perform its task exactly one time. * Implement "the task" just once. * Get rid of extraneous if statements. * Avoid using nested ?: operators . * Handle your special cases just once. * Avoid risky language idioms. * Don't needlessly mix operator types. If you must mix operators, use parentheses to isolate the operations. * Avoid calling functions that return errors. * **Chapter 7** * Don't reference memory that you don't own. * Don't reference memory that you have freed. * Don't use output memory as workspace buffers. * Don't pass data in static (or global) memory. * Don't write parasitic functions. * Don't abuse your programming language. * Tight C does not guarantee efficient machine code. * Write code for the "average" programmer. * **Chapter 8** * Bugs don't just "go away." * Don't fix bugs later; fix them now. * Fix the cause, not the symptom. * Don't clean up code unless the clean­ up is critical to the product's success. * Don't implement nonstrategic features. * There are no free features. * Don't allow unnecessary flexibility. * Don't keep "trying" solutions· until you find one that works. Take the time to find the correct solution. * Write and test code in small chunks. * Always test your code, even if that means your schedule will slip. * Don't rely on the testing group to find your bugs. * Don't blame testers for finding your bugs. * Establish your priorities and stick to them. * Never allow the same bug to bite you twice. * **A: CODING CHECKLISTS** * **B: MEMORY LOGGING ROUTINES** * **To sum up** * Charles Simonyi in the early 1970s. https://en.wikipedia.org/wiki/Hungarian_notation * char ch; * byte b; * flag f;/* flags that are always TRUE or FALSE*/ * symbol sym;/* some sort of symbol structure*/ * char byte flag symbol * *pch:/* character pointer*/ * *pb; * *pf: * *psym: * related link: [https://www.morling.dev/images/code_review_pyramid.svg](https://www.google.com/url?q=https%3A%2F%2Fwww.morling.dev%2Fimages%2Fcode_review_pyramid.svg&sa=D&sntz=1&usg=AOvVaw1y2It_PUzXAtFN7dPLegCa) * ## Shape Up: Stop Running in Circles and Ship Work that Matters * Six-week cycles: Our decisions are based on moving the product forward in the next six weeks, not micromanaging time. We don’t count hours or question how individual days are spent. We don’t have daily meetings. We don’t rethink our roadmap every two weeks. Our focus is at a higher level. We say to ourselves: “If this project ships after six weeks, we’ll be really happy. We’ll feel our time was well spent.” Then we commit the six weeks and leave the team alone to get it done * Shaping the work: key elements of a solution, appetite * **part one shaping:** * two separate tracks: one for shaping, one for building. * Steps to shaping * 1\. Set boundaries. * 2\. Rough out the elements: idea that solves the problem within the appetite but without all the fine details worked out * 3\. Address risks and rabbit holes. * 4\. Write the pitch. * fixed time, variable scope, * Where in the current system does the new thing fit? How do you get to it? What are the key components or interactions? Where does it take you? * five ingredients that we always want to include in a pitch: 1. Problem — The raw idea, a use case, or something we’ve seen that motivates us to work on this 2. Appetite — How much time we want to spend and how that constrains the solution 3. Solution — The core elements we came up with, presented in a form that’s easy for people to immediately understand 4. Rabbit holes — Details about the solution worth calling out to avoid problems 5. No-gos — Anything specifically excluded from the concept: functionality or use cases we intentionally aren’t covering to fit the appetite or make the problem tractable * **part two : Betting** * Some companies use two-week cycles (aka “sprints”). We learned that two weeks is too short to get anything meaningful done. Worse than that, two-week cycles are extremely costly due to the planning overhead. The amount of work you get out of two weeks isn’t worth the collective hours around the table to “sprint plan” or the opportunity cost of breaking everyone’s momentum to re-group. * after each six-week cycle, we schedule two weeks for cool-down. * bugs: Use cool-down. Bring it to the betting table. Schedule a bug smash. * We’ve noticed three phases of work when we build a new product from scratch. R&D mode (bet the time on spiking some key pieces of the new product idea. CEO and designer, we don’t expect to ship anything at the end of an R&D cycle) , Production mode (Shaping is deliberate again. bet multiple teams in parallel , Shipping is the goal) , Cleanup mode (There’s no shaping. There aren’t clear team boundaries. Work is “shipped”) [Cleanup shouldn’t last longer than two cycles] * Example: two years of development, R&D mode for the first year, a year of production mode cycles followed, two cycles of cleanup and significantly cut back the feature set, some overlap between R&D and production mode after that first year, * **Part three: Building** * We don’t start by assigning tasks to anyone. * we define and track the scopes as to-do lists. Each scope corresponds to a list name. Then any tasks for that scope go in that list. * **image 1, 2, 3,** * Mark nice-to-haves with ~ * First there’s the uphill phase of figuring out what our approach is and what we’re going to do. Then, once we can see all the work involved, there’s the downhill phase of execution * image 4, 5, * ## Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet * getting start: idea, technology, passion; What can I do well that I would love to do for an extended period of time? P: 19, 21 * 1 market segmentation: The single necessary and sufficient condition for a business is a paying customer. ; technological enthusiasts=early adopters=early majority ; P: 31, * 2 select a beachhead market: P: 44, * 3 build an end user profile: P: 52, * 4 Calculate the Total Addressable Market (TAM) Size for the Beachhead Market; $200 # [Essential Tips And Tricks For ](/book-summary/commonplace-book)[commonplace book](/book-summary/commonplace-book) # [knowledge management ](/book-summary/knowledge_management) # [PKM](/book-summary/knowledge_management/pkm) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/LMErpRo3SemuSDj3pLjSSh1xcteMOPvF9yzhoEb8_AB5vppySOpESQgqlBlCy2nymRjHv3i3BNkjBtED1LpjpWk=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/LMErpRo3SemuSDj3pLjSSh1xcteMOPvF9yzhoEb8_AB5vppySOpESQgqlBlCy2nymRjHv3i3BNkjBtED1LpjpWk=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Book Summary Books How to take smart Notes Writing Solid Code Shape Up: Stop Running in Circles and Ship Work that Matters Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet List of books I've read: Computer Vision: Algorithms and Applications Multiple View Geometry in Computer Vision Second Edition Complete to download the latest draft of Machine Learning Yearning Multiple view geometry in computer vision Learning OpenCV Book by Adrian Kaehler and Gary Bradski Digital Image Processing, Global Edition by Rafael C. Gonzalez Introduction to Algorithms, 3rd Edition (The MIT Press) The Mythical Man-Month: Essays on Software Engineering OpenCV-4-with-Python-Blueprints-Second-Edition Cracking the coding interview 189 programming questions and solutions Cracking the Tech Career: Insider Advice on Landing a Job at Google, Microsoft, Apple, Or Any Top Tech Company The Art of Startup Fundraising Mathematics for Machine Learning Principles of Economics (6th edition) The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) Reinventing Your Life: The Breakthough Program to End Negative Behavior Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf Magazine Papers Websites and links Tools YouTube - List channels Language: Essential Tips And Tricks For commonplace book knowledge management PKM # Books * ## How to take smart Notes * Introduction 4, The four underlying principles 4, the six steps to successful writing 6, afterword * introduction * write everything/ collect notes not related to any thing, organize notes, revirew, rewrite, * no reorganise no complex system, develop ideas by smart notes * 1 slip-box; 2 repeatable tasks that can become automatic and fit together seamlessly change routinges * overarching workflow "Getting Things Done" GTD * constantly have to ump back and forth between different tasks * 1- bibliographical slip-box: reference * 2- collected and generated ideas slip-box * 3- index: notes serve as entry point / thought/ topic * writing a paper step by step * 1 fleeting notes: it will delete within a day * I always have a slip of paper at hand, on which I note down the ideas of certain pages. on the backside I write down the bibliographic details. after finishing the book I go throught my notes and think how ehse notes might be relevant for already written notes in the slip-box. it means that I always read with an eye towards possible connections in the slip-box * 2 literature notes: contain the necessary information - in reference system or in the slip-box * I make a note with the bibliographic details. on the backside I would write "on page x is this, on page y is that" and then it goes into the bibliographic slib-box where I collect everything I read * read, think about its relevance * theoretical background, methodological approach or perspective of the text we read. * whether something adds to a discussion in the slip-box * we look at our slip-box for already existing lines of thought and think about the questions and problems already on our minds to which a new note might contribute. - assigning keywords is much more than just a bureaucratic act. it is a crucial part of the thinking process, which often leads to a deeper elaboration of the note itself and the connection to other notes. * * 3 permanent notes: develop ideas/new/ - only relevant to one particular project - in project specific folder - can be discarded or archived after project finished * add to slip-box behind the note you dirctyly refer to or behind the last note in the slip-box * add links to other notes or links on other notes to your new note * make sure it can be found from the index (MOC) add an entry in the index if necessary or refer to it from a note that is connected to the index * build a latticework of mental models * 4 behind multiple notes (link backward) - link to related note - link to MOC * make sure it can be found again * **keywords** can easily be added to a note like **tags** and will then show up in the **index** * a day * read and take notes * build connections within the slip-box - new idea * you write on your paper, notice a hole in the argument and have another look in the file system for the missing link. you follow up on a footnote, go back to research and might add a fitting quote to one of your papers in the making. * The four underlying principles * writing * simplicity * not from scratch * let the work carry you forward * the six steps to successful writing * separate and interlocking tasks * we use memo techniques is to bundle items together in a meaningful way and remember the bundles * the brain doesn't distinguish between an actual finished task and one that is postponed by taking a note * the fist step is to break down the amprphous task of "writing" into smaller pieces of different tasks that can be finished in **one go.** the second step is to make sure we always write down the outcome of our thinking, including possible connections to further inquiries. * ego depletion * read for understanding * take smart notes * develp ideas (MOC) * 1 overview of a topic - referred to from the index and used as an entry point into a topic has already develped * 2 keeping tarck of all topic - index - * share your insight * make it a habit * afterword * ## Writing Solid Code Writing Solid Code (Microsoft Programming Series) by Trendelles Store Note from Writing Solid Code Book [https://www.pirahansiah.com/describe/book- summary](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Fdescribe%2Fbook- summary&sa=D&sntz=1&usg=AOvVaw3ACzA6G0MBVN3nw6f-yMQo) #SolidCode #C++ #Python #pirahansiah * **Chapter 1** * Enable all optional compiler warnings. * Use lint to catch bugs that your compiler may miss. * If you have unit tests, use them. * **Chapter 2** * introduction assert page 16; assert.h * assert is nothing more than a repackaged form of the #ifdef code we saw before, but when you use * the macro, it takes one line instead of seven * That's why assert is a macro and not a function; if it were a function, calling * it could cause unexpected memory or code swapping. * Use assertions to validate function arguments . * Strip undefined behavior from your code, or use assertions to catch illegal uses of undefined behavior . * Don't waste people's time. Document unclear assertions . * Either remove implicit assumptions, or assert that they are valid . * Use assertions to detect impossible conditions. * Don't hide bugs when you program defensively. * Use a second algorithm to validate your results. * Don't wait for bugs to happen; use startup checks. * **Chapter 3** * Maintain shipping and debugging versions of your program. Shrink-wrap the shipping version, but use the debugging ver­sion as much as possible to catch bugs quickly. * Assertions are a shorthand way to write debugging checks. Use them to catch illegal conditions that should never arise. Don't confuse such conditions with error conditions, which you must handle in the final product. * Use assertions to validate function arguments and to alert pro­ grammars when they do something undefined. The more rigidly you define your functions, the easier it will be to validate the arguments. * Once you've written a function, review it and ask yourself, 'What am I assuming?" If you find an assumption, either assert that your assumption is always valid, or rewrite the code to re­ move the assumption. Also ask, 'What is most likely to be wrong in this code, and how can I automatically detect the prob­lem?" Strive to implement tests that catch bugs at the earliest possible moment. Textbooks encourage programmers to program defensively, but remember that this coding style hides bugs. When you write de­fensive code, use assertions to alert you if the "can't happen" cases do happen. * Eliminate random behavior. Force bugs to be reproducible . * Destroy your garbage so that it's not misused . * Not only did reallocate have to move the block of memory as it was expanding it, but the old memory had to be reallocated and filled with new data. In the assembler, both happened rarely. * This brings up another guideline for writing bug-free code: You don't want anything to happen rarely. You need to isolate those behaviors in your subsystems that may happen and make sure that they do happen. And of­ ten. If you find rare behavior in your subsystems, be sure to do something­ anything-to stir things up. * The assembler bug could have been found within hours, instead of years, if reallocate hadn't so rarely moved blocks when it expanded them. * If something happens rarely, force it to happen often. * Keep debug information to allow stronger error checking. * There is no question that the debug code will create differences be­ tween the ship and the debug versions of your code, but as long as you're careful not to change the underlying behavior of the code, those differences shouldn't matter. * Create thorough subsystem checks, and use them often. * Design your tests carefully. Nothing should be arbitrary. * Strive to implement transparent integrity checks. * Don't apply ship version constraints to the debug version. Trade size and speed for error detection. * **Chapter 4** * Don't wait until you have a bug to step through your code. * Step through every code path. * What About Sweeping Changes? In the past, programmers have asked, "What if I add a feature that touches code in many places? Stepping through all that new code is going to be time consuming." Let me answer that question with another: Can you make such sweeping changes without introducing any bugs? The habit of stepping through your code creates an interesting negative feedback loop. Programmers who step through their code soon learn to write small, easily testable functions because stepping through large func­tions is so painful. Programmers also spend more time thinking about how they can localize the changes they need to make-again, so that they can more easily test their code. And isn't this exactly what you want? No project lead likes it when programmers touch a lot of code; it's too destabilizing. Nor do leads like large, unmanageable functions; they're often unmaintainable. Try hard to localize your changes. If you find that you must make per­vasive changes, think twice before you decide not to step through all the new code. * Overflow and underflow bugs * Data conversion bugs * Off-by-one bugs * NULL pointer bugs * Bugs using garbage memory (0xA3 bugs) * Assignment bugs in which you've used = instead of == * Precedence bugs * Logic bugs * As you step through code, focus on data flow. Source level debuggers can hide execution details. Step through critical code at the instruction level . * **Chapter 5** * Make it hard to ignore error conditions. Don't bury error codes in return values. * Always look for, and eliminate, flaws in your interfaces. * Don't write multipurpose functions. Write separate functions to allow stronger argument validation. * Don't be wishy-washy. Define explicit function arguments. * Write functions that, given valid inputs, cannot fail. * Make the code intelligible at the point of call. Avoid Boolean arguments. * Write comments that emphasize potential hazards. * **Chapter 6** * Use well-defined data types. * Always ask ,"Can this variable or expression over- or underflow?" * Implement your designs as accurately as possible. Being kind a close is being kind a buggy. * these principles already: Don't accept special purpose arguments such as the NULL pointer, and Implement your design, not something that approximates it. The third principle is new: Strive to make every function perform its task exactly one time. * Implement "the task" just once. * Get rid of extraneous if statements. * Avoid using nested ?: operators . * Handle your special cases just once. * Avoid risky language idioms. * Don't needlessly mix operator types. If you must mix operators, use parentheses to isolate the operations. * Avoid calling functions that return errors. * **Chapter 7** * Don't reference memory that you don't own. * Don't reference memory that you have freed. * Don't use output memory as workspace buffers. * Don't pass data in static (or global) memory. * Don't write parasitic functions. * Don't abuse your programming language. * Tight C does not guarantee efficient machine code. * Write code for the "average" programmer. * **Chapter 8** * Bugs don't just "go away." * Don't fix bugs later; fix them now. * Fix the cause, not the symptom. * Don't clean up code unless the clean­ up is critical to the product's success. * Don't implement nonstrategic features. * There are no free features. * Don't allow unnecessary flexibility. * Don't keep "trying" solutions· until you find one that works. Take the time to find the correct solution. * Write and test code in small chunks. * Always test your code, even if that means your schedule will slip. * Don't rely on the testing group to find your bugs. * Don't blame testers for finding your bugs. * Establish your priorities and stick to them. * Never allow the same bug to bite you twice. * **A: CODING CHECKLISTS** * **B: MEMORY LOGGING ROUTINES** * **To sum up** * Charles Simonyi in the early 1970s. https://en.wikipedia.org/wiki/Hungarian_notation * char ch; * byte b; * flag f;/* flags that are always TRUE or FALSE*/ * symbol sym;/* some sort of symbol structure*/ * char byte flag symbol * *pch:/* character pointer*/ * *pb; * *pf: * *psym: * related link: [https://www.morling.dev/images/code_review_pyramid.svg](https://www.google.com/url?q=https%3A%2F%2Fwww.morling.dev%2Fimages%2Fcode_review_pyramid.svg&sa=D&sntz=1&usg=AOvVaw1y2It_PUzXAtFN7dPLegCa) * ## Shape Up: Stop Running in Circles and Ship Work that Matters * Six-week cycles: Our decisions are based on moving the product forward in the next six weeks, not micromanaging time. We don’t count hours or question how individual days are spent. We don’t have daily meetings. We don’t rethink our roadmap every two weeks. Our focus is at a higher level. We say to ourselves: “If this project ships after six weeks, we’ll be really happy. We’ll feel our time was well spent.” Then we commit the six weeks and leave the team alone to get it done * Shaping the work: key elements of a solution, appetite * **part one shaping:** * two separate tracks: one for shaping, one for building. * Steps to shaping * 1\. Set boundaries. * 2\. Rough out the elements: idea that solves the problem within the appetite but without all the fine details worked out * 3\. Address risks and rabbit holes. * 4\. Write the pitch. * fixed time, variable scope, * Where in the current system does the new thing fit? How do you get to it? What are the key components or interactions? Where does it take you? * five ingredients that we always want to include in a pitch: 1. Problem — The raw idea, a use case, or something we’ve seen that motivates us to work on this 2. Appetite — How much time we want to spend and how that constrains the solution 3. Solution — The core elements we came up with, presented in a form that’s easy for people to immediately understand 4. Rabbit holes — Details about the solution worth calling out to avoid problems 5. No-gos — Anything specifically excluded from the concept: functionality or use cases we intentionally aren’t covering to fit the appetite or make the problem tractable * **part two : Betting** * Some companies use two-week cycles (aka “sprints”). We learned that two weeks is too short to get anything meaningful done. Worse than that, two-week cycles are extremely costly due to the planning overhead. The amount of work you get out of two weeks isn’t worth the collective hours around the table to “sprint plan” or the opportunity cost of breaking everyone’s momentum to re-group. * after each six-week cycle, we schedule two weeks for cool-down. * bugs: Use cool-down. Bring it to the betting table. Schedule a bug smash. * We’ve noticed three phases of work when we build a new product from scratch. R&D mode (bet the time on spiking some key pieces of the new product idea. CEO and designer, we don’t expect to ship anything at the end of an R&D cycle) , Production mode (Shaping is deliberate again. bet multiple teams in parallel , Shipping is the goal) , Cleanup mode (There’s no shaping. There aren’t clear team boundaries. Work is “shipped”) [Cleanup shouldn’t last longer than two cycles] * Example: two years of development, R&D mode for the first year, a year of production mode cycles followed, two cycles of cleanup and significantly cut back the feature set, some overlap between R&D and production mode after that first year, * **Part three: Building** * We don’t start by assigning tasks to anyone. * we define and track the scopes as to-do lists. Each scope corresponds to a list name. Then any tasks for that scope go in that list. * **image 1, 2, 3,** * Mark nice-to-haves with ~ * First there’s the uphill phase of figuring out what our approach is and what we’re going to do. Then, once we can see all the work involved, there’s the downhill phase of execution * image 4, 5, * ## Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet * getting start: idea, technology, passion; What can I do well that I would love to do for an extended period of time? P: 19, 21 * 1 market segmentation: The single necessary and sufficient condition for a business is a paying customer. ; technological enthusiasts=early adopters=early majority ; P: 31, * 2 select a beachhead market: P: 44, * 3 build an end user profile: P: 52, * 4 Calculate the Total Addressable Market (TAM) Size for the Beachhead Market; $200 # [Essential Tips And Tricks For ](/book-summary/commonplace-book)[commonplace book](/book-summary/commonplace-book) # [knowledge management ](/book-summary/knowledge_management) # [PKM](/book-summary/knowledge_management/pkm) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. 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Gonzalez Introduction to Algorithms, 3rd Edition (The MIT Press) The Mythical Man-Month: Essays on Software Engineering OpenCV-4-with-Python-Blueprints-Second-Edition Cracking the coding interview 189 programming questions and solutions Cracking the Tech Career: Insider Advice on Landing a Job at Google, Microsoft, Apple, Or Any Top Tech Company The Art of Startup Fundraising Mathematics for Machine Learning Principles of Economics (6th edition) The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) Reinventing Your Life: The Breakthough Program to End Negative Behavior Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf Magazine Papers Websites and links Tools YouTube - List channels Language: Essential Tips And Tricks For commonplace book knowledge management PKM # Books * ## How to take smart Notes * Introduction 4, The four underlying principles 4, the six steps to successful writing 6, afterword * introduction * write everything/ collect notes not related to any thing, organize notes, revirew, rewrite, * no reorganise no complex system, develop ideas by smart notes * 1 slip-box; 2 repeatable tasks that can become automatic and fit together seamlessly change routinges * overarching workflow "Getting Things Done" GTD * constantly have to ump back and forth between different tasks * 1- bibliographical slip-box: reference * 2- collected and generated ideas slip-box * 3- index: notes serve as entry point / thought/ topic * writing a paper step by step * 1 fleeting notes: it will delete within a day * I always have a slip of paper at hand, on which I note down the ideas of certain pages. on the backside I write down the bibliographic details. after finishing the book I go throught my notes and think how ehse notes might be relevant for already written notes in the slip-box. it means that I always read with an eye towards possible connections in the slip-box * 2 literature notes: contain the necessary information - in reference system or in the slip-box * I make a note with the bibliographic details. on the backside I would write "on page x is this, on page y is that" and then it goes into the bibliographic slib-box where I collect everything I read * read, think about its relevance * theoretical background, methodological approach or perspective of the text we read. * whether something adds to a discussion in the slip-box * we look at our slip-box for already existing lines of thought and think about the questions and problems already on our minds to which a new note might contribute. - assigning keywords is much more than just a bureaucratic act. it is a crucial part of the thinking process, which often leads to a deeper elaboration of the note itself and the connection to other notes. * * 3 permanent notes: develop ideas/new/ - only relevant to one particular project - in project specific folder - can be discarded or archived after project finished * add to slip-box behind the note you dirctyly refer to or behind the last note in the slip-box * add links to other notes or links on other notes to your new note * make sure it can be found from the index (MOC) add an entry in the index if necessary or refer to it from a note that is connected to the index * build a latticework of mental models * 4 behind multiple notes (link backward) - link to related note - link to MOC * make sure it can be found again * **keywords** can easily be added to a note like **tags** and will then show up in the **index** * a day * read and take notes * build connections within the slip-box - new idea * you write on your paper, notice a hole in the argument and have another look in the file system for the missing link. you follow up on a footnote, go back to research and might add a fitting quote to one of your papers in the making. * The four underlying principles * writing * simplicity * not from scratch * let the work carry you forward * the six steps to successful writing * separate and interlocking tasks * we use memo techniques is to bundle items together in a meaningful way and remember the bundles * the brain doesn't distinguish between an actual finished task and one that is postponed by taking a note * the fist step is to break down the amprphous task of "writing" into smaller pieces of different tasks that can be finished in **one go.** the second step is to make sure we always write down the outcome of our thinking, including possible connections to further inquiries. * ego depletion * read for understanding * take smart notes * develp ideas (MOC) * 1 overview of a topic - referred to from the index and used as an entry point into a topic has already develped * 2 keeping tarck of all topic - index - * share your insight * make it a habit * afterword * ## Writing Solid Code Writing Solid Code (Microsoft Programming Series) by Trendelles Store Note from Writing Solid Code Book [https://www.pirahansiah.com/describe/book- summary](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Fdescribe%2Fbook- summary&sa=D&sntz=1&usg=AOvVaw3ACzA6G0MBVN3nw6f-yMQo) #SolidCode #C++ #Python #pirahansiah * **Chapter 1** * Enable all optional compiler warnings. * Use lint to catch bugs that your compiler may miss. * If you have unit tests, use them. * **Chapter 2** * introduction assert page 16; assert.h * assert is nothing more than a repackaged form of the #ifdef code we saw before, but when you use * the macro, it takes one line instead of seven * That's why assert is a macro and not a function; if it were a function, calling * it could cause unexpected memory or code swapping. * Use assertions to validate function arguments . * Strip undefined behavior from your code, or use assertions to catch illegal uses of undefined behavior . * Don't waste people's time. Document unclear assertions . * Either remove implicit assumptions, or assert that they are valid . * Use assertions to detect impossible conditions. * Don't hide bugs when you program defensively. * Use a second algorithm to validate your results. * Don't wait for bugs to happen; use startup checks. * **Chapter 3** * Maintain shipping and debugging versions of your program. Shrink-wrap the shipping version, but use the debugging ver­sion as much as possible to catch bugs quickly. * Assertions are a shorthand way to write debugging checks. Use them to catch illegal conditions that should never arise. Don't confuse such conditions with error conditions, which you must handle in the final product. * Use assertions to validate function arguments and to alert pro­ grammars when they do something undefined. The more rigidly you define your functions, the easier it will be to validate the arguments. * Once you've written a function, review it and ask yourself, 'What am I assuming?" If you find an assumption, either assert that your assumption is always valid, or rewrite the code to re­ move the assumption. Also ask, 'What is most likely to be wrong in this code, and how can I automatically detect the prob­lem?" Strive to implement tests that catch bugs at the earliest possible moment. Textbooks encourage programmers to program defensively, but remember that this coding style hides bugs. When you write de­fensive code, use assertions to alert you if the "can't happen" cases do happen. * Eliminate random behavior. Force bugs to be reproducible . * Destroy your garbage so that it's not misused . * Not only did reallocate have to move the block of memory as it was expanding it, but the old memory had to be reallocated and filled with new data. In the assembler, both happened rarely. * This brings up another guideline for writing bug-free code: You don't want anything to happen rarely. You need to isolate those behaviors in your subsystems that may happen and make sure that they do happen. And of­ ten. If you find rare behavior in your subsystems, be sure to do something­ anything-to stir things up. * The assembler bug could have been found within hours, instead of years, if reallocate hadn't so rarely moved blocks when it expanded them. * If something happens rarely, force it to happen often. * Keep debug information to allow stronger error checking. * There is no question that the debug code will create differences be­ tween the ship and the debug versions of your code, but as long as you're careful not to change the underlying behavior of the code, those differences shouldn't matter. * Create thorough subsystem checks, and use them often. * Design your tests carefully. Nothing should be arbitrary. * Strive to implement transparent integrity checks. * Don't apply ship version constraints to the debug version. Trade size and speed for error detection. * **Chapter 4** * Don't wait until you have a bug to step through your code. * Step through every code path. * What About Sweeping Changes? In the past, programmers have asked, "What if I add a feature that touches code in many places? Stepping through all that new code is going to be time consuming." Let me answer that question with another: Can you make such sweeping changes without introducing any bugs? The habit of stepping through your code creates an interesting negative feedback loop. Programmers who step through their code soon learn to write small, easily testable functions because stepping through large func­tions is so painful. Programmers also spend more time thinking about how they can localize the changes they need to make-again, so that they can more easily test their code. And isn't this exactly what you want? No project lead likes it when programmers touch a lot of code; it's too destabilizing. Nor do leads like large, unmanageable functions; they're often unmaintainable. Try hard to localize your changes. If you find that you must make per­vasive changes, think twice before you decide not to step through all the new code. * Overflow and underflow bugs * Data conversion bugs * Off-by-one bugs * NULL pointer bugs * Bugs using garbage memory (0xA3 bugs) * Assignment bugs in which you've used = instead of == * Precedence bugs * Logic bugs * As you step through code, focus on data flow. Source level debuggers can hide execution details. Step through critical code at the instruction level . * **Chapter 5** * Make it hard to ignore error conditions. Don't bury error codes in return values. * Always look for, and eliminate, flaws in your interfaces. * Don't write multipurpose functions. Write separate functions to allow stronger argument validation. * Don't be wishy-washy. Define explicit function arguments. * Write functions that, given valid inputs, cannot fail. * Make the code intelligible at the point of call. Avoid Boolean arguments. * Write comments that emphasize potential hazards. * **Chapter 6** * Use well-defined data types. * Always ask ,"Can this variable or expression over- or underflow?" * Implement your designs as accurately as possible. Being kind a close is being kind a buggy. * these principles already: Don't accept special purpose arguments such as the NULL pointer, and Implement your design, not something that approximates it. The third principle is new: Strive to make every function perform its task exactly one time. * Implement "the task" just once. * Get rid of extraneous if statements. * Avoid using nested ?: operators . * Handle your special cases just once. * Avoid risky language idioms. * Don't needlessly mix operator types. If you must mix operators, use parentheses to isolate the operations. * Avoid calling functions that return errors. * **Chapter 7** * Don't reference memory that you don't own. * Don't reference memory that you have freed. * Don't use output memory as workspace buffers. * Don't pass data in static (or global) memory. * Don't write parasitic functions. * Don't abuse your programming language. * Tight C does not guarantee efficient machine code. * Write code for the "average" programmer. * **Chapter 8** * Bugs don't just "go away." * Don't fix bugs later; fix them now. * Fix the cause, not the symptom. * Don't clean up code unless the clean­ up is critical to the product's success. * Don't implement nonstrategic features. * There are no free features. * Don't allow unnecessary flexibility. * Don't keep "trying" solutions· until you find one that works. Take the time to find the correct solution. * Write and test code in small chunks. * Always test your code, even if that means your schedule will slip. * Don't rely on the testing group to find your bugs. * Don't blame testers for finding your bugs. * Establish your priorities and stick to them. * Never allow the same bug to bite you twice. * **A: CODING CHECKLISTS** * **B: MEMORY LOGGING ROUTINES** * **To sum up** * Charles Simonyi in the early 1970s. https://en.wikipedia.org/wiki/Hungarian_notation * char ch; * byte b; * flag f;/* flags that are always TRUE or FALSE*/ * symbol sym;/* some sort of symbol structure*/ * char byte flag symbol * *pch:/* character pointer*/ * *pb; * *pf: * *psym: * related link: [https://www.morling.dev/images/code_review_pyramid.svg](https://www.google.com/url?q=https%3A%2F%2Fwww.morling.dev%2Fimages%2Fcode_review_pyramid.svg&sa=D&sntz=1&usg=AOvVaw1y2It_PUzXAtFN7dPLegCa) * ## Shape Up: Stop Running in Circles and Ship Work that Matters * Six-week cycles: Our decisions are based on moving the product forward in the next six weeks, not micromanaging time. We don’t count hours or question how individual days are spent. We don’t have daily meetings. We don’t rethink our roadmap every two weeks. Our focus is at a higher level. We say to ourselves: “If this project ships after six weeks, we’ll be really happy. We’ll feel our time was well spent.” Then we commit the six weeks and leave the team alone to get it done * Shaping the work: key elements of a solution, appetite * **part one shaping:** * two separate tracks: one for shaping, one for building. * Steps to shaping * 1\. Set boundaries. * 2\. Rough out the elements: idea that solves the problem within the appetite but without all the fine details worked out * 3\. Address risks and rabbit holes. * 4\. Write the pitch. * fixed time, variable scope, * Where in the current system does the new thing fit? How do you get to it? What are the key components or interactions? Where does it take you? * five ingredients that we always want to include in a pitch: 1. Problem — The raw idea, a use case, or something we’ve seen that motivates us to work on this 2. Appetite — How much time we want to spend and how that constrains the solution 3. Solution — The core elements we came up with, presented in a form that’s easy for people to immediately understand 4. Rabbit holes — Details about the solution worth calling out to avoid problems 5. No-gos — Anything specifically excluded from the concept: functionality or use cases we intentionally aren’t covering to fit the appetite or make the problem tractable * **part two : Betting** * Some companies use two-week cycles (aka “sprints”). We learned that two weeks is too short to get anything meaningful done. Worse than that, two-week cycles are extremely costly due to the planning overhead. The amount of work you get out of two weeks isn’t worth the collective hours around the table to “sprint plan” or the opportunity cost of breaking everyone’s momentum to re-group. * after each six-week cycle, we schedule two weeks for cool-down. * bugs: Use cool-down. Bring it to the betting table. Schedule a bug smash. * We’ve noticed three phases of work when we build a new product from scratch. R&D mode (bet the time on spiking some key pieces of the new product idea. CEO and designer, we don’t expect to ship anything at the end of an R&D cycle) , Production mode (Shaping is deliberate again. bet multiple teams in parallel , Shipping is the goal) , Cleanup mode (There’s no shaping. There aren’t clear team boundaries. Work is “shipped”) [Cleanup shouldn’t last longer than two cycles] * Example: two years of development, R&D mode for the first year, a year of production mode cycles followed, two cycles of cleanup and significantly cut back the feature set, some overlap between R&D and production mode after that first year, * **Part three: Building** * We don’t start by assigning tasks to anyone. * we define and track the scopes as to-do lists. Each scope corresponds to a list name. Then any tasks for that scope go in that list. * **image 1, 2, 3,** * Mark nice-to-haves with ~ * First there’s the uphill phase of figuring out what our approach is and what we’re going to do. Then, once we can see all the work involved, there’s the downhill phase of execution * image 4, 5, * ## Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet * getting start: idea, technology, passion; What can I do well that I would love to do for an extended period of time? P: 19, 21 * 1 market segmentation: The single necessary and sufficient condition for a business is a paying customer. ; technological enthusiasts=early adopters=early majority ; P: 31, * 2 select a beachhead market: P: 44, * 3 build an end user profile: P: 52, * 4 Calculate the Total Addressable Market (TAM) Size for the Beachhead Market; $200 # [Essential Tips And Tricks For ](/book-summary/commonplace-book)[commonplace book](/book-summary/commonplace-book) # [knowledge management ](/book-summary/knowledge_management) # [PKM](/book-summary/knowledge_management/pkm) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/qDZvojKIjUt_BmKSFeFiLcko8E9gUx2UPvpr5lUZqjKQRQ0VpykBihMNMzKE- OOh8UqTvee2oKfKd28QF1FzbWk=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/qDZvojKIjUt_BmKSFeFiLcko8E9gUx2UPvpr5lUZqjKQRQ0VpykBihMNMzKE- OOh8UqTvee2oKfKd28QF1FzbWk=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Book Summary Books How to take smart Notes Writing Solid Code Shape Up: Stop Running in Circles and Ship Work that Matters Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet List of books I've read: Computer Vision: Algorithms and Applications Multiple View Geometry in Computer Vision Second Edition Complete to download the latest draft of Machine Learning Yearning Multiple view geometry in computer vision Learning OpenCV Book by Adrian Kaehler and Gary Bradski Digital Image Processing, Global Edition by Rafael C. Gonzalez Introduction to Algorithms, 3rd Edition (The MIT Press) The Mythical Man-Month: Essays on Software Engineering OpenCV-4-with-Python-Blueprints-Second-Edition Cracking the coding interview 189 programming questions and solutions Cracking the Tech Career: Insider Advice on Landing a Job at Google, Microsoft, Apple, Or Any Top Tech Company The Art of Startup Fundraising Mathematics for Machine Learning Principles of Economics (6th edition) The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) Reinventing Your Life: The Breakthough Program to End Negative Behavior Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf Magazine Papers Websites and links Tools YouTube - List channels Language: Essential Tips And Tricks For commonplace book knowledge management PKM # Books * ## How to take smart Notes * Introduction 4, The four underlying principles 4, the six steps to successful writing 6, afterword * introduction * write everything/ collect notes not related to any thing, organize notes, revirew, rewrite, * no reorganise no complex system, develop ideas by smart notes * 1 slip-box; 2 repeatable tasks that can become automatic and fit together seamlessly change routinges * overarching workflow "Getting Things Done" GTD * constantly have to ump back and forth between different tasks * 1- bibliographical slip-box: reference * 2- collected and generated ideas slip-box * 3- index: notes serve as entry point / thought/ topic * writing a paper step by step * 1 fleeting notes: it will delete within a day * I always have a slip of paper at hand, on which I note down the ideas of certain pages. on the backside I write down the bibliographic details. after finishing the book I go throught my notes and think how ehse notes might be relevant for already written notes in the slip-box. it means that I always read with an eye towards possible connections in the slip-box * 2 literature notes: contain the necessary information - in reference system or in the slip-box * I make a note with the bibliographic details. on the backside I would write "on page x is this, on page y is that" and then it goes into the bibliographic slib-box where I collect everything I read * read, think about its relevance * theoretical background, methodological approach or perspective of the text we read. * whether something adds to a discussion in the slip-box * we look at our slip-box for already existing lines of thought and think about the questions and problems already on our minds to which a new note might contribute. - assigning keywords is much more than just a bureaucratic act. it is a crucial part of the thinking process, which often leads to a deeper elaboration of the note itself and the connection to other notes. * * 3 permanent notes: develop ideas/new/ - only relevant to one particular project - in project specific folder - can be discarded or archived after project finished * add to slip-box behind the note you dirctyly refer to or behind the last note in the slip-box * add links to other notes or links on other notes to your new note * make sure it can be found from the index (MOC) add an entry in the index if necessary or refer to it from a note that is connected to the index * build a latticework of mental models * 4 behind multiple notes (link backward) - link to related note - link to MOC * make sure it can be found again * **keywords** can easily be added to a note like **tags** and will then show up in the **index** * a day * read and take notes * build connections within the slip-box - new idea * you write on your paper, notice a hole in the argument and have another look in the file system for the missing link. you follow up on a footnote, go back to research and might add a fitting quote to one of your papers in the making. * The four underlying principles * writing * simplicity * not from scratch * let the work carry you forward * the six steps to successful writing * separate and interlocking tasks * we use memo techniques is to bundle items together in a meaningful way and remember the bundles * the brain doesn't distinguish between an actual finished task and one that is postponed by taking a note * the fist step is to break down the amprphous task of "writing" into smaller pieces of different tasks that can be finished in **one go.** the second step is to make sure we always write down the outcome of our thinking, including possible connections to further inquiries. * ego depletion * read for understanding * take smart notes * develp ideas (MOC) * 1 overview of a topic - referred to from the index and used as an entry point into a topic has already develped * 2 keeping tarck of all topic - index - * share your insight * make it a habit * afterword * ## Writing Solid Code Writing Solid Code (Microsoft Programming Series) by Trendelles Store Note from Writing Solid Code Book [https://www.pirahansiah.com/describe/book- summary](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Fdescribe%2Fbook- summary&sa=D&sntz=1&usg=AOvVaw3ACzA6G0MBVN3nw6f-yMQo) #SolidCode #C++ #Python #pirahansiah * **Chapter 1** * Enable all optional compiler warnings. * Use lint to catch bugs that your compiler may miss. * If you have unit tests, use them. * **Chapter 2** * introduction assert page 16; assert.h * assert is nothing more than a repackaged form of the #ifdef code we saw before, but when you use * the macro, it takes one line instead of seven * That's why assert is a macro and not a function; if it were a function, calling * it could cause unexpected memory or code swapping. * Use assertions to validate function arguments . * Strip undefined behavior from your code, or use assertions to catch illegal uses of undefined behavior . * Don't waste people's time. Document unclear assertions . * Either remove implicit assumptions, or assert that they are valid . * Use assertions to detect impossible conditions. * Don't hide bugs when you program defensively. * Use a second algorithm to validate your results. * Don't wait for bugs to happen; use startup checks. * **Chapter 3** * Maintain shipping and debugging versions of your program. Shrink-wrap the shipping version, but use the debugging ver­sion as much as possible to catch bugs quickly. * Assertions are a shorthand way to write debugging checks. Use them to catch illegal conditions that should never arise. Don't confuse such conditions with error conditions, which you must handle in the final product. * Use assertions to validate function arguments and to alert pro­ grammars when they do something undefined. The more rigidly you define your functions, the easier it will be to validate the arguments. * Once you've written a function, review it and ask yourself, 'What am I assuming?" If you find an assumption, either assert that your assumption is always valid, or rewrite the code to re­ move the assumption. Also ask, 'What is most likely to be wrong in this code, and how can I automatically detect the prob­lem?" Strive to implement tests that catch bugs at the earliest possible moment. Textbooks encourage programmers to program defensively, but remember that this coding style hides bugs. When you write de­fensive code, use assertions to alert you if the "can't happen" cases do happen. * Eliminate random behavior. Force bugs to be reproducible . * Destroy your garbage so that it's not misused . * Not only did reallocate have to move the block of memory as it was expanding it, but the old memory had to be reallocated and filled with new data. In the assembler, both happened rarely. * This brings up another guideline for writing bug-free code: You don't want anything to happen rarely. You need to isolate those behaviors in your subsystems that may happen and make sure that they do happen. And of­ ten. If you find rare behavior in your subsystems, be sure to do something­ anything-to stir things up. * The assembler bug could have been found within hours, instead of years, if reallocate hadn't so rarely moved blocks when it expanded them. * If something happens rarely, force it to happen often. * Keep debug information to allow stronger error checking. * There is no question that the debug code will create differences be­ tween the ship and the debug versions of your code, but as long as you're careful not to change the underlying behavior of the code, those differences shouldn't matter. * Create thorough subsystem checks, and use them often. * Design your tests carefully. Nothing should be arbitrary. * Strive to implement transparent integrity checks. * Don't apply ship version constraints to the debug version. Trade size and speed for error detection. * **Chapter 4** * Don't wait until you have a bug to step through your code. * Step through every code path. * What About Sweeping Changes? In the past, programmers have asked, "What if I add a feature that touches code in many places? Stepping through all that new code is going to be time consuming." Let me answer that question with another: Can you make such sweeping changes without introducing any bugs? The habit of stepping through your code creates an interesting negative feedback loop. Programmers who step through their code soon learn to write small, easily testable functions because stepping through large func­tions is so painful. Programmers also spend more time thinking about how they can localize the changes they need to make-again, so that they can more easily test their code. And isn't this exactly what you want? No project lead likes it when programmers touch a lot of code; it's too destabilizing. Nor do leads like large, unmanageable functions; they're often unmaintainable. Try hard to localize your changes. If you find that you must make per­vasive changes, think twice before you decide not to step through all the new code. * Overflow and underflow bugs * Data conversion bugs * Off-by-one bugs * NULL pointer bugs * Bugs using garbage memory (0xA3 bugs) * Assignment bugs in which you've used = instead of == * Precedence bugs * Logic bugs * As you step through code, focus on data flow. Source level debuggers can hide execution details. Step through critical code at the instruction level . * **Chapter 5** * Make it hard to ignore error conditions. Don't bury error codes in return values. * Always look for, and eliminate, flaws in your interfaces. * Don't write multipurpose functions. Write separate functions to allow stronger argument validation. * Don't be wishy-washy. Define explicit function arguments. * Write functions that, given valid inputs, cannot fail. * Make the code intelligible at the point of call. Avoid Boolean arguments. * Write comments that emphasize potential hazards. * **Chapter 6** * Use well-defined data types. * Always ask ,"Can this variable or expression over- or underflow?" * Implement your designs as accurately as possible. Being kind a close is being kind a buggy. * these principles already: Don't accept special purpose arguments such as the NULL pointer, and Implement your design, not something that approximates it. The third principle is new: Strive to make every function perform its task exactly one time. * Implement "the task" just once. * Get rid of extraneous if statements. * Avoid using nested ?: operators . * Handle your special cases just once. * Avoid risky language idioms. * Don't needlessly mix operator types. If you must mix operators, use parentheses to isolate the operations. * Avoid calling functions that return errors. * **Chapter 7** * Don't reference memory that you don't own. * Don't reference memory that you have freed. * Don't use output memory as workspace buffers. * Don't pass data in static (or global) memory. * Don't write parasitic functions. * Don't abuse your programming language. * Tight C does not guarantee efficient machine code. * Write code for the "average" programmer. * **Chapter 8** * Bugs don't just "go away." * Don't fix bugs later; fix them now. * Fix the cause, not the symptom. * Don't clean up code unless the clean­ up is critical to the product's success. * Don't implement nonstrategic features. * There are no free features. * Don't allow unnecessary flexibility. * Don't keep "trying" solutions· until you find one that works. Take the time to find the correct solution. * Write and test code in small chunks. * Always test your code, even if that means your schedule will slip. * Don't rely on the testing group to find your bugs. * Don't blame testers for finding your bugs. * Establish your priorities and stick to them. * Never allow the same bug to bite you twice. * **A: CODING CHECKLISTS** * **B: MEMORY LOGGING ROUTINES** * **To sum up** * Charles Simonyi in the early 1970s. https://en.wikipedia.org/wiki/Hungarian_notation * char ch; * byte b; * flag f;/* flags that are always TRUE or FALSE*/ * symbol sym;/* some sort of symbol structure*/ * char byte flag symbol * *pch:/* character pointer*/ * *pb; * *pf: * *psym: * related link: [https://www.morling.dev/images/code_review_pyramid.svg](https://www.google.com/url?q=https%3A%2F%2Fwww.morling.dev%2Fimages%2Fcode_review_pyramid.svg&sa=D&sntz=1&usg=AOvVaw1y2It_PUzXAtFN7dPLegCa) * ## Shape Up: Stop Running in Circles and Ship Work that Matters * Six-week cycles: Our decisions are based on moving the product forward in the next six weeks, not micromanaging time. We don’t count hours or question how individual days are spent. We don’t have daily meetings. We don’t rethink our roadmap every two weeks. Our focus is at a higher level. We say to ourselves: “If this project ships after six weeks, we’ll be really happy. We’ll feel our time was well spent.” Then we commit the six weeks and leave the team alone to get it done * Shaping the work: key elements of a solution, appetite * **part one shaping:** * two separate tracks: one for shaping, one for building. * Steps to shaping * 1\. Set boundaries. * 2\. Rough out the elements: idea that solves the problem within the appetite but without all the fine details worked out * 3\. Address risks and rabbit holes. * 4\. Write the pitch. * fixed time, variable scope, * Where in the current system does the new thing fit? How do you get to it? What are the key components or interactions? Where does it take you? * five ingredients that we always want to include in a pitch: 1. Problem — The raw idea, a use case, or something we’ve seen that motivates us to work on this 2. Appetite — How much time we want to spend and how that constrains the solution 3. Solution — The core elements we came up with, presented in a form that’s easy for people to immediately understand 4. Rabbit holes — Details about the solution worth calling out to avoid problems 5. No-gos — Anything specifically excluded from the concept: functionality or use cases we intentionally aren’t covering to fit the appetite or make the problem tractable * **part two : Betting** * Some companies use two-week cycles (aka “sprints”). We learned that two weeks is too short to get anything meaningful done. Worse than that, two-week cycles are extremely costly due to the planning overhead. The amount of work you get out of two weeks isn’t worth the collective hours around the table to “sprint plan” or the opportunity cost of breaking everyone’s momentum to re-group. * after each six-week cycle, we schedule two weeks for cool-down. * bugs: Use cool-down. Bring it to the betting table. Schedule a bug smash. * We’ve noticed three phases of work when we build a new product from scratch. R&D mode (bet the time on spiking some key pieces of the new product idea. CEO and designer, we don’t expect to ship anything at the end of an R&D cycle) , Production mode (Shaping is deliberate again. bet multiple teams in parallel , Shipping is the goal) , Cleanup mode (There’s no shaping. There aren’t clear team boundaries. Work is “shipped”) [Cleanup shouldn’t last longer than two cycles] * Example: two years of development, R&D mode for the first year, a year of production mode cycles followed, two cycles of cleanup and significantly cut back the feature set, some overlap between R&D and production mode after that first year, * **Part three: Building** * We don’t start by assigning tasks to anyone. * we define and track the scopes as to-do lists. Each scope corresponds to a list name. Then any tasks for that scope go in that list. * **image 1, 2, 3,** * Mark nice-to-haves with ~ * First there’s the uphill phase of figuring out what our approach is and what we’re going to do. Then, once we can see all the work involved, there’s the downhill phase of execution * image 4, 5, * ## Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet * getting start: idea, technology, passion; What can I do well that I would love to do for an extended period of time? P: 19, 21 * 1 market segmentation: The single necessary and sufficient condition for a business is a paying customer. ; technological enthusiasts=early adopters=early majority ; P: 31, * 2 select a beachhead market: P: 44, * 3 build an end user profile: P: 52, * 4 Calculate the Total Addressable Market (TAM) Size for the Beachhead Market; $200 # [Essential Tips And Tricks For ](/book-summary/commonplace-book)[commonplace book](/book-summary/commonplace-book) # [knowledge management ](/book-summary/knowledge_management) # [PKM](/book-summary/knowledge_management/pkm) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/qDZvojKIjUt_BmKSFeFiLcko8E9gUx2UPvpr5lUZqjKQRQ0VpykBihMNMzKE- OOh8UqTvee2oKfKd28QF1FzbWk=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/qDZvojKIjUt_BmKSFeFiLcko8E9gUx2UPvpr5lUZqjKQRQ0VpykBihMNMzKE- OOh8UqTvee2oKfKd28QF1FzbWk=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Book Summary Books How to take smart Notes Writing Solid Code Shape Up: Stop Running in Circles and Ship Work that Matters Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet List of books I've read: Computer Vision: Algorithms and Applications Multiple View Geometry in Computer Vision Second Edition Complete to download the latest draft of Machine Learning Yearning Multiple view geometry in computer vision Learning OpenCV Book by Adrian Kaehler and Gary Bradski Digital Image Processing, Global Edition by Rafael C. Gonzalez Introduction to Algorithms, 3rd Edition (The MIT Press) The Mythical Man-Month: Essays on Software Engineering OpenCV-4-with-Python-Blueprints-Second-Edition Cracking the coding interview 189 programming questions and solutions Cracking the Tech Career: Insider Advice on Landing a Job at Google, Microsoft, Apple, Or Any Top Tech Company The Art of Startup Fundraising Mathematics for Machine Learning Principles of Economics (6th edition) The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) Reinventing Your Life: The Breakthough Program to End Negative Behavior Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf Magazine Papers Websites and links Tools YouTube - List channels Language: Essential Tips And Tricks For commonplace book knowledge management PKM # Books * ## How to take smart Notes * Introduction 4, The four underlying principles 4, the six steps to successful writing 6, afterword * introduction * write everything/ collect notes not related to any thing, organize notes, revirew, rewrite, * no reorganise no complex system, develop ideas by smart notes * 1 slip-box; 2 repeatable tasks that can become automatic and fit together seamlessly change routinges * overarching workflow "Getting Things Done" GTD * constantly have to ump back and forth between different tasks * 1- bibliographical slip-box: reference * 2- collected and generated ideas slip-box * 3- index: notes serve as entry point / thought/ topic * writing a paper step by step * 1 fleeting notes: it will delete within a day * I always have a slip of paper at hand, on which I note down the ideas of certain pages. on the backside I write down the bibliographic details. after finishing the book I go throught my notes and think how ehse notes might be relevant for already written notes in the slip-box. it means that I always read with an eye towards possible connections in the slip-box * 2 literature notes: contain the necessary information - in reference system or in the slip-box * I make a note with the bibliographic details. on the backside I would write "on page x is this, on page y is that" and then it goes into the bibliographic slib-box where I collect everything I read * read, think about its relevance * theoretical background, methodological approach or perspective of the text we read. * whether something adds to a discussion in the slip-box * we look at our slip-box for already existing lines of thought and think about the questions and problems already on our minds to which a new note might contribute. - assigning keywords is much more than just a bureaucratic act. it is a crucial part of the thinking process, which often leads to a deeper elaboration of the note itself and the connection to other notes. * * 3 permanent notes: develop ideas/new/ - only relevant to one particular project - in project specific folder - can be discarded or archived after project finished * add to slip-box behind the note you dirctyly refer to or behind the last note in the slip-box * add links to other notes or links on other notes to your new note * make sure it can be found from the index (MOC) add an entry in the index if necessary or refer to it from a note that is connected to the index * build a latticework of mental models * 4 behind multiple notes (link backward) - link to related note - link to MOC * make sure it can be found again * **keywords** can easily be added to a note like **tags** and will then show up in the **index** * a day * read and take notes * build connections within the slip-box - new idea * you write on your paper, notice a hole in the argument and have another look in the file system for the missing link. you follow up on a footnote, go back to research and might add a fitting quote to one of your papers in the making. * The four underlying principles * writing * simplicity * not from scratch * let the work carry you forward * the six steps to successful writing * separate and interlocking tasks * we use memo techniques is to bundle items together in a meaningful way and remember the bundles * the brain doesn't distinguish between an actual finished task and one that is postponed by taking a note * the fist step is to break down the amprphous task of "writing" into smaller pieces of different tasks that can be finished in **one go.** the second step is to make sure we always write down the outcome of our thinking, including possible connections to further inquiries. * ego depletion * read for understanding * take smart notes * develp ideas (MOC) * 1 overview of a topic - referred to from the index and used as an entry point into a topic has already develped * 2 keeping tarck of all topic - index - * share your insight * make it a habit * afterword * ## Writing Solid Code Writing Solid Code (Microsoft Programming Series) by Trendelles Store Note from Writing Solid Code Book [https://www.pirahansiah.com/describe/book- summary](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Fdescribe%2Fbook- summary&sa=D&sntz=1&usg=AOvVaw3ACzA6G0MBVN3nw6f-yMQo) #SolidCode #C++ #Python #pirahansiah * **Chapter 1** * Enable all optional compiler warnings. * Use lint to catch bugs that your compiler may miss. * If you have unit tests, use them. * **Chapter 2** * introduction assert page 16; assert.h * assert is nothing more than a repackaged form of the #ifdef code we saw before, but when you use * the macro, it takes one line instead of seven * That's why assert is a macro and not a function; if it were a function, calling * it could cause unexpected memory or code swapping. * Use assertions to validate function arguments . * Strip undefined behavior from your code, or use assertions to catch illegal uses of undefined behavior . * Don't waste people's time. Document unclear assertions . * Either remove implicit assumptions, or assert that they are valid . * Use assertions to detect impossible conditions. * Don't hide bugs when you program defensively. * Use a second algorithm to validate your results. * Don't wait for bugs to happen; use startup checks. * **Chapter 3** * Maintain shipping and debugging versions of your program. Shrink-wrap the shipping version, but use the debugging ver­sion as much as possible to catch bugs quickly. * Assertions are a shorthand way to write debugging checks. Use them to catch illegal conditions that should never arise. Don't confuse such conditions with error conditions, which you must handle in the final product. * Use assertions to validate function arguments and to alert pro­ grammars when they do something undefined. The more rigidly you define your functions, the easier it will be to validate the arguments. * Once you've written a function, review it and ask yourself, 'What am I assuming?" If you find an assumption, either assert that your assumption is always valid, or rewrite the code to re­ move the assumption. Also ask, 'What is most likely to be wrong in this code, and how can I automatically detect the prob­lem?" Strive to implement tests that catch bugs at the earliest possible moment. Textbooks encourage programmers to program defensively, but remember that this coding style hides bugs. When you write de­fensive code, use assertions to alert you if the "can't happen" cases do happen. * Eliminate random behavior. Force bugs to be reproducible . * Destroy your garbage so that it's not misused . * Not only did reallocate have to move the block of memory as it was expanding it, but the old memory had to be reallocated and filled with new data. In the assembler, both happened rarely. * This brings up another guideline for writing bug-free code: You don't want anything to happen rarely. You need to isolate those behaviors in your subsystems that may happen and make sure that they do happen. And of­ ten. If you find rare behavior in your subsystems, be sure to do something­ anything-to stir things up. * The assembler bug could have been found within hours, instead of years, if reallocate hadn't so rarely moved blocks when it expanded them. * If something happens rarely, force it to happen often. * Keep debug information to allow stronger error checking. * There is no question that the debug code will create differences be­ tween the ship and the debug versions of your code, but as long as you're careful not to change the underlying behavior of the code, those differences shouldn't matter. * Create thorough subsystem checks, and use them often. * Design your tests carefully. Nothing should be arbitrary. * Strive to implement transparent integrity checks. * Don't apply ship version constraints to the debug version. Trade size and speed for error detection. * **Chapter 4** * Don't wait until you have a bug to step through your code. * Step through every code path. * What About Sweeping Changes? In the past, programmers have asked, "What if I add a feature that touches code in many places? Stepping through all that new code is going to be time consuming." Let me answer that question with another: Can you make such sweeping changes without introducing any bugs? The habit of stepping through your code creates an interesting negative feedback loop. Programmers who step through their code soon learn to write small, easily testable functions because stepping through large func­tions is so painful. Programmers also spend more time thinking about how they can localize the changes they need to make-again, so that they can more easily test their code. And isn't this exactly what you want? No project lead likes it when programmers touch a lot of code; it's too destabilizing. Nor do leads like large, unmanageable functions; they're often unmaintainable. Try hard to localize your changes. If you find that you must make per­vasive changes, think twice before you decide not to step through all the new code. * Overflow and underflow bugs * Data conversion bugs * Off-by-one bugs * NULL pointer bugs * Bugs using garbage memory (0xA3 bugs) * Assignment bugs in which you've used = instead of == * Precedence bugs * Logic bugs * As you step through code, focus on data flow. Source level debuggers can hide execution details. Step through critical code at the instruction level . * **Chapter 5** * Make it hard to ignore error conditions. Don't bury error codes in return values. * Always look for, and eliminate, flaws in your interfaces. * Don't write multipurpose functions. Write separate functions to allow stronger argument validation. * Don't be wishy-washy. Define explicit function arguments. * Write functions that, given valid inputs, cannot fail. * Make the code intelligible at the point of call. Avoid Boolean arguments. * Write comments that emphasize potential hazards. * **Chapter 6** * Use well-defined data types. * Always ask ,"Can this variable or expression over- or underflow?" * Implement your designs as accurately as possible. Being kind a close is being kind a buggy. * these principles already: Don't accept special purpose arguments such as the NULL pointer, and Implement your design, not something that approximates it. The third principle is new: Strive to make every function perform its task exactly one time. * Implement "the task" just once. * Get rid of extraneous if statements. * Avoid using nested ?: operators . * Handle your special cases just once. * Avoid risky language idioms. * Don't needlessly mix operator types. If you must mix operators, use parentheses to isolate the operations. * Avoid calling functions that return errors. * **Chapter 7** * Don't reference memory that you don't own. * Don't reference memory that you have freed. * Don't use output memory as workspace buffers. * Don't pass data in static (or global) memory. * Don't write parasitic functions. * Don't abuse your programming language. * Tight C does not guarantee efficient machine code. * Write code for the "average" programmer. * **Chapter 8** * Bugs don't just "go away." * Don't fix bugs later; fix them now. * Fix the cause, not the symptom. * Don't clean up code unless the clean­ up is critical to the product's success. * Don't implement nonstrategic features. * There are no free features. * Don't allow unnecessary flexibility. * Don't keep "trying" solutions· until you find one that works. Take the time to find the correct solution. * Write and test code in small chunks. * Always test your code, even if that means your schedule will slip. * Don't rely on the testing group to find your bugs. * Don't blame testers for finding your bugs. * Establish your priorities and stick to them. * Never allow the same bug to bite you twice. * **A: CODING CHECKLISTS** * **B: MEMORY LOGGING ROUTINES** * **To sum up** * Charles Simonyi in the early 1970s. https://en.wikipedia.org/wiki/Hungarian_notation * char ch; * byte b; * flag f;/* flags that are always TRUE or FALSE*/ * symbol sym;/* some sort of symbol structure*/ * char byte flag symbol * *pch:/* character pointer*/ * *pb; * *pf: * *psym: * related link: [https://www.morling.dev/images/code_review_pyramid.svg](https://www.google.com/url?q=https%3A%2F%2Fwww.morling.dev%2Fimages%2Fcode_review_pyramid.svg&sa=D&sntz=1&usg=AOvVaw1y2It_PUzXAtFN7dPLegCa) * ## Shape Up: Stop Running in Circles and Ship Work that Matters * Six-week cycles: Our decisions are based on moving the product forward in the next six weeks, not micromanaging time. We don’t count hours or question how individual days are spent. We don’t have daily meetings. We don’t rethink our roadmap every two weeks. Our focus is at a higher level. We say to ourselves: “If this project ships after six weeks, we’ll be really happy. We’ll feel our time was well spent.” Then we commit the six weeks and leave the team alone to get it done * Shaping the work: key elements of a solution, appetite * **part one shaping:** * two separate tracks: one for shaping, one for building. * Steps to shaping * 1\. Set boundaries. * 2\. Rough out the elements: idea that solves the problem within the appetite but without all the fine details worked out * 3\. Address risks and rabbit holes. * 4\. Write the pitch. * fixed time, variable scope, * Where in the current system does the new thing fit? How do you get to it? What are the key components or interactions? Where does it take you? * five ingredients that we always want to include in a pitch: 1. Problem — The raw idea, a use case, or something we’ve seen that motivates us to work on this 2. Appetite — How much time we want to spend and how that constrains the solution 3. Solution — The core elements we came up with, presented in a form that’s easy for people to immediately understand 4. Rabbit holes — Details about the solution worth calling out to avoid problems 5. No-gos — Anything specifically excluded from the concept: functionality or use cases we intentionally aren’t covering to fit the appetite or make the problem tractable * **part two : Betting** * Some companies use two-week cycles (aka “sprints”). We learned that two weeks is too short to get anything meaningful done. Worse than that, two-week cycles are extremely costly due to the planning overhead. The amount of work you get out of two weeks isn’t worth the collective hours around the table to “sprint plan” or the opportunity cost of breaking everyone’s momentum to re-group. * after each six-week cycle, we schedule two weeks for cool-down. * bugs: Use cool-down. Bring it to the betting table. Schedule a bug smash. * We’ve noticed three phases of work when we build a new product from scratch. R&D mode (bet the time on spiking some key pieces of the new product idea. CEO and designer, we don’t expect to ship anything at the end of an R&D cycle) , Production mode (Shaping is deliberate again. bet multiple teams in parallel , Shipping is the goal) , Cleanup mode (There’s no shaping. There aren’t clear team boundaries. Work is “shipped”) [Cleanup shouldn’t last longer than two cycles] * Example: two years of development, R&D mode for the first year, a year of production mode cycles followed, two cycles of cleanup and significantly cut back the feature set, some overlap between R&D and production mode after that first year, * **Part three: Building** * We don’t start by assigning tasks to anyone. * we define and track the scopes as to-do lists. Each scope corresponds to a list name. Then any tasks for that scope go in that list. * **image 1, 2, 3,** * Mark nice-to-haves with ~ * First there’s the uphill phase of figuring out what our approach is and what we’re going to do. Then, once we can see all the work involved, there’s the downhill phase of execution * image 4, 5, * ## Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet * getting start: idea, technology, passion; What can I do well that I would love to do for an extended period of time? P: 19, 21 * 1 market segmentation: The single necessary and sufficient condition for a business is a paying customer. ; technological enthusiasts=early adopters=early majority ; P: 31, * 2 select a beachhead market: P: 44, * 3 build an end user profile: P: 52, * 4 Calculate the Total Addressable Market (TAM) Size for the Beachhead Market; $200 # [Essential Tips And Tricks For ](/book-summary/commonplace-book)[commonplace book](/book-summary/commonplace-book) # [knowledge management ](/book-summary/knowledge_management) # [PKM](/book-summary/knowledge_management/pkm) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/qDZvojKIjUt_BmKSFeFiLcko8E9gUx2UPvpr5lUZqjKQRQ0VpykBihMNMzKE- OOh8UqTvee2oKfKd28QF1FzbWk=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/qDZvojKIjUt_BmKSFeFiLcko8E9gUx2UPvpr5lUZqjKQRQ0VpykBihMNMzKE- OOh8UqTvee2oKfKd28QF1FzbWk=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Book Summary Books How to take smart Notes Writing Solid Code Shape Up: Stop Running in Circles and Ship Work that Matters Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet List of books I've read: Computer Vision: Algorithms and Applications Multiple View Geometry in Computer Vision Second Edition Complete to download the latest draft of Machine Learning Yearning Multiple view geometry in computer vision Learning OpenCV Book by Adrian Kaehler and Gary Bradski Digital Image Processing, Global Edition by Rafael C. Gonzalez Introduction to Algorithms, 3rd Edition (The MIT Press) The Mythical Man-Month: Essays on Software Engineering OpenCV-4-with-Python-Blueprints-Second-Edition Cracking the coding interview 189 programming questions and solutions Cracking the Tech Career: Insider Advice on Landing a Job at Google, Microsoft, Apple, Or Any Top Tech Company The Art of Startup Fundraising Mathematics for Machine Learning Principles of Economics (6th edition) The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) Reinventing Your Life: The Breakthough Program to End Negative Behavior Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf Magazine Papers Websites and links Tools YouTube - List channels Language: Essential Tips And Tricks For commonplace book knowledge management PKM # Books * ## How to take smart Notes * Introduction 4, The four underlying principles 4, the six steps to successful writing 6, afterword * introduction * write everything/ collect notes not related to any thing, organize notes, revirew, rewrite, * no reorganise no complex system, develop ideas by smart notes * 1 slip-box; 2 repeatable tasks that can become automatic and fit together seamlessly change routinges * overarching workflow "Getting Things Done" GTD * constantly have to ump back and forth between different tasks * 1- bibliographical slip-box: reference * 2- collected and generated ideas slip-box * 3- index: notes serve as entry point / thought/ topic * writing a paper step by step * 1 fleeting notes: it will delete within a day * I always have a slip of paper at hand, on which I note down the ideas of certain pages. on the backside I write down the bibliographic details. after finishing the book I go throught my notes and think how ehse notes might be relevant for already written notes in the slip-box. it means that I always read with an eye towards possible connections in the slip-box * 2 literature notes: contain the necessary information - in reference system or in the slip-box * I make a note with the bibliographic details. on the backside I would write "on page x is this, on page y is that" and then it goes into the bibliographic slib-box where I collect everything I read * read, think about its relevance * theoretical background, methodological approach or perspective of the text we read. * whether something adds to a discussion in the slip-box * we look at our slip-box for already existing lines of thought and think about the questions and problems already on our minds to which a new note might contribute. - assigning keywords is much more than just a bureaucratic act. it is a crucial part of the thinking process, which often leads to a deeper elaboration of the note itself and the connection to other notes. * * 3 permanent notes: develop ideas/new/ - only relevant to one particular project - in project specific folder - can be discarded or archived after project finished * add to slip-box behind the note you dirctyly refer to or behind the last note in the slip-box * add links to other notes or links on other notes to your new note * make sure it can be found from the index (MOC) add an entry in the index if necessary or refer to it from a note that is connected to the index * build a latticework of mental models * 4 behind multiple notes (link backward) - link to related note - link to MOC * make sure it can be found again * **keywords** can easily be added to a note like **tags** and will then show up in the **index** * a day * read and take notes * build connections within the slip-box - new idea * you write on your paper, notice a hole in the argument and have another look in the file system for the missing link. you follow up on a footnote, go back to research and might add a fitting quote to one of your papers in the making. * The four underlying principles * writing * simplicity * not from scratch * let the work carry you forward * the six steps to successful writing * separate and interlocking tasks * we use memo techniques is to bundle items together in a meaningful way and remember the bundles * the brain doesn't distinguish between an actual finished task and one that is postponed by taking a note * the fist step is to break down the amprphous task of "writing" into smaller pieces of different tasks that can be finished in **one go.** the second step is to make sure we always write down the outcome of our thinking, including possible connections to further inquiries. * ego depletion * read for understanding * take smart notes * develp ideas (MOC) * 1 overview of a topic - referred to from the index and used as an entry point into a topic has already develped * 2 keeping tarck of all topic - index - * share your insight * make it a habit * afterword * ## Writing Solid Code Writing Solid Code (Microsoft Programming Series) by Trendelles Store Note from Writing Solid Code Book [https://www.pirahansiah.com/describe/book- summary](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Fdescribe%2Fbook- summary&sa=D&sntz=1&usg=AOvVaw3ACzA6G0MBVN3nw6f-yMQo) #SolidCode #C++ #Python #pirahansiah * **Chapter 1** * Enable all optional compiler warnings. * Use lint to catch bugs that your compiler may miss. * If you have unit tests, use them. * **Chapter 2** * introduction assert page 16; assert.h * assert is nothing more than a repackaged form of the #ifdef code we saw before, but when you use * the macro, it takes one line instead of seven * That's why assert is a macro and not a function; if it were a function, calling * it could cause unexpected memory or code swapping. * Use assertions to validate function arguments . * Strip undefined behavior from your code, or use assertions to catch illegal uses of undefined behavior . * Don't waste people's time. Document unclear assertions . * Either remove implicit assumptions, or assert that they are valid . * Use assertions to detect impossible conditions. * Don't hide bugs when you program defensively. * Use a second algorithm to validate your results. * Don't wait for bugs to happen; use startup checks. * **Chapter 3** * Maintain shipping and debugging versions of your program. Shrink-wrap the shipping version, but use the debugging ver­sion as much as possible to catch bugs quickly. * Assertions are a shorthand way to write debugging checks. Use them to catch illegal conditions that should never arise. Don't confuse such conditions with error conditions, which you must handle in the final product. * Use assertions to validate function arguments and to alert pro­ grammars when they do something undefined. The more rigidly you define your functions, the easier it will be to validate the arguments. * Once you've written a function, review it and ask yourself, 'What am I assuming?" If you find an assumption, either assert that your assumption is always valid, or rewrite the code to re­ move the assumption. Also ask, 'What is most likely to be wrong in this code, and how can I automatically detect the prob­lem?" Strive to implement tests that catch bugs at the earliest possible moment. Textbooks encourage programmers to program defensively, but remember that this coding style hides bugs. When you write de­fensive code, use assertions to alert you if the "can't happen" cases do happen. * Eliminate random behavior. Force bugs to be reproducible . * Destroy your garbage so that it's not misused . * Not only did reallocate have to move the block of memory as it was expanding it, but the old memory had to be reallocated and filled with new data. In the assembler, both happened rarely. * This brings up another guideline for writing bug-free code: You don't want anything to happen rarely. You need to isolate those behaviors in your subsystems that may happen and make sure that they do happen. And of­ ten. If you find rare behavior in your subsystems, be sure to do something­ anything-to stir things up. * The assembler bug could have been found within hours, instead of years, if reallocate hadn't so rarely moved blocks when it expanded them. * If something happens rarely, force it to happen often. * Keep debug information to allow stronger error checking. * There is no question that the debug code will create differences be­ tween the ship and the debug versions of your code, but as long as you're careful not to change the underlying behavior of the code, those differences shouldn't matter. * Create thorough subsystem checks, and use them often. * Design your tests carefully. Nothing should be arbitrary. * Strive to implement transparent integrity checks. * Don't apply ship version constraints to the debug version. Trade size and speed for error detection. * **Chapter 4** * Don't wait until you have a bug to step through your code. * Step through every code path. * What About Sweeping Changes? In the past, programmers have asked, "What if I add a feature that touches code in many places? Stepping through all that new code is going to be time consuming." Let me answer that question with another: Can you make such sweeping changes without introducing any bugs? The habit of stepping through your code creates an interesting negative feedback loop. Programmers who step through their code soon learn to write small, easily testable functions because stepping through large func­tions is so painful. Programmers also spend more time thinking about how they can localize the changes they need to make-again, so that they can more easily test their code. And isn't this exactly what you want? No project lead likes it when programmers touch a lot of code; it's too destabilizing. Nor do leads like large, unmanageable functions; they're often unmaintainable. Try hard to localize your changes. If you find that you must make per­vasive changes, think twice before you decide not to step through all the new code. * Overflow and underflow bugs * Data conversion bugs * Off-by-one bugs * NULL pointer bugs * Bugs using garbage memory (0xA3 bugs) * Assignment bugs in which you've used = instead of == * Precedence bugs * Logic bugs * As you step through code, focus on data flow. Source level debuggers can hide execution details. Step through critical code at the instruction level . * **Chapter 5** * Make it hard to ignore error conditions. Don't bury error codes in return values. * Always look for, and eliminate, flaws in your interfaces. * Don't write multipurpose functions. Write separate functions to allow stronger argument validation. * Don't be wishy-washy. Define explicit function arguments. * Write functions that, given valid inputs, cannot fail. * Make the code intelligible at the point of call. Avoid Boolean arguments. * Write comments that emphasize potential hazards. * **Chapter 6** * Use well-defined data types. * Always ask ,"Can this variable or expression over- or underflow?" * Implement your designs as accurately as possible. Being kind a close is being kind a buggy. * these principles already: Don't accept special purpose arguments such as the NULL pointer, and Implement your design, not something that approximates it. The third principle is new: Strive to make every function perform its task exactly one time. * Implement "the task" just once. * Get rid of extraneous if statements. * Avoid using nested ?: operators . * Handle your special cases just once. * Avoid risky language idioms. * Don't needlessly mix operator types. If you must mix operators, use parentheses to isolate the operations. * Avoid calling functions that return errors. * **Chapter 7** * Don't reference memory that you don't own. * Don't reference memory that you have freed. * Don't use output memory as workspace buffers. * Don't pass data in static (or global) memory. * Don't write parasitic functions. * Don't abuse your programming language. * Tight C does not guarantee efficient machine code. * Write code for the "average" programmer. * **Chapter 8** * Bugs don't just "go away." * Don't fix bugs later; fix them now. * Fix the cause, not the symptom. * Don't clean up code unless the clean­ up is critical to the product's success. * Don't implement nonstrategic features. * There are no free features. * Don't allow unnecessary flexibility. * Don't keep "trying" solutions· until you find one that works. Take the time to find the correct solution. * Write and test code in small chunks. * Always test your code, even if that means your schedule will slip. * Don't rely on the testing group to find your bugs. * Don't blame testers for finding your bugs. * Establish your priorities and stick to them. * Never allow the same bug to bite you twice. * **A: CODING CHECKLISTS** * **B: MEMORY LOGGING ROUTINES** * **To sum up** * Charles Simonyi in the early 1970s. https://en.wikipedia.org/wiki/Hungarian_notation * char ch; * byte b; * flag f;/* flags that are always TRUE or FALSE*/ * symbol sym;/* some sort of symbol structure*/ * char byte flag symbol * *pch:/* character pointer*/ * *pb; * *pf: * *psym: * related link: [https://www.morling.dev/images/code_review_pyramid.svg](https://www.google.com/url?q=https%3A%2F%2Fwww.morling.dev%2Fimages%2Fcode_review_pyramid.svg&sa=D&sntz=1&usg=AOvVaw1y2It_PUzXAtFN7dPLegCa) * ## Shape Up: Stop Running in Circles and Ship Work that Matters * Six-week cycles: Our decisions are based on moving the product forward in the next six weeks, not micromanaging time. We don’t count hours or question how individual days are spent. We don’t have daily meetings. We don’t rethink our roadmap every two weeks. Our focus is at a higher level. We say to ourselves: “If this project ships after six weeks, we’ll be really happy. We’ll feel our time was well spent.” Then we commit the six weeks and leave the team alone to get it done * Shaping the work: key elements of a solution, appetite * **part one shaping:** * two separate tracks: one for shaping, one for building. * Steps to shaping * 1\. Set boundaries. * 2\. Rough out the elements: idea that solves the problem within the appetite but without all the fine details worked out * 3\. Address risks and rabbit holes. * 4\. Write the pitch. * fixed time, variable scope, * Where in the current system does the new thing fit? How do you get to it? What are the key components or interactions? Where does it take you? * five ingredients that we always want to include in a pitch: 1. Problem — The raw idea, a use case, or something we’ve seen that motivates us to work on this 2. Appetite — How much time we want to spend and how that constrains the solution 3. Solution — The core elements we came up with, presented in a form that’s easy for people to immediately understand 4. Rabbit holes — Details about the solution worth calling out to avoid problems 5. No-gos — Anything specifically excluded from the concept: functionality or use cases we intentionally aren’t covering to fit the appetite or make the problem tractable * **part two : Betting** * Some companies use two-week cycles (aka “sprints”). We learned that two weeks is too short to get anything meaningful done. Worse than that, two-week cycles are extremely costly due to the planning overhead. The amount of work you get out of two weeks isn’t worth the collective hours around the table to “sprint plan” or the opportunity cost of breaking everyone’s momentum to re-group. * after each six-week cycle, we schedule two weeks for cool-down. * bugs: Use cool-down. Bring it to the betting table. Schedule a bug smash. * We’ve noticed three phases of work when we build a new product from scratch. R&D mode (bet the time on spiking some key pieces of the new product idea. CEO and designer, we don’t expect to ship anything at the end of an R&D cycle) , Production mode (Shaping is deliberate again. bet multiple teams in parallel , Shipping is the goal) , Cleanup mode (There’s no shaping. There aren’t clear team boundaries. Work is “shipped”) [Cleanup shouldn’t last longer than two cycles] * Example: two years of development, R&D mode for the first year, a year of production mode cycles followed, two cycles of cleanup and significantly cut back the feature set, some overlap between R&D and production mode after that first year, * **Part three: Building** * We don’t start by assigning tasks to anyone. * we define and track the scopes as to-do lists. Each scope corresponds to a list name. Then any tasks for that scope go in that list. * **image 1, 2, 3,** * Mark nice-to-haves with ~ * First there’s the uphill phase of figuring out what our approach is and what we’re going to do. Then, once we can see all the work involved, there’s the downhill phase of execution * image 4, 5, * ## Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet * getting start: idea, technology, passion; What can I do well that I would love to do for an extended period of time? P: 19, 21 * 1 market segmentation: The single necessary and sufficient condition for a business is a paying customer. ; technological enthusiasts=early adopters=early majority ; P: 31, * 2 select a beachhead market: P: 44, * 3 build an end user profile: P: 52, * 4 Calculate the Total Addressable Market (TAM) Size for the Beachhead Market; $200 # [Essential Tips And Tricks For ](/book-summary/commonplace-book)[commonplace book](/book-summary/commonplace-book) # [knowledge management ](/book-summary/knowledge_management) # [PKM](/book-summary/knowledge_management/pkm) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/qDZvojKIjUt_BmKSFeFiLcko8E9gUx2UPvpr5lUZqjKQRQ0VpykBihMNMzKE- OOh8UqTvee2oKfKd28QF1FzbWk=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/qDZvojKIjUt_BmKSFeFiLcko8E9gUx2UPvpr5lUZqjKQRQ0VpykBihMNMzKE- OOh8UqTvee2oKfKd28QF1FzbWk=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Book Summary Books How to take smart Notes Writing Solid Code Shape Up: Stop Running in Circles and Ship Work that Matters Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet List of books I've read: Computer Vision: Algorithms and Applications Multiple View Geometry in Computer Vision Second Edition Complete to download the latest draft of Machine Learning Yearning Multiple view geometry in computer vision Learning OpenCV Book by Adrian Kaehler and Gary Bradski Digital Image Processing, Global Edition by Rafael C. Gonzalez Introduction to Algorithms, 3rd Edition (The MIT Press) The Mythical Man-Month: Essays on Software Engineering OpenCV-4-with-Python-Blueprints-Second-Edition Cracking the coding interview 189 programming questions and solutions Cracking the Tech Career: Insider Advice on Landing a Job at Google, Microsoft, Apple, Or Any Top Tech Company The Art of Startup Fundraising Mathematics for Machine Learning Principles of Economics (6th edition) The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) Reinventing Your Life: The Breakthough Program to End Negative Behavior Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf Magazine Papers Websites and links Tools YouTube - List channels Language: Essential Tips And Tricks For commonplace book knowledge management PKM # Books * ## How to take smart Notes * Introduction 4, The four underlying principles 4, the six steps to successful writing 6, afterword * introduction * write everything/ collect notes not related to any thing, organize notes, revirew, rewrite, * no reorganise no complex system, develop ideas by smart notes * 1 slip-box; 2 repeatable tasks that can become automatic and fit together seamlessly change routinges * overarching workflow "Getting Things Done" GTD * constantly have to ump back and forth between different tasks * 1- bibliographical slip-box: reference * 2- collected and generated ideas slip-box * 3- index: notes serve as entry point / thought/ topic * writing a paper step by step * 1 fleeting notes: it will delete within a day * I always have a slip of paper at hand, on which I note down the ideas of certain pages. on the backside I write down the bibliographic details. after finishing the book I go throught my notes and think how ehse notes might be relevant for already written notes in the slip-box. it means that I always read with an eye towards possible connections in the slip-box * 2 literature notes: contain the necessary information - in reference system or in the slip-box * I make a note with the bibliographic details. on the backside I would write "on page x is this, on page y is that" and then it goes into the bibliographic slib-box where I collect everything I read * read, think about its relevance * theoretical background, methodological approach or perspective of the text we read. * whether something adds to a discussion in the slip-box * we look at our slip-box for already existing lines of thought and think about the questions and problems already on our minds to which a new note might contribute. - assigning keywords is much more than just a bureaucratic act. it is a crucial part of the thinking process, which often leads to a deeper elaboration of the note itself and the connection to other notes. * * 3 permanent notes: develop ideas/new/ - only relevant to one particular project - in project specific folder - can be discarded or archived after project finished * add to slip-box behind the note you dirctyly refer to or behind the last note in the slip-box * add links to other notes or links on other notes to your new note * make sure it can be found from the index (MOC) add an entry in the index if necessary or refer to it from a note that is connected to the index * build a latticework of mental models * 4 behind multiple notes (link backward) - link to related note - link to MOC * make sure it can be found again * **keywords** can easily be added to a note like **tags** and will then show up in the **index** * a day * read and take notes * build connections within the slip-box - new idea * you write on your paper, notice a hole in the argument and have another look in the file system for the missing link. you follow up on a footnote, go back to research and might add a fitting quote to one of your papers in the making. * The four underlying principles * writing * simplicity * not from scratch * let the work carry you forward * the six steps to successful writing * separate and interlocking tasks * we use memo techniques is to bundle items together in a meaningful way and remember the bundles * the brain doesn't distinguish between an actual finished task and one that is postponed by taking a note * the fist step is to break down the amprphous task of "writing" into smaller pieces of different tasks that can be finished in **one go.** the second step is to make sure we always write down the outcome of our thinking, including possible connections to further inquiries. * ego depletion * read for understanding * take smart notes * develp ideas (MOC) * 1 overview of a topic - referred to from the index and used as an entry point into a topic has already develped * 2 keeping tarck of all topic - index - * share your insight * make it a habit * afterword * ## Writing Solid Code Writing Solid Code (Microsoft Programming Series) by Trendelles Store Note from Writing Solid Code Book [https://www.pirahansiah.com/describe/book- summary](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Fdescribe%2Fbook- summary&sa=D&sntz=1&usg=AOvVaw3ACzA6G0MBVN3nw6f-yMQo) #SolidCode #C++ #Python #pirahansiah * **Chapter 1** * Enable all optional compiler warnings. * Use lint to catch bugs that your compiler may miss. * If you have unit tests, use them. * **Chapter 2** * introduction assert page 16; assert.h * assert is nothing more than a repackaged form of the #ifdef code we saw before, but when you use * the macro, it takes one line instead of seven * That's why assert is a macro and not a function; if it were a function, calling * it could cause unexpected memory or code swapping. * Use assertions to validate function arguments . * Strip undefined behavior from your code, or use assertions to catch illegal uses of undefined behavior . * Don't waste people's time. Document unclear assertions . * Either remove implicit assumptions, or assert that they are valid . * Use assertions to detect impossible conditions. * Don't hide bugs when you program defensively. * Use a second algorithm to validate your results. * Don't wait for bugs to happen; use startup checks. * **Chapter 3** * Maintain shipping and debugging versions of your program. Shrink-wrap the shipping version, but use the debugging ver­sion as much as possible to catch bugs quickly. * Assertions are a shorthand way to write debugging checks. Use them to catch illegal conditions that should never arise. Don't confuse such conditions with error conditions, which you must handle in the final product. * Use assertions to validate function arguments and to alert pro­ grammars when they do something undefined. The more rigidly you define your functions, the easier it will be to validate the arguments. * Once you've written a function, review it and ask yourself, 'What am I assuming?" If you find an assumption, either assert that your assumption is always valid, or rewrite the code to re­ move the assumption. Also ask, 'What is most likely to be wrong in this code, and how can I automatically detect the prob­lem?" Strive to implement tests that catch bugs at the earliest possible moment. Textbooks encourage programmers to program defensively, but remember that this coding style hides bugs. When you write de­fensive code, use assertions to alert you if the "can't happen" cases do happen. * Eliminate random behavior. Force bugs to be reproducible . * Destroy your garbage so that it's not misused . * Not only did reallocate have to move the block of memory as it was expanding it, but the old memory had to be reallocated and filled with new data. In the assembler, both happened rarely. * This brings up another guideline for writing bug-free code: You don't want anything to happen rarely. You need to isolate those behaviors in your subsystems that may happen and make sure that they do happen. And of­ ten. If you find rare behavior in your subsystems, be sure to do something­ anything-to stir things up. * The assembler bug could have been found within hours, instead of years, if reallocate hadn't so rarely moved blocks when it expanded them. * If something happens rarely, force it to happen often. * Keep debug information to allow stronger error checking. * There is no question that the debug code will create differences be­ tween the ship and the debug versions of your code, but as long as you're careful not to change the underlying behavior of the code, those differences shouldn't matter. * Create thorough subsystem checks, and use them often. * Design your tests carefully. Nothing should be arbitrary. * Strive to implement transparent integrity checks. * Don't apply ship version constraints to the debug version. Trade size and speed for error detection. * **Chapter 4** * Don't wait until you have a bug to step through your code. * Step through every code path. * What About Sweeping Changes? In the past, programmers have asked, "What if I add a feature that touches code in many places? Stepping through all that new code is going to be time consuming." Let me answer that question with another: Can you make such sweeping changes without introducing any bugs? The habit of stepping through your code creates an interesting negative feedback loop. Programmers who step through their code soon learn to write small, easily testable functions because stepping through large func­tions is so painful. Programmers also spend more time thinking about how they can localize the changes they need to make-again, so that they can more easily test their code. And isn't this exactly what you want? No project lead likes it when programmers touch a lot of code; it's too destabilizing. Nor do leads like large, unmanageable functions; they're often unmaintainable. Try hard to localize your changes. If you find that you must make per­vasive changes, think twice before you decide not to step through all the new code. * Overflow and underflow bugs * Data conversion bugs * Off-by-one bugs * NULL pointer bugs * Bugs using garbage memory (0xA3 bugs) * Assignment bugs in which you've used = instead of == * Precedence bugs * Logic bugs * As you step through code, focus on data flow. Source level debuggers can hide execution details. Step through critical code at the instruction level . * **Chapter 5** * Make it hard to ignore error conditions. Don't bury error codes in return values. * Always look for, and eliminate, flaws in your interfaces. * Don't write multipurpose functions. Write separate functions to allow stronger argument validation. * Don't be wishy-washy. Define explicit function arguments. * Write functions that, given valid inputs, cannot fail. * Make the code intelligible at the point of call. Avoid Boolean arguments. * Write comments that emphasize potential hazards. * **Chapter 6** * Use well-defined data types. * Always ask ,"Can this variable or expression over- or underflow?" * Implement your designs as accurately as possible. Being kind a close is being kind a buggy. * these principles already: Don't accept special purpose arguments such as the NULL pointer, and Implement your design, not something that approximates it. The third principle is new: Strive to make every function perform its task exactly one time. * Implement "the task" just once. * Get rid of extraneous if statements. * Avoid using nested ?: operators . * Handle your special cases just once. * Avoid risky language idioms. * Don't needlessly mix operator types. If you must mix operators, use parentheses to isolate the operations. * Avoid calling functions that return errors. * **Chapter 7** * Don't reference memory that you don't own. * Don't reference memory that you have freed. * Don't use output memory as workspace buffers. * Don't pass data in static (or global) memory. * Don't write parasitic functions. * Don't abuse your programming language. * Tight C does not guarantee efficient machine code. * Write code for the "average" programmer. * **Chapter 8** * Bugs don't just "go away." * Don't fix bugs later; fix them now. * Fix the cause, not the symptom. * Don't clean up code unless the clean­ up is critical to the product's success. * Don't implement nonstrategic features. * There are no free features. * Don't allow unnecessary flexibility. * Don't keep "trying" solutions· until you find one that works. Take the time to find the correct solution. * Write and test code in small chunks. * Always test your code, even if that means your schedule will slip. * Don't rely on the testing group to find your bugs. * Don't blame testers for finding your bugs. * Establish your priorities and stick to them. * Never allow the same bug to bite you twice. * **A: CODING CHECKLISTS** * **B: MEMORY LOGGING ROUTINES** * **To sum up** * Charles Simonyi in the early 1970s. https://en.wikipedia.org/wiki/Hungarian_notation * char ch; * byte b; * flag f;/* flags that are always TRUE or FALSE*/ * symbol sym;/* some sort of symbol structure*/ * char byte flag symbol * *pch:/* character pointer*/ * *pb; * *pf: * *psym: * related link: [https://www.morling.dev/images/code_review_pyramid.svg](https://www.google.com/url?q=https%3A%2F%2Fwww.morling.dev%2Fimages%2Fcode_review_pyramid.svg&sa=D&sntz=1&usg=AOvVaw1y2It_PUzXAtFN7dPLegCa) * ## Shape Up: Stop Running in Circles and Ship Work that Matters * Six-week cycles: Our decisions are based on moving the product forward in the next six weeks, not micromanaging time. We don’t count hours or question how individual days are spent. We don’t have daily meetings. We don’t rethink our roadmap every two weeks. Our focus is at a higher level. We say to ourselves: “If this project ships after six weeks, we’ll be really happy. We’ll feel our time was well spent.” Then we commit the six weeks and leave the team alone to get it done * Shaping the work: key elements of a solution, appetite * **part one shaping:** * two separate tracks: one for shaping, one for building. * Steps to shaping * 1\. Set boundaries. * 2\. Rough out the elements: idea that solves the problem within the appetite but without all the fine details worked out * 3\. Address risks and rabbit holes. * 4\. Write the pitch. * fixed time, variable scope, * Where in the current system does the new thing fit? How do you get to it? What are the key components or interactions? Where does it take you? * five ingredients that we always want to include in a pitch: 1. Problem — The raw idea, a use case, or something we’ve seen that motivates us to work on this 2. Appetite — How much time we want to spend and how that constrains the solution 3. Solution — The core elements we came up with, presented in a form that’s easy for people to immediately understand 4. Rabbit holes — Details about the solution worth calling out to avoid problems 5. No-gos — Anything specifically excluded from the concept: functionality or use cases we intentionally aren’t covering to fit the appetite or make the problem tractable * **part two : Betting** * Some companies use two-week cycles (aka “sprints”). We learned that two weeks is too short to get anything meaningful done. Worse than that, two-week cycles are extremely costly due to the planning overhead. The amount of work you get out of two weeks isn’t worth the collective hours around the table to “sprint plan” or the opportunity cost of breaking everyone’s momentum to re-group. * after each six-week cycle, we schedule two weeks for cool-down. * bugs: Use cool-down. Bring it to the betting table. Schedule a bug smash. * We’ve noticed three phases of work when we build a new product from scratch. R&D mode (bet the time on spiking some key pieces of the new product idea. CEO and designer, we don’t expect to ship anything at the end of an R&D cycle) , Production mode (Shaping is deliberate again. bet multiple teams in parallel , Shipping is the goal) , Cleanup mode (There’s no shaping. There aren’t clear team boundaries. Work is “shipped”) [Cleanup shouldn’t last longer than two cycles] * Example: two years of development, R&D mode for the first year, a year of production mode cycles followed, two cycles of cleanup and significantly cut back the feature set, some overlap between R&D and production mode after that first year, * **Part three: Building** * We don’t start by assigning tasks to anyone. * we define and track the scopes as to-do lists. Each scope corresponds to a list name. Then any tasks for that scope go in that list. * **image 1, 2, 3,** * Mark nice-to-haves with ~ * First there’s the uphill phase of figuring out what our approach is and what we’re going to do. Then, once we can see all the work involved, there’s the downhill phase of execution * image 4, 5, * ## Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet * getting start: idea, technology, passion; What can I do well that I would love to do for an extended period of time? P: 19, 21 * 1 market segmentation: The single necessary and sufficient condition for a business is a paying customer. ; technological enthusiasts=early adopters=early majority ; P: 31, * 2 select a beachhead market: P: 44, * 3 build an end user profile: P: 52, * 4 Calculate the Total Addressable Market (TAM) Size for the Beachhead Market; $200 # [Essential Tips And Tricks For ](/book-summary/commonplace-book)[commonplace book](/book-summary/commonplace-book) # [knowledge management ](/book-summary/knowledge_management) # [PKM](/book-summary/knowledge_management/pkm) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. 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Gonzalez Introduction to Algorithms, 3rd Edition (The MIT Press) The Mythical Man-Month: Essays on Software Engineering OpenCV-4-with-Python-Blueprints-Second-Edition Cracking the coding interview 189 programming questions and solutions Cracking the Tech Career: Insider Advice on Landing a Job at Google, Microsoft, Apple, Or Any Top Tech Company The Art of Startup Fundraising Mathematics for Machine Learning Principles of Economics (6th edition) The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) Reinventing Your Life: The Breakthough Program to End Negative Behavior Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf Magazine Papers Websites and links Tools YouTube - List channels Language: Essential Tips And Tricks For commonplace book knowledge management PKM # Books * ## How to take smart Notes * Introduction 4, The four underlying principles 4, the six steps to successful writing 6, afterword * introduction * write everything/ collect notes not related to any thing, organize notes, revirew, rewrite, * no reorganise no complex system, develop ideas by smart notes * 1 slip-box; 2 repeatable tasks that can become automatic and fit together seamlessly change routinges * overarching workflow "Getting Things Done" GTD * constantly have to ump back and forth between different tasks * 1- bibliographical slip-box: reference * 2- collected and generated ideas slip-box * 3- index: notes serve as entry point / thought/ topic * writing a paper step by step * 1 fleeting notes: it will delete within a day * I always have a slip of paper at hand, on which I note down the ideas of certain pages. on the backside I write down the bibliographic details. after finishing the book I go throught my notes and think how ehse notes might be relevant for already written notes in the slip-box. it means that I always read with an eye towards possible connections in the slip-box * 2 literature notes: contain the necessary information - in reference system or in the slip-box * I make a note with the bibliographic details. on the backside I would write "on page x is this, on page y is that" and then it goes into the bibliographic slib-box where I collect everything I read * read, think about its relevance * theoretical background, methodological approach or perspective of the text we read. * whether something adds to a discussion in the slip-box * we look at our slip-box for already existing lines of thought and think about the questions and problems already on our minds to which a new note might contribute. - assigning keywords is much more than just a bureaucratic act. it is a crucial part of the thinking process, which often leads to a deeper elaboration of the note itself and the connection to other notes. * * 3 permanent notes: develop ideas/new/ - only relevant to one particular project - in project specific folder - can be discarded or archived after project finished * add to slip-box behind the note you dirctyly refer to or behind the last note in the slip-box * add links to other notes or links on other notes to your new note * make sure it can be found from the index (MOC) add an entry in the index if necessary or refer to it from a note that is connected to the index * build a latticework of mental models * 4 behind multiple notes (link backward) - link to related note - link to MOC * make sure it can be found again * **keywords** can easily be added to a note like **tags** and will then show up in the **index** * a day * read and take notes * build connections within the slip-box - new idea * you write on your paper, notice a hole in the argument and have another look in the file system for the missing link. you follow up on a footnote, go back to research and might add a fitting quote to one of your papers in the making. * The four underlying principles * writing * simplicity * not from scratch * let the work carry you forward * the six steps to successful writing * separate and interlocking tasks * we use memo techniques is to bundle items together in a meaningful way and remember the bundles * the brain doesn't distinguish between an actual finished task and one that is postponed by taking a note * the fist step is to break down the amprphous task of "writing" into smaller pieces of different tasks that can be finished in **one go.** the second step is to make sure we always write down the outcome of our thinking, including possible connections to further inquiries. * ego depletion * read for understanding * take smart notes * develp ideas (MOC) * 1 overview of a topic - referred to from the index and used as an entry point into a topic has already develped * 2 keeping tarck of all topic - index - * share your insight * make it a habit * afterword * ## Writing Solid Code Writing Solid Code (Microsoft Programming Series) by Trendelles Store Note from Writing Solid Code Book [https://www.pirahansiah.com/describe/book- summary](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Fdescribe%2Fbook- summary&sa=D&sntz=1&usg=AOvVaw3ACzA6G0MBVN3nw6f-yMQo) #SolidCode #C++ #Python #pirahansiah * **Chapter 1** * Enable all optional compiler warnings. * Use lint to catch bugs that your compiler may miss. * If you have unit tests, use them. * **Chapter 2** * introduction assert page 16; assert.h * assert is nothing more than a repackaged form of the #ifdef code we saw before, but when you use * the macro, it takes one line instead of seven * That's why assert is a macro and not a function; if it were a function, calling * it could cause unexpected memory or code swapping. * Use assertions to validate function arguments . * Strip undefined behavior from your code, or use assertions to catch illegal uses of undefined behavior . * Don't waste people's time. Document unclear assertions . * Either remove implicit assumptions, or assert that they are valid . * Use assertions to detect impossible conditions. * Don't hide bugs when you program defensively. * Use a second algorithm to validate your results. * Don't wait for bugs to happen; use startup checks. * **Chapter 3** * Maintain shipping and debugging versions of your program. Shrink-wrap the shipping version, but use the debugging ver­sion as much as possible to catch bugs quickly. * Assertions are a shorthand way to write debugging checks. Use them to catch illegal conditions that should never arise. Don't confuse such conditions with error conditions, which you must handle in the final product. * Use assertions to validate function arguments and to alert pro­ grammars when they do something undefined. The more rigidly you define your functions, the easier it will be to validate the arguments. * Once you've written a function, review it and ask yourself, 'What am I assuming?" If you find an assumption, either assert that your assumption is always valid, or rewrite the code to re­ move the assumption. Also ask, 'What is most likely to be wrong in this code, and how can I automatically detect the prob­lem?" Strive to implement tests that catch bugs at the earliest possible moment. Textbooks encourage programmers to program defensively, but remember that this coding style hides bugs. When you write de­fensive code, use assertions to alert you if the "can't happen" cases do happen. * Eliminate random behavior. Force bugs to be reproducible . * Destroy your garbage so that it's not misused . * Not only did reallocate have to move the block of memory as it was expanding it, but the old memory had to be reallocated and filled with new data. In the assembler, both happened rarely. * This brings up another guideline for writing bug-free code: You don't want anything to happen rarely. You need to isolate those behaviors in your subsystems that may happen and make sure that they do happen. And of­ ten. If you find rare behavior in your subsystems, be sure to do something­ anything-to stir things up. * The assembler bug could have been found within hours, instead of years, if reallocate hadn't so rarely moved blocks when it expanded them. * If something happens rarely, force it to happen often. * Keep debug information to allow stronger error checking. * There is no question that the debug code will create differences be­ tween the ship and the debug versions of your code, but as long as you're careful not to change the underlying behavior of the code, those differences shouldn't matter. * Create thorough subsystem checks, and use them often. * Design your tests carefully. Nothing should be arbitrary. * Strive to implement transparent integrity checks. * Don't apply ship version constraints to the debug version. Trade size and speed for error detection. * **Chapter 4** * Don't wait until you have a bug to step through your code. * Step through every code path. * What About Sweeping Changes? In the past, programmers have asked, "What if I add a feature that touches code in many places? Stepping through all that new code is going to be time consuming." Let me answer that question with another: Can you make such sweeping changes without introducing any bugs? The habit of stepping through your code creates an interesting negative feedback loop. Programmers who step through their code soon learn to write small, easily testable functions because stepping through large func­tions is so painful. Programmers also spend more time thinking about how they can localize the changes they need to make-again, so that they can more easily test their code. And isn't this exactly what you want? No project lead likes it when programmers touch a lot of code; it's too destabilizing. Nor do leads like large, unmanageable functions; they're often unmaintainable. Try hard to localize your changes. If you find that you must make per­vasive changes, think twice before you decide not to step through all the new code. * Overflow and underflow bugs * Data conversion bugs * Off-by-one bugs * NULL pointer bugs * Bugs using garbage memory (0xA3 bugs) * Assignment bugs in which you've used = instead of == * Precedence bugs * Logic bugs * As you step through code, focus on data flow. Source level debuggers can hide execution details. Step through critical code at the instruction level . * **Chapter 5** * Make it hard to ignore error conditions. Don't bury error codes in return values. * Always look for, and eliminate, flaws in your interfaces. * Don't write multipurpose functions. Write separate functions to allow stronger argument validation. * Don't be wishy-washy. Define explicit function arguments. * Write functions that, given valid inputs, cannot fail. * Make the code intelligible at the point of call. Avoid Boolean arguments. * Write comments that emphasize potential hazards. * **Chapter 6** * Use well-defined data types. * Always ask ,"Can this variable or expression over- or underflow?" * Implement your designs as accurately as possible. Being kind a close is being kind a buggy. * these principles already: Don't accept special purpose arguments such as the NULL pointer, and Implement your design, not something that approximates it. The third principle is new: Strive to make every function perform its task exactly one time. * Implement "the task" just once. * Get rid of extraneous if statements. * Avoid using nested ?: operators . * Handle your special cases just once. * Avoid risky language idioms. * Don't needlessly mix operator types. If you must mix operators, use parentheses to isolate the operations. * Avoid calling functions that return errors. * **Chapter 7** * Don't reference memory that you don't own. * Don't reference memory that you have freed. * Don't use output memory as workspace buffers. * Don't pass data in static (or global) memory. * Don't write parasitic functions. * Don't abuse your programming language. * Tight C does not guarantee efficient machine code. * Write code for the "average" programmer. * **Chapter 8** * Bugs don't just "go away." * Don't fix bugs later; fix them now. * Fix the cause, not the symptom. * Don't clean up code unless the clean­ up is critical to the product's success. * Don't implement nonstrategic features. * There are no free features. * Don't allow unnecessary flexibility. * Don't keep "trying" solutions· until you find one that works. Take the time to find the correct solution. * Write and test code in small chunks. * Always test your code, even if that means your schedule will slip. * Don't rely on the testing group to find your bugs. * Don't blame testers for finding your bugs. * Establish your priorities and stick to them. * Never allow the same bug to bite you twice. * **A: CODING CHECKLISTS** * **B: MEMORY LOGGING ROUTINES** * **To sum up** * Charles Simonyi in the early 1970s. https://en.wikipedia.org/wiki/Hungarian_notation * char ch; * byte b; * flag f;/* flags that are always TRUE or FALSE*/ * symbol sym;/* some sort of symbol structure*/ * char byte flag symbol * *pch:/* character pointer*/ * *pb; * *pf: * *psym: * related link: [https://www.morling.dev/images/code_review_pyramid.svg](https://www.google.com/url?q=https%3A%2F%2Fwww.morling.dev%2Fimages%2Fcode_review_pyramid.svg&sa=D&sntz=1&usg=AOvVaw1y2It_PUzXAtFN7dPLegCa) * ## Shape Up: Stop Running in Circles and Ship Work that Matters * Six-week cycles: Our decisions are based on moving the product forward in the next six weeks, not micromanaging time. We don’t count hours or question how individual days are spent. We don’t have daily meetings. We don’t rethink our roadmap every two weeks. Our focus is at a higher level. We say to ourselves: “If this project ships after six weeks, we’ll be really happy. We’ll feel our time was well spent.” Then we commit the six weeks and leave the team alone to get it done * Shaping the work: key elements of a solution, appetite * **part one shaping:** * two separate tracks: one for shaping, one for building. * Steps to shaping * 1\. Set boundaries. * 2\. Rough out the elements: idea that solves the problem within the appetite but without all the fine details worked out * 3\. Address risks and rabbit holes. * 4\. Write the pitch. * fixed time, variable scope, * Where in the current system does the new thing fit? How do you get to it? What are the key components or interactions? Where does it take you? * five ingredients that we always want to include in a pitch: 1. Problem — The raw idea, a use case, or something we’ve seen that motivates us to work on this 2. Appetite — How much time we want to spend and how that constrains the solution 3. Solution — The core elements we came up with, presented in a form that’s easy for people to immediately understand 4. Rabbit holes — Details about the solution worth calling out to avoid problems 5. No-gos — Anything specifically excluded from the concept: functionality or use cases we intentionally aren’t covering to fit the appetite or make the problem tractable * **part two : Betting** * Some companies use two-week cycles (aka “sprints”). We learned that two weeks is too short to get anything meaningful done. Worse than that, two-week cycles are extremely costly due to the planning overhead. The amount of work you get out of two weeks isn’t worth the collective hours around the table to “sprint plan” or the opportunity cost of breaking everyone’s momentum to re-group. * after each six-week cycle, we schedule two weeks for cool-down. * bugs: Use cool-down. Bring it to the betting table. Schedule a bug smash. * We’ve noticed three phases of work when we build a new product from scratch. R&D mode (bet the time on spiking some key pieces of the new product idea. CEO and designer, we don’t expect to ship anything at the end of an R&D cycle) , Production mode (Shaping is deliberate again. bet multiple teams in parallel , Shipping is the goal) , Cleanup mode (There’s no shaping. There aren’t clear team boundaries. Work is “shipped”) [Cleanup shouldn’t last longer than two cycles] * Example: two years of development, R&D mode for the first year, a year of production mode cycles followed, two cycles of cleanup and significantly cut back the feature set, some overlap between R&D and production mode after that first year, * **Part three: Building** * We don’t start by assigning tasks to anyone. * we define and track the scopes as to-do lists. Each scope corresponds to a list name. Then any tasks for that scope go in that list. * **image 1, 2, 3,** * Mark nice-to-haves with ~ * First there’s the uphill phase of figuring out what our approach is and what we’re going to do. Then, once we can see all the work involved, there’s the downhill phase of execution * image 4, 5, * ## Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet * getting start: idea, technology, passion; What can I do well that I would love to do for an extended period of time? P: 19, 21 * 1 market segmentation: The single necessary and sufficient condition for a business is a paying customer. ; technological enthusiasts=early adopters=early majority ; P: 31, * 2 select a beachhead market: P: 44, * 3 build an end user profile: P: 52, * 4 Calculate the Total Addressable Market (TAM) Size for the Beachhead Market; $200 # [Essential Tips And Tricks For ](/book-summary/commonplace-book)[commonplace book](/book-summary/commonplace-book) # [knowledge management ](/book-summary/knowledge_management) # [PKM](/book-summary/knowledge_management/pkm) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/qDZvojKIjUt_BmKSFeFiLcko8E9gUx2UPvpr5lUZqjKQRQ0VpykBihMNMzKE- OOh8UqTvee2oKfKd28QF1FzbWk=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/qDZvojKIjUt_BmKSFeFiLcko8E9gUx2UPvpr5lUZqjKQRQ0VpykBihMNMzKE- OOh8UqTvee2oKfKd28QF1FzbWk=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Book Summary Books How to take smart Notes Writing Solid Code Shape Up: Stop Running in Circles and Ship Work that Matters Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet List of books I've read: Computer Vision: Algorithms and Applications Multiple View Geometry in Computer Vision Second Edition Complete to download the latest draft of Machine Learning Yearning Multiple view geometry in computer vision Learning OpenCV Book by Adrian Kaehler and Gary Bradski Digital Image Processing, Global Edition by Rafael C. Gonzalez Introduction to Algorithms, 3rd Edition (The MIT Press) The Mythical Man-Month: Essays on Software Engineering OpenCV-4-with-Python-Blueprints-Second-Edition Cracking the coding interview 189 programming questions and solutions Cracking the Tech Career: Insider Advice on Landing a Job at Google, Microsoft, Apple, Or Any Top Tech Company The Art of Startup Fundraising Mathematics for Machine Learning Principles of Economics (6th edition) The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) Reinventing Your Life: The Breakthough Program to End Negative Behavior Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf Magazine Papers Websites and links Tools YouTube - List channels Language: Essential Tips And Tricks For commonplace book knowledge management PKM # Books * ## How to take smart Notes * Introduction 4, The four underlying principles 4, the six steps to successful writing 6, afterword * introduction * write everything/ collect notes not related to any thing, organize notes, revirew, rewrite, * no reorganise no complex system, develop ideas by smart notes * 1 slip-box; 2 repeatable tasks that can become automatic and fit together seamlessly change routinges * overarching workflow "Getting Things Done" GTD * constantly have to ump back and forth between different tasks * 1- bibliographical slip-box: reference * 2- collected and generated ideas slip-box * 3- index: notes serve as entry point / thought/ topic * writing a paper step by step * 1 fleeting notes: it will delete within a day * I always have a slip of paper at hand, on which I note down the ideas of certain pages. on the backside I write down the bibliographic details. after finishing the book I go throught my notes and think how ehse notes might be relevant for already written notes in the slip-box. it means that I always read with an eye towards possible connections in the slip-box * 2 literature notes: contain the necessary information - in reference system or in the slip-box * I make a note with the bibliographic details. on the backside I would write "on page x is this, on page y is that" and then it goes into the bibliographic slib-box where I collect everything I read * read, think about its relevance * theoretical background, methodological approach or perspective of the text we read. * whether something adds to a discussion in the slip-box * we look at our slip-box for already existing lines of thought and think about the questions and problems already on our minds to which a new note might contribute. - assigning keywords is much more than just a bureaucratic act. it is a crucial part of the thinking process, which often leads to a deeper elaboration of the note itself and the connection to other notes. * * 3 permanent notes: develop ideas/new/ - only relevant to one particular project - in project specific folder - can be discarded or archived after project finished * add to slip-box behind the note you dirctyly refer to or behind the last note in the slip-box * add links to other notes or links on other notes to your new note * make sure it can be found from the index (MOC) add an entry in the index if necessary or refer to it from a note that is connected to the index * build a latticework of mental models * 4 behind multiple notes (link backward) - link to related note - link to MOC * make sure it can be found again * **keywords** can easily be added to a note like **tags** and will then show up in the **index** * a day * read and take notes * build connections within the slip-box - new idea * you write on your paper, notice a hole in the argument and have another look in the file system for the missing link. you follow up on a footnote, go back to research and might add a fitting quote to one of your papers in the making. * The four underlying principles * writing * simplicity * not from scratch * let the work carry you forward * the six steps to successful writing * separate and interlocking tasks * we use memo techniques is to bundle items together in a meaningful way and remember the bundles * the brain doesn't distinguish between an actual finished task and one that is postponed by taking a note * the fist step is to break down the amprphous task of "writing" into smaller pieces of different tasks that can be finished in **one go.** the second step is to make sure we always write down the outcome of our thinking, including possible connections to further inquiries. * ego depletion * read for understanding * take smart notes * develp ideas (MOC) * 1 overview of a topic - referred to from the index and used as an entry point into a topic has already develped * 2 keeping tarck of all topic - index - * share your insight * make it a habit * afterword * ## Writing Solid Code Writing Solid Code (Microsoft Programming Series) by Trendelles Store Note from Writing Solid Code Book [https://www.pirahansiah.com/describe/book- summary](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Fdescribe%2Fbook- summary&sa=D&sntz=1&usg=AOvVaw3ACzA6G0MBVN3nw6f-yMQo) #SolidCode #C++ #Python #pirahansiah * **Chapter 1** * Enable all optional compiler warnings. * Use lint to catch bugs that your compiler may miss. * If you have unit tests, use them. * **Chapter 2** * introduction assert page 16; assert.h * assert is nothing more than a repackaged form of the #ifdef code we saw before, but when you use * the macro, it takes one line instead of seven * That's why assert is a macro and not a function; if it were a function, calling * it could cause unexpected memory or code swapping. * Use assertions to validate function arguments . * Strip undefined behavior from your code, or use assertions to catch illegal uses of undefined behavior . * Don't waste people's time. Document unclear assertions . * Either remove implicit assumptions, or assert that they are valid . * Use assertions to detect impossible conditions. * Don't hide bugs when you program defensively. * Use a second algorithm to validate your results. * Don't wait for bugs to happen; use startup checks. * **Chapter 3** * Maintain shipping and debugging versions of your program. Shrink-wrap the shipping version, but use the debugging ver­sion as much as possible to catch bugs quickly. * Assertions are a shorthand way to write debugging checks. Use them to catch illegal conditions that should never arise. Don't confuse such conditions with error conditions, which you must handle in the final product. * Use assertions to validate function arguments and to alert pro­ grammars when they do something undefined. The more rigidly you define your functions, the easier it will be to validate the arguments. * Once you've written a function, review it and ask yourself, 'What am I assuming?" If you find an assumption, either assert that your assumption is always valid, or rewrite the code to re­ move the assumption. Also ask, 'What is most likely to be wrong in this code, and how can I automatically detect the prob­lem?" Strive to implement tests that catch bugs at the earliest possible moment. Textbooks encourage programmers to program defensively, but remember that this coding style hides bugs. When you write de­fensive code, use assertions to alert you if the "can't happen" cases do happen. * Eliminate random behavior. Force bugs to be reproducible . * Destroy your garbage so that it's not misused . * Not only did reallocate have to move the block of memory as it was expanding it, but the old memory had to be reallocated and filled with new data. In the assembler, both happened rarely. * This brings up another guideline for writing bug-free code: You don't want anything to happen rarely. You need to isolate those behaviors in your subsystems that may happen and make sure that they do happen. And of­ ten. If you find rare behavior in your subsystems, be sure to do something­ anything-to stir things up. * The assembler bug could have been found within hours, instead of years, if reallocate hadn't so rarely moved blocks when it expanded them. * If something happens rarely, force it to happen often. * Keep debug information to allow stronger error checking. * There is no question that the debug code will create differences be­ tween the ship and the debug versions of your code, but as long as you're careful not to change the underlying behavior of the code, those differences shouldn't matter. * Create thorough subsystem checks, and use them often. * Design your tests carefully. Nothing should be arbitrary. * Strive to implement transparent integrity checks. * Don't apply ship version constraints to the debug version. Trade size and speed for error detection. * **Chapter 4** * Don't wait until you have a bug to step through your code. * Step through every code path. * What About Sweeping Changes? In the past, programmers have asked, "What if I add a feature that touches code in many places? Stepping through all that new code is going to be time consuming." Let me answer that question with another: Can you make such sweeping changes without introducing any bugs? The habit of stepping through your code creates an interesting negative feedback loop. Programmers who step through their code soon learn to write small, easily testable functions because stepping through large func­tions is so painful. Programmers also spend more time thinking about how they can localize the changes they need to make-again, so that they can more easily test their code. And isn't this exactly what you want? No project lead likes it when programmers touch a lot of code; it's too destabilizing. Nor do leads like large, unmanageable functions; they're often unmaintainable. Try hard to localize your changes. If you find that you must make per­vasive changes, think twice before you decide not to step through all the new code. * Overflow and underflow bugs * Data conversion bugs * Off-by-one bugs * NULL pointer bugs * Bugs using garbage memory (0xA3 bugs) * Assignment bugs in which you've used = instead of == * Precedence bugs * Logic bugs * As you step through code, focus on data flow. Source level debuggers can hide execution details. Step through critical code at the instruction level . * **Chapter 5** * Make it hard to ignore error conditions. Don't bury error codes in return values. * Always look for, and eliminate, flaws in your interfaces. * Don't write multipurpose functions. Write separate functions to allow stronger argument validation. * Don't be wishy-washy. Define explicit function arguments. * Write functions that, given valid inputs, cannot fail. * Make the code intelligible at the point of call. Avoid Boolean arguments. * Write comments that emphasize potential hazards. * **Chapter 6** * Use well-defined data types. * Always ask ,"Can this variable or expression over- or underflow?" * Implement your designs as accurately as possible. Being kind a close is being kind a buggy. * these principles already: Don't accept special purpose arguments such as the NULL pointer, and Implement your design, not something that approximates it. The third principle is new: Strive to make every function perform its task exactly one time. * Implement "the task" just once. * Get rid of extraneous if statements. * Avoid using nested ?: operators . * Handle your special cases just once. * Avoid risky language idioms. * Don't needlessly mix operator types. If you must mix operators, use parentheses to isolate the operations. * Avoid calling functions that return errors. * **Chapter 7** * Don't reference memory that you don't own. * Don't reference memory that you have freed. * Don't use output memory as workspace buffers. * Don't pass data in static (or global) memory. * Don't write parasitic functions. * Don't abuse your programming language. * Tight C does not guarantee efficient machine code. * Write code for the "average" programmer. * **Chapter 8** * Bugs don't just "go away." * Don't fix bugs later; fix them now. * Fix the cause, not the symptom. * Don't clean up code unless the clean­ up is critical to the product's success. * Don't implement nonstrategic features. * There are no free features. * Don't allow unnecessary flexibility. * Don't keep "trying" solutions· until you find one that works. Take the time to find the correct solution. * Write and test code in small chunks. * Always test your code, even if that means your schedule will slip. * Don't rely on the testing group to find your bugs. * Don't blame testers for finding your bugs. * Establish your priorities and stick to them. * Never allow the same bug to bite you twice. * **A: CODING CHECKLISTS** * **B: MEMORY LOGGING ROUTINES** * **To sum up** * Charles Simonyi in the early 1970s. https://en.wikipedia.org/wiki/Hungarian_notation * char ch; * byte b; * flag f;/* flags that are always TRUE or FALSE*/ * symbol sym;/* some sort of symbol structure*/ * char byte flag symbol * *pch:/* character pointer*/ * *pb; * *pf: * *psym: * related link: [https://www.morling.dev/images/code_review_pyramid.svg](https://www.google.com/url?q=https%3A%2F%2Fwww.morling.dev%2Fimages%2Fcode_review_pyramid.svg&sa=D&sntz=1&usg=AOvVaw1y2It_PUzXAtFN7dPLegCa) * ## Shape Up: Stop Running in Circles and Ship Work that Matters * Six-week cycles: Our decisions are based on moving the product forward in the next six weeks, not micromanaging time. We don’t count hours or question how individual days are spent. We don’t have daily meetings. We don’t rethink our roadmap every two weeks. Our focus is at a higher level. We say to ourselves: “If this project ships after six weeks, we’ll be really happy. We’ll feel our time was well spent.” Then we commit the six weeks and leave the team alone to get it done * Shaping the work: key elements of a solution, appetite * **part one shaping:** * two separate tracks: one for shaping, one for building. * Steps to shaping * 1\. Set boundaries. * 2\. Rough out the elements: idea that solves the problem within the appetite but without all the fine details worked out * 3\. Address risks and rabbit holes. * 4\. Write the pitch. * fixed time, variable scope, * Where in the current system does the new thing fit? How do you get to it? What are the key components or interactions? Where does it take you? * five ingredients that we always want to include in a pitch: 1. Problem — The raw idea, a use case, or something we’ve seen that motivates us to work on this 2. Appetite — How much time we want to spend and how that constrains the solution 3. Solution — The core elements we came up with, presented in a form that’s easy for people to immediately understand 4. Rabbit holes — Details about the solution worth calling out to avoid problems 5. No-gos — Anything specifically excluded from the concept: functionality or use cases we intentionally aren’t covering to fit the appetite or make the problem tractable * **part two : Betting** * Some companies use two-week cycles (aka “sprints”). We learned that two weeks is too short to get anything meaningful done. Worse than that, two-week cycles are extremely costly due to the planning overhead. The amount of work you get out of two weeks isn’t worth the collective hours around the table to “sprint plan” or the opportunity cost of breaking everyone’s momentum to re-group. * after each six-week cycle, we schedule two weeks for cool-down. * bugs: Use cool-down. Bring it to the betting table. Schedule a bug smash. * We’ve noticed three phases of work when we build a new product from scratch. R&D mode (bet the time on spiking some key pieces of the new product idea. CEO and designer, we don’t expect to ship anything at the end of an R&D cycle) , Production mode (Shaping is deliberate again. bet multiple teams in parallel , Shipping is the goal) , Cleanup mode (There’s no shaping. There aren’t clear team boundaries. Work is “shipped”) [Cleanup shouldn’t last longer than two cycles] * Example: two years of development, R&D mode for the first year, a year of production mode cycles followed, two cycles of cleanup and significantly cut back the feature set, some overlap between R&D and production mode after that first year, * **Part three: Building** * We don’t start by assigning tasks to anyone. * we define and track the scopes as to-do lists. Each scope corresponds to a list name. Then any tasks for that scope go in that list. * **image 1, 2, 3,** * Mark nice-to-haves with ~ * First there’s the uphill phase of figuring out what our approach is and what we’re going to do. Then, once we can see all the work involved, there’s the downhill phase of execution * image 4, 5, * ## Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet * getting start: idea, technology, passion; What can I do well that I would love to do for an extended period of time? P: 19, 21 * 1 market segmentation: The single necessary and sufficient condition for a business is a paying customer. ; technological enthusiasts=early adopters=early majority ; P: 31, * 2 select a beachhead market: P: 44, * 3 build an end user profile: P: 52, * 4 Calculate the Total Addressable Market (TAM) Size for the Beachhead Market; $200 # [Essential Tips And Tricks For ](/book-summary/commonplace-book)[commonplace book](/book-summary/commonplace-book) # [knowledge management ](/book-summary/knowledge_management) # [PKM](/book-summary/knowledge_management/pkm) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/qDZvojKIjUt_BmKSFeFiLcko8E9gUx2UPvpr5lUZqjKQRQ0VpykBihMNMzKE- OOh8UqTvee2oKfKd28QF1FzbWk=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/qDZvojKIjUt_BmKSFeFiLcko8E9gUx2UPvpr5lUZqjKQRQ0VpykBihMNMzKE- OOh8UqTvee2oKfKd28QF1FzbWk=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Book Summary Books How to take smart Notes Writing Solid Code Shape Up: Stop Running in Circles and Ship Work that Matters Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet List of books I've read: Computer Vision: Algorithms and Applications Multiple View Geometry in Computer Vision Second Edition Complete to download the latest draft of Machine Learning Yearning Multiple view geometry in computer vision Learning OpenCV Book by Adrian Kaehler and Gary Bradski Digital Image Processing, Global Edition by Rafael C. Gonzalez Introduction to Algorithms, 3rd Edition (The MIT Press) The Mythical Man-Month: Essays on Software Engineering OpenCV-4-with-Python-Blueprints-Second-Edition Cracking the coding interview 189 programming questions and solutions Cracking the Tech Career: Insider Advice on Landing a Job at Google, Microsoft, Apple, Or Any Top Tech Company The Art of Startup Fundraising Mathematics for Machine Learning Principles of Economics (6th edition) The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) Reinventing Your Life: The Breakthough Program to End Negative Behavior Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf Magazine Papers Websites and links Tools YouTube - List channels Language: Essential Tips And Tricks For commonplace book knowledge management PKM # Books * ## How to take smart Notes * Introduction 4, The four underlying principles 4, the six steps to successful writing 6, afterword * introduction * write everything/ collect notes not related to any thing, organize notes, revirew, rewrite, * no reorganise no complex system, develop ideas by smart notes * 1 slip-box; 2 repeatable tasks that can become automatic and fit together seamlessly change routinges * overarching workflow "Getting Things Done" GTD * constantly have to ump back and forth between different tasks * 1- bibliographical slip-box: reference * 2- collected and generated ideas slip-box * 3- index: notes serve as entry point / thought/ topic * writing a paper step by step * 1 fleeting notes: it will delete within a day * I always have a slip of paper at hand, on which I note down the ideas of certain pages. on the backside I write down the bibliographic details. after finishing the book I go throught my notes and think how ehse notes might be relevant for already written notes in the slip-box. it means that I always read with an eye towards possible connections in the slip-box * 2 literature notes: contain the necessary information - in reference system or in the slip-box * I make a note with the bibliographic details. on the backside I would write "on page x is this, on page y is that" and then it goes into the bibliographic slib-box where I collect everything I read * read, think about its relevance * theoretical background, methodological approach or perspective of the text we read. * whether something adds to a discussion in the slip-box * we look at our slip-box for already existing lines of thought and think about the questions and problems already on our minds to which a new note might contribute. - assigning keywords is much more than just a bureaucratic act. it is a crucial part of the thinking process, which often leads to a deeper elaboration of the note itself and the connection to other notes. * * 3 permanent notes: develop ideas/new/ - only relevant to one particular project - in project specific folder - can be discarded or archived after project finished * add to slip-box behind the note you dirctyly refer to or behind the last note in the slip-box * add links to other notes or links on other notes to your new note * make sure it can be found from the index (MOC) add an entry in the index if necessary or refer to it from a note that is connected to the index * build a latticework of mental models * 4 behind multiple notes (link backward) - link to related note - link to MOC * make sure it can be found again * **keywords** can easily be added to a note like **tags** and will then show up in the **index** * a day * read and take notes * build connections within the slip-box - new idea * you write on your paper, notice a hole in the argument and have another look in the file system for the missing link. you follow up on a footnote, go back to research and might add a fitting quote to one of your papers in the making. * The four underlying principles * writing * simplicity * not from scratch * let the work carry you forward * the six steps to successful writing * separate and interlocking tasks * we use memo techniques is to bundle items together in a meaningful way and remember the bundles * the brain doesn't distinguish between an actual finished task and one that is postponed by taking a note * the fist step is to break down the amprphous task of "writing" into smaller pieces of different tasks that can be finished in **one go.** the second step is to make sure we always write down the outcome of our thinking, including possible connections to further inquiries. * ego depletion * read for understanding * take smart notes * develp ideas (MOC) * 1 overview of a topic - referred to from the index and used as an entry point into a topic has already develped * 2 keeping tarck of all topic - index - * share your insight * make it a habit * afterword * ## Writing Solid Code Writing Solid Code (Microsoft Programming Series) by Trendelles Store Note from Writing Solid Code Book [https://www.pirahansiah.com/describe/book- summary](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Fdescribe%2Fbook- summary&sa=D&sntz=1&usg=AOvVaw3ACzA6G0MBVN3nw6f-yMQo) #SolidCode #C++ #Python #pirahansiah * **Chapter 1** * Enable all optional compiler warnings. * Use lint to catch bugs that your compiler may miss. * If you have unit tests, use them. * **Chapter 2** * introduction assert page 16; assert.h * assert is nothing more than a repackaged form of the #ifdef code we saw before, but when you use * the macro, it takes one line instead of seven * That's why assert is a macro and not a function; if it were a function, calling * it could cause unexpected memory or code swapping. * Use assertions to validate function arguments . * Strip undefined behavior from your code, or use assertions to catch illegal uses of undefined behavior . * Don't waste people's time. Document unclear assertions . * Either remove implicit assumptions, or assert that they are valid . * Use assertions to detect impossible conditions. * Don't hide bugs when you program defensively. * Use a second algorithm to validate your results. * Don't wait for bugs to happen; use startup checks. * **Chapter 3** * Maintain shipping and debugging versions of your program. Shrink-wrap the shipping version, but use the debugging ver­sion as much as possible to catch bugs quickly. * Assertions are a shorthand way to write debugging checks. Use them to catch illegal conditions that should never arise. Don't confuse such conditions with error conditions, which you must handle in the final product. * Use assertions to validate function arguments and to alert pro­ grammars when they do something undefined. The more rigidly you define your functions, the easier it will be to validate the arguments. * Once you've written a function, review it and ask yourself, 'What am I assuming?" If you find an assumption, either assert that your assumption is always valid, or rewrite the code to re­ move the assumption. Also ask, 'What is most likely to be wrong in this code, and how can I automatically detect the prob­lem?" Strive to implement tests that catch bugs at the earliest possible moment. Textbooks encourage programmers to program defensively, but remember that this coding style hides bugs. When you write de­fensive code, use assertions to alert you if the "can't happen" cases do happen. * Eliminate random behavior. Force bugs to be reproducible . * Destroy your garbage so that it's not misused . * Not only did reallocate have to move the block of memory as it was expanding it, but the old memory had to be reallocated and filled with new data. In the assembler, both happened rarely. * This brings up another guideline for writing bug-free code: You don't want anything to happen rarely. You need to isolate those behaviors in your subsystems that may happen and make sure that they do happen. And of­ ten. If you find rare behavior in your subsystems, be sure to do something­ anything-to stir things up. * The assembler bug could have been found within hours, instead of years, if reallocate hadn't so rarely moved blocks when it expanded them. * If something happens rarely, force it to happen often. * Keep debug information to allow stronger error checking. * There is no question that the debug code will create differences be­ tween the ship and the debug versions of your code, but as long as you're careful not to change the underlying behavior of the code, those differences shouldn't matter. * Create thorough subsystem checks, and use them often. * Design your tests carefully. Nothing should be arbitrary. * Strive to implement transparent integrity checks. * Don't apply ship version constraints to the debug version. Trade size and speed for error detection. * **Chapter 4** * Don't wait until you have a bug to step through your code. * Step through every code path. * What About Sweeping Changes? In the past, programmers have asked, "What if I add a feature that touches code in many places? Stepping through all that new code is going to be time consuming." Let me answer that question with another: Can you make such sweeping changes without introducing any bugs? The habit of stepping through your code creates an interesting negative feedback loop. Programmers who step through their code soon learn to write small, easily testable functions because stepping through large func­tions is so painful. Programmers also spend more time thinking about how they can localize the changes they need to make-again, so that they can more easily test their code. And isn't this exactly what you want? No project lead likes it when programmers touch a lot of code; it's too destabilizing. Nor do leads like large, unmanageable functions; they're often unmaintainable. Try hard to localize your changes. If you find that you must make per­vasive changes, think twice before you decide not to step through all the new code. * Overflow and underflow bugs * Data conversion bugs * Off-by-one bugs * NULL pointer bugs * Bugs using garbage memory (0xA3 bugs) * Assignment bugs in which you've used = instead of == * Precedence bugs * Logic bugs * As you step through code, focus on data flow. Source level debuggers can hide execution details. Step through critical code at the instruction level . * **Chapter 5** * Make it hard to ignore error conditions. Don't bury error codes in return values. * Always look for, and eliminate, flaws in your interfaces. * Don't write multipurpose functions. Write separate functions to allow stronger argument validation. * Don't be wishy-washy. Define explicit function arguments. * Write functions that, given valid inputs, cannot fail. * Make the code intelligible at the point of call. Avoid Boolean arguments. * Write comments that emphasize potential hazards. * **Chapter 6** * Use well-defined data types. * Always ask ,"Can this variable or expression over- or underflow?" * Implement your designs as accurately as possible. Being kind a close is being kind a buggy. * these principles already: Don't accept special purpose arguments such as the NULL pointer, and Implement your design, not something that approximates it. The third principle is new: Strive to make every function perform its task exactly one time. * Implement "the task" just once. * Get rid of extraneous if statements. * Avoid using nested ?: operators . * Handle your special cases just once. * Avoid risky language idioms. * Don't needlessly mix operator types. If you must mix operators, use parentheses to isolate the operations. * Avoid calling functions that return errors. * **Chapter 7** * Don't reference memory that you don't own. * Don't reference memory that you have freed. * Don't use output memory as workspace buffers. * Don't pass data in static (or global) memory. * Don't write parasitic functions. * Don't abuse your programming language. * Tight C does not guarantee efficient machine code. * Write code for the "average" programmer. * **Chapter 8** * Bugs don't just "go away." * Don't fix bugs later; fix them now. * Fix the cause, not the symptom. * Don't clean up code unless the clean­ up is critical to the product's success. * Don't implement nonstrategic features. * There are no free features. * Don't allow unnecessary flexibility. * Don't keep "trying" solutions· until you find one that works. Take the time to find the correct solution. * Write and test code in small chunks. * Always test your code, even if that means your schedule will slip. * Don't rely on the testing group to find your bugs. * Don't blame testers for finding your bugs. * Establish your priorities and stick to them. * Never allow the same bug to bite you twice. * **A: CODING CHECKLISTS** * **B: MEMORY LOGGING ROUTINES** * **To sum up** * Charles Simonyi in the early 1970s. https://en.wikipedia.org/wiki/Hungarian_notation * char ch; * byte b; * flag f;/* flags that are always TRUE or FALSE*/ * symbol sym;/* some sort of symbol structure*/ * char byte flag symbol * *pch:/* character pointer*/ * *pb; * *pf: * *psym: * related link: [https://www.morling.dev/images/code_review_pyramid.svg](https://www.google.com/url?q=https%3A%2F%2Fwww.morling.dev%2Fimages%2Fcode_review_pyramid.svg&sa=D&sntz=1&usg=AOvVaw1y2It_PUzXAtFN7dPLegCa) * ## Shape Up: Stop Running in Circles and Ship Work that Matters * Six-week cycles: Our decisions are based on moving the product forward in the next six weeks, not micromanaging time. We don’t count hours or question how individual days are spent. We don’t have daily meetings. We don’t rethink our roadmap every two weeks. Our focus is at a higher level. We say to ourselves: “If this project ships after six weeks, we’ll be really happy. We’ll feel our time was well spent.” Then we commit the six weeks and leave the team alone to get it done * Shaping the work: key elements of a solution, appetite * **part one shaping:** * two separate tracks: one for shaping, one for building. * Steps to shaping * 1\. Set boundaries. * 2\. Rough out the elements: idea that solves the problem within the appetite but without all the fine details worked out * 3\. Address risks and rabbit holes. * 4\. Write the pitch. * fixed time, variable scope, * Where in the current system does the new thing fit? How do you get to it? What are the key components or interactions? Where does it take you? * five ingredients that we always want to include in a pitch: 1. Problem — The raw idea, a use case, or something we’ve seen that motivates us to work on this 2. Appetite — How much time we want to spend and how that constrains the solution 3. Solution — The core elements we came up with, presented in a form that’s easy for people to immediately understand 4. Rabbit holes — Details about the solution worth calling out to avoid problems 5. No-gos — Anything specifically excluded from the concept: functionality or use cases we intentionally aren’t covering to fit the appetite or make the problem tractable * **part two : Betting** * Some companies use two-week cycles (aka “sprints”). We learned that two weeks is too short to get anything meaningful done. Worse than that, two-week cycles are extremely costly due to the planning overhead. The amount of work you get out of two weeks isn’t worth the collective hours around the table to “sprint plan” or the opportunity cost of breaking everyone’s momentum to re-group. * after each six-week cycle, we schedule two weeks for cool-down. * bugs: Use cool-down. Bring it to the betting table. Schedule a bug smash. * We’ve noticed three phases of work when we build a new product from scratch. R&D mode (bet the time on spiking some key pieces of the new product idea. CEO and designer, we don’t expect to ship anything at the end of an R&D cycle) , Production mode (Shaping is deliberate again. bet multiple teams in parallel , Shipping is the goal) , Cleanup mode (There’s no shaping. There aren’t clear team boundaries. Work is “shipped”) [Cleanup shouldn’t last longer than two cycles] * Example: two years of development, R&D mode for the first year, a year of production mode cycles followed, two cycles of cleanup and significantly cut back the feature set, some overlap between R&D and production mode after that first year, * **Part three: Building** * We don’t start by assigning tasks to anyone. * we define and track the scopes as to-do lists. Each scope corresponds to a list name. Then any tasks for that scope go in that list. * **image 1, 2, 3,** * Mark nice-to-haves with ~ * First there’s the uphill phase of figuring out what our approach is and what we’re going to do. Then, once we can see all the work involved, there’s the downhill phase of execution * image 4, 5, * ## Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet * getting start: idea, technology, passion; What can I do well that I would love to do for an extended period of time? P: 19, 21 * 1 market segmentation: The single necessary and sufficient condition for a business is a paying customer. ; technological enthusiasts=early adopters=early majority ; P: 31, * 2 select a beachhead market: P: 44, * 3 build an end user profile: P: 52, * 4 Calculate the Total Addressable Market (TAM) Size for the Beachhead Market; $200 # [Essential Tips And Tricks For ](/book-summary/commonplace-book)[commonplace book](/book-summary/commonplace-book) # [knowledge management ](/book-summary/knowledge_management) # [PKM](/book-summary/knowledge_management/pkm) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/qDZvojKIjUt_BmKSFeFiLcko8E9gUx2UPvpr5lUZqjKQRQ0VpykBihMNMzKE- OOh8UqTvee2oKfKd28QF1FzbWk=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/qDZvojKIjUt_BmKSFeFiLcko8E9gUx2UPvpr5lUZqjKQRQ0VpykBihMNMzKE- OOh8UqTvee2oKfKd28QF1FzbWk=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Book Summary Books How to take smart Notes Writing Solid Code Shape Up: Stop Running in Circles and Ship Work that Matters Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet List of books I've read: Computer Vision: Algorithms and Applications Multiple View Geometry in Computer Vision Second Edition Complete to download the latest draft of Machine Learning Yearning Multiple view geometry in computer vision Learning OpenCV Book by Adrian Kaehler and Gary Bradski Digital Image Processing, Global Edition by Rafael C. Gonzalez Introduction to Algorithms, 3rd Edition (The MIT Press) The Mythical Man-Month: Essays on Software Engineering OpenCV-4-with-Python-Blueprints-Second-Edition Cracking the coding interview 189 programming questions and solutions Cracking the Tech Career: Insider Advice on Landing a Job at Google, Microsoft, Apple, Or Any Top Tech Company The Art of Startup Fundraising Mathematics for Machine Learning Principles of Economics (6th edition) The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) Reinventing Your Life: The Breakthough Program to End Negative Behavior Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf Magazine Papers Websites and links Tools YouTube - List channels Language: Essential Tips And Tricks For commonplace book knowledge management PKM # Books * ## How to take smart Notes * Introduction 4, The four underlying principles 4, the six steps to successful writing 6, afterword * introduction * write everything/ collect notes not related to any thing, organize notes, revirew, rewrite, * no reorganise no complex system, develop ideas by smart notes * 1 slip-box; 2 repeatable tasks that can become automatic and fit together seamlessly change routinges * overarching workflow "Getting Things Done" GTD * constantly have to ump back and forth between different tasks * 1- bibliographical slip-box: reference * 2- collected and generated ideas slip-box * 3- index: notes serve as entry point / thought/ topic * writing a paper step by step * 1 fleeting notes: it will delete within a day * I always have a slip of paper at hand, on which I note down the ideas of certain pages. on the backside I write down the bibliographic details. after finishing the book I go throught my notes and think how ehse notes might be relevant for already written notes in the slip-box. it means that I always read with an eye towards possible connections in the slip-box * 2 literature notes: contain the necessary information - in reference system or in the slip-box * I make a note with the bibliographic details. on the backside I would write "on page x is this, on page y is that" and then it goes into the bibliographic slib-box where I collect everything I read * read, think about its relevance * theoretical background, methodological approach or perspective of the text we read. * whether something adds to a discussion in the slip-box * we look at our slip-box for already existing lines of thought and think about the questions and problems already on our minds to which a new note might contribute. - assigning keywords is much more than just a bureaucratic act. it is a crucial part of the thinking process, which often leads to a deeper elaboration of the note itself and the connection to other notes. * * 3 permanent notes: develop ideas/new/ - only relevant to one particular project - in project specific folder - can be discarded or archived after project finished * add to slip-box behind the note you dirctyly refer to or behind the last note in the slip-box * add links to other notes or links on other notes to your new note * make sure it can be found from the index (MOC) add an entry in the index if necessary or refer to it from a note that is connected to the index * build a latticework of mental models * 4 behind multiple notes (link backward) - link to related note - link to MOC * make sure it can be found again * **keywords** can easily be added to a note like **tags** and will then show up in the **index** * a day * read and take notes * build connections within the slip-box - new idea * you write on your paper, notice a hole in the argument and have another look in the file system for the missing link. you follow up on a footnote, go back to research and might add a fitting quote to one of your papers in the making. * The four underlying principles * writing * simplicity * not from scratch * let the work carry you forward * the six steps to successful writing * separate and interlocking tasks * we use memo techniques is to bundle items together in a meaningful way and remember the bundles * the brain doesn't distinguish between an actual finished task and one that is postponed by taking a note * the fist step is to break down the amprphous task of "writing" into smaller pieces of different tasks that can be finished in **one go.** the second step is to make sure we always write down the outcome of our thinking, including possible connections to further inquiries. * ego depletion * read for understanding * take smart notes * develp ideas (MOC) * 1 overview of a topic - referred to from the index and used as an entry point into a topic has already develped * 2 keeping tarck of all topic - index - * share your insight * make it a habit * afterword * ## Writing Solid Code Writing Solid Code (Microsoft Programming Series) by Trendelles Store Note from Writing Solid Code Book [https://www.pirahansiah.com/describe/book- summary](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Fdescribe%2Fbook- summary&sa=D&sntz=1&usg=AOvVaw3ACzA6G0MBVN3nw6f-yMQo) #SolidCode #C++ #Python #pirahansiah * **Chapter 1** * Enable all optional compiler warnings. * Use lint to catch bugs that your compiler may miss. * If you have unit tests, use them. * **Chapter 2** * introduction assert page 16; assert.h * assert is nothing more than a repackaged form of the #ifdef code we saw before, but when you use * the macro, it takes one line instead of seven * That's why assert is a macro and not a function; if it were a function, calling * it could cause unexpected memory or code swapping. * Use assertions to validate function arguments . * Strip undefined behavior from your code, or use assertions to catch illegal uses of undefined behavior . * Don't waste people's time. Document unclear assertions . * Either remove implicit assumptions, or assert that they are valid . * Use assertions to detect impossible conditions. * Don't hide bugs when you program defensively. * Use a second algorithm to validate your results. * Don't wait for bugs to happen; use startup checks. * **Chapter 3** * Maintain shipping and debugging versions of your program. Shrink-wrap the shipping version, but use the debugging ver­sion as much as possible to catch bugs quickly. * Assertions are a shorthand way to write debugging checks. Use them to catch illegal conditions that should never arise. Don't confuse such conditions with error conditions, which you must handle in the final product. * Use assertions to validate function arguments and to alert pro­ grammars when they do something undefined. The more rigidly you define your functions, the easier it will be to validate the arguments. * Once you've written a function, review it and ask yourself, 'What am I assuming?" If you find an assumption, either assert that your assumption is always valid, or rewrite the code to re­ move the assumption. Also ask, 'What is most likely to be wrong in this code, and how can I automatically detect the prob­lem?" Strive to implement tests that catch bugs at the earliest possible moment. Textbooks encourage programmers to program defensively, but remember that this coding style hides bugs. When you write de­fensive code, use assertions to alert you if the "can't happen" cases do happen. * Eliminate random behavior. Force bugs to be reproducible . * Destroy your garbage so that it's not misused . * Not only did reallocate have to move the block of memory as it was expanding it, but the old memory had to be reallocated and filled with new data. In the assembler, both happened rarely. * This brings up another guideline for writing bug-free code: You don't want anything to happen rarely. You need to isolate those behaviors in your subsystems that may happen and make sure that they do happen. And of­ ten. If you find rare behavior in your subsystems, be sure to do something­ anything-to stir things up. * The assembler bug could have been found within hours, instead of years, if reallocate hadn't so rarely moved blocks when it expanded them. * If something happens rarely, force it to happen often. * Keep debug information to allow stronger error checking. * There is no question that the debug code will create differences be­ tween the ship and the debug versions of your code, but as long as you're careful not to change the underlying behavior of the code, those differences shouldn't matter. * Create thorough subsystem checks, and use them often. * Design your tests carefully. Nothing should be arbitrary. * Strive to implement transparent integrity checks. * Don't apply ship version constraints to the debug version. Trade size and speed for error detection. * **Chapter 4** * Don't wait until you have a bug to step through your code. * Step through every code path. * What About Sweeping Changes? In the past, programmers have asked, "What if I add a feature that touches code in many places? Stepping through all that new code is going to be time consuming." Let me answer that question with another: Can you make such sweeping changes without introducing any bugs? The habit of stepping through your code creates an interesting negative feedback loop. Programmers who step through their code soon learn to write small, easily testable functions because stepping through large func­tions is so painful. Programmers also spend more time thinking about how they can localize the changes they need to make-again, so that they can more easily test their code. And isn't this exactly what you want? No project lead likes it when programmers touch a lot of code; it's too destabilizing. Nor do leads like large, unmanageable functions; they're often unmaintainable. Try hard to localize your changes. If you find that you must make per­vasive changes, think twice before you decide not to step through all the new code. * Overflow and underflow bugs * Data conversion bugs * Off-by-one bugs * NULL pointer bugs * Bugs using garbage memory (0xA3 bugs) * Assignment bugs in which you've used = instead of == * Precedence bugs * Logic bugs * As you step through code, focus on data flow. Source level debuggers can hide execution details. Step through critical code at the instruction level . * **Chapter 5** * Make it hard to ignore error conditions. Don't bury error codes in return values. * Always look for, and eliminate, flaws in your interfaces. * Don't write multipurpose functions. Write separate functions to allow stronger argument validation. * Don't be wishy-washy. Define explicit function arguments. * Write functions that, given valid inputs, cannot fail. * Make the code intelligible at the point of call. Avoid Boolean arguments. * Write comments that emphasize potential hazards. * **Chapter 6** * Use well-defined data types. * Always ask ,"Can this variable or expression over- or underflow?" * Implement your designs as accurately as possible. Being kind a close is being kind a buggy. * these principles already: Don't accept special purpose arguments such as the NULL pointer, and Implement your design, not something that approximates it. The third principle is new: Strive to make every function perform its task exactly one time. * Implement "the task" just once. * Get rid of extraneous if statements. * Avoid using nested ?: operators . * Handle your special cases just once. * Avoid risky language idioms. * Don't needlessly mix operator types. If you must mix operators, use parentheses to isolate the operations. * Avoid calling functions that return errors. * **Chapter 7** * Don't reference memory that you don't own. * Don't reference memory that you have freed. * Don't use output memory as workspace buffers. * Don't pass data in static (or global) memory. * Don't write parasitic functions. * Don't abuse your programming language. * Tight C does not guarantee efficient machine code. * Write code for the "average" programmer. * **Chapter 8** * Bugs don't just "go away." * Don't fix bugs later; fix them now. * Fix the cause, not the symptom. * Don't clean up code unless the clean­ up is critical to the product's success. * Don't implement nonstrategic features. * There are no free features. * Don't allow unnecessary flexibility. * Don't keep "trying" solutions· until you find one that works. Take the time to find the correct solution. * Write and test code in small chunks. * Always test your code, even if that means your schedule will slip. * Don't rely on the testing group to find your bugs. * Don't blame testers for finding your bugs. * Establish your priorities and stick to them. * Never allow the same bug to bite you twice. * **A: CODING CHECKLISTS** * **B: MEMORY LOGGING ROUTINES** * **To sum up** * Charles Simonyi in the early 1970s. https://en.wikipedia.org/wiki/Hungarian_notation * char ch; * byte b; * flag f;/* flags that are always TRUE or FALSE*/ * symbol sym;/* some sort of symbol structure*/ * char byte flag symbol * *pch:/* character pointer*/ * *pb; * *pf: * *psym: * related link: [https://www.morling.dev/images/code_review_pyramid.svg](https://www.google.com/url?q=https%3A%2F%2Fwww.morling.dev%2Fimages%2Fcode_review_pyramid.svg&sa=D&sntz=1&usg=AOvVaw1y2It_PUzXAtFN7dPLegCa) * ## Shape Up: Stop Running in Circles and Ship Work that Matters * Six-week cycles: Our decisions are based on moving the product forward in the next six weeks, not micromanaging time. We don’t count hours or question how individual days are spent. We don’t have daily meetings. We don’t rethink our roadmap every two weeks. Our focus is at a higher level. We say to ourselves: “If this project ships after six weeks, we’ll be really happy. We’ll feel our time was well spent.” Then we commit the six weeks and leave the team alone to get it done * Shaping the work: key elements of a solution, appetite * **part one shaping:** * two separate tracks: one for shaping, one for building. * Steps to shaping * 1\. Set boundaries. * 2\. Rough out the elements: idea that solves the problem within the appetite but without all the fine details worked out * 3\. Address risks and rabbit holes. * 4\. Write the pitch. * fixed time, variable scope, * Where in the current system does the new thing fit? How do you get to it? What are the key components or interactions? Where does it take you? * five ingredients that we always want to include in a pitch: 1. Problem — The raw idea, a use case, or something we’ve seen that motivates us to work on this 2. Appetite — How much time we want to spend and how that constrains the solution 3. Solution — The core elements we came up with, presented in a form that’s easy for people to immediately understand 4. Rabbit holes — Details about the solution worth calling out to avoid problems 5. No-gos — Anything specifically excluded from the concept: functionality or use cases we intentionally aren’t covering to fit the appetite or make the problem tractable * **part two : Betting** * Some companies use two-week cycles (aka “sprints”). We learned that two weeks is too short to get anything meaningful done. Worse than that, two-week cycles are extremely costly due to the planning overhead. The amount of work you get out of two weeks isn’t worth the collective hours around the table to “sprint plan” or the opportunity cost of breaking everyone’s momentum to re-group. * after each six-week cycle, we schedule two weeks for cool-down. * bugs: Use cool-down. Bring it to the betting table. Schedule a bug smash. * We’ve noticed three phases of work when we build a new product from scratch. R&D mode (bet the time on spiking some key pieces of the new product idea. CEO and designer, we don’t expect to ship anything at the end of an R&D cycle) , Production mode (Shaping is deliberate again. bet multiple teams in parallel , Shipping is the goal) , Cleanup mode (There’s no shaping. There aren’t clear team boundaries. Work is “shipped”) [Cleanup shouldn’t last longer than two cycles] * Example: two years of development, R&D mode for the first year, a year of production mode cycles followed, two cycles of cleanup and significantly cut back the feature set, some overlap between R&D and production mode after that first year, * **Part three: Building** * We don’t start by assigning tasks to anyone. * we define and track the scopes as to-do lists. Each scope corresponds to a list name. Then any tasks for that scope go in that list. * **image 1, 2, 3,** * Mark nice-to-haves with ~ * First there’s the uphill phase of figuring out what our approach is and what we’re going to do. Then, once we can see all the work involved, there’s the downhill phase of execution * image 4, 5, * ## Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet * getting start: idea, technology, passion; What can I do well that I would love to do for an extended period of time? P: 19, 21 * 1 market segmentation: The single necessary and sufficient condition for a business is a paying customer. ; technological enthusiasts=early adopters=early majority ; P: 31, * 2 select a beachhead market: P: 44, * 3 build an end user profile: P: 52, * 4 Calculate the Total Addressable Market (TAM) Size for the Beachhead Market; $200 # [Essential Tips And Tricks For ](/book-summary/commonplace-book)[commonplace book](/book-summary/commonplace-book) # [knowledge management ](/book-summary/knowledge_management) # [PKM](/book-summary/knowledge_management/pkm) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/qDZvojKIjUt_BmKSFeFiLcko8E9gUx2UPvpr5lUZqjKQRQ0VpykBihMNMzKE- OOh8UqTvee2oKfKd28QF1FzbWk=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/qDZvojKIjUt_BmKSFeFiLcko8E9gUx2UPvpr5lUZqjKQRQ0VpykBihMNMzKE- OOh8UqTvee2oKfKd28QF1FzbWk=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Book Summary Books How to take smart Notes Writing Solid Code Shape Up: Stop Running in Circles and Ship Work that Matters Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet List of books I've read: Computer Vision: Algorithms and Applications Multiple View Geometry in Computer Vision Second Edition Complete to download the latest draft of Machine Learning Yearning Multiple view geometry in computer vision Learning OpenCV Book by Adrian Kaehler and Gary Bradski Digital Image Processing, Global Edition by Rafael C. Gonzalez Introduction to Algorithms, 3rd Edition (The MIT Press) The Mythical Man-Month: Essays on Software Engineering OpenCV-4-with-Python-Blueprints-Second-Edition Cracking the coding interview 189 programming questions and solutions Cracking the Tech Career: Insider Advice on Landing a Job at Google, Microsoft, Apple, Or Any Top Tech Company The Art of Startup Fundraising Mathematics for Machine Learning Principles of Economics (6th edition) The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) Reinventing Your Life: The Breakthough Program to End Negative Behavior Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf Magazine Papers Websites and links Tools YouTube - List channels Language: Essential Tips And Tricks For commonplace book knowledge management PKM # Books * ## How to take smart Notes * Introduction 4, The four underlying principles 4, the six steps to successful writing 6, afterword * introduction * write everything/ collect notes not related to any thing, organize notes, revirew, rewrite, * no reorganise no complex system, develop ideas by smart notes * 1 slip-box; 2 repeatable tasks that can become automatic and fit together seamlessly change routinges * overarching workflow "Getting Things Done" GTD * constantly have to ump back and forth between different tasks * 1- bibliographical slip-box: reference * 2- collected and generated ideas slip-box * 3- index: notes serve as entry point / thought/ topic * writing a paper step by step * 1 fleeting notes: it will delete within a day * I always have a slip of paper at hand, on which I note down the ideas of certain pages. on the backside I write down the bibliographic details. after finishing the book I go throught my notes and think how ehse notes might be relevant for already written notes in the slip-box. it means that I always read with an eye towards possible connections in the slip-box * 2 literature notes: contain the necessary information - in reference system or in the slip-box * I make a note with the bibliographic details. on the backside I would write "on page x is this, on page y is that" and then it goes into the bibliographic slib-box where I collect everything I read * read, think about its relevance * theoretical background, methodological approach or perspective of the text we read. * whether something adds to a discussion in the slip-box * we look at our slip-box for already existing lines of thought and think about the questions and problems already on our minds to which a new note might contribute. - assigning keywords is much more than just a bureaucratic act. it is a crucial part of the thinking process, which often leads to a deeper elaboration of the note itself and the connection to other notes. * * 3 permanent notes: develop ideas/new/ - only relevant to one particular project - in project specific folder - can be discarded or archived after project finished * add to slip-box behind the note you dirctyly refer to or behind the last note in the slip-box * add links to other notes or links on other notes to your new note * make sure it can be found from the index (MOC) add an entry in the index if necessary or refer to it from a note that is connected to the index * build a latticework of mental models * 4 behind multiple notes (link backward) - link to related note - link to MOC * make sure it can be found again * **keywords** can easily be added to a note like **tags** and will then show up in the **index** * a day * read and take notes * build connections within the slip-box - new idea * you write on your paper, notice a hole in the argument and have another look in the file system for the missing link. you follow up on a footnote, go back to research and might add a fitting quote to one of your papers in the making. * The four underlying principles * writing * simplicity * not from scratch * let the work carry you forward * the six steps to successful writing * separate and interlocking tasks * we use memo techniques is to bundle items together in a meaningful way and remember the bundles * the brain doesn't distinguish between an actual finished task and one that is postponed by taking a note * the fist step is to break down the amprphous task of "writing" into smaller pieces of different tasks that can be finished in **one go.** the second step is to make sure we always write down the outcome of our thinking, including possible connections to further inquiries. * ego depletion * read for understanding * take smart notes * develp ideas (MOC) * 1 overview of a topic - referred to from the index and used as an entry point into a topic has already develped * 2 keeping tarck of all topic - index - * share your insight * make it a habit * afterword * ## Writing Solid Code Writing Solid Code (Microsoft Programming Series) by Trendelles Store Note from Writing Solid Code Book [https://www.pirahansiah.com/describe/book- summary](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Fdescribe%2Fbook- summary&sa=D&sntz=1&usg=AOvVaw3ACzA6G0MBVN3nw6f-yMQo) #SolidCode #C++ #Python #pirahansiah * **Chapter 1** * Enable all optional compiler warnings. * Use lint to catch bugs that your compiler may miss. * If you have unit tests, use them. * **Chapter 2** * introduction assert page 16; assert.h * assert is nothing more than a repackaged form of the #ifdef code we saw before, but when you use * the macro, it takes one line instead of seven * That's why assert is a macro and not a function; if it were a function, calling * it could cause unexpected memory or code swapping. * Use assertions to validate function arguments . * Strip undefined behavior from your code, or use assertions to catch illegal uses of undefined behavior . * Don't waste people's time. Document unclear assertions . * Either remove implicit assumptions, or assert that they are valid . * Use assertions to detect impossible conditions. * Don't hide bugs when you program defensively. * Use a second algorithm to validate your results. * Don't wait for bugs to happen; use startup checks. * **Chapter 3** * Maintain shipping and debugging versions of your program. Shrink-wrap the shipping version, but use the debugging ver­sion as much as possible to catch bugs quickly. * Assertions are a shorthand way to write debugging checks. Use them to catch illegal conditions that should never arise. Don't confuse such conditions with error conditions, which you must handle in the final product. * Use assertions to validate function arguments and to alert pro­ grammars when they do something undefined. The more rigidly you define your functions, the easier it will be to validate the arguments. * Once you've written a function, review it and ask yourself, 'What am I assuming?" If you find an assumption, either assert that your assumption is always valid, or rewrite the code to re­ move the assumption. Also ask, 'What is most likely to be wrong in this code, and how can I automatically detect the prob­lem?" Strive to implement tests that catch bugs at the earliest possible moment. Textbooks encourage programmers to program defensively, but remember that this coding style hides bugs. When you write de­fensive code, use assertions to alert you if the "can't happen" cases do happen. * Eliminate random behavior. Force bugs to be reproducible . * Destroy your garbage so that it's not misused . * Not only did reallocate have to move the block of memory as it was expanding it, but the old memory had to be reallocated and filled with new data. In the assembler, both happened rarely. * This brings up another guideline for writing bug-free code: You don't want anything to happen rarely. You need to isolate those behaviors in your subsystems that may happen and make sure that they do happen. And of­ ten. If you find rare behavior in your subsystems, be sure to do something­ anything-to stir things up. * The assembler bug could have been found within hours, instead of years, if reallocate hadn't so rarely moved blocks when it expanded them. * If something happens rarely, force it to happen often. * Keep debug information to allow stronger error checking. * There is no question that the debug code will create differences be­ tween the ship and the debug versions of your code, but as long as you're careful not to change the underlying behavior of the code, those differences shouldn't matter. * Create thorough subsystem checks, and use them often. * Design your tests carefully. Nothing should be arbitrary. * Strive to implement transparent integrity checks. * Don't apply ship version constraints to the debug version. Trade size and speed for error detection. * **Chapter 4** * Don't wait until you have a bug to step through your code. * Step through every code path. * What About Sweeping Changes? In the past, programmers have asked, "What if I add a feature that touches code in many places? Stepping through all that new code is going to be time consuming." Let me answer that question with another: Can you make such sweeping changes without introducing any bugs? The habit of stepping through your code creates an interesting negative feedback loop. Programmers who step through their code soon learn to write small, easily testable functions because stepping through large func­tions is so painful. Programmers also spend more time thinking about how they can localize the changes they need to make-again, so that they can more easily test their code. And isn't this exactly what you want? No project lead likes it when programmers touch a lot of code; it's too destabilizing. Nor do leads like large, unmanageable functions; they're often unmaintainable. Try hard to localize your changes. If you find that you must make per­vasive changes, think twice before you decide not to step through all the new code. * Overflow and underflow bugs * Data conversion bugs * Off-by-one bugs * NULL pointer bugs * Bugs using garbage memory (0xA3 bugs) * Assignment bugs in which you've used = instead of == * Precedence bugs * Logic bugs * As you step through code, focus on data flow. Source level debuggers can hide execution details. Step through critical code at the instruction level . * **Chapter 5** * Make it hard to ignore error conditions. Don't bury error codes in return values. * Always look for, and eliminate, flaws in your interfaces. * Don't write multipurpose functions. Write separate functions to allow stronger argument validation. * Don't be wishy-washy. Define explicit function arguments. * Write functions that, given valid inputs, cannot fail. * Make the code intelligible at the point of call. Avoid Boolean arguments. * Write comments that emphasize potential hazards. * **Chapter 6** * Use well-defined data types. * Always ask ,"Can this variable or expression over- or underflow?" * Implement your designs as accurately as possible. Being kind a close is being kind a buggy. * these principles already: Don't accept special purpose arguments such as the NULL pointer, and Implement your design, not something that approximates it. The third principle is new: Strive to make every function perform its task exactly one time. * Implement "the task" just once. * Get rid of extraneous if statements. * Avoid using nested ?: operators . * Handle your special cases just once. * Avoid risky language idioms. * Don't needlessly mix operator types. If you must mix operators, use parentheses to isolate the operations. * Avoid calling functions that return errors. * **Chapter 7** * Don't reference memory that you don't own. * Don't reference memory that you have freed. * Don't use output memory as workspace buffers. * Don't pass data in static (or global) memory. * Don't write parasitic functions. * Don't abuse your programming language. * Tight C does not guarantee efficient machine code. * Write code for the "average" programmer. * **Chapter 8** * Bugs don't just "go away." * Don't fix bugs later; fix them now. * Fix the cause, not the symptom. * Don't clean up code unless the clean­ up is critical to the product's success. * Don't implement nonstrategic features. * There are no free features. * Don't allow unnecessary flexibility. * Don't keep "trying" solutions· until you find one that works. Take the time to find the correct solution. * Write and test code in small chunks. * Always test your code, even if that means your schedule will slip. * Don't rely on the testing group to find your bugs. * Don't blame testers for finding your bugs. * Establish your priorities and stick to them. * Never allow the same bug to bite you twice. * **A: CODING CHECKLISTS** * **B: MEMORY LOGGING ROUTINES** * **To sum up** * Charles Simonyi in the early 1970s. https://en.wikipedia.org/wiki/Hungarian_notation * char ch; * byte b; * flag f;/* flags that are always TRUE or FALSE*/ * symbol sym;/* some sort of symbol structure*/ * char byte flag symbol * *pch:/* character pointer*/ * *pb; * *pf: * *psym: * related link: [https://www.morling.dev/images/code_review_pyramid.svg](https://www.google.com/url?q=https%3A%2F%2Fwww.morling.dev%2Fimages%2Fcode_review_pyramid.svg&sa=D&sntz=1&usg=AOvVaw1y2It_PUzXAtFN7dPLegCa) * ## Shape Up: Stop Running in Circles and Ship Work that Matters * Six-week cycles: Our decisions are based on moving the product forward in the next six weeks, not micromanaging time. We don’t count hours or question how individual days are spent. We don’t have daily meetings. We don’t rethink our roadmap every two weeks. Our focus is at a higher level. We say to ourselves: “If this project ships after six weeks, we’ll be really happy. We’ll feel our time was well spent.” Then we commit the six weeks and leave the team alone to get it done * Shaping the work: key elements of a solution, appetite * **part one shaping:** * two separate tracks: one for shaping, one for building. * Steps to shaping * 1\. Set boundaries. * 2\. Rough out the elements: idea that solves the problem within the appetite but without all the fine details worked out * 3\. Address risks and rabbit holes. * 4\. Write the pitch. * fixed time, variable scope, * Where in the current system does the new thing fit? How do you get to it? What are the key components or interactions? Where does it take you? * five ingredients that we always want to include in a pitch: 1. Problem — The raw idea, a use case, or something we’ve seen that motivates us to work on this 2. Appetite — How much time we want to spend and how that constrains the solution 3. Solution — The core elements we came up with, presented in a form that’s easy for people to immediately understand 4. Rabbit holes — Details about the solution worth calling out to avoid problems 5. No-gos — Anything specifically excluded from the concept: functionality or use cases we intentionally aren’t covering to fit the appetite or make the problem tractable * **part two : Betting** * Some companies use two-week cycles (aka “sprints”). We learned that two weeks is too short to get anything meaningful done. Worse than that, two-week cycles are extremely costly due to the planning overhead. The amount of work you get out of two weeks isn’t worth the collective hours around the table to “sprint plan” or the opportunity cost of breaking everyone’s momentum to re-group. * after each six-week cycle, we schedule two weeks for cool-down. * bugs: Use cool-down. Bring it to the betting table. Schedule a bug smash. * We’ve noticed three phases of work when we build a new product from scratch. R&D mode (bet the time on spiking some key pieces of the new product idea. CEO and designer, we don’t expect to ship anything at the end of an R&D cycle) , Production mode (Shaping is deliberate again. bet multiple teams in parallel , Shipping is the goal) , Cleanup mode (There’s no shaping. There aren’t clear team boundaries. Work is “shipped”) [Cleanup shouldn’t last longer than two cycles] * Example: two years of development, R&D mode for the first year, a year of production mode cycles followed, two cycles of cleanup and significantly cut back the feature set, some overlap between R&D and production mode after that first year, * **Part three: Building** * We don’t start by assigning tasks to anyone. * we define and track the scopes as to-do lists. Each scope corresponds to a list name. Then any tasks for that scope go in that list. * **image 1, 2, 3,** * Mark nice-to-haves with ~ * First there’s the uphill phase of figuring out what our approach is and what we’re going to do. Then, once we can see all the work involved, there’s the downhill phase of execution * image 4, 5, * ## Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet * getting start: idea, technology, passion; What can I do well that I would love to do for an extended period of time? P: 19, 21 * 1 market segmentation: The single necessary and sufficient condition for a business is a paying customer. ; technological enthusiasts=early adopters=early majority ; P: 31, * 2 select a beachhead market: P: 44, * 3 build an end user profile: P: 52, * 4 Calculate the Total Addressable Market (TAM) Size for the Beachhead Market; $200 # [Essential Tips And Tricks For ](/book-summary/commonplace-book)[commonplace book](/book-summary/commonplace-book) # [knowledge management ](/book-summary/knowledge_management) # [PKM](/book-summary/knowledge_management/pkm) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. 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Gonzalez Introduction to Algorithms, 3rd Edition (The MIT Press) The Mythical Man-Month: Essays on Software Engineering OpenCV-4-with-Python-Blueprints-Second-Edition Cracking the coding interview 189 programming questions and solutions Cracking the Tech Career: Insider Advice on Landing a Job at Google, Microsoft, Apple, Or Any Top Tech Company The Art of Startup Fundraising Mathematics for Machine Learning Principles of Economics (6th edition) The E-Myth Revisited Why Most Small Businesses Don't Work and What to Do About It Clean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin) Reinventing Your Life: The Breakthough Program to End Negative Behavior Ways Of Thinking: The Limits Of Rational Thought And Artificial Intelligence https://images.kw.com/docs/2/1/2/212345/1285134779158_htwfaip.pdf Magazine Papers Websites and links Tools YouTube - List channels Language: Essential Tips And Tricks For commonplace book knowledge management PKM # Books * ## How to take smart Notes * Introduction 4, The four underlying principles 4, the six steps to successful writing 6, afterword * introduction * write everything/ collect notes not related to any thing, organize notes, revirew, rewrite, * no reorganise no complex system, develop ideas by smart notes * 1 slip-box; 2 repeatable tasks that can become automatic and fit together seamlessly change routinges * overarching workflow "Getting Things Done" GTD * constantly have to ump back and forth between different tasks * 1- bibliographical slip-box: reference * 2- collected and generated ideas slip-box * 3- index: notes serve as entry point / thought/ topic * writing a paper step by step * 1 fleeting notes: it will delete within a day * I always have a slip of paper at hand, on which I note down the ideas of certain pages. on the backside I write down the bibliographic details. after finishing the book I go throught my notes and think how ehse notes might be relevant for already written notes in the slip-box. it means that I always read with an eye towards possible connections in the slip-box * 2 literature notes: contain the necessary information - in reference system or in the slip-box * I make a note with the bibliographic details. on the backside I would write "on page x is this, on page y is that" and then it goes into the bibliographic slib-box where I collect everything I read * read, think about its relevance * theoretical background, methodological approach or perspective of the text we read. * whether something adds to a discussion in the slip-box * we look at our slip-box for already existing lines of thought and think about the questions and problems already on our minds to which a new note might contribute. - assigning keywords is much more than just a bureaucratic act. it is a crucial part of the thinking process, which often leads to a deeper elaboration of the note itself and the connection to other notes. * * 3 permanent notes: develop ideas/new/ - only relevant to one particular project - in project specific folder - can be discarded or archived after project finished * add to slip-box behind the note you dirctyly refer to or behind the last note in the slip-box * add links to other notes or links on other notes to your new note * make sure it can be found from the index (MOC) add an entry in the index if necessary or refer to it from a note that is connected to the index * build a latticework of mental models * 4 behind multiple notes (link backward) - link to related note - link to MOC * make sure it can be found again * **keywords** can easily be added to a note like **tags** and will then show up in the **index** * a day * read and take notes * build connections within the slip-box - new idea * you write on your paper, notice a hole in the argument and have another look in the file system for the missing link. you follow up on a footnote, go back to research and might add a fitting quote to one of your papers in the making. * The four underlying principles * writing * simplicity * not from scratch * let the work carry you forward * the six steps to successful writing * separate and interlocking tasks * we use memo techniques is to bundle items together in a meaningful way and remember the bundles * the brain doesn't distinguish between an actual finished task and one that is postponed by taking a note * the fist step is to break down the amprphous task of "writing" into smaller pieces of different tasks that can be finished in **one go.** the second step is to make sure we always write down the outcome of our thinking, including possible connections to further inquiries. * ego depletion * read for understanding * take smart notes * develp ideas (MOC) * 1 overview of a topic - referred to from the index and used as an entry point into a topic has already develped * 2 keeping tarck of all topic - index - * share your insight * make it a habit * afterword * ## Writing Solid Code Writing Solid Code (Microsoft Programming Series) by Trendelles Store Note from Writing Solid Code Book [https://www.pirahansiah.com/describe/book- summary](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Fdescribe%2Fbook- summary&sa=D&sntz=1&usg=AOvVaw3ACzA6G0MBVN3nw6f-yMQo) #SolidCode #C++ #Python #pirahansiah * **Chapter 1** * Enable all optional compiler warnings. * Use lint to catch bugs that your compiler may miss. * If you have unit tests, use them. * **Chapter 2** * introduction assert page 16; assert.h * assert is nothing more than a repackaged form of the #ifdef code we saw before, but when you use * the macro, it takes one line instead of seven * That's why assert is a macro and not a function; if it were a function, calling * it could cause unexpected memory or code swapping. * Use assertions to validate function arguments . * Strip undefined behavior from your code, or use assertions to catch illegal uses of undefined behavior . * Don't waste people's time. Document unclear assertions . * Either remove implicit assumptions, or assert that they are valid . * Use assertions to detect impossible conditions. * Don't hide bugs when you program defensively. * Use a second algorithm to validate your results. * Don't wait for bugs to happen; use startup checks. * **Chapter 3** * Maintain shipping and debugging versions of your program. Shrink-wrap the shipping version, but use the debugging ver­sion as much as possible to catch bugs quickly. * Assertions are a shorthand way to write debugging checks. Use them to catch illegal conditions that should never arise. Don't confuse such conditions with error conditions, which you must handle in the final product. * Use assertions to validate function arguments and to alert pro­ grammars when they do something undefined. The more rigidly you define your functions, the easier it will be to validate the arguments. * Once you've written a function, review it and ask yourself, 'What am I assuming?" If you find an assumption, either assert that your assumption is always valid, or rewrite the code to re­ move the assumption. Also ask, 'What is most likely to be wrong in this code, and how can I automatically detect the prob­lem?" Strive to implement tests that catch bugs at the earliest possible moment. Textbooks encourage programmers to program defensively, but remember that this coding style hides bugs. When you write de­fensive code, use assertions to alert you if the "can't happen" cases do happen. * Eliminate random behavior. Force bugs to be reproducible . * Destroy your garbage so that it's not misused . * Not only did reallocate have to move the block of memory as it was expanding it, but the old memory had to be reallocated and filled with new data. In the assembler, both happened rarely. * This brings up another guideline for writing bug-free code: You don't want anything to happen rarely. You need to isolate those behaviors in your subsystems that may happen and make sure that they do happen. And of­ ten. If you find rare behavior in your subsystems, be sure to do something­ anything-to stir things up. * The assembler bug could have been found within hours, instead of years, if reallocate hadn't so rarely moved blocks when it expanded them. * If something happens rarely, force it to happen often. * Keep debug information to allow stronger error checking. * There is no question that the debug code will create differences be­ tween the ship and the debug versions of your code, but as long as you're careful not to change the underlying behavior of the code, those differences shouldn't matter. * Create thorough subsystem checks, and use them often. * Design your tests carefully. Nothing should be arbitrary. * Strive to implement transparent integrity checks. * Don't apply ship version constraints to the debug version. Trade size and speed for error detection. * **Chapter 4** * Don't wait until you have a bug to step through your code. * Step through every code path. * What About Sweeping Changes? In the past, programmers have asked, "What if I add a feature that touches code in many places? Stepping through all that new code is going to be time consuming." Let me answer that question with another: Can you make such sweeping changes without introducing any bugs? The habit of stepping through your code creates an interesting negative feedback loop. Programmers who step through their code soon learn to write small, easily testable functions because stepping through large func­tions is so painful. Programmers also spend more time thinking about how they can localize the changes they need to make-again, so that they can more easily test their code. And isn't this exactly what you want? No project lead likes it when programmers touch a lot of code; it's too destabilizing. Nor do leads like large, unmanageable functions; they're often unmaintainable. Try hard to localize your changes. If you find that you must make per­vasive changes, think twice before you decide not to step through all the new code. * Overflow and underflow bugs * Data conversion bugs * Off-by-one bugs * NULL pointer bugs * Bugs using garbage memory (0xA3 bugs) * Assignment bugs in which you've used = instead of == * Precedence bugs * Logic bugs * As you step through code, focus on data flow. Source level debuggers can hide execution details. Step through critical code at the instruction level . * **Chapter 5** * Make it hard to ignore error conditions. Don't bury error codes in return values. * Always look for, and eliminate, flaws in your interfaces. * Don't write multipurpose functions. Write separate functions to allow stronger argument validation. * Don't be wishy-washy. Define explicit function arguments. * Write functions that, given valid inputs, cannot fail. * Make the code intelligible at the point of call. Avoid Boolean arguments. * Write comments that emphasize potential hazards. * **Chapter 6** * Use well-defined data types. * Always ask ,"Can this variable or expression over- or underflow?" * Implement your designs as accurately as possible. Being kind a close is being kind a buggy. * these principles already: Don't accept special purpose arguments such as the NULL pointer, and Implement your design, not something that approximates it. The third principle is new: Strive to make every function perform its task exactly one time. * Implement "the task" just once. * Get rid of extraneous if statements. * Avoid using nested ?: operators . * Handle your special cases just once. * Avoid risky language idioms. * Don't needlessly mix operator types. If you must mix operators, use parentheses to isolate the operations. * Avoid calling functions that return errors. * **Chapter 7** * Don't reference memory that you don't own. * Don't reference memory that you have freed. * Don't use output memory as workspace buffers. * Don't pass data in static (or global) memory. * Don't write parasitic functions. * Don't abuse your programming language. * Tight C does not guarantee efficient machine code. * Write code for the "average" programmer. * **Chapter 8** * Bugs don't just "go away." * Don't fix bugs later; fix them now. * Fix the cause, not the symptom. * Don't clean up code unless the clean­ up is critical to the product's success. * Don't implement nonstrategic features. * There are no free features. * Don't allow unnecessary flexibility. * Don't keep "trying" solutions· until you find one that works. Take the time to find the correct solution. * Write and test code in small chunks. * Always test your code, even if that means your schedule will slip. * Don't rely on the testing group to find your bugs. * Don't blame testers for finding your bugs. * Establish your priorities and stick to them. * Never allow the same bug to bite you twice. * **A: CODING CHECKLISTS** * **B: MEMORY LOGGING ROUTINES** * **To sum up** * Charles Simonyi in the early 1970s. https://en.wikipedia.org/wiki/Hungarian_notation * char ch; * byte b; * flag f;/* flags that are always TRUE or FALSE*/ * symbol sym;/* some sort of symbol structure*/ * char byte flag symbol * *pch:/* character pointer*/ * *pb; * *pf: * *psym: * related link: [https://www.morling.dev/images/code_review_pyramid.svg](https://www.google.com/url?q=https%3A%2F%2Fwww.morling.dev%2Fimages%2Fcode_review_pyramid.svg&sa=D&sntz=1&usg=AOvVaw1y2It_PUzXAtFN7dPLegCa) * ## Shape Up: Stop Running in Circles and Ship Work that Matters * Six-week cycles: Our decisions are based on moving the product forward in the next six weeks, not micromanaging time. We don’t count hours or question how individual days are spent. We don’t have daily meetings. We don’t rethink our roadmap every two weeks. Our focus is at a higher level. We say to ourselves: “If this project ships after six weeks, we’ll be really happy. We’ll feel our time was well spent.” Then we commit the six weeks and leave the team alone to get it done * Shaping the work: key elements of a solution, appetite * **part one shaping:** * two separate tracks: one for shaping, one for building. * Steps to shaping * 1\. Set boundaries. * 2\. Rough out the elements: idea that solves the problem within the appetite but without all the fine details worked out * 3\. Address risks and rabbit holes. * 4\. Write the pitch. * fixed time, variable scope, * Where in the current system does the new thing fit? How do you get to it? What are the key components or interactions? Where does it take you? * five ingredients that we always want to include in a pitch: 1. Problem — The raw idea, a use case, or something we’ve seen that motivates us to work on this 2. Appetite — How much time we want to spend and how that constrains the solution 3. Solution — The core elements we came up with, presented in a form that’s easy for people to immediately understand 4. Rabbit holes — Details about the solution worth calling out to avoid problems 5. No-gos — Anything specifically excluded from the concept: functionality or use cases we intentionally aren’t covering to fit the appetite or make the problem tractable * **part two : Betting** * Some companies use two-week cycles (aka “sprints”). We learned that two weeks is too short to get anything meaningful done. Worse than that, two-week cycles are extremely costly due to the planning overhead. The amount of work you get out of two weeks isn’t worth the collective hours around the table to “sprint plan” or the opportunity cost of breaking everyone’s momentum to re-group. * after each six-week cycle, we schedule two weeks for cool-down. * bugs: Use cool-down. Bring it to the betting table. Schedule a bug smash. * We’ve noticed three phases of work when we build a new product from scratch. R&D mode (bet the time on spiking some key pieces of the new product idea. CEO and designer, we don’t expect to ship anything at the end of an R&D cycle) , Production mode (Shaping is deliberate again. bet multiple teams in parallel , Shipping is the goal) , Cleanup mode (There’s no shaping. There aren’t clear team boundaries. Work is “shipped”) [Cleanup shouldn’t last longer than two cycles] * Example: two years of development, R&D mode for the first year, a year of production mode cycles followed, two cycles of cleanup and significantly cut back the feature set, some overlap between R&D and production mode after that first year, * **Part three: Building** * We don’t start by assigning tasks to anyone. * we define and track the scopes as to-do lists. Each scope corresponds to a list name. Then any tasks for that scope go in that list. * **image 1, 2, 3,** * Mark nice-to-haves with ~ * First there’s the uphill phase of figuring out what our approach is and what we’re going to do. Then, once we can see all the work involved, there’s the downhill phase of execution * image 4, 5, * ## Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet * getting start: idea, technology, passion; What can I do well that I would love to do for an extended period of time? P: 19, 21 * 1 market segmentation: The single necessary and sufficient condition for a business is a paying customer. ; technological enthusiasts=early adopters=early majority ; P: 31, * 2 select a beachhead market: P: 44, * 3 build an end user profile: P: 52, * 4 Calculate the Total Addressable Market (TAM) Size for the Beachhead Market; $200 # [Essential Tips And Tricks For ](/book-summary/commonplace-book)[commonplace book](/book-summary/commonplace-book) # [knowledge management ](/book-summary/knowledge_management) # [PKM](/book-summary/knowledge_management/pkm) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/3LTMUMvLqIOxMLGRMkavoKhjR0yGpVpsaeb_NnspLybaPexjoQBnvVa2C2973HUQ30RiIqubg5B9F6eLjnxKXlE=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/3LTMUMvLqIOxMLGRMkavoKhjR0yGpVpsaeb_NnspLybaPexjoQBnvVa2C2973HUQ30RiIqubg5B9F6eLjnxKXlE=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # _ **Workbench**_ OpenVINO Deep Learning ## _ **Workbench**_ OpenVINO™ Deep Learning: OpenVINO™ Deep Learning Workbench and how to use the DL workbench to analyze and optimize neural networks. Discover what first steps you’ll have to take towards optimizing your model. Download and see more: [https://www.pirahansiah.com/topics/events/openvino-deep- learning](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fevents%2Fopenvino- deep-learning&sa=D&sntz=1&usg=AOvVaw1t9dUtU_TOqDDP-CxxE9ns) #OpenVINO #ComputerVision #pirahansiah [https://intel.ly/openvino](https://www.google.com/url?q=https%3A%2F%2Fintel.ly%2Fopenvino&sa=D&sntz=1&usg=AOvVaw0xaMIrnhPOwlgb4nqYB5MG) pip install openvino build; optimize; deploy ![](https://www.google.com/images/icons/product/drive-32.png)Workbench OpenVINO Deep Learning.png Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/G7JKnoBc5s5bG3WK7alpRCEIdQazcLj2L1DLGACGDrsMeHOK9CTS5fh5v74shZzmMJ8YN6hl77hXFxOIDH_8b3M=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/G7JKnoBc5s5bG3WK7alpRCEIdQazcLj2L1DLGACGDrsMeHOK9CTS5fh5v74shZzmMJ8YN6hl77hXFxOIDH_8b3M=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # _ **Workbench**_ OpenVINO Deep Learning ## _ **Workbench**_ OpenVINO™ Deep Learning: OpenVINO™ Deep Learning Workbench and how to use the DL workbench to analyze and optimize neural networks. Discover what first steps you’ll have to take towards optimizing your model. Download and see more: [https://www.pirahansiah.com/topics/events/openvino-deep- learning](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fevents%2Fopenvino- deep-learning&sa=D&sntz=1&usg=AOvVaw1t9dUtU_TOqDDP-CxxE9ns) #OpenVINO #ComputerVision #pirahansiah [https://intel.ly/openvino](https://www.google.com/url?q=https%3A%2F%2Fintel.ly%2Fopenvino&sa=D&sntz=1&usg=AOvVaw0xaMIrnhPOwlgb4nqYB5MG) pip install openvino build; optimize; deploy ![](https://www.google.com/images/icons/product/drive-32.png)Workbench OpenVINO Deep Learning.png Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/G7JKnoBc5s5bG3WK7alpRCEIdQazcLj2L1DLGACGDrsMeHOK9CTS5fh5v74shZzmMJ8YN6hl77hXFxOIDH_8b3M=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/G7JKnoBc5s5bG3WK7alpRCEIdQazcLj2L1DLGACGDrsMeHOK9CTS5fh5v74shZzmMJ8YN6hl77hXFxOIDH_8b3M=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # AI-Hardware What I learn from AI HARDWARE EUROPE SUMMIT (July 2020): Very short summary of the presentations in 3 days. You can access to all presentation[ ](https://www.google.com/url?q=https%3A%2F%2Faihardwaresummiteu.com%2Fevents%2Fai- hardware-summit- europe&sa=D&sntz=1&usg=AOvVaw3C_9PS0mNFEg5bebdW3OEq)[here](https://www.google.com/url?q=https%3A%2F%2Faihardwaresummiteu.com%2Fevents%2Fai- hardware-summit-europe&sa=D&sntz=1&usg=AOvVaw3C_9PS0mNFEg5bebdW3OEq) Summary of AI HARDWARE EUROPE SUMMIT (July 2020) Some of the problems FPGAs solve: \- excessive heat \- electricity consumption \- resistance to environmental factors and motion \- lifespan The goal is artificial general intelligence (AGI). AI software and hardware should work together to achieve this goal. A lot of research in this area work with new methods of algorithms and hardware together to achieve high performance. Most of new hardware working with TensorFlow and Pytorch. In most case new hardware come with software solution that have high performance in special use cade or scenario. However, the new hardware can be modify by programmer like Embedded FPGA (eFPGA) in order to implement their requirement. Some of the best presentations are: Machine Intelligent Systems & Software by Victoria Rege from Graphcore, Leveraging sparsity to enable ultra-low latency inferences demonstrated using GrAI One by Orlando Moreira and Remi Poittevin from GrAI Matter Labs, Challenges for Using Machine Learning in Industrial Automation by Ingo Thon from Siemens [Convolutional autoencoder for anomaly detection], From Training to Production Inference for Automotive AI - Transforming Research into Reality ( From laboratory training to automotive inference: the realities of embedding AI) by Tony King-Smith and Marton Feher from AIMotive ,Towards Embedded Intelligence by Michaela Blott from Xilinx, and the last good presentation is MLIR: Accelerating Artificial Intelligence by Albert Cohen from Google. Some key words for the event: FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs, The MLIR project is a novel approach to building reusable and extensible compiler infrastructure, Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services at mlperf.org, Graphcore IPU which is the new type of processor for AI with very high performance. **Day1:** **Day 2:** **Day 3:** **OPENING KEYNOTE** : Past Chip Childhood and System Teenage: Why We Need to Build a Mature Ecosystem Olivier Temam - DeepMind **PRESENTATION: Power and Cost Efficient High Performance Inference at the Edge** Geoff Tate - Flex Logix ***PRESENTATION: Machine Intelligent Systems & Software** Victoria Rege - Graphcore **PANEL: High Performance and Low Energy Consumption: Developments in Performing at the Edge** Moderator - Luca Benini - Professor Orr Danon - Hailo Technologies Eric Flamand - GreenWaves Technologies **Ask an Analyst: Moderated Q &A and Group Discussion** Michael Azoff - Kisaco Research Brett Simpson - Arete Research ****PRESENTATION: Leveraging sparsity to enable ultra-low latency inferences demonstrated using GrAI One** Orlando Moreira - GrAI Matter Labs Remi Poittevin - GrAI Matter Labs ***PANEL: Investment Trends & Dynamics in AI Hardware and the Startup Ecosystem** Moderator - Brett Simpson - Arete Research Christian Patze - M Ventures Sascha Fritz - Robert Bosch Venture Capital GmbH ***PRESENTATION: Challenges for Using Machine Learning in Industrial Automation** Ingo Thon - Siemens **PANEL: Designing Safe, Power-Efficient and Affordable Autonomous Systems** Moderator - Robert Krutsch - Zenuity Arnaud Van Den Bossche - NXP Gordon Cooper - Synopsys **ASK AN EXPERT: Moderated Q &A and Group Discussion About Possibilities For Real Time AI enabled by Edge Compute** Eric Flamand - GreenWaves Technologies ***PRESENTATION: From Training to Production Inference for Automotive AI - Transforming Research into Reality** Tony King-Smith - AI Motive Marton Feher - AIMotive **PRESENTATION: Neuromorphic Computing at BMW** Oliver Wick - BMW **PANEL: Applications of Neuromorphic Hardware in Industry, Automotive & Robotics** Moderator - Yulia Sandamirskaya - Intel Christian Mayr - TU Dresden Steve Furber - University of Manchester **PRESENTATION: Computer Architecture - The Next Step: Energy Efficient Machine Learning** Uri Weiser - Technion **PRESENTATION: Energy Efficient AI and Carbon Offsetting** Rick Calle - Microsoft **PRESENTATION: Edge Processors for Deep Learning - Practical Perspectives** Orr Danon - Hailo Technologies **PRESENTATION - Why heterogenous multi-processing is a critical requirement for Edge Computing: Example of Automotive** Eric Baissus - Kalray ****PRESENTATION: Towards Embedded Intelligence** Michaela Blott - Xilinx ****PRESENTATION: MLIR: Accelerating Artificial Intelligence** Albert Cohen - Google 1. **OPENING KEYNOTE: Past Chip Childhood and System Teenage: Why We Need to Build a Mature Ecosystem** Olivier Temam - DeepMind Temam @google.com AI progress comes at a heavy computing DRL, AutoML, AGI need higher computing requirements Chanelle: 1. Hyper focused on chips 2. Idiosyncratic hardware 3. AI algorithms evolve very fast 4. Efficiency/flexibility tradeoff He talk about AI chip and said AI progress comes at a heavy computing. Algorithm like DRL, AutoML, AGI need higher computing requirements. The challenge on AI chip is tradeoff between efficiency and flexibility, AI algorithms evolve very fast which change the methods of processing, Idiosyncratic hardware for specific domain, hyper focused on chips not in the system. I am impress about "Accelerator Benchmarking on Read Edge-inference Applications" presentation at AI HW summit and I like to see live demo of " InferX X1" on October. Do you have any benchmarking of InferX X1 for the deep reinforcement learning? 1. **PRESENTATION: Power and Cost Efficient High Performance Inference at the Edge** Geoff Tate - Flex Logix-; geoff@flex-logix.com Accelerator Benchmarking on Read Edge-inference Applications, He talk about Embedded FPGA (eFPGA) and new product which is "inferX X1" that will come soon. It use TDP:7-13W compare to Nvidia Xavier NX 15W. He mentioned The ResNet-50 with the image size of 224*224 will not tell you the robustness of the memory system that been required with megapixel images. That’s why not good for comparison the best for customer to use and compare is YOLOv3. YOLOv3 intermediate activations are 64MB peak for 2 Megapixel images, this stresses memory subsystems much more than ResNet-50. Their chip is good because of efficiency is in Data packing and transposition. It allowed efficiency 3D convolutions which for each layer ad dedicated path RAM to compute to RAM and deep layer fusion reduces memory requirement and DRAM access in "hidden" in the background. Embedded FPGA, eFPGA, clustering MACs with a reconfigurable interconnect delivered high inference throughput at low cost, inferX X1 in fab now TDP:7-13W (compare to Nvidia Xavier NX 15W), not Pytorch, the best for customer to use and compare is YOLOv3. YOLOv3 intermediate activations are 64MB peak for 2 Megapixel images, this stresses memory subsystems much more than ResNet-50. They said the normal default image size is 224*224 but the activation intermediate (max layer) growth exponentially by increasing the size of images. **The ResNet-50 with the image size of 224*224 will not tell you the robustness of the memory system that been required with megapixel images. That’s why not good for comparison.** Nvidia Xavier NX: has 3 inference (GPU, 2x DLA):self-driving car multiple models are running simultaneously But in most AI system one camera one model one system process images frame per frame. If we have stream of data coming in 15FPS one image at time 15FPS Key to inferX X1 efficiency is in Data packing and transposition, efficiency 3D convolutions , for each layer ad dedicated path RAM to compute to RAM, deep layer fusion reduces memory requirement, DRAM access in "hidden" in the background, TSMC,GUC,synopsys,arteris,analog bits, cadence, mentor I am impress about " **Machine Intelligent Systems & Software GRAPHCORE IPU**" presentation at AI HW summit. Do you have any benchmarking of **GRAPHCORE IPU** for the deep reinforcement learning? 1. **PRESENTATION: Machine Intelligent Systems & Software** Victoria Rege - Graphcore; victoria@graphcore.ai info@graphcore.ai ; @fleurdevie This presentation is one of the best. She talk about GRAPHCORE IPU(INTELLIGENCE PROCESSING UNIT) and POPLAR SDK. The Graphcore IPU can run training of sample model in 3 hours which require 40 hours on GPU. In another use case Graphcore IPU accelerated medical imaging on azure can process 2000 images/sec compare to 166 images/sec on GPU. **GRAPHCORE IPU(INTELLIGENCE PROC** **ESSING UNIT)** The poplar SDK, [https://www.graphcore.ai/finance](https://www.google.com/url?q=https%3A%2F%2Fwww.graphcore.ai%2Ffinance&sa=D&sntz=1&usg=AOvVaw3H3ZErMe45lTls5mcZYdmA) **(40 hours on GPU to 3 hours)** [Running a PyTorch model on the Graphcore IPU: ResNeXt-101 example](https://youtu.be/BQmpBtXGuf0) **Ipu accelerated medical imaging on azure (IPU 2000 images/sec vs 166 images/sec)** [https://www.graphcore.ai/posts/microsoft-accelerates-resnext-50-medical- imaging-inference-with-the- ipu](https://www.google.com/url?q=https%3A%2F%2Fwww.graphcore.ai%2Fposts%2Fmicrosoft- accelerates-resnext-50-medical-imaging-inference-with-the- ipu&sa=D&sntz=1&usg=AOvVaw2ZnH6NrKX2_3wd030hR7xh) **** 1. **PANEL: High Performance and Low Energy Consumption: Developments in Performing at the Edge** Moderator - Luca Benini - Professor: ML processors Orr Danon - Hailo Technologies Eric Flamand - GreenWaves Technologies 1. **Ask an Analyst: Moderated Q &A and Group Discussion** Michael Azoff - Kisaco Research Brett Simpson - Arete Research 1. **PRESENTATION: Leveraging sparsity to enable ultra-low latency inferences demonstrated using GrAI One** *Orlando Moreira - GrAI Matter Labs Remi Poittevin - GrAI Matter Labs ******* **Edge workloads involve real-time:** responsive smart devices, closely coupled feedback loops, autonomous systems **Input data streams are continuous** : video/Audio feeds, industrial sensor ensembles, bio signals (EEG,EKG, movement) **The data rate is much higher than the real information rate:** voice 512-> information 39 bits/s UXGA video 79 MB/s -> information 95% Frame based processing: apply single frame algorithm independently to each input frame in a stream. Advantages: Many popular sensors are frame based Simple, easy to scale: image -> video stream Disadvantage: Repeated and redundant data is processed over and over **Sparsity in video** **Sparsity in structure:** pruning of needless weights and kernels in network **Sparsity in space** : most pixels in an image have no relevant feature data. ; results in 0-valued activations **Sparsity in time:** image changes little from instant to instant; why should we always re-process the whole frame? Event based computation of networks (process only the data that change); single events in an input layer fan out (typically 1:9 to 1:49 per feature map); only the affected pixels in the convolutional layer need be computed. **; locality of change** is preserved downstream. ; the events than fan in typically 4:1 **in pooling** layers. ; additionally, events are only 2 5% likely to change the pixel state (tpy. 2*2 max pooling **).; locality of change** is preserved downstream. ; sparNet: sparse and event-based execution model Exploits time-sparsity in a time series Converts frame. Based network to evet based inference Event based: change is sent sporadically, so no frame structure to input data Only propagates changes, thus less work needs to be done Requires resilient neuron state Threshold: per neuron, defines how much change is needed to warrant propagation. To convert a CNN to sparNet, they set a threshold per neuron **Pilotnet in sparnet** **Execution pilotnet with sparnet dramatically reduces the number of operations required.** Effect becomes dramatic at high fps: Same amount change per same time interval But for frame based processing, load increases linearly with frame rate Higher fps => lower sampling period => lower latency Consequences of sparsity for computer architecture: Requires that they store resilient neuron states Suggests in/near memory computation Frame structure is lost: Event based scheduling of computation Suggests data flow synchronization Significantly less sequential memory accesses occur Reduced value of caching, network bursts, bulk dma transfers; Reduced opportunities for latency hiding Suggests in/near memory computation **Conclusion:** Neuronflow is designed to exploit sparsity Sparsity in structure Neuronflow: Event based activation skips 0-weight(pruned) synapses/kernels. Sparsity in space Neuronflow: 0-valued activations are neither sent nor processed. Sparsity in time Neuronflow: if change between frames is below threshold, it is neither sent nor processed. **Neuron state to 200 to store the space/…..** 1. **PANEL: Investment Trends & Dynamics in AI Hardware and the Startup Ecosystem** Moderator - Brett Simpson - Arete Research Christian Patze - M Ventures Sascha Fritz - Robert Bosch Venture Capital GmbH Day 2========================================= **PRESENTATION: Challenges for Using Machine Learning in Industrial Automation** Dr. Ingo Thon - Siemens- Ingo.Thon@siemens.com He presented some challenge in hardware. Some key notes are time series data chip is missing, AI should sit at hardware level, Imagine automating the unpredictable. Drivers for new developments in industries: time to market, flexibility (PID), quality, efficiency Convolutional autoencoder for anomaly detection; reptile algorithm; trick is wide product in line after trained can used pre trained to adept to other type Cost come on reliability/performance/easy to use (man power) - Drivers for new developments in industries: time to market, flexibility (PID), quality, efficiency AI should site at HW level Imagine automating the unpredictable … Visual quality inspection solved but hard: Convolutional autoencoder for anomaly detection; reptile algorithm; trick is wide product in line after trained can used pre trained to adept to other type Cost come on reliability/performance/easy to use (man power) - Time series data chip is missing **PANEL: Designing Safe, Power-Efficient and A;** **ffordable Autonomous Systems** Moderator - Robert Krutsch - Zenuity Arnaud Van Den Bossche - NXP Gordon Cooper - Synopsys **ASK AN EXPERT: Moderated Q &A and Group Discussion About Possibilities For Real Time AI enabled by Edge Compute** Eric Flamand - GreenWaves Technologies **Watch again** **PRESENTATION: From Training to Production Inference for Automotive AI - Transforming Research into Reality** Tony King-Smith - AI Motive Marton Feher - AIMotive **From laboratory training to automotive inference: the realities of embedding AI** Convolution is Not the same as matrix multiplication. Matrix multipliers used extensively in GPUs and DSPs - so many algorithm implementations use them BUT matrix multipliers need pre and post processing to re order data for convolution. Need to accelerate the NN algorithms, not just implementations of them. **Convolution is Not the same as matrix multiplication** Matrix multipliers used extensively in GPUs and DSPs - so many algorithm implementations use them BUT matrix multipliers need pre and post processing to re order data for convolution. Need to accelerate the NN algorithms, not just implementations of them. Manually optimization; Convolution 5*5 kernel ; relu 5*5 conv and 5*5 de conv ; **PRESENTATION: Neuromorphic Computing at BMW** Oliver Wick - BMW; oliver.wick@bmw.de Neuromorphic computing. Building up a neuromorphic computing readiness for BMW. **PANEL: Applications of Neuromorphic Hardware in Industry, Automotive & Robotics** Moderator - Yulia Sandamirskaya - Intel Christian Mayr - TU Dresden Steve Furber - University of Manchester **Day 3=============================================** **\---PRESENTATION: Computer Architecture - The Next Step: Energy Efficient Machine Learning** Professor Uri Weiser - Technion --- Technical hardware talk. Deep learning is everywhere: pedestrian detection, vehicle detection, collision avoidance, parking assist, speech understanding, plate/traffic sign detection, passenger control, face recognition. We are at the beginning stages of comprehending the environment and where we are. In AI hardware the Performance is the king and the efficiency is the next step. 1. **Spatial correlation and value prediction in convolutional neural networks** 2. **Non blocking simultaneous multithreading: embracing the resiliency of deep neural network** **ML architecture is resilient to inaccuracies - > SMT is suitable in DNN approximation environment** **Why resNet for benchmarking?[ ](https://mlperf.org/)** Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services. **PRESENTATION: Energy Efficient AI and Carbon Offsetting** Rick Calle - Microsoft ; M2 the venture Arm of Microsoft What can AI industry do to reduce AI computational energy? (in a world of trillion X?) Paper: M12 meta analysis of research papers: energy and ploicy considerations for deep learning in NLP Language models are few shot learners (OpenAI GPT-3) BERTology, learning and evaluationg general linguistic intelligence (Google) **\---PRESENTATION: Edge Processors for Deep Learning - Practical Perspectives** Orr Danon - Hailo Technologies DNN accelerators in the wild; use case driven overview Video analytics platforms pipeline start with frame grab. Then, analyse 1 which consists of detection, quality classification and grip orientation. Then, analyse 2 which consist of decision logic. Finally, act which consists of pick and place. **PRESENTATION - Why heterogenous multi-processing is a critical requirement for Edge Computing: Example of Automotive** Eric Baissus - Kalray: new type processor and solutions, 3rd MPPA processor, Investors: nxp, renault nissan mistubishi, safran, mbda Multicore and many core processors: homogeneous multicore processor (mix of FPGA,GPU,ASIC,CPUs), PGGPU manycore processor, CPU based manycore processor. Only 25% of usable data will reach a data centre the 75% need to be analysed locally in real time. I would like to know more about FINN compiler. **PRESENTATION: Towards Embedded Intelligence: opportunities and challenges in the technology landscape** Michaela Blott - Xilinx In memory computing; waverscale computing specialized architectures DPU; How can enable a broader spectrum of end-users to be able to specialize hardware architectures and co design solutions? [https://arxiv.org/abs/2004.03021](https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F2004.03021&sa=D&sntz=1&usg=AOvVaw0Wtw19dhHe8EqYEjzL- JpC) FINN(10k-10M FPS); logicNets (100M+ FPS) Innovative architectures emerge to address the needs of embedded intelligence; specialization of hardware architecture are key; with more flexibility, more opportunity to customization (potential to exploit with FPGAs and ACAPs, allow to specialize to the specifics of individual use cases; tools such as FINN are needed to address of complexity in the design entry); future: key challenge in the community remains around how to compare (focussed on embedded; [ ](https://www.google.com/url?q=https%3A%2F%2Frcl- lab.github.io%2FQutibenchWeb%2F&sa=D&sntz=1&usg=AOvVaw2vgv-hm- sUXCHhqXhsHzgN)[https://rcl- lab.github.io/QutibenchWeb/](https://www.google.com/url?q=https%3A%2F%2Frcl- lab.github.io%2FQutibenchWeb%2F&sa=D&sntz=1&usg=AOvVaw2vgv-hm-sUXCHhqXhsHzgN)) [https://xilinx.github.io/finn/](https://www.google.com/url?q=https%3A%2F%2Fxilinx.github.io%2Ffinn%2F&sa=D&sntz=1&usg=AOvVaw2maYrSPzEpDJH0TBGSUxcc) **PRESENTATION: MLIR: Accelerating Artificial Intelligence** Albert Cohen - Google **Mlir-hiring@google.com** A new golden age for computer architecture, a call to action for software stack construction **compilers** , execution environments, tools MLIR: Multi Level Intermediate Representation Mlir.llvm.org **===================** **Compiler research** ; unification ;[ ](https://www.google.com/url?q=https%3A%2F%2Fllvm.org%2F&sa=D&sntz=1&usg=AOvVaw0bpdW9vWr0vS190nbiFHwy)[https://llvm.org/](https://www.google.com/url?q=https%3A%2F%2Fllvm.org%2F&sa=D&sntz=1&usg=AOvVaw0bpdW9vWr0vS190nbiFHwy) ; Price rate is **€999 + VAT**. you have the opportunity to access all **19 presentations and panel discussions** on-demand for the cost of only **€149+VAT.** ****[**Register online and receive immediate access.**](https://www.google.com/url?q=https%3A%2F%2Fkisacoresearch.acemlnb.com%2Flt.php%3Fs%3Dfc3bf0097d93b4d9ef4d8c9ae35df9ed%26i%3D4249A16249A1480A48636&sa=D&sntz=1&usg=AOvVaw0tWbE4MgeBScPNed9jsYHG) **Appendix:** Software and AI/ML [SPARTRONIX](https://www.google.com/url?q=https%3A%2F%2Fspartronix.eu%2F&sa=D&sntz=1&usg=AOvVaw3DdLcWVaWoSEkd5DfhumnD) **Software and AI/ML** Either for soft-processors (Microblaze, NIOS) or physical microcontrollers. Bare metal, RTOS or Linux-based applications. Software deployed Neural Networks **Real Time Operating Systems (RTOS)** FreeRTOS, VxWorks, pSOS, Ecos, Nucleus, Proprietary **Vast experience with RTOS** **Microprocessors/Microcontrollers** x86 68x Freescale Power ARch Tech ARM MIPS SuperH Symbian XScale **Embedded Operating Systems** Linux WinCE Windows Embedded CE.NET 4.x QNX Symbian **We Love Linux** **Application & Kernel Dev.** Embedded Linux Windows CE VxWorks ThreadX QNX **Custom BSP and Driver Dev.** Embedded Linux Windows CE VxWorks ThreadX QNX **Tailor-made** **Custom Driver Development** Network & Communications Storage Drivers Device Drivers Experience **AI** Detection/Recognition of objects and faces ADAS Security Data centers and more… **Experts in AI/ML** **Real Time Operating Systems (RTOS):** FreeRTOS, VxWorks, pSOS, Ecos, Nucleus, Proprietary 1. [https://xilinx.github.io/finn/](https://www.google.com/url?q=https%3A%2F%2Fxilinx.github.io%2Ffinn%2F&sa=D&sntz=1&usg=AOvVaw2maYrSPzEpDJH0TBGSUxcc) : FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. 2. [Mlir.llvm.org](http://www.google.com/url?q=http%3A%2F%2Fmlir.llvm.org%2F&sa=D&sntz=1&usg=AOvVaw1APVu2XvSUlvFYI81oUWMM) : The MLIR project is a novel approach to building reusable and extensible compiler infrastructure. MLIR aims to address software fragmentation, improve compilation for heterogeneous hardware, significantly reduce the cost of building domain specific compilers, and aid in connecting existing compilers together. 3. : Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services. 4. Graphcore IPU: [https://www.graphcore.ai/posts/microsoft-accelerates-resnext-50-medical-imaging-inference-with-the-ipu](https://www.google.com/url?q=https%3A%2F%2Fwww.graphcore.ai%2Fposts%2Fmicrosoft-accelerates-resnext-50-medical-imaging-inference-with-the-ipu&sa=D&sntz=1&usg=AOvVaw2ZnH6NrKX2_3wd030hR7xh) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. 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Transformer 3. RNN 4. **Spatial RNN video module** ![](https://lh3.googleusercontent.com/lRooRXX9SlvoUzUeYLK6WN8JWWuEJzX7ntbzuKWetLhH2072YciFfzdSx0WW4XkMokONgz9GUOw12IoNzf2MfVg97ABChSvJetqdlM0FqXb1YnBgwhyuZSDAAAxgWAGvrQ=w1280) ![](https://lh4.googleusercontent.com/ywBOI4sYcdvIZqur2N8K6b6ETfgvNFvRkbWUOf2dxnO0gP4VMZbXRGTfD35yrdE107Ktc2HdcSrnw51FKntjFZFWnqtc44EnJXnesR9o924HRfHnG_0K-h874pmnG4ZSjQ=w1280) Tesla AI day (2021) vision system #deeplearning #computervision #dataset ![](https://lh3.googleusercontent.com/B0F_YnSwIA9riGd3qCS01di1VgGNbVTW2fqltrBZJFvFcwdmuQ76zBp0d3xwieLlSiEAc --1UKkVf1uuoCboaGclxMPgOgnjHARkHTFYn4w9z6zKBhFfXL8EG9Qn0Ig1PA=w1280) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. 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Transformer 3. RNN 4. **Spatial RNN video module** ![](https://lh3.googleusercontent.com/lRooRXX9SlvoUzUeYLK6WN8JWWuEJzX7ntbzuKWetLhH2072YciFfzdSx0WW4XkMokONgz9GUOw12IoNzf2MfVg97ABChSvJetqdlM0FqXb1YnBgwhyuZSDAAAxgWAGvrQ=w1280) ![](https://lh4.googleusercontent.com/ywBOI4sYcdvIZqur2N8K6b6ETfgvNFvRkbWUOf2dxnO0gP4VMZbXRGTfD35yrdE107Ktc2HdcSrnw51FKntjFZFWnqtc44EnJXnesR9o924HRfHnG_0K-h874pmnG4ZSjQ=w1280) Tesla AI day (2021) vision system #deeplearning #computervision #dataset ![](https://lh3.googleusercontent.com/B0F_YnSwIA9riGd3qCS01di1VgGNbVTW2fqltrBZJFvFcwdmuQ76zBp0d3xwieLlSiEAc --1UKkVf1uuoCboaGclxMPgOgnjHARkHTFYn4w9z6zKBhFfXL8EG9Qn0Ig1PA=w1280) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. 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Transformer 3. RNN 4. **Spatial RNN video module** ![](https://lh3.googleusercontent.com/C1KVpCqBQzG8R6Sq945hMrLT8TwJuJgmxXcauw1hM1pjRcrypWlIDIzWZhavzBxMgNalyubk6lBH9vHpGcHm3n9e7XKKTx55nEpu50fEYo0YUBM3K13fYUfK9e8nCvYpxw=w1280) ![](https://lh5.googleusercontent.com/tV7o5ec2cC802iox- nXrpd9WvLLPlcSKHIog1IahuZKwmBxIqco_tQkS6GSpnX1asI3Cwd0_2vP1MRqjEtatWGZ4kvVBwU3lEeEV1WzXlyTbwWVWw_jkxNlcasisTZNFZA=w1280) Tesla AI day (2021) vision system #deeplearning #computervision #dataset ![](https://lh5.googleusercontent.com/mkxZ8KYP-X1lbasl- TvOiEJxkcZzWexOKGjBfbKzUhaGsILCcd0X-QLR- ZeIIncbCB9mpWlgmLuCtQeebTommw9HK2I-dJvB9fDqLVHOYAj8Xn44Z_SexXDAn8VS8yUc7Q=w1280) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/G7JKnoBc5s5bG3WK7alpRCEIdQazcLj2L1DLGACGDrsMeHOK9CTS5fh5v74shZzmMJ8YN6hl77hXFxOIDH_8b3M=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/G7JKnoBc5s5bG3WK7alpRCEIdQazcLj2L1DLGACGDrsMeHOK9CTS5fh5v74shZzmMJ8YN6hl77hXFxOIDH_8b3M=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Embedded IoT Embedded IoT World 2021 April 28-29 #eiotworld 1- 87.1 B devices; Tensorflow lite without OS in microcontroller, RISC-V, FPGA; TinyML, ; Docker for embedded; CORE-V; eFPGA, 2- Embedded IoT: Devices, Solutions Architect, Security, Safety, Standardization and certification, secured connectivity, end to end security, modern development practices, 3- Safe access of local IoT devices from the internet OAuth 2.0 & OIDC relies on TLS; MQTT, 4- Scale4Edge RISC- V edge computing ecosystem: Virtual prototyping first! Performance, Energy efficiency, low cost, quality, robustness (safety, security, reliability); virtual system prototypes(VSP); E2E AI performance assessment: NN + ML-Compiler + Virtual Hardware Model => KPI based evaluation TC-ResNet; Sparsity extraction, microcoded data path; * Machine learning on edge devices requires tailored hardware/software solutions * support and integration of different target architectures: RISC-V; UltraTrail; SpinnEDGE * deployment software flow based on TVM * application specific vs domain specific 5 - A structured approach to comprehensive IoT security in the smart home Network Protocols: BLE, Bluetooth mesh, WiFi, Zigbee, Sub-G, GPRS, 4G/5G, NB- IoT, GNSS *6 - Code size compiler optimizations and techniques for embedded systems Code size of embedded application has been a concern for a very long time. While storage becomes cheaper and smaller, developers find creative ways to increase code size by adding features or unnecessary software engineering. Compilers have come a long way in optimizing applications for code size. While most compiler optimization work were focused on application performance, we have seen increase in the code size optimizations in recent years. This session will cover classical as well as recent compiler optimizations for code size, a few of which Aditya has implemented in the LLVM compiler. Some optimizations (hot cold splitting, function entry instrumentation) require collecting data from the field while the application is running. The presentation will provide an overview of how those compiler techniques help reduce code size. We will also explore some tips and techniques (compiler flags to reduce code size, tuning of compiler options like inline threshold), that help reduce binary size. Having knowledge of the code generated by the compiler and the instruction set architecture can help engineers chose appropriate programming abstractions and idioms. Optimize applications for code size using available compiler techniques and software engineering techniques ; Various code-size and performance trade offs ; Understanding the code size requirements embedded application. code size optimization flags: * -Os * -Oz (only in llvm) * -fno-unroll-loops * -fno-exceptions * -fno-rtti * -fno-jump-tables * -fno-function-sections/ -ffunction-sections * -Wl, --strip-all ( or do not pass '-g' flag) * *7 - Condition monitoring through machine learning Data acquisition: acquisition sensor setup; retrieve data over wired/wireless connectivity; label data; store data condition monitoring: data cleaning/denoising; data visualization; preprocessing and feature extraction; feature engineering anomaly detection & classification: machine learning of the system behavior; semi-supervised learning at the edge for anomaly detection; supervised learning to classify anomalies predictive maintenance: model deployment; remaining life prediction models; overall efficiency optimization; operational systems integration **8 - Optimizing machine learning models for IoT applications ML/AI in embedded applications is tightly constrained and performance intensive; best-in-class optimization makes it possible; structuring your code effectively can help; *9 - Edge AI processing in real time edge sensing with cloud AI: data may be filtered, compressed, or pre-processed at edge edge AI with cloud data upstream: results send cloud; AI inference at the edge for efficiency, latency, cost, or scale edge AI real time interactive system: everything on edge ioFog; 10 - Enabling machine learning on Arm Cortex M0-powered IoT nodes using Qeexo AutoML (qeexo.com) benefits of ML + IoT = low latency, low bandwidth, low power consumption, high availability, data privacy and security ml pipeline for IoT: data processing, feature extraction, model training, model conversion, training & conversion, _**ensemble methods**_ : * feature-based, compact representation, easy to reduce model size, model size can be reduced post-training * _ **bagging**_ is a type of ensemble method where multiple models are trained in parallel on subsampled datasets (reduces error due to variance); many models to combine output to make a single classification; every model get pictures on dataset , over fit, but combine get same accuracy, picture the noise is cancelled for each model. * _**boosting**_ is a type of ensemble method where multiple models are trained in sequence to improve upon the errors of the previous model (reduces error due to bias) 11 - Managing ROS2 applications at the edge ROS 2: end-to-end application lifecycle 12 - Edge computing: Use cases, requirements, architectures and implementations heterogeneous processors; challenges: latency, network bandwidth, trustworthiness (safety, security, resilience, reliability, privacy), scalability, data models/ data ownership, IT/OT disconnect, justifying the cost, ... Company: Zephyr Project, QuickLoginc, Antmicro, Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/ssWXVQN4wWnp524T8UjHyQEdArMpTIaM4OalIHhpfDv0Sv6HC6tLnwxjfrdaemWa002sbPUNKYrtuvBhEcn6DIQ=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/ssWXVQN4wWnp524T8UjHyQEdArMpTIaM4OalIHhpfDv0Sv6HC6tLnwxjfrdaemWa002sbPUNKYrtuvBhEcn6DIQ=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Edge AI summit Short summary of the edge AI summit 18-20 November 2020 Wednesday, November 18, 2020 A Software Solution Enabling Predictive Maintenance at the Sensor Level Helping Fish Farmers Feed The World With Deep Learning tinyMLPerf: Benchmarking Ultra-low Power Machine Learning Systems Ultra-low power neuromorphic intelligence for the sensor edge * How is AI affecting hearables and sensors? Breaking the Barriers to Deploy DNNs on Low-Power Hardware Optimizing ML Models At The Edge Made Simple Thursday, November 19, 2020 Developing Edge AI Solutions For A Post-Pandemic Society The Evolving Landscape of Edge AI InferX X1, The Fastest and Most Efficient Edge Inference Accelerator Implementing Edge Technologies in Retail: Walmart Case Study The Era of Analog AI Compute Is Here Using Edge AI To Detect Repetitive Mot Friday, November 20, 2020 *Spatial Computing: A Collision of Edge and Cloud-Based Computing Building An Autonomous Network For IoT and Edge Applications Practical Edge Inferencing: Enabling fastest AI inferencing per Watt leveraging sparsity **Large Scale Deep Learning and AI models on the Edge The Edge: The Hottest Market for AI Accelerator Chips - Introducing the Kisaco Leadership Chart on AI Hardware Accelerators 2020-21: Edge and Automotive # Short summary of the edge AI summit 18-20 November 2020 Best of Wednesday, November 18, 2020; tinyMLPerf, Breaking the Barriers to Deploy DNNs on Low-Power Hardware, Optimizing ML Models At The Edge Made Simple **Thursday, November 19, 2020** * 8:00 AM - 8:30 AM (PST) KEYNOTE PRESENTATION: Developing Edge AI Solutions For A Post-Pandemic Society Sastry Malladi - FogHorn Systems * 8:35 AM - 9:05 AM (PST) PRESENTATION: The Evolving Landscape of Edge AI Ajay Nair - Google * 9:05 AM - 9:20 AM (PST) Comfort Break * 9:20 AM - 9:45 AM (PST) PRESENTATION: InferX X1, The Fastest and Most Efficient Edge Inference Accelerator Cheng Wang - Flex Logix Technologies Inc. * 9:50 AM - 10:20 AM (PST) PRESENTATION: Implementing Edge Technologies in Retail: Walmart Case Study Alex Sabatier - Nvidia * 10:20 AM - 10:35 AM (PST) Comfort Break * 10:35 AM - 11:20 AM (PST) Meet speaker! * **11:20 AM - 11:50 AM (PST) PRESENTATION: The Era of Analog AI Compute Is Here Mike Henry - Mythic** * 11:55 AM - 12:25 PM (PST) PRESENTATION: Using Edge AI To Detect Repetitive Mot Marcellino Gemelli - Bosch Sensortec * 12:30 PM - 2:30 PM (PST) NETWORKING - Dedicated Networking 2 hours for 1-2-1 Video Meetings **Friday, November 20, 2020** * 8:00 AM - 8:30 AM (PST) PRESENTATION: Spatial Computing: A Collision of Edge and Cloud-Based Computing Ashwin Swaminathan - Magic Leap * 8:35 AM - 9:05 AM (PST) PRESENTATION: Building An Autonomous Network For IoT and Edge Applications Anshul Bhatt - Rakuten Mobile * 9:05 AM - 9:20 AM (PST) Comfort Break * 9:20 AM - 9:45 AM (PST) PRESENTATION: Practical Edge Inferencing: Enabling fastest AI inferencing per Watt leveraging sparsity Mahesh Makhijani - GrAI Matter Labs * **9:50 AM - 10:20 AM (PST) PRESENTATION: Large Scale Deep Learning and AI models on the Edge Chandra Khatri - Got It AI** * 10:20 AM - 10:35 AM (PST) Comfort Break * 10:35 AM - 11:20 AM (PST) NETWORKING: Interest Groups (18 people per room, topic-specific discussions) * 11:20 AM - 11:50 AM (PST) PANEL DISCUSSION: The Symbiotic Relationship between 5G and Edge AI Sami Badri - Credit Suisse, Christos Kolias - Orange, Rima Raouda - Independent * 11:55 AM - 12:25 PM (PST) PANEL DISCUSSION: Investment Trends & Dynamics Panel Rashmi Gopinath - B Capital Group, Yvonne Lutsch - Bosch Venture Capital, Eileen Tanghal - In-Q-Tel, Albert Wang - Qualcomm Ventures * **12:30 PM - 12:50 PM (PST) PRESENTATION: The Edge: The Hottest Market for AI Accelerator Chips - Introducing the Kisaco Leadership Chart on AI Hardware Accelerators 2020-21: Edge and Automotive Michael Azoff - Kisaco Research** ## Wednesday, November 18, 2020 * ### A Software Solution Enabling Predictive Maintenance at the Sensor Level * SensiML Toolkit enables AI for a broad array of resource constrained time-series sensor endpoint applications. These include a wide range of consumer and industrial sensing applications. * The problem is machine learning engineer do not have experience with embedded system and moving model to embedded system takes long time. * AutoML for Embedded system usage. it is on the cloud. * using the compiler for that device for this tools * cost edge and cloud. easy to work on cloud. streaming data to cloud is difficult. faster if working on edge. * TinyML addresses problems, battery powered, limited internet connectivity, security/privacy, latency, economic * [https://sensiml.com/products/#process](https://www.google.com/url?q=https%3A%2F%2Fsensiml.com%2Fproducts%2F%23process&sa=D&sntz=1&usg=AOvVaw116wjx6mBnEo9x0htyL2pr) * ### Helping Fish Farmers Feed The World With Deep Learning * [https://s3-us-west-1.amazonaws.com/aquabyte-static/videos/welcome_to_aquabyte_subtitled.mp4](https://www.google.com/url?q=https%3A%2F%2Fs3-us-west-1.amazonaws.com%2Faquabyte-static%2Fvideos%2Fwelcome_to_aquabyte_subtitled.mp4&sa=D&sntz=1&usg=AOvVaw1osNtsRVM9TMCmKoPsTnj_) * Count sea lice and accurately measure biomass in real-time while reducing cage furniture. Our experts‑in‑the‑loop ensure that every single prediction is correct. * Aquabyte is seeking a Machine Learning Platform Engineer to drive the development, testing, and delivery of machine learning models that enable cutting-edge analytics and automation of fish farms around the world. * Aquabyte is on a mission to revolutionize the sustainability and efficiency of aquaculture. It is an audacious, and incredibly rewarding mission. By making fish farming cheaper and more viable than livestock production, we aim to mitigate one of the biggest causes of climate change and help prepare our planet for impending population growth. Aquaculture is the single fastest growing food-production sector in the world, and now is the time to define how technology is used to harvest the sea for generations to come. * We are currently focused on helping Norwegian salmon farmers better understand their fish populations and make environmentally-sound decisions. Through custom underwater cameras, computer vision, and machine learning we are able to quantify fish weights, detect sea lice infestations, and generate optimal feeding plans in real time. Our product operates at three levels: on-site hardware for image capture, cloud pipelines for data processing, and a user-facing web application. As a result, there are hundreds of moving pieces and no shortage of fascinating challenges across all levels of the stack. * * ### tinyMLPerf: Benchmarking Ultra-low Power Machine Learning Systems * [https://github.com/mlperf/tiny](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fmlperf%2Ftiny&sa=D&sntz=1&usg=AOvVaw1TgviYAuh83PMxDPYljOjs) * tinyMLPerf Deep Learning Benchmarks for Embedded Devices * The goal of TinyMLPerf is to provide a representative set of deep neural nets and benchmarking code to compare performance between embedded devices. Embedded devices include microcontrollers, DSPs, and tiny NN accelerators. These devics typically run at between 10MHz and 250MHz, and can perform inference using less then 50mW of power. TinyMLPerf submissions will allow device makers and researchers to choose the best hardware for their use case, and allows hardware vendors to showcase their offerings. TinyMLPerf is primarily intended to benchmark hardware rather than new network archietctures, or embedded neural net runtimes. The reference benchmarks are provided using TensorFlow Lite for Microcontrollers (TFLM). Submitters can directly use the TFLM, although submitters are encouraged to use the software stack that works best on thier hardware. * anomaly detection benchmark, visual wake words benchmark, * ### Ultra-low power neuromorphic intelligence for the sensor edge * Innatera Nanosystems BV (Innatera, (Innatera, innatera.com) is a rapidly-growing Dutch semiconductor company that develops ultra-efficient neuromorphic processors for AI at the edge. These microprocessors mimic the brain’s mechanisms for processing fast data streams from sensors, enabling complex turn-key sensor analytics functionalities, with 10,000x higher performance per watt than competing solutions. Innatera's technology serves as a critical enabler for next-generation use-cases in the IoT, wearable, embedded, and automotive domains. * ### * How is AI affecting hearables and sensors? * [https://github.com/greenwaves-technologies/nn_menu](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fgreenwaves-technologies%2Fnn_menu&sa=D&sntz=1&usg=AOvVaw2JKYYAPrnA9Mkalw2qenUQ) * The Neural Network Menu* is a collection of software that implements Neural Networks on Greenwaves Application Processors (GAP). This repository contains common mobile and edge NN archtecture examples, NN sample applications and full flagged reference designs. Our tools maps a TFLITE model (quantized or unquantized) onto gap. There is also a flow in the ingredients directory showing how to hand map from a Pytorch Model onto GAP. * [https://greenwaves-technologies.com/store/](https://www.google.com/url?q=https%3A%2F%2Fgreenwaves-technologies.com%2Fstore%2F&sa=D&sntz=1&usg=AOvVaw0Ya_w_NBAr4AbIxBe2j_YX) * GAPPoc-A is a Proof of Concept Board that can be used for demonstration of battery-operated, edge computer vision applications based on GAP8. * It incorporates GAPmod, a surface-mount module that implements all the layout sensitive portion of a GAP8 design, along with a VGA image sensor and a Bluetooth Low Energy radio. * The GAPPoc-A board enables battery-operated applications developed around algorithms such as people counting, face-identification and many others to be quickly assembled and evaluated in the field. * [https://riscv.org/blog/2019/08/risc-v-emea-roadshow-spotlight-greenwaves-technologies/](https://www.google.com/url?q=https%3A%2F%2Friscv.org%2Fblog%2F2019%2F08%2Frisc-v-emea-roadshow-spotlight-greenwaves-technologies%2F&sa=D&sntz=1&usg=AOvVaw1ikZjtEoYTgFb-S_eGEB3i) * ### Breaking the Barriers to Deploy DNNs on Low-Power Hardware * Deeplite, named to the 2020 CB Insights AI100 List of Most Innovative Artificial Intelligence Startups, is devoted to making fundamental advancements in accessible and efficient deep learning. Our solution helps deep learning engineers and experts automatically create faster, smaller and more energy-efficient deep neural networks. Industry leaders in computer vision, augmented reality and autonomous driving use our technology to unlock new possibilities for deep learning in the real world. At Deeplite, our vision is to create a lightweight intelligence that’s accessible for daily life. * [https://www.deeplite.ai/](https://www.google.com/url?q=https%3A%2F%2Fwww.deeplite.ai%2F&sa=D&sntz=1&usg=AOvVaw1r7NiQGt1hRi6S_xiJ522C) * At Deeplite, we are tackling inference optimization of deep neural networks, making them faster and energy-efficient from cloud to edge computing. Our solution leverages state-of-the-art technology from elite universities to make deep neural networks applicable for any device, and our team works hard on the iterative evolution of the science behind deep neural networks to directly improve daily life. * reduce the size of model 40x * ### Optimizing ML Models At The Edge Made Simple * [https://octoml.ai/](https://www.google.com/url?q=https%3A%2F%2Foctoml.ai%2F&sa=D&sntz=1&usg=AOvVaw2uXg6ESgQgVGrF9nQJKFve) * OctoML is an energetic new company changing how developers optimize and deploy machine learning models for their AI needs. We’re a team of machine learning systems leaders focused on making ML more efficient and easier to deploy by… applying machine learning to it! * OctoML is leveraging the power and traction of Apache TVM, an open source project originated by our founding team, to enable companies of every size to harness the power of deep learning without the expensive heavy lifting of tuning and securing models to each hardware configuration that a customer might need. * Apache TVM and Deep Learning Compilation Conference, Wed-Fri, December 2nd-4th 2020, Free Virtual Event. ## Thursday, November 19, 2020 * ### Developing Edge AI Solutions For A Post-Pandemic Society * [https://www.foghorn.io/](https://www.google.com/url?q=https%3A%2F%2Fwww.foghorn.io%2F&sa=D&sntz=1&usg=AOvVaw2aRKISC9BdrrriEej5Xb_I) * ogHorn’s Lightning™ Edge AI platform brings a groundbreaking dimension to IIoT and edge computing by embedding AI as close to the source of streaming sensor data as possible. The Edge AI software platform is a highly compact, advanced and feature-rich edge solution that delivers unprecedented low latency for onsite data processing, real-time analytics, ML and AI capabilities. It delivers the industry’s lowest total cost for computing requirements, communications services, and cloud processing and storage. * temperature detection, social distancing, cough detection, PPE/Mask detection * Flexible, customizable, integrated, actionable * ### The Evolving Landscape of Edge AI * * Coral’s local AI technology enables new possibilities across almost any kind of industry * The Coral Dev Board is a single-board computer that contains an Edge TPU coprocessor. It's ideal for prototyping new projects that demand fast on-device inferencing for machine learning models. This page is your guide to get started. The setup requires flashing Mendel Linux to the board, and then accessing the board's shell terminal. Once you have terminal access and update some of the software, we'll show you how to run an image classification model on the board. If you want to learn more about the hardware, see the Dev Board datasheet. * TPU v3, 32 to 512 TOPS, Q2 2021 * ### InferX X1, The Fastest and Most Efficient Edge Inference Accelerator * InferX X1: World's fastest and most efficient Edge Inference Accelerator. We have just launched our first inference chip and it is the best in the world for edge inference. We are bringing up neural network models now and moving forward on the steps required for Q2/2021 chip and board production and Inference Compiler availability. * mbedded FPGA, or eFPGA, enables your SoC to have flexibility in critical areas where algorithm, protocol or market needs are changing. FPGA can also accelerate many workloads faster than processors: Microsoft Azure uses one FPGA accelerator for every 2 Xeons.Flex Logix provides eFPGA cores which have density and performance similar to leading FPGAs in the same process node. Our EFLX eFPGA is silicon proven in 40nm, 28/22nm, 16nm and 12nm. 6/7nm EFLX eFPGA is planned. Our eFPGA is based on a “tile” called EFLX 4K, which comes in two versions: all logic or mostly logic with some MACs (multiply-accumulators). The programmable logic is called LUTs (look up tables) that can implement any Boolean function. EFLX 4K Logix has 4000 LUT4 equivalents, EFLX 4K DSP has 3000 LUT4s and 40 Multiplier-Accumulators (MACs): the MAC has a 22-bit pre-adder, a 22×22 multiple and a 48-bit post adder/accumulator. MACs can be combined or cascaded to form fast DSP functions. (For 40nm-180nm we offer an EFLX 1K tile). * depth-wise conv2d * ### Implementing Edge Technologies in Retail: Walmart Case Study * NVidia * ### The Era of Analog AI Compute Is Here * Mythic products are based on a unique tile-based AI compute architecture that features three fundamental hardware technologies – Compute-in-Memory, Dataflow Architecture, and Analog Computing. For AI developers, the Mythic SDK streamlines the preparation of trained neural networks for edge and low-latency datacenter deployments, and also performs automatic optimization and compilation of dataflow graphs for our unique architecture. * low power consumption, ultra-low latency, high ai performance, large weight capacity, small form factor, cost effective solution * ### **Us** ing Edge AI To Detect Repetitive Mot * Bosch Sensortec develops and markets a wide portfolio of MEMS sensors and solutions for applications in smartphones, tablets, wearables, AR/VR devices, drones, robots, smart home and the Internet of Things. Striving to meet the demanding requirements of the consumer electronics market, we provide best-in-class sensing solutions in terms of customer focus, quality and reliability, performance, sustainability and competitiveness. * [https://github.com/BoschSensortec](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FBoschSensortec&sa=D&sntz=1&usg=AOvVaw0Xr8dUHPERsj-rYH7ZAnP1) ## Friday, November 20, 2020 * ### *Spatial Computing: A Collision of Edge and Cloud-Based Computing * [https://github.com/magicleap](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fmagicleap&sa=D&sntz=1&usg=AOvVaw22R0IwwKYOdwv06BvMpvZF) * instance semantic segmentation contextual computing * spatial computing * SLAM: tracking/localization, mapping: * latency is critical for see through displays * weight is critical cannot compensate for lack of compute with more sensors * thermal is critical more sensors and more compute lead to heat * rigidity leads to weight our device should be light * very stringent requirements for MR * why build a map: drift correction, robustness (pose recovery), persistence * feature descriptors * matching across large baselines and illumination changes is challenging * most of the SOTA methods based on deep learning and not feasible withing compute budget * our deep descriptor is optimized for SLAM and provides the best trade off in terms of performance and compute * semantic segmentation 3d point cloud * ### Building An Autonomous Network For IoT and Edge Applications * 5G + AI * ### Practical Edge Inferencing: Enabling fastest AI inferencing per Watt leveraging sparsity * [https://www.graimatterlabs.ai/](https://www.google.com/url?q=https%3A%2F%2Fwww.graimatterlabs.ai%2F&sa=D&sntz=1&usg=AOvVaw1qLIEaRzrtXoYDAb_Y4tQb) * The world’s first sparsity-enabled AI processor optimized for ultra-low latency and low power processing at the edge. * GrAI One drastically reduces application latency, for instance, it reduces the end-to-end latencies for deep learning networks such as PilotNet to the order of milliseconds. The GrAI One chip is based on GML’s innovative NeuronFlow™ technology that combines the dynamic Dataflow paradigm with sparse computing to produce massively parallel in-network processing. * GrAI Matter Labs ([www.graimatterlabs.ai](http://www.google.com/url?q=http%3A%2F%2Fwww.graimatterlabs.ai%2F&sa=D&sntz=1&usg=AOvVaw3eJ9oROjswyCFHO-68LiDi)), a fabless semiconductor company specialized in brain-inspired technology, designs and develops fully programmable ultra-low power neuromorphic HW for sensor analytics and machine learning. The company has offices in Eindhoven (NL), Paris (FR) and San Jose (USA) and has strong relations with top-ranking research groups on neuroscience, human vision and natural computation * ### **Large Scale Deep Learning and AI models on the Edge * deployment pipelines * there are several steps involved in the AI/ML life-cycle * several tools to help simplify the whole process * tensorflow extended (TFX): an end to end platform for deploying production ML pipelines * MLflow (other options michelangelo): an open source platform for the end to end machine learning life cycle * apache airflow (other options kubeflow): an open source workflow management platform * dataiku data science studio (DSS): collaborative data science software platform for teams of data scientist , data analysts, and engineers to explore prototype build and deliver * ### The Edge: The Hottest Market for AI Accelerator Chips - Introducing the Kisaco Leadership Chart on AI Hardware Accelerators 2020-21: Edge and Automotive * [https://www.kisacoresearch.com/#about-us](https://www.google.com/url?q=https%3A%2F%2Fwww.kisacoresearch.com%2F%23about-us&sa=D&sntz=1&usg=AOvVaw0nasAOo80KuyOwbm4OeiOb) [OpenHTF is a Python library that provides a set of convenient abstractions designed to remove as much boilerplate as possible from hardware test setup and execution, so test engineers can focus primarily on test logic.](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fgoogle%2Fopenhtf&sa=D&sntz=1&usg=AOvVaw0zU3RKntPn4N8JIkPvriIu) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/ssWXVQN4wWnp524T8UjHyQEdArMpTIaM4OalIHhpfDv0Sv6HC6tLnwxjfrdaemWa002sbPUNKYrtuvBhEcn6DIQ=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/ssWXVQN4wWnp524T8UjHyQEdArMpTIaM4OalIHhpfDv0Sv6HC6tLnwxjfrdaemWa002sbPUNKYrtuvBhEcn6DIQ=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Edge AI summit Short summary of the edge AI summit 18-20 November 2020 Wednesday, November 18, 2020 A Software Solution Enabling Predictive Maintenance at the Sensor Level Helping Fish Farmers Feed The World With Deep Learning tinyMLPerf: Benchmarking Ultra-low Power Machine Learning Systems Ultra-low power neuromorphic intelligence for the sensor edge * How is AI affecting hearables and sensors? Breaking the Barriers to Deploy DNNs on Low-Power Hardware Optimizing ML Models At The Edge Made Simple Thursday, November 19, 2020 Developing Edge AI Solutions For A Post-Pandemic Society The Evolving Landscape of Edge AI InferX X1, The Fastest and Most Efficient Edge Inference Accelerator Implementing Edge Technologies in Retail: Walmart Case Study The Era of Analog AI Compute Is Here Using Edge AI To Detect Repetitive Mot Friday, November 20, 2020 *Spatial Computing: A Collision of Edge and Cloud-Based Computing Building An Autonomous Network For IoT and Edge Applications Practical Edge Inferencing: Enabling fastest AI inferencing per Watt leveraging sparsity **Large Scale Deep Learning and AI models on the Edge The Edge: The Hottest Market for AI Accelerator Chips - Introducing the Kisaco Leadership Chart on AI Hardware Accelerators 2020-21: Edge and Automotive # Short summary of the edge AI summit 18-20 November 2020 Best of Wednesday, November 18, 2020; tinyMLPerf, Breaking the Barriers to Deploy DNNs on Low-Power Hardware, Optimizing ML Models At The Edge Made Simple **Thursday, November 19, 2020** * 8:00 AM - 8:30 AM (PST) KEYNOTE PRESENTATION: Developing Edge AI Solutions For A Post-Pandemic Society Sastry Malladi - FogHorn Systems * 8:35 AM - 9:05 AM (PST) PRESENTATION: The Evolving Landscape of Edge AI Ajay Nair - Google * 9:05 AM - 9:20 AM (PST) Comfort Break * 9:20 AM - 9:45 AM (PST) PRESENTATION: InferX X1, The Fastest and Most Efficient Edge Inference Accelerator Cheng Wang - Flex Logix Technologies Inc. * 9:50 AM - 10:20 AM (PST) PRESENTATION: Implementing Edge Technologies in Retail: Walmart Case Study Alex Sabatier - Nvidia * 10:20 AM - 10:35 AM (PST) Comfort Break * 10:35 AM - 11:20 AM (PST) Meet speaker! * **11:20 AM - 11:50 AM (PST) PRESENTATION: The Era of Analog AI Compute Is Here Mike Henry - Mythic** * 11:55 AM - 12:25 PM (PST) PRESENTATION: Using Edge AI To Detect Repetitive Mot Marcellino Gemelli - Bosch Sensortec * 12:30 PM - 2:30 PM (PST) NETWORKING - Dedicated Networking 2 hours for 1-2-1 Video Meetings **Friday, November 20, 2020** * 8:00 AM - 8:30 AM (PST) PRESENTATION: Spatial Computing: A Collision of Edge and Cloud-Based Computing Ashwin Swaminathan - Magic Leap * 8:35 AM - 9:05 AM (PST) PRESENTATION: Building An Autonomous Network For IoT and Edge Applications Anshul Bhatt - Rakuten Mobile * 9:05 AM - 9:20 AM (PST) Comfort Break * 9:20 AM - 9:45 AM (PST) PRESENTATION: Practical Edge Inferencing: Enabling fastest AI inferencing per Watt leveraging sparsity Mahesh Makhijani - GrAI Matter Labs * **9:50 AM - 10:20 AM (PST) PRESENTATION: Large Scale Deep Learning and AI models on the Edge Chandra Khatri - Got It AI** * 10:20 AM - 10:35 AM (PST) Comfort Break * 10:35 AM - 11:20 AM (PST) NETWORKING: Interest Groups (18 people per room, topic-specific discussions) * 11:20 AM - 11:50 AM (PST) PANEL DISCUSSION: The Symbiotic Relationship between 5G and Edge AI Sami Badri - Credit Suisse, Christos Kolias - Orange, Rima Raouda - Independent * 11:55 AM - 12:25 PM (PST) PANEL DISCUSSION: Investment Trends & Dynamics Panel Rashmi Gopinath - B Capital Group, Yvonne Lutsch - Bosch Venture Capital, Eileen Tanghal - In-Q-Tel, Albert Wang - Qualcomm Ventures * **12:30 PM - 12:50 PM (PST) PRESENTATION: The Edge: The Hottest Market for AI Accelerator Chips - Introducing the Kisaco Leadership Chart on AI Hardware Accelerators 2020-21: Edge and Automotive Michael Azoff - Kisaco Research** ## Wednesday, November 18, 2020 * ### A Software Solution Enabling Predictive Maintenance at the Sensor Level * SensiML Toolkit enables AI for a broad array of resource constrained time-series sensor endpoint applications. These include a wide range of consumer and industrial sensing applications. * The problem is machine learning engineer do not have experience with embedded system and moving model to embedded system takes long time. * AutoML for Embedded system usage. it is on the cloud. * using the compiler for that device for this tools * cost edge and cloud. easy to work on cloud. streaming data to cloud is difficult. faster if working on edge. * TinyML addresses problems, battery powered, limited internet connectivity, security/privacy, latency, economic * [https://sensiml.com/products/#process](https://www.google.com/url?q=https%3A%2F%2Fsensiml.com%2Fproducts%2F%23process&sa=D&sntz=1&usg=AOvVaw116wjx6mBnEo9x0htyL2pr) * ### Helping Fish Farmers Feed The World With Deep Learning * [https://s3-us-west-1.amazonaws.com/aquabyte-static/videos/welcome_to_aquabyte_subtitled.mp4](https://www.google.com/url?q=https%3A%2F%2Fs3-us-west-1.amazonaws.com%2Faquabyte-static%2Fvideos%2Fwelcome_to_aquabyte_subtitled.mp4&sa=D&sntz=1&usg=AOvVaw1osNtsRVM9TMCmKoPsTnj_) * Count sea lice and accurately measure biomass in real-time while reducing cage furniture. Our experts‑in‑the‑loop ensure that every single prediction is correct. * Aquabyte is seeking a Machine Learning Platform Engineer to drive the development, testing, and delivery of machine learning models that enable cutting-edge analytics and automation of fish farms around the world. * Aquabyte is on a mission to revolutionize the sustainability and efficiency of aquaculture. It is an audacious, and incredibly rewarding mission. By making fish farming cheaper and more viable than livestock production, we aim to mitigate one of the biggest causes of climate change and help prepare our planet for impending population growth. Aquaculture is the single fastest growing food-production sector in the world, and now is the time to define how technology is used to harvest the sea for generations to come. * We are currently focused on helping Norwegian salmon farmers better understand their fish populations and make environmentally-sound decisions. Through custom underwater cameras, computer vision, and machine learning we are able to quantify fish weights, detect sea lice infestations, and generate optimal feeding plans in real time. Our product operates at three levels: on-site hardware for image capture, cloud pipelines for data processing, and a user-facing web application. As a result, there are hundreds of moving pieces and no shortage of fascinating challenges across all levels of the stack. * * ### tinyMLPerf: Benchmarking Ultra-low Power Machine Learning Systems * [https://github.com/mlperf/tiny](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fmlperf%2Ftiny&sa=D&sntz=1&usg=AOvVaw1TgviYAuh83PMxDPYljOjs) * tinyMLPerf Deep Learning Benchmarks for Embedded Devices * The goal of TinyMLPerf is to provide a representative set of deep neural nets and benchmarking code to compare performance between embedded devices. Embedded devices include microcontrollers, DSPs, and tiny NN accelerators. These devics typically run at between 10MHz and 250MHz, and can perform inference using less then 50mW of power. TinyMLPerf submissions will allow device makers and researchers to choose the best hardware for their use case, and allows hardware vendors to showcase their offerings. TinyMLPerf is primarily intended to benchmark hardware rather than new network archietctures, or embedded neural net runtimes. The reference benchmarks are provided using TensorFlow Lite for Microcontrollers (TFLM). Submitters can directly use the TFLM, although submitters are encouraged to use the software stack that works best on thier hardware. * anomaly detection benchmark, visual wake words benchmark, * ### Ultra-low power neuromorphic intelligence for the sensor edge * Innatera Nanosystems BV (Innatera, (Innatera, innatera.com) is a rapidly-growing Dutch semiconductor company that develops ultra-efficient neuromorphic processors for AI at the edge. These microprocessors mimic the brain’s mechanisms for processing fast data streams from sensors, enabling complex turn-key sensor analytics functionalities, with 10,000x higher performance per watt than competing solutions. Innatera's technology serves as a critical enabler for next-generation use-cases in the IoT, wearable, embedded, and automotive domains. * ### * How is AI affecting hearables and sensors? * [https://github.com/greenwaves-technologies/nn_menu](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fgreenwaves-technologies%2Fnn_menu&sa=D&sntz=1&usg=AOvVaw2JKYYAPrnA9Mkalw2qenUQ) * The Neural Network Menu* is a collection of software that implements Neural Networks on Greenwaves Application Processors (GAP). This repository contains common mobile and edge NN archtecture examples, NN sample applications and full flagged reference designs. Our tools maps a TFLITE model (quantized or unquantized) onto gap. There is also a flow in the ingredients directory showing how to hand map from a Pytorch Model onto GAP. * [https://greenwaves-technologies.com/store/](https://www.google.com/url?q=https%3A%2F%2Fgreenwaves-technologies.com%2Fstore%2F&sa=D&sntz=1&usg=AOvVaw0Ya_w_NBAr4AbIxBe2j_YX) * GAPPoc-A is a Proof of Concept Board that can be used for demonstration of battery-operated, edge computer vision applications based on GAP8. * It incorporates GAPmod, a surface-mount module that implements all the layout sensitive portion of a GAP8 design, along with a VGA image sensor and a Bluetooth Low Energy radio. * The GAPPoc-A board enables battery-operated applications developed around algorithms such as people counting, face-identification and many others to be quickly assembled and evaluated in the field. * [https://riscv.org/blog/2019/08/risc-v-emea-roadshow-spotlight-greenwaves-technologies/](https://www.google.com/url?q=https%3A%2F%2Friscv.org%2Fblog%2F2019%2F08%2Frisc-v-emea-roadshow-spotlight-greenwaves-technologies%2F&sa=D&sntz=1&usg=AOvVaw1ikZjtEoYTgFb-S_eGEB3i) * ### Breaking the Barriers to Deploy DNNs on Low-Power Hardware * Deeplite, named to the 2020 CB Insights AI100 List of Most Innovative Artificial Intelligence Startups, is devoted to making fundamental advancements in accessible and efficient deep learning. Our solution helps deep learning engineers and experts automatically create faster, smaller and more energy-efficient deep neural networks. Industry leaders in computer vision, augmented reality and autonomous driving use our technology to unlock new possibilities for deep learning in the real world. At Deeplite, our vision is to create a lightweight intelligence that’s accessible for daily life. * [https://www.deeplite.ai/](https://www.google.com/url?q=https%3A%2F%2Fwww.deeplite.ai%2F&sa=D&sntz=1&usg=AOvVaw1r7NiQGt1hRi6S_xiJ522C) * At Deeplite, we are tackling inference optimization of deep neural networks, making them faster and energy-efficient from cloud to edge computing. Our solution leverages state-of-the-art technology from elite universities to make deep neural networks applicable for any device, and our team works hard on the iterative evolution of the science behind deep neural networks to directly improve daily life. * reduce the size of model 40x * ### Optimizing ML Models At The Edge Made Simple * [https://octoml.ai/](https://www.google.com/url?q=https%3A%2F%2Foctoml.ai%2F&sa=D&sntz=1&usg=AOvVaw2uXg6ESgQgVGrF9nQJKFve) * OctoML is an energetic new company changing how developers optimize and deploy machine learning models for their AI needs. We’re a team of machine learning systems leaders focused on making ML more efficient and easier to deploy by… applying machine learning to it! * OctoML is leveraging the power and traction of Apache TVM, an open source project originated by our founding team, to enable companies of every size to harness the power of deep learning without the expensive heavy lifting of tuning and securing models to each hardware configuration that a customer might need. * Apache TVM and Deep Learning Compilation Conference, Wed-Fri, December 2nd-4th 2020, Free Virtual Event. ## Thursday, November 19, 2020 * ### Developing Edge AI Solutions For A Post-Pandemic Society * [https://www.foghorn.io/](https://www.google.com/url?q=https%3A%2F%2Fwww.foghorn.io%2F&sa=D&sntz=1&usg=AOvVaw2aRKISC9BdrrriEej5Xb_I) * ogHorn’s Lightning™ Edge AI platform brings a groundbreaking dimension to IIoT and edge computing by embedding AI as close to the source of streaming sensor data as possible. The Edge AI software platform is a highly compact, advanced and feature-rich edge solution that delivers unprecedented low latency for onsite data processing, real-time analytics, ML and AI capabilities. It delivers the industry’s lowest total cost for computing requirements, communications services, and cloud processing and storage. * temperature detection, social distancing, cough detection, PPE/Mask detection * Flexible, customizable, integrated, actionable * ### The Evolving Landscape of Edge AI * * Coral’s local AI technology enables new possibilities across almost any kind of industry * The Coral Dev Board is a single-board computer that contains an Edge TPU coprocessor. It's ideal for prototyping new projects that demand fast on-device inferencing for machine learning models. This page is your guide to get started. The setup requires flashing Mendel Linux to the board, and then accessing the board's shell terminal. Once you have terminal access and update some of the software, we'll show you how to run an image classification model on the board. If you want to learn more about the hardware, see the Dev Board datasheet. * TPU v3, 32 to 512 TOPS, Q2 2021 * ### InferX X1, The Fastest and Most Efficient Edge Inference Accelerator * InferX X1: World's fastest and most efficient Edge Inference Accelerator. We have just launched our first inference chip and it is the best in the world for edge inference. We are bringing up neural network models now and moving forward on the steps required for Q2/2021 chip and board production and Inference Compiler availability. * mbedded FPGA, or eFPGA, enables your SoC to have flexibility in critical areas where algorithm, protocol or market needs are changing. FPGA can also accelerate many workloads faster than processors: Microsoft Azure uses one FPGA accelerator for every 2 Xeons.Flex Logix provides eFPGA cores which have density and performance similar to leading FPGAs in the same process node. Our EFLX eFPGA is silicon proven in 40nm, 28/22nm, 16nm and 12nm. 6/7nm EFLX eFPGA is planned. Our eFPGA is based on a “tile” called EFLX 4K, which comes in two versions: all logic or mostly logic with some MACs (multiply-accumulators). The programmable logic is called LUTs (look up tables) that can implement any Boolean function. EFLX 4K Logix has 4000 LUT4 equivalents, EFLX 4K DSP has 3000 LUT4s and 40 Multiplier-Accumulators (MACs): the MAC has a 22-bit pre-adder, a 22×22 multiple and a 48-bit post adder/accumulator. MACs can be combined or cascaded to form fast DSP functions. (For 40nm-180nm we offer an EFLX 1K tile). * depth-wise conv2d * ### Implementing Edge Technologies in Retail: Walmart Case Study * NVidia * ### The Era of Analog AI Compute Is Here * Mythic products are based on a unique tile-based AI compute architecture that features three fundamental hardware technologies – Compute-in-Memory, Dataflow Architecture, and Analog Computing. For AI developers, the Mythic SDK streamlines the preparation of trained neural networks for edge and low-latency datacenter deployments, and also performs automatic optimization and compilation of dataflow graphs for our unique architecture. * low power consumption, ultra-low latency, high ai performance, large weight capacity, small form factor, cost effective solution * ### **Us** ing Edge AI To Detect Repetitive Mot * Bosch Sensortec develops and markets a wide portfolio of MEMS sensors and solutions for applications in smartphones, tablets, wearables, AR/VR devices, drones, robots, smart home and the Internet of Things. Striving to meet the demanding requirements of the consumer electronics market, we provide best-in-class sensing solutions in terms of customer focus, quality and reliability, performance, sustainability and competitiveness. * [https://github.com/BoschSensortec](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FBoschSensortec&sa=D&sntz=1&usg=AOvVaw0Xr8dUHPERsj-rYH7ZAnP1) ## Friday, November 20, 2020 * ### *Spatial Computing: A Collision of Edge and Cloud-Based Computing * [https://github.com/magicleap](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fmagicleap&sa=D&sntz=1&usg=AOvVaw22R0IwwKYOdwv06BvMpvZF) * instance semantic segmentation contextual computing * spatial computing * SLAM: tracking/localization, mapping: * latency is critical for see through displays * weight is critical cannot compensate for lack of compute with more sensors * thermal is critical more sensors and more compute lead to heat * rigidity leads to weight our device should be light * very stringent requirements for MR * why build a map: drift correction, robustness (pose recovery), persistence * feature descriptors * matching across large baselines and illumination changes is challenging * most of the SOTA methods based on deep learning and not feasible withing compute budget * our deep descriptor is optimized for SLAM and provides the best trade off in terms of performance and compute * semantic segmentation 3d point cloud * ### Building An Autonomous Network For IoT and Edge Applications * 5G + AI * ### Practical Edge Inferencing: Enabling fastest AI inferencing per Watt leveraging sparsity * [https://www.graimatterlabs.ai/](https://www.google.com/url?q=https%3A%2F%2Fwww.graimatterlabs.ai%2F&sa=D&sntz=1&usg=AOvVaw1qLIEaRzrtXoYDAb_Y4tQb) * The world’s first sparsity-enabled AI processor optimized for ultra-low latency and low power processing at the edge. * GrAI One drastically reduces application latency, for instance, it reduces the end-to-end latencies for deep learning networks such as PilotNet to the order of milliseconds. The GrAI One chip is based on GML’s innovative NeuronFlow™ technology that combines the dynamic Dataflow paradigm with sparse computing to produce massively parallel in-network processing. * GrAI Matter Labs ([www.graimatterlabs.ai](http://www.google.com/url?q=http%3A%2F%2Fwww.graimatterlabs.ai%2F&sa=D&sntz=1&usg=AOvVaw3eJ9oROjswyCFHO-68LiDi)), a fabless semiconductor company specialized in brain-inspired technology, designs and develops fully programmable ultra-low power neuromorphic HW for sensor analytics and machine learning. The company has offices in Eindhoven (NL), Paris (FR) and San Jose (USA) and has strong relations with top-ranking research groups on neuroscience, human vision and natural computation * ### **Large Scale Deep Learning and AI models on the Edge * deployment pipelines * there are several steps involved in the AI/ML life-cycle * several tools to help simplify the whole process * tensorflow extended (TFX): an end to end platform for deploying production ML pipelines * MLflow (other options michelangelo): an open source platform for the end to end machine learning life cycle * apache airflow (other options kubeflow): an open source workflow management platform * dataiku data science studio (DSS): collaborative data science software platform for teams of data scientist , data analysts, and engineers to explore prototype build and deliver * ### The Edge: The Hottest Market for AI Accelerator Chips - Introducing the Kisaco Leadership Chart on AI Hardware Accelerators 2020-21: Edge and Automotive * [https://www.kisacoresearch.com/#about-us](https://www.google.com/url?q=https%3A%2F%2Fwww.kisacoresearch.com%2F%23about-us&sa=D&sntz=1&usg=AOvVaw0nasAOo80KuyOwbm4OeiOb) [OpenHTF is a Python library that provides a set of convenient abstractions designed to remove as much boilerplate as possible from hardware test setup and execution, so test engineers can focus primarily on test logic.](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fgoogle%2Fopenhtf&sa=D&sntz=1&usg=AOvVaw0zU3RKntPn4N8JIkPvriIu) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/ssWXVQN4wWnp524T8UjHyQEdArMpTIaM4OalIHhpfDv0Sv6HC6tLnwxjfrdaemWa002sbPUNKYrtuvBhEcn6DIQ=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/ssWXVQN4wWnp524T8UjHyQEdArMpTIaM4OalIHhpfDv0Sv6HC6tLnwxjfrdaemWa002sbPUNKYrtuvBhEcn6DIQ=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Edge AI summit Short summary of the edge AI summit 18-20 November 2020 Wednesday, November 18, 2020 A Software Solution Enabling Predictive Maintenance at the Sensor Level Helping Fish Farmers Feed The World With Deep Learning tinyMLPerf: Benchmarking Ultra-low Power Machine Learning Systems Ultra-low power neuromorphic intelligence for the sensor edge * How is AI affecting hearables and sensors? Breaking the Barriers to Deploy DNNs on Low-Power Hardware Optimizing ML Models At The Edge Made Simple Thursday, November 19, 2020 Developing Edge AI Solutions For A Post-Pandemic Society The Evolving Landscape of Edge AI InferX X1, The Fastest and Most Efficient Edge Inference Accelerator Implementing Edge Technologies in Retail: Walmart Case Study The Era of Analog AI Compute Is Here Using Edge AI To Detect Repetitive Mot Friday, November 20, 2020 *Spatial Computing: A Collision of Edge and Cloud-Based Computing Building An Autonomous Network For IoT and Edge Applications Practical Edge Inferencing: Enabling fastest AI inferencing per Watt leveraging sparsity **Large Scale Deep Learning and AI models on the Edge The Edge: The Hottest Market for AI Accelerator Chips - Introducing the Kisaco Leadership Chart on AI Hardware Accelerators 2020-21: Edge and Automotive # Short summary of the edge AI summit 18-20 November 2020 Best of Wednesday, November 18, 2020; tinyMLPerf, Breaking the Barriers to Deploy DNNs on Low-Power Hardware, Optimizing ML Models At The Edge Made Simple **Thursday, November 19, 2020** * 8:00 AM - 8:30 AM (PST) KEYNOTE PRESENTATION: Developing Edge AI Solutions For A Post-Pandemic Society Sastry Malladi - FogHorn Systems * 8:35 AM - 9:05 AM (PST) PRESENTATION: The Evolving Landscape of Edge AI Ajay Nair - Google * 9:05 AM - 9:20 AM (PST) Comfort Break * 9:20 AM - 9:45 AM (PST) PRESENTATION: InferX X1, The Fastest and Most Efficient Edge Inference Accelerator Cheng Wang - Flex Logix Technologies Inc. * 9:50 AM - 10:20 AM (PST) PRESENTATION: Implementing Edge Technologies in Retail: Walmart Case Study Alex Sabatier - Nvidia * 10:20 AM - 10:35 AM (PST) Comfort Break * 10:35 AM - 11:20 AM (PST) Meet speaker! * **11:20 AM - 11:50 AM (PST) PRESENTATION: The Era of Analog AI Compute Is Here Mike Henry - Mythic** * 11:55 AM - 12:25 PM (PST) PRESENTATION: Using Edge AI To Detect Repetitive Mot Marcellino Gemelli - Bosch Sensortec * 12:30 PM - 2:30 PM (PST) NETWORKING - Dedicated Networking 2 hours for 1-2-1 Video Meetings **Friday, November 20, 2020** * 8:00 AM - 8:30 AM (PST) PRESENTATION: Spatial Computing: A Collision of Edge and Cloud-Based Computing Ashwin Swaminathan - Magic Leap * 8:35 AM - 9:05 AM (PST) PRESENTATION: Building An Autonomous Network For IoT and Edge Applications Anshul Bhatt - Rakuten Mobile * 9:05 AM - 9:20 AM (PST) Comfort Break * 9:20 AM - 9:45 AM (PST) PRESENTATION: Practical Edge Inferencing: Enabling fastest AI inferencing per Watt leveraging sparsity Mahesh Makhijani - GrAI Matter Labs * **9:50 AM - 10:20 AM (PST) PRESENTATION: Large Scale Deep Learning and AI models on the Edge Chandra Khatri - Got It AI** * 10:20 AM - 10:35 AM (PST) Comfort Break * 10:35 AM - 11:20 AM (PST) NETWORKING: Interest Groups (18 people per room, topic-specific discussions) * 11:20 AM - 11:50 AM (PST) PANEL DISCUSSION: The Symbiotic Relationship between 5G and Edge AI Sami Badri - Credit Suisse, Christos Kolias - Orange, Rima Raouda - Independent * 11:55 AM - 12:25 PM (PST) PANEL DISCUSSION: Investment Trends & Dynamics Panel Rashmi Gopinath - B Capital Group, Yvonne Lutsch - Bosch Venture Capital, Eileen Tanghal - In-Q-Tel, Albert Wang - Qualcomm Ventures * **12:30 PM - 12:50 PM (PST) PRESENTATION: The Edge: The Hottest Market for AI Accelerator Chips - Introducing the Kisaco Leadership Chart on AI Hardware Accelerators 2020-21: Edge and Automotive Michael Azoff - Kisaco Research** ## Wednesday, November 18, 2020 * ### A Software Solution Enabling Predictive Maintenance at the Sensor Level * SensiML Toolkit enables AI for a broad array of resource constrained time-series sensor endpoint applications. These include a wide range of consumer and industrial sensing applications. * The problem is machine learning engineer do not have experience with embedded system and moving model to embedded system takes long time. * AutoML for Embedded system usage. it is on the cloud. * using the compiler for that device for this tools * cost edge and cloud. easy to work on cloud. streaming data to cloud is difficult. faster if working on edge. * TinyML addresses problems, battery powered, limited internet connectivity, security/privacy, latency, economic * [https://sensiml.com/products/#process](https://www.google.com/url?q=https%3A%2F%2Fsensiml.com%2Fproducts%2F%23process&sa=D&sntz=1&usg=AOvVaw116wjx6mBnEo9x0htyL2pr) * ### Helping Fish Farmers Feed The World With Deep Learning * [https://s3-us-west-1.amazonaws.com/aquabyte-static/videos/welcome_to_aquabyte_subtitled.mp4](https://www.google.com/url?q=https%3A%2F%2Fs3-us-west-1.amazonaws.com%2Faquabyte-static%2Fvideos%2Fwelcome_to_aquabyte_subtitled.mp4&sa=D&sntz=1&usg=AOvVaw1osNtsRVM9TMCmKoPsTnj_) * Count sea lice and accurately measure biomass in real-time while reducing cage furniture. Our experts‑in‑the‑loop ensure that every single prediction is correct. * Aquabyte is seeking a Machine Learning Platform Engineer to drive the development, testing, and delivery of machine learning models that enable cutting-edge analytics and automation of fish farms around the world. * Aquabyte is on a mission to revolutionize the sustainability and efficiency of aquaculture. It is an audacious, and incredibly rewarding mission. By making fish farming cheaper and more viable than livestock production, we aim to mitigate one of the biggest causes of climate change and help prepare our planet for impending population growth. Aquaculture is the single fastest growing food-production sector in the world, and now is the time to define how technology is used to harvest the sea for generations to come. * We are currently focused on helping Norwegian salmon farmers better understand their fish populations and make environmentally-sound decisions. Through custom underwater cameras, computer vision, and machine learning we are able to quantify fish weights, detect sea lice infestations, and generate optimal feeding plans in real time. Our product operates at three levels: on-site hardware for image capture, cloud pipelines for data processing, and a user-facing web application. As a result, there are hundreds of moving pieces and no shortage of fascinating challenges across all levels of the stack. * * ### tinyMLPerf: Benchmarking Ultra-low Power Machine Learning Systems * [https://github.com/mlperf/tiny](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fmlperf%2Ftiny&sa=D&sntz=1&usg=AOvVaw1TgviYAuh83PMxDPYljOjs) * tinyMLPerf Deep Learning Benchmarks for Embedded Devices * The goal of TinyMLPerf is to provide a representative set of deep neural nets and benchmarking code to compare performance between embedded devices. Embedded devices include microcontrollers, DSPs, and tiny NN accelerators. These devics typically run at between 10MHz and 250MHz, and can perform inference using less then 50mW of power. TinyMLPerf submissions will allow device makers and researchers to choose the best hardware for their use case, and allows hardware vendors to showcase their offerings. TinyMLPerf is primarily intended to benchmark hardware rather than new network archietctures, or embedded neural net runtimes. The reference benchmarks are provided using TensorFlow Lite for Microcontrollers (TFLM). Submitters can directly use the TFLM, although submitters are encouraged to use the software stack that works best on thier hardware. * anomaly detection benchmark, visual wake words benchmark, * ### Ultra-low power neuromorphic intelligence for the sensor edge * Innatera Nanosystems BV (Innatera, (Innatera, innatera.com) is a rapidly-growing Dutch semiconductor company that develops ultra-efficient neuromorphic processors for AI at the edge. These microprocessors mimic the brain’s mechanisms for processing fast data streams from sensors, enabling complex turn-key sensor analytics functionalities, with 10,000x higher performance per watt than competing solutions. Innatera's technology serves as a critical enabler for next-generation use-cases in the IoT, wearable, embedded, and automotive domains. * ### * How is AI affecting hearables and sensors? * [https://github.com/greenwaves-technologies/nn_menu](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fgreenwaves-technologies%2Fnn_menu&sa=D&sntz=1&usg=AOvVaw2JKYYAPrnA9Mkalw2qenUQ) * The Neural Network Menu* is a collection of software that implements Neural Networks on Greenwaves Application Processors (GAP). This repository contains common mobile and edge NN archtecture examples, NN sample applications and full flagged reference designs. Our tools maps a TFLITE model (quantized or unquantized) onto gap. There is also a flow in the ingredients directory showing how to hand map from a Pytorch Model onto GAP. * [https://greenwaves-technologies.com/store/](https://www.google.com/url?q=https%3A%2F%2Fgreenwaves-technologies.com%2Fstore%2F&sa=D&sntz=1&usg=AOvVaw0Ya_w_NBAr4AbIxBe2j_YX) * GAPPoc-A is a Proof of Concept Board that can be used for demonstration of battery-operated, edge computer vision applications based on GAP8. * It incorporates GAPmod, a surface-mount module that implements all the layout sensitive portion of a GAP8 design, along with a VGA image sensor and a Bluetooth Low Energy radio. * The GAPPoc-A board enables battery-operated applications developed around algorithms such as people counting, face-identification and many others to be quickly assembled and evaluated in the field. * [https://riscv.org/blog/2019/08/risc-v-emea-roadshow-spotlight-greenwaves-technologies/](https://www.google.com/url?q=https%3A%2F%2Friscv.org%2Fblog%2F2019%2F08%2Frisc-v-emea-roadshow-spotlight-greenwaves-technologies%2F&sa=D&sntz=1&usg=AOvVaw1ikZjtEoYTgFb-S_eGEB3i) * ### Breaking the Barriers to Deploy DNNs on Low-Power Hardware * Deeplite, named to the 2020 CB Insights AI100 List of Most Innovative Artificial Intelligence Startups, is devoted to making fundamental advancements in accessible and efficient deep learning. Our solution helps deep learning engineers and experts automatically create faster, smaller and more energy-efficient deep neural networks. Industry leaders in computer vision, augmented reality and autonomous driving use our technology to unlock new possibilities for deep learning in the real world. At Deeplite, our vision is to create a lightweight intelligence that’s accessible for daily life. * [https://www.deeplite.ai/](https://www.google.com/url?q=https%3A%2F%2Fwww.deeplite.ai%2F&sa=D&sntz=1&usg=AOvVaw1r7NiQGt1hRi6S_xiJ522C) * At Deeplite, we are tackling inference optimization of deep neural networks, making them faster and energy-efficient from cloud to edge computing. Our solution leverages state-of-the-art technology from elite universities to make deep neural networks applicable for any device, and our team works hard on the iterative evolution of the science behind deep neural networks to directly improve daily life. * reduce the size of model 40x * ### Optimizing ML Models At The Edge Made Simple * [https://octoml.ai/](https://www.google.com/url?q=https%3A%2F%2Foctoml.ai%2F&sa=D&sntz=1&usg=AOvVaw2uXg6ESgQgVGrF9nQJKFve) * OctoML is an energetic new company changing how developers optimize and deploy machine learning models for their AI needs. We’re a team of machine learning systems leaders focused on making ML more efficient and easier to deploy by… applying machine learning to it! * OctoML is leveraging the power and traction of Apache TVM, an open source project originated by our founding team, to enable companies of every size to harness the power of deep learning without the expensive heavy lifting of tuning and securing models to each hardware configuration that a customer might need. * Apache TVM and Deep Learning Compilation Conference, Wed-Fri, December 2nd-4th 2020, Free Virtual Event. ## Thursday, November 19, 2020 * ### Developing Edge AI Solutions For A Post-Pandemic Society * [https://www.foghorn.io/](https://www.google.com/url?q=https%3A%2F%2Fwww.foghorn.io%2F&sa=D&sntz=1&usg=AOvVaw2aRKISC9BdrrriEej5Xb_I) * ogHorn’s Lightning™ Edge AI platform brings a groundbreaking dimension to IIoT and edge computing by embedding AI as close to the source of streaming sensor data as possible. The Edge AI software platform is a highly compact, advanced and feature-rich edge solution that delivers unprecedented low latency for onsite data processing, real-time analytics, ML and AI capabilities. It delivers the industry’s lowest total cost for computing requirements, communications services, and cloud processing and storage. * temperature detection, social distancing, cough detection, PPE/Mask detection * Flexible, customizable, integrated, actionable * ### The Evolving Landscape of Edge AI * * Coral’s local AI technology enables new possibilities across almost any kind of industry * The Coral Dev Board is a single-board computer that contains an Edge TPU coprocessor. It's ideal for prototyping new projects that demand fast on-device inferencing for machine learning models. This page is your guide to get started. The setup requires flashing Mendel Linux to the board, and then accessing the board's shell terminal. Once you have terminal access and update some of the software, we'll show you how to run an image classification model on the board. If you want to learn more about the hardware, see the Dev Board datasheet. * TPU v3, 32 to 512 TOPS, Q2 2021 * ### InferX X1, The Fastest and Most Efficient Edge Inference Accelerator * InferX X1: World's fastest and most efficient Edge Inference Accelerator. We have just launched our first inference chip and it is the best in the world for edge inference. We are bringing up neural network models now and moving forward on the steps required for Q2/2021 chip and board production and Inference Compiler availability. * mbedded FPGA, or eFPGA, enables your SoC to have flexibility in critical areas where algorithm, protocol or market needs are changing. FPGA can also accelerate many workloads faster than processors: Microsoft Azure uses one FPGA accelerator for every 2 Xeons.Flex Logix provides eFPGA cores which have density and performance similar to leading FPGAs in the same process node. Our EFLX eFPGA is silicon proven in 40nm, 28/22nm, 16nm and 12nm. 6/7nm EFLX eFPGA is planned. Our eFPGA is based on a “tile” called EFLX 4K, which comes in two versions: all logic or mostly logic with some MACs (multiply-accumulators). The programmable logic is called LUTs (look up tables) that can implement any Boolean function. EFLX 4K Logix has 4000 LUT4 equivalents, EFLX 4K DSP has 3000 LUT4s and 40 Multiplier-Accumulators (MACs): the MAC has a 22-bit pre-adder, a 22×22 multiple and a 48-bit post adder/accumulator. MACs can be combined or cascaded to form fast DSP functions. (For 40nm-180nm we offer an EFLX 1K tile). * depth-wise conv2d * ### Implementing Edge Technologies in Retail: Walmart Case Study * NVidia * ### The Era of Analog AI Compute Is Here * Mythic products are based on a unique tile-based AI compute architecture that features three fundamental hardware technologies – Compute-in-Memory, Dataflow Architecture, and Analog Computing. For AI developers, the Mythic SDK streamlines the preparation of trained neural networks for edge and low-latency datacenter deployments, and also performs automatic optimization and compilation of dataflow graphs for our unique architecture. * low power consumption, ultra-low latency, high ai performance, large weight capacity, small form factor, cost effective solution * ### **Us** ing Edge AI To Detect Repetitive Mot * Bosch Sensortec develops and markets a wide portfolio of MEMS sensors and solutions for applications in smartphones, tablets, wearables, AR/VR devices, drones, robots, smart home and the Internet of Things. Striving to meet the demanding requirements of the consumer electronics market, we provide best-in-class sensing solutions in terms of customer focus, quality and reliability, performance, sustainability and competitiveness. * [https://github.com/BoschSensortec](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FBoschSensortec&sa=D&sntz=1&usg=AOvVaw0Xr8dUHPERsj-rYH7ZAnP1) ## Friday, November 20, 2020 * ### *Spatial Computing: A Collision of Edge and Cloud-Based Computing * [https://github.com/magicleap](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fmagicleap&sa=D&sntz=1&usg=AOvVaw22R0IwwKYOdwv06BvMpvZF) * instance semantic segmentation contextual computing * spatial computing * SLAM: tracking/localization, mapping: * latency is critical for see through displays * weight is critical cannot compensate for lack of compute with more sensors * thermal is critical more sensors and more compute lead to heat * rigidity leads to weight our device should be light * very stringent requirements for MR * why build a map: drift correction, robustness (pose recovery), persistence * feature descriptors * matching across large baselines and illumination changes is challenging * most of the SOTA methods based on deep learning and not feasible withing compute budget * our deep descriptor is optimized for SLAM and provides the best trade off in terms of performance and compute * semantic segmentation 3d point cloud * ### Building An Autonomous Network For IoT and Edge Applications * 5G + AI * ### Practical Edge Inferencing: Enabling fastest AI inferencing per Watt leveraging sparsity * [https://www.graimatterlabs.ai/](https://www.google.com/url?q=https%3A%2F%2Fwww.graimatterlabs.ai%2F&sa=D&sntz=1&usg=AOvVaw1qLIEaRzrtXoYDAb_Y4tQb) * The world’s first sparsity-enabled AI processor optimized for ultra-low latency and low power processing at the edge. * GrAI One drastically reduces application latency, for instance, it reduces the end-to-end latencies for deep learning networks such as PilotNet to the order of milliseconds. The GrAI One chip is based on GML’s innovative NeuronFlow™ technology that combines the dynamic Dataflow paradigm with sparse computing to produce massively parallel in-network processing. * GrAI Matter Labs ([www.graimatterlabs.ai](http://www.google.com/url?q=http%3A%2F%2Fwww.graimatterlabs.ai%2F&sa=D&sntz=1&usg=AOvVaw3eJ9oROjswyCFHO-68LiDi)), a fabless semiconductor company specialized in brain-inspired technology, designs and develops fully programmable ultra-low power neuromorphic HW for sensor analytics and machine learning. The company has offices in Eindhoven (NL), Paris (FR) and San Jose (USA) and has strong relations with top-ranking research groups on neuroscience, human vision and natural computation * ### **Large Scale Deep Learning and AI models on the Edge * deployment pipelines * there are several steps involved in the AI/ML life-cycle * several tools to help simplify the whole process * tensorflow extended (TFX): an end to end platform for deploying production ML pipelines * MLflow (other options michelangelo): an open source platform for the end to end machine learning life cycle * apache airflow (other options kubeflow): an open source workflow management platform * dataiku data science studio (DSS): collaborative data science software platform for teams of data scientist , data analysts, and engineers to explore prototype build and deliver * ### The Edge: The Hottest Market for AI Accelerator Chips - Introducing the Kisaco Leadership Chart on AI Hardware Accelerators 2020-21: Edge and Automotive * [https://www.kisacoresearch.com/#about-us](https://www.google.com/url?q=https%3A%2F%2Fwww.kisacoresearch.com%2F%23about-us&sa=D&sntz=1&usg=AOvVaw0nasAOo80KuyOwbm4OeiOb) [OpenHTF is a Python library that provides a set of convenient abstractions designed to remove as much boilerplate as possible from hardware test setup and execution, so test engineers can focus primarily on test logic.](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fgoogle%2Fopenhtf&sa=D&sntz=1&usg=AOvVaw0zU3RKntPn4N8JIkPvriIu) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/ssWXVQN4wWnp524T8UjHyQEdArMpTIaM4OalIHhpfDv0Sv6HC6tLnwxjfrdaemWa002sbPUNKYrtuvBhEcn6DIQ=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/ssWXVQN4wWnp524T8UjHyQEdArMpTIaM4OalIHhpfDv0Sv6HC6tLnwxjfrdaemWa002sbPUNKYrtuvBhEcn6DIQ=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Edge AI summit Short summary of the edge AI summit 18-20 November 2020 Wednesday, November 18, 2020 A Software Solution Enabling Predictive Maintenance at the Sensor Level Helping Fish Farmers Feed The World With Deep Learning tinyMLPerf: Benchmarking Ultra-low Power Machine Learning Systems Ultra-low power neuromorphic intelligence for the sensor edge * How is AI affecting hearables and sensors? Breaking the Barriers to Deploy DNNs on Low-Power Hardware Optimizing ML Models At The Edge Made Simple Thursday, November 19, 2020 Developing Edge AI Solutions For A Post-Pandemic Society The Evolving Landscape of Edge AI InferX X1, The Fastest and Most Efficient Edge Inference Accelerator Implementing Edge Technologies in Retail: Walmart Case Study The Era of Analog AI Compute Is Here Using Edge AI To Detect Repetitive Mot Friday, November 20, 2020 *Spatial Computing: A Collision of Edge and Cloud-Based Computing Building An Autonomous Network For IoT and Edge Applications Practical Edge Inferencing: Enabling fastest AI inferencing per Watt leveraging sparsity **Large Scale Deep Learning and AI models on the Edge The Edge: The Hottest Market for AI Accelerator Chips - Introducing the Kisaco Leadership Chart on AI Hardware Accelerators 2020-21: Edge and Automotive # Short summary of the edge AI summit 18-20 November 2020 Best of Wednesday, November 18, 2020; tinyMLPerf, Breaking the Barriers to Deploy DNNs on Low-Power Hardware, Optimizing ML Models At The Edge Made Simple **Thursday, November 19, 2020** * 8:00 AM - 8:30 AM (PST) KEYNOTE PRESENTATION: Developing Edge AI Solutions For A Post-Pandemic Society Sastry Malladi - FogHorn Systems * 8:35 AM - 9:05 AM (PST) PRESENTATION: The Evolving Landscape of Edge AI Ajay Nair - Google * 9:05 AM - 9:20 AM (PST) Comfort Break * 9:20 AM - 9:45 AM (PST) PRESENTATION: InferX X1, The Fastest and Most Efficient Edge Inference Accelerator Cheng Wang - Flex Logix Technologies Inc. * 9:50 AM - 10:20 AM (PST) PRESENTATION: Implementing Edge Technologies in Retail: Walmart Case Study Alex Sabatier - Nvidia * 10:20 AM - 10:35 AM (PST) Comfort Break * 10:35 AM - 11:20 AM (PST) Meet speaker! * **11:20 AM - 11:50 AM (PST) PRESENTATION: The Era of Analog AI Compute Is Here Mike Henry - Mythic** * 11:55 AM - 12:25 PM (PST) PRESENTATION: Using Edge AI To Detect Repetitive Mot Marcellino Gemelli - Bosch Sensortec * 12:30 PM - 2:30 PM (PST) NETWORKING - Dedicated Networking 2 hours for 1-2-1 Video Meetings **Friday, November 20, 2020** * 8:00 AM - 8:30 AM (PST) PRESENTATION: Spatial Computing: A Collision of Edge and Cloud-Based Computing Ashwin Swaminathan - Magic Leap * 8:35 AM - 9:05 AM (PST) PRESENTATION: Building An Autonomous Network For IoT and Edge Applications Anshul Bhatt - Rakuten Mobile * 9:05 AM - 9:20 AM (PST) Comfort Break * 9:20 AM - 9:45 AM (PST) PRESENTATION: Practical Edge Inferencing: Enabling fastest AI inferencing per Watt leveraging sparsity Mahesh Makhijani - GrAI Matter Labs * **9:50 AM - 10:20 AM (PST) PRESENTATION: Large Scale Deep Learning and AI models on the Edge Chandra Khatri - Got It AI** * 10:20 AM - 10:35 AM (PST) Comfort Break * 10:35 AM - 11:20 AM (PST) NETWORKING: Interest Groups (18 people per room, topic-specific discussions) * 11:20 AM - 11:50 AM (PST) PANEL DISCUSSION: The Symbiotic Relationship between 5G and Edge AI Sami Badri - Credit Suisse, Christos Kolias - Orange, Rima Raouda - Independent * 11:55 AM - 12:25 PM (PST) PANEL DISCUSSION: Investment Trends & Dynamics Panel Rashmi Gopinath - B Capital Group, Yvonne Lutsch - Bosch Venture Capital, Eileen Tanghal - In-Q-Tel, Albert Wang - Qualcomm Ventures * **12:30 PM - 12:50 PM (PST) PRESENTATION: The Edge: The Hottest Market for AI Accelerator Chips - Introducing the Kisaco Leadership Chart on AI Hardware Accelerators 2020-21: Edge and Automotive Michael Azoff - Kisaco Research** ## Wednesday, November 18, 2020 * ### A Software Solution Enabling Predictive Maintenance at the Sensor Level * SensiML Toolkit enables AI for a broad array of resource constrained time-series sensor endpoint applications. These include a wide range of consumer and industrial sensing applications. * The problem is machine learning engineer do not have experience with embedded system and moving model to embedded system takes long time. * AutoML for Embedded system usage. it is on the cloud. * using the compiler for that device for this tools * cost edge and cloud. easy to work on cloud. streaming data to cloud is difficult. faster if working on edge. * TinyML addresses problems, battery powered, limited internet connectivity, security/privacy, latency, economic * [https://sensiml.com/products/#process](https://www.google.com/url?q=https%3A%2F%2Fsensiml.com%2Fproducts%2F%23process&sa=D&sntz=1&usg=AOvVaw116wjx6mBnEo9x0htyL2pr) * ### Helping Fish Farmers Feed The World With Deep Learning * [https://s3-us-west-1.amazonaws.com/aquabyte-static/videos/welcome_to_aquabyte_subtitled.mp4](https://www.google.com/url?q=https%3A%2F%2Fs3-us-west-1.amazonaws.com%2Faquabyte-static%2Fvideos%2Fwelcome_to_aquabyte_subtitled.mp4&sa=D&sntz=1&usg=AOvVaw1osNtsRVM9TMCmKoPsTnj_) * Count sea lice and accurately measure biomass in real-time while reducing cage furniture. Our experts‑in‑the‑loop ensure that every single prediction is correct. * Aquabyte is seeking a Machine Learning Platform Engineer to drive the development, testing, and delivery of machine learning models that enable cutting-edge analytics and automation of fish farms around the world. * Aquabyte is on a mission to revolutionize the sustainability and efficiency of aquaculture. It is an audacious, and incredibly rewarding mission. By making fish farming cheaper and more viable than livestock production, we aim to mitigate one of the biggest causes of climate change and help prepare our planet for impending population growth. Aquaculture is the single fastest growing food-production sector in the world, and now is the time to define how technology is used to harvest the sea for generations to come. * We are currently focused on helping Norwegian salmon farmers better understand their fish populations and make environmentally-sound decisions. Through custom underwater cameras, computer vision, and machine learning we are able to quantify fish weights, detect sea lice infestations, and generate optimal feeding plans in real time. Our product operates at three levels: on-site hardware for image capture, cloud pipelines for data processing, and a user-facing web application. As a result, there are hundreds of moving pieces and no shortage of fascinating challenges across all levels of the stack. * * ### tinyMLPerf: Benchmarking Ultra-low Power Machine Learning Systems * [https://github.com/mlperf/tiny](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fmlperf%2Ftiny&sa=D&sntz=1&usg=AOvVaw1TgviYAuh83PMxDPYljOjs) * tinyMLPerf Deep Learning Benchmarks for Embedded Devices * The goal of TinyMLPerf is to provide a representative set of deep neural nets and benchmarking code to compare performance between embedded devices. Embedded devices include microcontrollers, DSPs, and tiny NN accelerators. These devics typically run at between 10MHz and 250MHz, and can perform inference using less then 50mW of power. TinyMLPerf submissions will allow device makers and researchers to choose the best hardware for their use case, and allows hardware vendors to showcase their offerings. TinyMLPerf is primarily intended to benchmark hardware rather than new network archietctures, or embedded neural net runtimes. The reference benchmarks are provided using TensorFlow Lite for Microcontrollers (TFLM). Submitters can directly use the TFLM, although submitters are encouraged to use the software stack that works best on thier hardware. * anomaly detection benchmark, visual wake words benchmark, * ### Ultra-low power neuromorphic intelligence for the sensor edge * Innatera Nanosystems BV (Innatera, (Innatera, innatera.com) is a rapidly-growing Dutch semiconductor company that develops ultra-efficient neuromorphic processors for AI at the edge. These microprocessors mimic the brain’s mechanisms for processing fast data streams from sensors, enabling complex turn-key sensor analytics functionalities, with 10,000x higher performance per watt than competing solutions. Innatera's technology serves as a critical enabler for next-generation use-cases in the IoT, wearable, embedded, and automotive domains. * ### * How is AI affecting hearables and sensors? * [https://github.com/greenwaves-technologies/nn_menu](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fgreenwaves-technologies%2Fnn_menu&sa=D&sntz=1&usg=AOvVaw2JKYYAPrnA9Mkalw2qenUQ) * The Neural Network Menu* is a collection of software that implements Neural Networks on Greenwaves Application Processors (GAP). This repository contains common mobile and edge NN archtecture examples, NN sample applications and full flagged reference designs. Our tools maps a TFLITE model (quantized or unquantized) onto gap. There is also a flow in the ingredients directory showing how to hand map from a Pytorch Model onto GAP. * [https://greenwaves-technologies.com/store/](https://www.google.com/url?q=https%3A%2F%2Fgreenwaves-technologies.com%2Fstore%2F&sa=D&sntz=1&usg=AOvVaw0Ya_w_NBAr4AbIxBe2j_YX) * GAPPoc-A is a Proof of Concept Board that can be used for demonstration of battery-operated, edge computer vision applications based on GAP8. * It incorporates GAPmod, a surface-mount module that implements all the layout sensitive portion of a GAP8 design, along with a VGA image sensor and a Bluetooth Low Energy radio. * The GAPPoc-A board enables battery-operated applications developed around algorithms such as people counting, face-identification and many others to be quickly assembled and evaluated in the field. * [https://riscv.org/blog/2019/08/risc-v-emea-roadshow-spotlight-greenwaves-technologies/](https://www.google.com/url?q=https%3A%2F%2Friscv.org%2Fblog%2F2019%2F08%2Frisc-v-emea-roadshow-spotlight-greenwaves-technologies%2F&sa=D&sntz=1&usg=AOvVaw1ikZjtEoYTgFb-S_eGEB3i) * ### Breaking the Barriers to Deploy DNNs on Low-Power Hardware * Deeplite, named to the 2020 CB Insights AI100 List of Most Innovative Artificial Intelligence Startups, is devoted to making fundamental advancements in accessible and efficient deep learning. Our solution helps deep learning engineers and experts automatically create faster, smaller and more energy-efficient deep neural networks. Industry leaders in computer vision, augmented reality and autonomous driving use our technology to unlock new possibilities for deep learning in the real world. At Deeplite, our vision is to create a lightweight intelligence that’s accessible for daily life. * [https://www.deeplite.ai/](https://www.google.com/url?q=https%3A%2F%2Fwww.deeplite.ai%2F&sa=D&sntz=1&usg=AOvVaw1r7NiQGt1hRi6S_xiJ522C) * At Deeplite, we are tackling inference optimization of deep neural networks, making them faster and energy-efficient from cloud to edge computing. Our solution leverages state-of-the-art technology from elite universities to make deep neural networks applicable for any device, and our team works hard on the iterative evolution of the science behind deep neural networks to directly improve daily life. * reduce the size of model 40x * ### Optimizing ML Models At The Edge Made Simple * [https://octoml.ai/](https://www.google.com/url?q=https%3A%2F%2Foctoml.ai%2F&sa=D&sntz=1&usg=AOvVaw2uXg6ESgQgVGrF9nQJKFve) * OctoML is an energetic new company changing how developers optimize and deploy machine learning models for their AI needs. We’re a team of machine learning systems leaders focused on making ML more efficient and easier to deploy by… applying machine learning to it! * OctoML is leveraging the power and traction of Apache TVM, an open source project originated by our founding team, to enable companies of every size to harness the power of deep learning without the expensive heavy lifting of tuning and securing models to each hardware configuration that a customer might need. * Apache TVM and Deep Learning Compilation Conference, Wed-Fri, December 2nd-4th 2020, Free Virtual Event. ## Thursday, November 19, 2020 * ### Developing Edge AI Solutions For A Post-Pandemic Society * [https://www.foghorn.io/](https://www.google.com/url?q=https%3A%2F%2Fwww.foghorn.io%2F&sa=D&sntz=1&usg=AOvVaw2aRKISC9BdrrriEej5Xb_I) * ogHorn’s Lightning™ Edge AI platform brings a groundbreaking dimension to IIoT and edge computing by embedding AI as close to the source of streaming sensor data as possible. The Edge AI software platform is a highly compact, advanced and feature-rich edge solution that delivers unprecedented low latency for onsite data processing, real-time analytics, ML and AI capabilities. It delivers the industry’s lowest total cost for computing requirements, communications services, and cloud processing and storage. * temperature detection, social distancing, cough detection, PPE/Mask detection * Flexible, customizable, integrated, actionable * ### The Evolving Landscape of Edge AI * * Coral’s local AI technology enables new possibilities across almost any kind of industry * The Coral Dev Board is a single-board computer that contains an Edge TPU coprocessor. It's ideal for prototyping new projects that demand fast on-device inferencing for machine learning models. This page is your guide to get started. The setup requires flashing Mendel Linux to the board, and then accessing the board's shell terminal. Once you have terminal access and update some of the software, we'll show you how to run an image classification model on the board. If you want to learn more about the hardware, see the Dev Board datasheet. * TPU v3, 32 to 512 TOPS, Q2 2021 * ### InferX X1, The Fastest and Most Efficient Edge Inference Accelerator * InferX X1: World's fastest and most efficient Edge Inference Accelerator. We have just launched our first inference chip and it is the best in the world for edge inference. We are bringing up neural network models now and moving forward on the steps required for Q2/2021 chip and board production and Inference Compiler availability. * mbedded FPGA, or eFPGA, enables your SoC to have flexibility in critical areas where algorithm, protocol or market needs are changing. FPGA can also accelerate many workloads faster than processors: Microsoft Azure uses one FPGA accelerator for every 2 Xeons.Flex Logix provides eFPGA cores which have density and performance similar to leading FPGAs in the same process node. Our EFLX eFPGA is silicon proven in 40nm, 28/22nm, 16nm and 12nm. 6/7nm EFLX eFPGA is planned. Our eFPGA is based on a “tile” called EFLX 4K, which comes in two versions: all logic or mostly logic with some MACs (multiply-accumulators). The programmable logic is called LUTs (look up tables) that can implement any Boolean function. EFLX 4K Logix has 4000 LUT4 equivalents, EFLX 4K DSP has 3000 LUT4s and 40 Multiplier-Accumulators (MACs): the MAC has a 22-bit pre-adder, a 22×22 multiple and a 48-bit post adder/accumulator. MACs can be combined or cascaded to form fast DSP functions. (For 40nm-180nm we offer an EFLX 1K tile). * depth-wise conv2d * ### Implementing Edge Technologies in Retail: Walmart Case Study * NVidia * ### The Era of Analog AI Compute Is Here * Mythic products are based on a unique tile-based AI compute architecture that features three fundamental hardware technologies – Compute-in-Memory, Dataflow Architecture, and Analog Computing. For AI developers, the Mythic SDK streamlines the preparation of trained neural networks for edge and low-latency datacenter deployments, and also performs automatic optimization and compilation of dataflow graphs for our unique architecture. * low power consumption, ultra-low latency, high ai performance, large weight capacity, small form factor, cost effective solution * ### **Us** ing Edge AI To Detect Repetitive Mot * Bosch Sensortec develops and markets a wide portfolio of MEMS sensors and solutions for applications in smartphones, tablets, wearables, AR/VR devices, drones, robots, smart home and the Internet of Things. Striving to meet the demanding requirements of the consumer electronics market, we provide best-in-class sensing solutions in terms of customer focus, quality and reliability, performance, sustainability and competitiveness. * [https://github.com/BoschSensortec](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FBoschSensortec&sa=D&sntz=1&usg=AOvVaw0Xr8dUHPERsj-rYH7ZAnP1) ## Friday, November 20, 2020 * ### *Spatial Computing: A Collision of Edge and Cloud-Based Computing * [https://github.com/magicleap](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fmagicleap&sa=D&sntz=1&usg=AOvVaw22R0IwwKYOdwv06BvMpvZF) * instance semantic segmentation contextual computing * spatial computing * SLAM: tracking/localization, mapping: * latency is critical for see through displays * weight is critical cannot compensate for lack of compute with more sensors * thermal is critical more sensors and more compute lead to heat * rigidity leads to weight our device should be light * very stringent requirements for MR * why build a map: drift correction, robustness (pose recovery), persistence * feature descriptors * matching across large baselines and illumination changes is challenging * most of the SOTA methods based on deep learning and not feasible withing compute budget * our deep descriptor is optimized for SLAM and provides the best trade off in terms of performance and compute * semantic segmentation 3d point cloud * ### Building An Autonomous Network For IoT and Edge Applications * 5G + AI * ### Practical Edge Inferencing: Enabling fastest AI inferencing per Watt leveraging sparsity * [https://www.graimatterlabs.ai/](https://www.google.com/url?q=https%3A%2F%2Fwww.graimatterlabs.ai%2F&sa=D&sntz=1&usg=AOvVaw1qLIEaRzrtXoYDAb_Y4tQb) * The world’s first sparsity-enabled AI processor optimized for ultra-low latency and low power processing at the edge. * GrAI One drastically reduces application latency, for instance, it reduces the end-to-end latencies for deep learning networks such as PilotNet to the order of milliseconds. The GrAI One chip is based on GML’s innovative NeuronFlow™ technology that combines the dynamic Dataflow paradigm with sparse computing to produce massively parallel in-network processing. * GrAI Matter Labs ([www.graimatterlabs.ai](http://www.google.com/url?q=http%3A%2F%2Fwww.graimatterlabs.ai%2F&sa=D&sntz=1&usg=AOvVaw3eJ9oROjswyCFHO-68LiDi)), a fabless semiconductor company specialized in brain-inspired technology, designs and develops fully programmable ultra-low power neuromorphic HW for sensor analytics and machine learning. The company has offices in Eindhoven (NL), Paris (FR) and San Jose (USA) and has strong relations with top-ranking research groups on neuroscience, human vision and natural computation * ### **Large Scale Deep Learning and AI models on the Edge * deployment pipelines * there are several steps involved in the AI/ML life-cycle * several tools to help simplify the whole process * tensorflow extended (TFX): an end to end platform for deploying production ML pipelines * MLflow (other options michelangelo): an open source platform for the end to end machine learning life cycle * apache airflow (other options kubeflow): an open source workflow management platform * dataiku data science studio (DSS): collaborative data science software platform for teams of data scientist , data analysts, and engineers to explore prototype build and deliver * ### The Edge: The Hottest Market for AI Accelerator Chips - Introducing the Kisaco Leadership Chart on AI Hardware Accelerators 2020-21: Edge and Automotive * [https://www.kisacoresearch.com/#about-us](https://www.google.com/url?q=https%3A%2F%2Fwww.kisacoresearch.com%2F%23about-us&sa=D&sntz=1&usg=AOvVaw0nasAOo80KuyOwbm4OeiOb) [OpenHTF is a Python library that provides a set of convenient abstractions designed to remove as much boilerplate as possible from hardware test setup and execution, so test engineers can focus primarily on test logic.](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fgoogle%2Fopenhtf&sa=D&sntz=1&usg=AOvVaw0zU3RKntPn4N8JIkPvriIu) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/ssWXVQN4wWnp524T8UjHyQEdArMpTIaM4OalIHhpfDv0Sv6HC6tLnwxjfrdaemWa002sbPUNKYrtuvBhEcn6DIQ=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/ssWXVQN4wWnp524T8UjHyQEdArMpTIaM4OalIHhpfDv0Sv6HC6tLnwxjfrdaemWa002sbPUNKYrtuvBhEcn6DIQ=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Edge AI summit Short summary of the edge AI summit 18-20 November 2020 Wednesday, November 18, 2020 A Software Solution Enabling Predictive Maintenance at the Sensor Level Helping Fish Farmers Feed The World With Deep Learning tinyMLPerf: Benchmarking Ultra-low Power Machine Learning Systems Ultra-low power neuromorphic intelligence for the sensor edge * How is AI affecting hearables and sensors? Breaking the Barriers to Deploy DNNs on Low-Power Hardware Optimizing ML Models At The Edge Made Simple Thursday, November 19, 2020 Developing Edge AI Solutions For A Post-Pandemic Society The Evolving Landscape of Edge AI InferX X1, The Fastest and Most Efficient Edge Inference Accelerator Implementing Edge Technologies in Retail: Walmart Case Study The Era of Analog AI Compute Is Here Using Edge AI To Detect Repetitive Mot Friday, November 20, 2020 *Spatial Computing: A Collision of Edge and Cloud-Based Computing Building An Autonomous Network For IoT and Edge Applications Practical Edge Inferencing: Enabling fastest AI inferencing per Watt leveraging sparsity **Large Scale Deep Learning and AI models on the Edge The Edge: The Hottest Market for AI Accelerator Chips - Introducing the Kisaco Leadership Chart on AI Hardware Accelerators 2020-21: Edge and Automotive # Short summary of the edge AI summit 18-20 November 2020 Best of Wednesday, November 18, 2020; tinyMLPerf, Breaking the Barriers to Deploy DNNs on Low-Power Hardware, Optimizing ML Models At The Edge Made Simple **Thursday, November 19, 2020** * 8:00 AM - 8:30 AM (PST) KEYNOTE PRESENTATION: Developing Edge AI Solutions For A Post-Pandemic Society Sastry Malladi - FogHorn Systems * 8:35 AM - 9:05 AM (PST) PRESENTATION: The Evolving Landscape of Edge AI Ajay Nair - Google * 9:05 AM - 9:20 AM (PST) Comfort Break * 9:20 AM - 9:45 AM (PST) PRESENTATION: InferX X1, The Fastest and Most Efficient Edge Inference Accelerator Cheng Wang - Flex Logix Technologies Inc. * 9:50 AM - 10:20 AM (PST) PRESENTATION: Implementing Edge Technologies in Retail: Walmart Case Study Alex Sabatier - Nvidia * 10:20 AM - 10:35 AM (PST) Comfort Break * 10:35 AM - 11:20 AM (PST) Meet speaker! * **11:20 AM - 11:50 AM (PST) PRESENTATION: The Era of Analog AI Compute Is Here Mike Henry - Mythic** * 11:55 AM - 12:25 PM (PST) PRESENTATION: Using Edge AI To Detect Repetitive Mot Marcellino Gemelli - Bosch Sensortec * 12:30 PM - 2:30 PM (PST) NETWORKING - Dedicated Networking 2 hours for 1-2-1 Video Meetings **Friday, November 20, 2020** * 8:00 AM - 8:30 AM (PST) PRESENTATION: Spatial Computing: A Collision of Edge and Cloud-Based Computing Ashwin Swaminathan - Magic Leap * 8:35 AM - 9:05 AM (PST) PRESENTATION: Building An Autonomous Network For IoT and Edge Applications Anshul Bhatt - Rakuten Mobile * 9:05 AM - 9:20 AM (PST) Comfort Break * 9:20 AM - 9:45 AM (PST) PRESENTATION: Practical Edge Inferencing: Enabling fastest AI inferencing per Watt leveraging sparsity Mahesh Makhijani - GrAI Matter Labs * **9:50 AM - 10:20 AM (PST) PRESENTATION: Large Scale Deep Learning and AI models on the Edge Chandra Khatri - Got It AI** * 10:20 AM - 10:35 AM (PST) Comfort Break * 10:35 AM - 11:20 AM (PST) NETWORKING: Interest Groups (18 people per room, topic-specific discussions) * 11:20 AM - 11:50 AM (PST) PANEL DISCUSSION: The Symbiotic Relationship between 5G and Edge AI Sami Badri - Credit Suisse, Christos Kolias - Orange, Rima Raouda - Independent * 11:55 AM - 12:25 PM (PST) PANEL DISCUSSION: Investment Trends & Dynamics Panel Rashmi Gopinath - B Capital Group, Yvonne Lutsch - Bosch Venture Capital, Eileen Tanghal - In-Q-Tel, Albert Wang - Qualcomm Ventures * **12:30 PM - 12:50 PM (PST) PRESENTATION: The Edge: The Hottest Market for AI Accelerator Chips - Introducing the Kisaco Leadership Chart on AI Hardware Accelerators 2020-21: Edge and Automotive Michael Azoff - Kisaco Research** ## Wednesday, November 18, 2020 * ### A Software Solution Enabling Predictive Maintenance at the Sensor Level * SensiML Toolkit enables AI for a broad array of resource constrained time-series sensor endpoint applications. These include a wide range of consumer and industrial sensing applications. * The problem is machine learning engineer do not have experience with embedded system and moving model to embedded system takes long time. * AutoML for Embedded system usage. it is on the cloud. * using the compiler for that device for this tools * cost edge and cloud. easy to work on cloud. streaming data to cloud is difficult. faster if working on edge. * TinyML addresses problems, battery powered, limited internet connectivity, security/privacy, latency, economic * [https://sensiml.com/products/#process](https://www.google.com/url?q=https%3A%2F%2Fsensiml.com%2Fproducts%2F%23process&sa=D&sntz=1&usg=AOvVaw116wjx6mBnEo9x0htyL2pr) * ### Helping Fish Farmers Feed The World With Deep Learning * [https://s3-us-west-1.amazonaws.com/aquabyte-static/videos/welcome_to_aquabyte_subtitled.mp4](https://www.google.com/url?q=https%3A%2F%2Fs3-us-west-1.amazonaws.com%2Faquabyte-static%2Fvideos%2Fwelcome_to_aquabyte_subtitled.mp4&sa=D&sntz=1&usg=AOvVaw1osNtsRVM9TMCmKoPsTnj_) * Count sea lice and accurately measure biomass in real-time while reducing cage furniture. Our experts‑in‑the‑loop ensure that every single prediction is correct. * Aquabyte is seeking a Machine Learning Platform Engineer to drive the development, testing, and delivery of machine learning models that enable cutting-edge analytics and automation of fish farms around the world. * Aquabyte is on a mission to revolutionize the sustainability and efficiency of aquaculture. It is an audacious, and incredibly rewarding mission. By making fish farming cheaper and more viable than livestock production, we aim to mitigate one of the biggest causes of climate change and help prepare our planet for impending population growth. Aquaculture is the single fastest growing food-production sector in the world, and now is the time to define how technology is used to harvest the sea for generations to come. * We are currently focused on helping Norwegian salmon farmers better understand their fish populations and make environmentally-sound decisions. Through custom underwater cameras, computer vision, and machine learning we are able to quantify fish weights, detect sea lice infestations, and generate optimal feeding plans in real time. Our product operates at three levels: on-site hardware for image capture, cloud pipelines for data processing, and a user-facing web application. As a result, there are hundreds of moving pieces and no shortage of fascinating challenges across all levels of the stack. * * ### tinyMLPerf: Benchmarking Ultra-low Power Machine Learning Systems * [https://github.com/mlperf/tiny](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fmlperf%2Ftiny&sa=D&sntz=1&usg=AOvVaw1TgviYAuh83PMxDPYljOjs) * tinyMLPerf Deep Learning Benchmarks for Embedded Devices * The goal of TinyMLPerf is to provide a representative set of deep neural nets and benchmarking code to compare performance between embedded devices. Embedded devices include microcontrollers, DSPs, and tiny NN accelerators. These devics typically run at between 10MHz and 250MHz, and can perform inference using less then 50mW of power. TinyMLPerf submissions will allow device makers and researchers to choose the best hardware for their use case, and allows hardware vendors to showcase their offerings. TinyMLPerf is primarily intended to benchmark hardware rather than new network archietctures, or embedded neural net runtimes. The reference benchmarks are provided using TensorFlow Lite for Microcontrollers (TFLM). Submitters can directly use the TFLM, although submitters are encouraged to use the software stack that works best on thier hardware. * anomaly detection benchmark, visual wake words benchmark, * ### Ultra-low power neuromorphic intelligence for the sensor edge * Innatera Nanosystems BV (Innatera, (Innatera, innatera.com) is a rapidly-growing Dutch semiconductor company that develops ultra-efficient neuromorphic processors for AI at the edge. These microprocessors mimic the brain’s mechanisms for processing fast data streams from sensors, enabling complex turn-key sensor analytics functionalities, with 10,000x higher performance per watt than competing solutions. Innatera's technology serves as a critical enabler for next-generation use-cases in the IoT, wearable, embedded, and automotive domains. * ### * How is AI affecting hearables and sensors? * [https://github.com/greenwaves-technologies/nn_menu](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fgreenwaves-technologies%2Fnn_menu&sa=D&sntz=1&usg=AOvVaw2JKYYAPrnA9Mkalw2qenUQ) * The Neural Network Menu* is a collection of software that implements Neural Networks on Greenwaves Application Processors (GAP). This repository contains common mobile and edge NN archtecture examples, NN sample applications and full flagged reference designs. Our tools maps a TFLITE model (quantized or unquantized) onto gap. There is also a flow in the ingredients directory showing how to hand map from a Pytorch Model onto GAP. * [https://greenwaves-technologies.com/store/](https://www.google.com/url?q=https%3A%2F%2Fgreenwaves-technologies.com%2Fstore%2F&sa=D&sntz=1&usg=AOvVaw0Ya_w_NBAr4AbIxBe2j_YX) * GAPPoc-A is a Proof of Concept Board that can be used for demonstration of battery-operated, edge computer vision applications based on GAP8. * It incorporates GAPmod, a surface-mount module that implements all the layout sensitive portion of a GAP8 design, along with a VGA image sensor and a Bluetooth Low Energy radio. * The GAPPoc-A board enables battery-operated applications developed around algorithms such as people counting, face-identification and many others to be quickly assembled and evaluated in the field. * [https://riscv.org/blog/2019/08/risc-v-emea-roadshow-spotlight-greenwaves-technologies/](https://www.google.com/url?q=https%3A%2F%2Friscv.org%2Fblog%2F2019%2F08%2Frisc-v-emea-roadshow-spotlight-greenwaves-technologies%2F&sa=D&sntz=1&usg=AOvVaw1ikZjtEoYTgFb-S_eGEB3i) * ### Breaking the Barriers to Deploy DNNs on Low-Power Hardware * Deeplite, named to the 2020 CB Insights AI100 List of Most Innovative Artificial Intelligence Startups, is devoted to making fundamental advancements in accessible and efficient deep learning. Our solution helps deep learning engineers and experts automatically create faster, smaller and more energy-efficient deep neural networks. Industry leaders in computer vision, augmented reality and autonomous driving use our technology to unlock new possibilities for deep learning in the real world. At Deeplite, our vision is to create a lightweight intelligence that’s accessible for daily life. * [https://www.deeplite.ai/](https://www.google.com/url?q=https%3A%2F%2Fwww.deeplite.ai%2F&sa=D&sntz=1&usg=AOvVaw1r7NiQGt1hRi6S_xiJ522C) * At Deeplite, we are tackling inference optimization of deep neural networks, making them faster and energy-efficient from cloud to edge computing. Our solution leverages state-of-the-art technology from elite universities to make deep neural networks applicable for any device, and our team works hard on the iterative evolution of the science behind deep neural networks to directly improve daily life. * reduce the size of model 40x * ### Optimizing ML Models At The Edge Made Simple * [https://octoml.ai/](https://www.google.com/url?q=https%3A%2F%2Foctoml.ai%2F&sa=D&sntz=1&usg=AOvVaw2uXg6ESgQgVGrF9nQJKFve) * OctoML is an energetic new company changing how developers optimize and deploy machine learning models for their AI needs. We’re a team of machine learning systems leaders focused on making ML more efficient and easier to deploy by… applying machine learning to it! * OctoML is leveraging the power and traction of Apache TVM, an open source project originated by our founding team, to enable companies of every size to harness the power of deep learning without the expensive heavy lifting of tuning and securing models to each hardware configuration that a customer might need. * Apache TVM and Deep Learning Compilation Conference, Wed-Fri, December 2nd-4th 2020, Free Virtual Event. ## Thursday, November 19, 2020 * ### Developing Edge AI Solutions For A Post-Pandemic Society * [https://www.foghorn.io/](https://www.google.com/url?q=https%3A%2F%2Fwww.foghorn.io%2F&sa=D&sntz=1&usg=AOvVaw2aRKISC9BdrrriEej5Xb_I) * ogHorn’s Lightning™ Edge AI platform brings a groundbreaking dimension to IIoT and edge computing by embedding AI as close to the source of streaming sensor data as possible. The Edge AI software platform is a highly compact, advanced and feature-rich edge solution that delivers unprecedented low latency for onsite data processing, real-time analytics, ML and AI capabilities. It delivers the industry’s lowest total cost for computing requirements, communications services, and cloud processing and storage. * temperature detection, social distancing, cough detection, PPE/Mask detection * Flexible, customizable, integrated, actionable * ### The Evolving Landscape of Edge AI * * Coral’s local AI technology enables new possibilities across almost any kind of industry * The Coral Dev Board is a single-board computer that contains an Edge TPU coprocessor. It's ideal for prototyping new projects that demand fast on-device inferencing for machine learning models. This page is your guide to get started. The setup requires flashing Mendel Linux to the board, and then accessing the board's shell terminal. Once you have terminal access and update some of the software, we'll show you how to run an image classification model on the board. If you want to learn more about the hardware, see the Dev Board datasheet. * TPU v3, 32 to 512 TOPS, Q2 2021 * ### InferX X1, The Fastest and Most Efficient Edge Inference Accelerator * InferX X1: World's fastest and most efficient Edge Inference Accelerator. We have just launched our first inference chip and it is the best in the world for edge inference. We are bringing up neural network models now and moving forward on the steps required for Q2/2021 chip and board production and Inference Compiler availability. * mbedded FPGA, or eFPGA, enables your SoC to have flexibility in critical areas where algorithm, protocol or market needs are changing. FPGA can also accelerate many workloads faster than processors: Microsoft Azure uses one FPGA accelerator for every 2 Xeons.Flex Logix provides eFPGA cores which have density and performance similar to leading FPGAs in the same process node. Our EFLX eFPGA is silicon proven in 40nm, 28/22nm, 16nm and 12nm. 6/7nm EFLX eFPGA is planned. Our eFPGA is based on a “tile” called EFLX 4K, which comes in two versions: all logic or mostly logic with some MACs (multiply-accumulators). The programmable logic is called LUTs (look up tables) that can implement any Boolean function. EFLX 4K Logix has 4000 LUT4 equivalents, EFLX 4K DSP has 3000 LUT4s and 40 Multiplier-Accumulators (MACs): the MAC has a 22-bit pre-adder, a 22×22 multiple and a 48-bit post adder/accumulator. MACs can be combined or cascaded to form fast DSP functions. (For 40nm-180nm we offer an EFLX 1K tile). * depth-wise conv2d * ### Implementing Edge Technologies in Retail: Walmart Case Study * NVidia * ### The Era of Analog AI Compute Is Here * Mythic products are based on a unique tile-based AI compute architecture that features three fundamental hardware technologies – Compute-in-Memory, Dataflow Architecture, and Analog Computing. For AI developers, the Mythic SDK streamlines the preparation of trained neural networks for edge and low-latency datacenter deployments, and also performs automatic optimization and compilation of dataflow graphs for our unique architecture. * low power consumption, ultra-low latency, high ai performance, large weight capacity, small form factor, cost effective solution * ### **Us** ing Edge AI To Detect Repetitive Mot * Bosch Sensortec develops and markets a wide portfolio of MEMS sensors and solutions for applications in smartphones, tablets, wearables, AR/VR devices, drones, robots, smart home and the Internet of Things. Striving to meet the demanding requirements of the consumer electronics market, we provide best-in-class sensing solutions in terms of customer focus, quality and reliability, performance, sustainability and competitiveness. * [https://github.com/BoschSensortec](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FBoschSensortec&sa=D&sntz=1&usg=AOvVaw0Xr8dUHPERsj-rYH7ZAnP1) ## Friday, November 20, 2020 * ### *Spatial Computing: A Collision of Edge and Cloud-Based Computing * [https://github.com/magicleap](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fmagicleap&sa=D&sntz=1&usg=AOvVaw22R0IwwKYOdwv06BvMpvZF) * instance semantic segmentation contextual computing * spatial computing * SLAM: tracking/localization, mapping: * latency is critical for see through displays * weight is critical cannot compensate for lack of compute with more sensors * thermal is critical more sensors and more compute lead to heat * rigidity leads to weight our device should be light * very stringent requirements for MR * why build a map: drift correction, robustness (pose recovery), persistence * feature descriptors * matching across large baselines and illumination changes is challenging * most of the SOTA methods based on deep learning and not feasible withing compute budget * our deep descriptor is optimized for SLAM and provides the best trade off in terms of performance and compute * semantic segmentation 3d point cloud * ### Building An Autonomous Network For IoT and Edge Applications * 5G + AI * ### Practical Edge Inferencing: Enabling fastest AI inferencing per Watt leveraging sparsity * [https://www.graimatterlabs.ai/](https://www.google.com/url?q=https%3A%2F%2Fwww.graimatterlabs.ai%2F&sa=D&sntz=1&usg=AOvVaw1qLIEaRzrtXoYDAb_Y4tQb) * The world’s first sparsity-enabled AI processor optimized for ultra-low latency and low power processing at the edge. * GrAI One drastically reduces application latency, for instance, it reduces the end-to-end latencies for deep learning networks such as PilotNet to the order of milliseconds. The GrAI One chip is based on GML’s innovative NeuronFlow™ technology that combines the dynamic Dataflow paradigm with sparse computing to produce massively parallel in-network processing. * GrAI Matter Labs ([www.graimatterlabs.ai](http://www.google.com/url?q=http%3A%2F%2Fwww.graimatterlabs.ai%2F&sa=D&sntz=1&usg=AOvVaw3eJ9oROjswyCFHO-68LiDi)), a fabless semiconductor company specialized in brain-inspired technology, designs and develops fully programmable ultra-low power neuromorphic HW for sensor analytics and machine learning. The company has offices in Eindhoven (NL), Paris (FR) and San Jose (USA) and has strong relations with top-ranking research groups on neuroscience, human vision and natural computation * ### **Large Scale Deep Learning and AI models on the Edge * deployment pipelines * there are several steps involved in the AI/ML life-cycle * several tools to help simplify the whole process * tensorflow extended (TFX): an end to end platform for deploying production ML pipelines * MLflow (other options michelangelo): an open source platform for the end to end machine learning life cycle * apache airflow (other options kubeflow): an open source workflow management platform * dataiku data science studio (DSS): collaborative data science software platform for teams of data scientist , data analysts, and engineers to explore prototype build and deliver * ### The Edge: The Hottest Market for AI Accelerator Chips - Introducing the Kisaco Leadership Chart on AI Hardware Accelerators 2020-21: Edge and Automotive * [https://www.kisacoresearch.com/#about-us](https://www.google.com/url?q=https%3A%2F%2Fwww.kisacoresearch.com%2F%23about-us&sa=D&sntz=1&usg=AOvVaw0nasAOo80KuyOwbm4OeiOb) [OpenHTF is a Python library that provides a set of convenient abstractions designed to remove as much boilerplate as possible from hardware test setup and execution, so test engineers can focus primarily on test logic.](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fgoogle%2Fopenhtf&sa=D&sntz=1&usg=AOvVaw0zU3RKntPn4N8JIkPvriIu) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/ssWXVQN4wWnp524T8UjHyQEdArMpTIaM4OalIHhpfDv0Sv6HC6tLnwxjfrdaemWa002sbPUNKYrtuvBhEcn6DIQ=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/ssWXVQN4wWnp524T8UjHyQEdArMpTIaM4OalIHhpfDv0Sv6HC6tLnwxjfrdaemWa002sbPUNKYrtuvBhEcn6DIQ=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Edge AI summit Short summary of the edge AI summit 18-20 November 2020 Wednesday, November 18, 2020 A Software Solution Enabling Predictive Maintenance at the Sensor Level Helping Fish Farmers Feed The World With Deep Learning tinyMLPerf: Benchmarking Ultra-low Power Machine Learning Systems Ultra-low power neuromorphic intelligence for the sensor edge * How is AI affecting hearables and sensors? Breaking the Barriers to Deploy DNNs on Low-Power Hardware Optimizing ML Models At The Edge Made Simple Thursday, November 19, 2020 Developing Edge AI Solutions For A Post-Pandemic Society The Evolving Landscape of Edge AI InferX X1, The Fastest and Most Efficient Edge Inference Accelerator Implementing Edge Technologies in Retail: Walmart Case Study The Era of Analog AI Compute Is Here Using Edge AI To Detect Repetitive Mot Friday, November 20, 2020 *Spatial Computing: A Collision of Edge and Cloud-Based Computing Building An Autonomous Network For IoT and Edge Applications Practical Edge Inferencing: Enabling fastest AI inferencing per Watt leveraging sparsity **Large Scale Deep Learning and AI models on the Edge The Edge: The Hottest Market for AI Accelerator Chips - Introducing the Kisaco Leadership Chart on AI Hardware Accelerators 2020-21: Edge and Automotive # Short summary of the edge AI summit 18-20 November 2020 Best of Wednesday, November 18, 2020; tinyMLPerf, Breaking the Barriers to Deploy DNNs on Low-Power Hardware, Optimizing ML Models At The Edge Made Simple **Thursday, November 19, 2020** * 8:00 AM - 8:30 AM (PST) KEYNOTE PRESENTATION: Developing Edge AI Solutions For A Post-Pandemic Society Sastry Malladi - FogHorn Systems * 8:35 AM - 9:05 AM (PST) PRESENTATION: The Evolving Landscape of Edge AI Ajay Nair - Google * 9:05 AM - 9:20 AM (PST) Comfort Break * 9:20 AM - 9:45 AM (PST) PRESENTATION: InferX X1, The Fastest and Most Efficient Edge Inference Accelerator Cheng Wang - Flex Logix Technologies Inc. * 9:50 AM - 10:20 AM (PST) PRESENTATION: Implementing Edge Technologies in Retail: Walmart Case Study Alex Sabatier - Nvidia * 10:20 AM - 10:35 AM (PST) Comfort Break * 10:35 AM - 11:20 AM (PST) Meet speaker! * **11:20 AM - 11:50 AM (PST) PRESENTATION: The Era of Analog AI Compute Is Here Mike Henry - Mythic** * 11:55 AM - 12:25 PM (PST) PRESENTATION: Using Edge AI To Detect Repetitive Mot Marcellino Gemelli - Bosch Sensortec * 12:30 PM - 2:30 PM (PST) NETWORKING - Dedicated Networking 2 hours for 1-2-1 Video Meetings **Friday, November 20, 2020** * 8:00 AM - 8:30 AM (PST) PRESENTATION: Spatial Computing: A Collision of Edge and Cloud-Based Computing Ashwin Swaminathan - Magic Leap * 8:35 AM - 9:05 AM (PST) PRESENTATION: Building An Autonomous Network For IoT and Edge Applications Anshul Bhatt - Rakuten Mobile * 9:05 AM - 9:20 AM (PST) Comfort Break * 9:20 AM - 9:45 AM (PST) PRESENTATION: Practical Edge Inferencing: Enabling fastest AI inferencing per Watt leveraging sparsity Mahesh Makhijani - GrAI Matter Labs * **9:50 AM - 10:20 AM (PST) PRESENTATION: Large Scale Deep Learning and AI models on the Edge Chandra Khatri - Got It AI** * 10:20 AM - 10:35 AM (PST) Comfort Break * 10:35 AM - 11:20 AM (PST) NETWORKING: Interest Groups (18 people per room, topic-specific discussions) * 11:20 AM - 11:50 AM (PST) PANEL DISCUSSION: The Symbiotic Relationship between 5G and Edge AI Sami Badri - Credit Suisse, Christos Kolias - Orange, Rima Raouda - Independent * 11:55 AM - 12:25 PM (PST) PANEL DISCUSSION: Investment Trends & Dynamics Panel Rashmi Gopinath - B Capital Group, Yvonne Lutsch - Bosch Venture Capital, Eileen Tanghal - In-Q-Tel, Albert Wang - Qualcomm Ventures * **12:30 PM - 12:50 PM (PST) PRESENTATION: The Edge: The Hottest Market for AI Accelerator Chips - Introducing the Kisaco Leadership Chart on AI Hardware Accelerators 2020-21: Edge and Automotive Michael Azoff - Kisaco Research** ## Wednesday, November 18, 2020 * ### A Software Solution Enabling Predictive Maintenance at the Sensor Level * SensiML Toolkit enables AI for a broad array of resource constrained time-series sensor endpoint applications. These include a wide range of consumer and industrial sensing applications. * The problem is machine learning engineer do not have experience with embedded system and moving model to embedded system takes long time. * AutoML for Embedded system usage. it is on the cloud. * using the compiler for that device for this tools * cost edge and cloud. easy to work on cloud. streaming data to cloud is difficult. faster if working on edge. * TinyML addresses problems, battery powered, limited internet connectivity, security/privacy, latency, economic * [https://sensiml.com/products/#process](https://www.google.com/url?q=https%3A%2F%2Fsensiml.com%2Fproducts%2F%23process&sa=D&sntz=1&usg=AOvVaw116wjx6mBnEo9x0htyL2pr) * ### Helping Fish Farmers Feed The World With Deep Learning * [https://s3-us-west-1.amazonaws.com/aquabyte-static/videos/welcome_to_aquabyte_subtitled.mp4](https://www.google.com/url?q=https%3A%2F%2Fs3-us-west-1.amazonaws.com%2Faquabyte-static%2Fvideos%2Fwelcome_to_aquabyte_subtitled.mp4&sa=D&sntz=1&usg=AOvVaw1osNtsRVM9TMCmKoPsTnj_) * Count sea lice and accurately measure biomass in real-time while reducing cage furniture. Our experts‑in‑the‑loop ensure that every single prediction is correct. * Aquabyte is seeking a Machine Learning Platform Engineer to drive the development, testing, and delivery of machine learning models that enable cutting-edge analytics and automation of fish farms around the world. * Aquabyte is on a mission to revolutionize the sustainability and efficiency of aquaculture. It is an audacious, and incredibly rewarding mission. By making fish farming cheaper and more viable than livestock production, we aim to mitigate one of the biggest causes of climate change and help prepare our planet for impending population growth. Aquaculture is the single fastest growing food-production sector in the world, and now is the time to define how technology is used to harvest the sea for generations to come. * We are currently focused on helping Norwegian salmon farmers better understand their fish populations and make environmentally-sound decisions. Through custom underwater cameras, computer vision, and machine learning we are able to quantify fish weights, detect sea lice infestations, and generate optimal feeding plans in real time. Our product operates at three levels: on-site hardware for image capture, cloud pipelines for data processing, and a user-facing web application. As a result, there are hundreds of moving pieces and no shortage of fascinating challenges across all levels of the stack. * * ### tinyMLPerf: Benchmarking Ultra-low Power Machine Learning Systems * [https://github.com/mlperf/tiny](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fmlperf%2Ftiny&sa=D&sntz=1&usg=AOvVaw1TgviYAuh83PMxDPYljOjs) * tinyMLPerf Deep Learning Benchmarks for Embedded Devices * The goal of TinyMLPerf is to provide a representative set of deep neural nets and benchmarking code to compare performance between embedded devices. Embedded devices include microcontrollers, DSPs, and tiny NN accelerators. These devics typically run at between 10MHz and 250MHz, and can perform inference using less then 50mW of power. TinyMLPerf submissions will allow device makers and researchers to choose the best hardware for their use case, and allows hardware vendors to showcase their offerings. TinyMLPerf is primarily intended to benchmark hardware rather than new network archietctures, or embedded neural net runtimes. The reference benchmarks are provided using TensorFlow Lite for Microcontrollers (TFLM). Submitters can directly use the TFLM, although submitters are encouraged to use the software stack that works best on thier hardware. * anomaly detection benchmark, visual wake words benchmark, * ### Ultra-low power neuromorphic intelligence for the sensor edge * Innatera Nanosystems BV (Innatera, (Innatera, innatera.com) is a rapidly-growing Dutch semiconductor company that develops ultra-efficient neuromorphic processors for AI at the edge. These microprocessors mimic the brain’s mechanisms for processing fast data streams from sensors, enabling complex turn-key sensor analytics functionalities, with 10,000x higher performance per watt than competing solutions. Innatera's technology serves as a critical enabler for next-generation use-cases in the IoT, wearable, embedded, and automotive domains. * ### * How is AI affecting hearables and sensors? * [https://github.com/greenwaves-technologies/nn_menu](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fgreenwaves-technologies%2Fnn_menu&sa=D&sntz=1&usg=AOvVaw2JKYYAPrnA9Mkalw2qenUQ) * The Neural Network Menu* is a collection of software that implements Neural Networks on Greenwaves Application Processors (GAP). This repository contains common mobile and edge NN archtecture examples, NN sample applications and full flagged reference designs. Our tools maps a TFLITE model (quantized or unquantized) onto gap. There is also a flow in the ingredients directory showing how to hand map from a Pytorch Model onto GAP. * [https://greenwaves-technologies.com/store/](https://www.google.com/url?q=https%3A%2F%2Fgreenwaves-technologies.com%2Fstore%2F&sa=D&sntz=1&usg=AOvVaw0Ya_w_NBAr4AbIxBe2j_YX) * GAPPoc-A is a Proof of Concept Board that can be used for demonstration of battery-operated, edge computer vision applications based on GAP8. * It incorporates GAPmod, a surface-mount module that implements all the layout sensitive portion of a GAP8 design, along with a VGA image sensor and a Bluetooth Low Energy radio. * The GAPPoc-A board enables battery-operated applications developed around algorithms such as people counting, face-identification and many others to be quickly assembled and evaluated in the field. * [https://riscv.org/blog/2019/08/risc-v-emea-roadshow-spotlight-greenwaves-technologies/](https://www.google.com/url?q=https%3A%2F%2Friscv.org%2Fblog%2F2019%2F08%2Frisc-v-emea-roadshow-spotlight-greenwaves-technologies%2F&sa=D&sntz=1&usg=AOvVaw1ikZjtEoYTgFb-S_eGEB3i) * ### Breaking the Barriers to Deploy DNNs on Low-Power Hardware * Deeplite, named to the 2020 CB Insights AI100 List of Most Innovative Artificial Intelligence Startups, is devoted to making fundamental advancements in accessible and efficient deep learning. Our solution helps deep learning engineers and experts automatically create faster, smaller and more energy-efficient deep neural networks. Industry leaders in computer vision, augmented reality and autonomous driving use our technology to unlock new possibilities for deep learning in the real world. At Deeplite, our vision is to create a lightweight intelligence that’s accessible for daily life. * [https://www.deeplite.ai/](https://www.google.com/url?q=https%3A%2F%2Fwww.deeplite.ai%2F&sa=D&sntz=1&usg=AOvVaw1r7NiQGt1hRi6S_xiJ522C) * At Deeplite, we are tackling inference optimization of deep neural networks, making them faster and energy-efficient from cloud to edge computing. Our solution leverages state-of-the-art technology from elite universities to make deep neural networks applicable for any device, and our team works hard on the iterative evolution of the science behind deep neural networks to directly improve daily life. * reduce the size of model 40x * ### Optimizing ML Models At The Edge Made Simple * [https://octoml.ai/](https://www.google.com/url?q=https%3A%2F%2Foctoml.ai%2F&sa=D&sntz=1&usg=AOvVaw2uXg6ESgQgVGrF9nQJKFve) * OctoML is an energetic new company changing how developers optimize and deploy machine learning models for their AI needs. We’re a team of machine learning systems leaders focused on making ML more efficient and easier to deploy by… applying machine learning to it! * OctoML is leveraging the power and traction of Apache TVM, an open source project originated by our founding team, to enable companies of every size to harness the power of deep learning without the expensive heavy lifting of tuning and securing models to each hardware configuration that a customer might need. * Apache TVM and Deep Learning Compilation Conference, Wed-Fri, December 2nd-4th 2020, Free Virtual Event. ## Thursday, November 19, 2020 * ### Developing Edge AI Solutions For A Post-Pandemic Society * [https://www.foghorn.io/](https://www.google.com/url?q=https%3A%2F%2Fwww.foghorn.io%2F&sa=D&sntz=1&usg=AOvVaw2aRKISC9BdrrriEej5Xb_I) * ogHorn’s Lightning™ Edge AI platform brings a groundbreaking dimension to IIoT and edge computing by embedding AI as close to the source of streaming sensor data as possible. The Edge AI software platform is a highly compact, advanced and feature-rich edge solution that delivers unprecedented low latency for onsite data processing, real-time analytics, ML and AI capabilities. It delivers the industry’s lowest total cost for computing requirements, communications services, and cloud processing and storage. * temperature detection, social distancing, cough detection, PPE/Mask detection * Flexible, customizable, integrated, actionable * ### The Evolving Landscape of Edge AI * * Coral’s local AI technology enables new possibilities across almost any kind of industry * The Coral Dev Board is a single-board computer that contains an Edge TPU coprocessor. It's ideal for prototyping new projects that demand fast on-device inferencing for machine learning models. This page is your guide to get started. The setup requires flashing Mendel Linux to the board, and then accessing the board's shell terminal. Once you have terminal access and update some of the software, we'll show you how to run an image classification model on the board. If you want to learn more about the hardware, see the Dev Board datasheet. * TPU v3, 32 to 512 TOPS, Q2 2021 * ### InferX X1, The Fastest and Most Efficient Edge Inference Accelerator * InferX X1: World's fastest and most efficient Edge Inference Accelerator. We have just launched our first inference chip and it is the best in the world for edge inference. We are bringing up neural network models now and moving forward on the steps required for Q2/2021 chip and board production and Inference Compiler availability. * mbedded FPGA, or eFPGA, enables your SoC to have flexibility in critical areas where algorithm, protocol or market needs are changing. FPGA can also accelerate many workloads faster than processors: Microsoft Azure uses one FPGA accelerator for every 2 Xeons.Flex Logix provides eFPGA cores which have density and performance similar to leading FPGAs in the same process node. Our EFLX eFPGA is silicon proven in 40nm, 28/22nm, 16nm and 12nm. 6/7nm EFLX eFPGA is planned. Our eFPGA is based on a “tile” called EFLX 4K, which comes in two versions: all logic or mostly logic with some MACs (multiply-accumulators). The programmable logic is called LUTs (look up tables) that can implement any Boolean function. EFLX 4K Logix has 4000 LUT4 equivalents, EFLX 4K DSP has 3000 LUT4s and 40 Multiplier-Accumulators (MACs): the MAC has a 22-bit pre-adder, a 22×22 multiple and a 48-bit post adder/accumulator. MACs can be combined or cascaded to form fast DSP functions. (For 40nm-180nm we offer an EFLX 1K tile). * depth-wise conv2d * ### Implementing Edge Technologies in Retail: Walmart Case Study * NVidia * ### The Era of Analog AI Compute Is Here * Mythic products are based on a unique tile-based AI compute architecture that features three fundamental hardware technologies – Compute-in-Memory, Dataflow Architecture, and Analog Computing. For AI developers, the Mythic SDK streamlines the preparation of trained neural networks for edge and low-latency datacenter deployments, and also performs automatic optimization and compilation of dataflow graphs for our unique architecture. * low power consumption, ultra-low latency, high ai performance, large weight capacity, small form factor, cost effective solution * ### **Us** ing Edge AI To Detect Repetitive Mot * Bosch Sensortec develops and markets a wide portfolio of MEMS sensors and solutions for applications in smartphones, tablets, wearables, AR/VR devices, drones, robots, smart home and the Internet of Things. Striving to meet the demanding requirements of the consumer electronics market, we provide best-in-class sensing solutions in terms of customer focus, quality and reliability, performance, sustainability and competitiveness. * [https://github.com/BoschSensortec](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FBoschSensortec&sa=D&sntz=1&usg=AOvVaw0Xr8dUHPERsj-rYH7ZAnP1) ## Friday, November 20, 2020 * ### *Spatial Computing: A Collision of Edge and Cloud-Based Computing * [https://github.com/magicleap](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fmagicleap&sa=D&sntz=1&usg=AOvVaw22R0IwwKYOdwv06BvMpvZF) * instance semantic segmentation contextual computing * spatial computing * SLAM: tracking/localization, mapping: * latency is critical for see through displays * weight is critical cannot compensate for lack of compute with more sensors * thermal is critical more sensors and more compute lead to heat * rigidity leads to weight our device should be light * very stringent requirements for MR * why build a map: drift correction, robustness (pose recovery), persistence * feature descriptors * matching across large baselines and illumination changes is challenging * most of the SOTA methods based on deep learning and not feasible withing compute budget * our deep descriptor is optimized for SLAM and provides the best trade off in terms of performance and compute * semantic segmentation 3d point cloud * ### Building An Autonomous Network For IoT and Edge Applications * 5G + AI * ### Practical Edge Inferencing: Enabling fastest AI inferencing per Watt leveraging sparsity * [https://www.graimatterlabs.ai/](https://www.google.com/url?q=https%3A%2F%2Fwww.graimatterlabs.ai%2F&sa=D&sntz=1&usg=AOvVaw1qLIEaRzrtXoYDAb_Y4tQb) * The world’s first sparsity-enabled AI processor optimized for ultra-low latency and low power processing at the edge. * GrAI One drastically reduces application latency, for instance, it reduces the end-to-end latencies for deep learning networks such as PilotNet to the order of milliseconds. The GrAI One chip is based on GML’s innovative NeuronFlow™ technology that combines the dynamic Dataflow paradigm with sparse computing to produce massively parallel in-network processing. * GrAI Matter Labs ([www.graimatterlabs.ai](http://www.google.com/url?q=http%3A%2F%2Fwww.graimatterlabs.ai%2F&sa=D&sntz=1&usg=AOvVaw3eJ9oROjswyCFHO-68LiDi)), a fabless semiconductor company specialized in brain-inspired technology, designs and develops fully programmable ultra-low power neuromorphic HW for sensor analytics and machine learning. The company has offices in Eindhoven (NL), Paris (FR) and San Jose (USA) and has strong relations with top-ranking research groups on neuroscience, human vision and natural computation * ### **Large Scale Deep Learning and AI models on the Edge * deployment pipelines * there are several steps involved in the AI/ML life-cycle * several tools to help simplify the whole process * tensorflow extended (TFX): an end to end platform for deploying production ML pipelines * MLflow (other options michelangelo): an open source platform for the end to end machine learning life cycle * apache airflow (other options kubeflow): an open source workflow management platform * dataiku data science studio (DSS): collaborative data science software platform for teams of data scientist , data analysts, and engineers to explore prototype build and deliver * ### The Edge: The Hottest Market for AI Accelerator Chips - Introducing the Kisaco Leadership Chart on AI Hardware Accelerators 2020-21: Edge and Automotive * [https://www.kisacoresearch.com/#about-us](https://www.google.com/url?q=https%3A%2F%2Fwww.kisacoresearch.com%2F%23about-us&sa=D&sntz=1&usg=AOvVaw0nasAOo80KuyOwbm4OeiOb) [OpenHTF is a Python library that provides a set of convenient abstractions designed to remove as much boilerplate as possible from hardware test setup and execution, so test engineers can focus primarily on test logic.](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fgoogle%2Fopenhtf&sa=D&sntz=1&usg=AOvVaw0zU3RKntPn4N8JIkPvriIu) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/3LTMUMvLqIOxMLGRMkavoKhjR0yGpVpsaeb_NnspLybaPexjoQBnvVa2C2973HUQ30RiIqubg5B9F6eLjnxKXlE=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/3LTMUMvLqIOxMLGRMkavoKhjR0yGpVpsaeb_NnspLybaPexjoQBnvVa2C2973HUQ30RiIqubg5B9F6eLjnxKXlE=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Edge AI summit Short summary of the edge AI summit 18-20 November 2020 Wednesday, November 18, 2020 A Software Solution Enabling Predictive Maintenance at the Sensor Level Helping Fish Farmers Feed The World With Deep Learning tinyMLPerf: Benchmarking Ultra-low Power Machine Learning Systems Ultra-low power neuromorphic intelligence for the sensor edge * How is AI affecting hearables and sensors? Breaking the Barriers to Deploy DNNs on Low-Power Hardware Optimizing ML Models At The Edge Made Simple Thursday, November 19, 2020 Developing Edge AI Solutions For A Post-Pandemic Society The Evolving Landscape of Edge AI InferX X1, The Fastest and Most Efficient Edge Inference Accelerator Implementing Edge Technologies in Retail: Walmart Case Study The Era of Analog AI Compute Is Here Using Edge AI To Detect Repetitive Mot Friday, November 20, 2020 *Spatial Computing: A Collision of Edge and Cloud-Based Computing Building An Autonomous Network For IoT and Edge Applications Practical Edge Inferencing: Enabling fastest AI inferencing per Watt leveraging sparsity **Large Scale Deep Learning and AI models on the Edge The Edge: The Hottest Market for AI Accelerator Chips - Introducing the Kisaco Leadership Chart on AI Hardware Accelerators 2020-21: Edge and Automotive # Short summary of the edge AI summit 18-20 November 2020 Best of Wednesday, November 18, 2020; tinyMLPerf, Breaking the Barriers to Deploy DNNs on Low-Power Hardware, Optimizing ML Models At The Edge Made Simple **Thursday, November 19, 2020** * 8:00 AM - 8:30 AM (PST) KEYNOTE PRESENTATION: Developing Edge AI Solutions For A Post-Pandemic Society Sastry Malladi - FogHorn Systems * 8:35 AM - 9:05 AM (PST) PRESENTATION: The Evolving Landscape of Edge AI Ajay Nair - Google * 9:05 AM - 9:20 AM (PST) Comfort Break * 9:20 AM - 9:45 AM (PST) PRESENTATION: InferX X1, The Fastest and Most Efficient Edge Inference Accelerator Cheng Wang - Flex Logix Technologies Inc. * 9:50 AM - 10:20 AM (PST) PRESENTATION: Implementing Edge Technologies in Retail: Walmart Case Study Alex Sabatier - Nvidia * 10:20 AM - 10:35 AM (PST) Comfort Break * 10:35 AM - 11:20 AM (PST) Meet speaker! * **11:20 AM - 11:50 AM (PST) PRESENTATION: The Era of Analog AI Compute Is Here Mike Henry - Mythic** * 11:55 AM - 12:25 PM (PST) PRESENTATION: Using Edge AI To Detect Repetitive Mot Marcellino Gemelli - Bosch Sensortec * 12:30 PM - 2:30 PM (PST) NETWORKING - Dedicated Networking 2 hours for 1-2-1 Video Meetings **Friday, November 20, 2020** * 8:00 AM - 8:30 AM (PST) PRESENTATION: Spatial Computing: A Collision of Edge and Cloud-Based Computing Ashwin Swaminathan - Magic Leap * 8:35 AM - 9:05 AM (PST) PRESENTATION: Building An Autonomous Network For IoT and Edge Applications Anshul Bhatt - Rakuten Mobile * 9:05 AM - 9:20 AM (PST) Comfort Break * 9:20 AM - 9:45 AM (PST) PRESENTATION: Practical Edge Inferencing: Enabling fastest AI inferencing per Watt leveraging sparsity Mahesh Makhijani - GrAI Matter Labs * **9:50 AM - 10:20 AM (PST) PRESENTATION: Large Scale Deep Learning and AI models on the Edge Chandra Khatri - Got It AI** * 10:20 AM - 10:35 AM (PST) Comfort Break * 10:35 AM - 11:20 AM (PST) NETWORKING: Interest Groups (18 people per room, topic-specific discussions) * 11:20 AM - 11:50 AM (PST) PANEL DISCUSSION: The Symbiotic Relationship between 5G and Edge AI Sami Badri - Credit Suisse, Christos Kolias - Orange, Rima Raouda - Independent * 11:55 AM - 12:25 PM (PST) PANEL DISCUSSION: Investment Trends & Dynamics Panel Rashmi Gopinath - B Capital Group, Yvonne Lutsch - Bosch Venture Capital, Eileen Tanghal - In-Q-Tel, Albert Wang - Qualcomm Ventures * **12:30 PM - 12:50 PM (PST) PRESENTATION: The Edge: The Hottest Market for AI Accelerator Chips - Introducing the Kisaco Leadership Chart on AI Hardware Accelerators 2020-21: Edge and Automotive Michael Azoff - Kisaco Research** ## Wednesday, November 18, 2020 * ### A Software Solution Enabling Predictive Maintenance at the Sensor Level * SensiML Toolkit enables AI for a broad array of resource constrained time-series sensor endpoint applications. These include a wide range of consumer and industrial sensing applications. * The problem is machine learning engineer do not have experience with embedded system and moving model to embedded system takes long time. * AutoML for Embedded system usage. it is on the cloud. * using the compiler for that device for this tools * cost edge and cloud. easy to work on cloud. streaming data to cloud is difficult. faster if working on edge. * TinyML addresses problems, battery powered, limited internet connectivity, security/privacy, latency, economic * [https://sensiml.com/products/#process](https://www.google.com/url?q=https%3A%2F%2Fsensiml.com%2Fproducts%2F%23process&sa=D&sntz=1&usg=AOvVaw116wjx6mBnEo9x0htyL2pr) * ### Helping Fish Farmers Feed The World With Deep Learning * [https://s3-us-west-1.amazonaws.com/aquabyte-static/videos/welcome_to_aquabyte_subtitled.mp4](https://www.google.com/url?q=https%3A%2F%2Fs3-us-west-1.amazonaws.com%2Faquabyte-static%2Fvideos%2Fwelcome_to_aquabyte_subtitled.mp4&sa=D&sntz=1&usg=AOvVaw1osNtsRVM9TMCmKoPsTnj_) * Count sea lice and accurately measure biomass in real-time while reducing cage furniture. Our experts‑in‑the‑loop ensure that every single prediction is correct. * Aquabyte is seeking a Machine Learning Platform Engineer to drive the development, testing, and delivery of machine learning models that enable cutting-edge analytics and automation of fish farms around the world. * Aquabyte is on a mission to revolutionize the sustainability and efficiency of aquaculture. It is an audacious, and incredibly rewarding mission. By making fish farming cheaper and more viable than livestock production, we aim to mitigate one of the biggest causes of climate change and help prepare our planet for impending population growth. Aquaculture is the single fastest growing food-production sector in the world, and now is the time to define how technology is used to harvest the sea for generations to come. * We are currently focused on helping Norwegian salmon farmers better understand their fish populations and make environmentally-sound decisions. Through custom underwater cameras, computer vision, and machine learning we are able to quantify fish weights, detect sea lice infestations, and generate optimal feeding plans in real time. Our product operates at three levels: on-site hardware for image capture, cloud pipelines for data processing, and a user-facing web application. As a result, there are hundreds of moving pieces and no shortage of fascinating challenges across all levels of the stack. * * ### tinyMLPerf: Benchmarking Ultra-low Power Machine Learning Systems * [https://github.com/mlperf/tiny](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fmlperf%2Ftiny&sa=D&sntz=1&usg=AOvVaw1TgviYAuh83PMxDPYljOjs) * tinyMLPerf Deep Learning Benchmarks for Embedded Devices * The goal of TinyMLPerf is to provide a representative set of deep neural nets and benchmarking code to compare performance between embedded devices. Embedded devices include microcontrollers, DSPs, and tiny NN accelerators. These devics typically run at between 10MHz and 250MHz, and can perform inference using less then 50mW of power. TinyMLPerf submissions will allow device makers and researchers to choose the best hardware for their use case, and allows hardware vendors to showcase their offerings. TinyMLPerf is primarily intended to benchmark hardware rather than new network archietctures, or embedded neural net runtimes. The reference benchmarks are provided using TensorFlow Lite for Microcontrollers (TFLM). Submitters can directly use the TFLM, although submitters are encouraged to use the software stack that works best on thier hardware. * anomaly detection benchmark, visual wake words benchmark, * ### Ultra-low power neuromorphic intelligence for the sensor edge * Innatera Nanosystems BV (Innatera, (Innatera, innatera.com) is a rapidly-growing Dutch semiconductor company that develops ultra-efficient neuromorphic processors for AI at the edge. These microprocessors mimic the brain’s mechanisms for processing fast data streams from sensors, enabling complex turn-key sensor analytics functionalities, with 10,000x higher performance per watt than competing solutions. Innatera's technology serves as a critical enabler for next-generation use-cases in the IoT, wearable, embedded, and automotive domains. * ### * How is AI affecting hearables and sensors? * [https://github.com/greenwaves-technologies/nn_menu](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fgreenwaves-technologies%2Fnn_menu&sa=D&sntz=1&usg=AOvVaw2JKYYAPrnA9Mkalw2qenUQ) * The Neural Network Menu* is a collection of software that implements Neural Networks on Greenwaves Application Processors (GAP). This repository contains common mobile and edge NN archtecture examples, NN sample applications and full flagged reference designs. Our tools maps a TFLITE model (quantized or unquantized) onto gap. There is also a flow in the ingredients directory showing how to hand map from a Pytorch Model onto GAP. * [https://greenwaves-technologies.com/store/](https://www.google.com/url?q=https%3A%2F%2Fgreenwaves-technologies.com%2Fstore%2F&sa=D&sntz=1&usg=AOvVaw0Ya_w_NBAr4AbIxBe2j_YX) * GAPPoc-A is a Proof of Concept Board that can be used for demonstration of battery-operated, edge computer vision applications based on GAP8. * It incorporates GAPmod, a surface-mount module that implements all the layout sensitive portion of a GAP8 design, along with a VGA image sensor and a Bluetooth Low Energy radio. * The GAPPoc-A board enables battery-operated applications developed around algorithms such as people counting, face-identification and many others to be quickly assembled and evaluated in the field. * [https://riscv.org/blog/2019/08/risc-v-emea-roadshow-spotlight-greenwaves-technologies/](https://www.google.com/url?q=https%3A%2F%2Friscv.org%2Fblog%2F2019%2F08%2Frisc-v-emea-roadshow-spotlight-greenwaves-technologies%2F&sa=D&sntz=1&usg=AOvVaw1ikZjtEoYTgFb-S_eGEB3i) * ### Breaking the Barriers to Deploy DNNs on Low-Power Hardware * Deeplite, named to the 2020 CB Insights AI100 List of Most Innovative Artificial Intelligence Startups, is devoted to making fundamental advancements in accessible and efficient deep learning. Our solution helps deep learning engineers and experts automatically create faster, smaller and more energy-efficient deep neural networks. Industry leaders in computer vision, augmented reality and autonomous driving use our technology to unlock new possibilities for deep learning in the real world. At Deeplite, our vision is to create a lightweight intelligence that’s accessible for daily life. * [https://www.deeplite.ai/](https://www.google.com/url?q=https%3A%2F%2Fwww.deeplite.ai%2F&sa=D&sntz=1&usg=AOvVaw1r7NiQGt1hRi6S_xiJ522C) * At Deeplite, we are tackling inference optimization of deep neural networks, making them faster and energy-efficient from cloud to edge computing. Our solution leverages state-of-the-art technology from elite universities to make deep neural networks applicable for any device, and our team works hard on the iterative evolution of the science behind deep neural networks to directly improve daily life. * reduce the size of model 40x * ### Optimizing ML Models At The Edge Made Simple * [https://octoml.ai/](https://www.google.com/url?q=https%3A%2F%2Foctoml.ai%2F&sa=D&sntz=1&usg=AOvVaw2uXg6ESgQgVGrF9nQJKFve) * OctoML is an energetic new company changing how developers optimize and deploy machine learning models for their AI needs. We’re a team of machine learning systems leaders focused on making ML more efficient and easier to deploy by… applying machine learning to it! * OctoML is leveraging the power and traction of Apache TVM, an open source project originated by our founding team, to enable companies of every size to harness the power of deep learning without the expensive heavy lifting of tuning and securing models to each hardware configuration that a customer might need. * Apache TVM and Deep Learning Compilation Conference, Wed-Fri, December 2nd-4th 2020, Free Virtual Event. ## Thursday, November 19, 2020 * ### Developing Edge AI Solutions For A Post-Pandemic Society * [https://www.foghorn.io/](https://www.google.com/url?q=https%3A%2F%2Fwww.foghorn.io%2F&sa=D&sntz=1&usg=AOvVaw2aRKISC9BdrrriEej5Xb_I) * ogHorn’s Lightning™ Edge AI platform brings a groundbreaking dimension to IIoT and edge computing by embedding AI as close to the source of streaming sensor data as possible. The Edge AI software platform is a highly compact, advanced and feature-rich edge solution that delivers unprecedented low latency for onsite data processing, real-time analytics, ML and AI capabilities. It delivers the industry’s lowest total cost for computing requirements, communications services, and cloud processing and storage. * temperature detection, social distancing, cough detection, PPE/Mask detection * Flexible, customizable, integrated, actionable * ### The Evolving Landscape of Edge AI * * Coral’s local AI technology enables new possibilities across almost any kind of industry * The Coral Dev Board is a single-board computer that contains an Edge TPU coprocessor. It's ideal for prototyping new projects that demand fast on-device inferencing for machine learning models. This page is your guide to get started. The setup requires flashing Mendel Linux to the board, and then accessing the board's shell terminal. Once you have terminal access and update some of the software, we'll show you how to run an image classification model on the board. If you want to learn more about the hardware, see the Dev Board datasheet. * TPU v3, 32 to 512 TOPS, Q2 2021 * ### InferX X1, The Fastest and Most Efficient Edge Inference Accelerator * InferX X1: World's fastest and most efficient Edge Inference Accelerator. We have just launched our first inference chip and it is the best in the world for edge inference. We are bringing up neural network models now and moving forward on the steps required for Q2/2021 chip and board production and Inference Compiler availability. * mbedded FPGA, or eFPGA, enables your SoC to have flexibility in critical areas where algorithm, protocol or market needs are changing. FPGA can also accelerate many workloads faster than processors: Microsoft Azure uses one FPGA accelerator for every 2 Xeons.Flex Logix provides eFPGA cores which have density and performance similar to leading FPGAs in the same process node. Our EFLX eFPGA is silicon proven in 40nm, 28/22nm, 16nm and 12nm. 6/7nm EFLX eFPGA is planned. Our eFPGA is based on a “tile” called EFLX 4K, which comes in two versions: all logic or mostly logic with some MACs (multiply-accumulators). The programmable logic is called LUTs (look up tables) that can implement any Boolean function. EFLX 4K Logix has 4000 LUT4 equivalents, EFLX 4K DSP has 3000 LUT4s and 40 Multiplier-Accumulators (MACs): the MAC has a 22-bit pre-adder, a 22×22 multiple and a 48-bit post adder/accumulator. MACs can be combined or cascaded to form fast DSP functions. (For 40nm-180nm we offer an EFLX 1K tile). * depth-wise conv2d * ### Implementing Edge Technologies in Retail: Walmart Case Study * NVidia * ### The Era of Analog AI Compute Is Here * Mythic products are based on a unique tile-based AI compute architecture that features three fundamental hardware technologies – Compute-in-Memory, Dataflow Architecture, and Analog Computing. For AI developers, the Mythic SDK streamlines the preparation of trained neural networks for edge and low-latency datacenter deployments, and also performs automatic optimization and compilation of dataflow graphs for our unique architecture. * low power consumption, ultra-low latency, high ai performance, large weight capacity, small form factor, cost effective solution * ### **Us** ing Edge AI To Detect Repetitive Mot * Bosch Sensortec develops and markets a wide portfolio of MEMS sensors and solutions for applications in smartphones, tablets, wearables, AR/VR devices, drones, robots, smart home and the Internet of Things. Striving to meet the demanding requirements of the consumer electronics market, we provide best-in-class sensing solutions in terms of customer focus, quality and reliability, performance, sustainability and competitiveness. * [https://github.com/BoschSensortec](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FBoschSensortec&sa=D&sntz=1&usg=AOvVaw0Xr8dUHPERsj-rYH7ZAnP1) ## Friday, November 20, 2020 * ### *Spatial Computing: A Collision of Edge and Cloud-Based Computing * [https://github.com/magicleap](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fmagicleap&sa=D&sntz=1&usg=AOvVaw22R0IwwKYOdwv06BvMpvZF) * instance semantic segmentation contextual computing * spatial computing * SLAM: tracking/localization, mapping: * latency is critical for see through displays * weight is critical cannot compensate for lack of compute with more sensors * thermal is critical more sensors and more compute lead to heat * rigidity leads to weight our device should be light * very stringent requirements for MR * why build a map: drift correction, robustness (pose recovery), persistence * feature descriptors * matching across large baselines and illumination changes is challenging * most of the SOTA methods based on deep learning and not feasible withing compute budget * our deep descriptor is optimized for SLAM and provides the best trade off in terms of performance and compute * semantic segmentation 3d point cloud * ### Building An Autonomous Network For IoT and Edge Applications * 5G + AI * ### Practical Edge Inferencing: Enabling fastest AI inferencing per Watt leveraging sparsity * [https://www.graimatterlabs.ai/](https://www.google.com/url?q=https%3A%2F%2Fwww.graimatterlabs.ai%2F&sa=D&sntz=1&usg=AOvVaw1qLIEaRzrtXoYDAb_Y4tQb) * The world’s first sparsity-enabled AI processor optimized for ultra-low latency and low power processing at the edge. * GrAI One drastically reduces application latency, for instance, it reduces the end-to-end latencies for deep learning networks such as PilotNet to the order of milliseconds. The GrAI One chip is based on GML’s innovative NeuronFlow™ technology that combines the dynamic Dataflow paradigm with sparse computing to produce massively parallel in-network processing. * GrAI Matter Labs ([www.graimatterlabs.ai](http://www.google.com/url?q=http%3A%2F%2Fwww.graimatterlabs.ai%2F&sa=D&sntz=1&usg=AOvVaw3eJ9oROjswyCFHO-68LiDi)), a fabless semiconductor company specialized in brain-inspired technology, designs and develops fully programmable ultra-low power neuromorphic HW for sensor analytics and machine learning. The company has offices in Eindhoven (NL), Paris (FR) and San Jose (USA) and has strong relations with top-ranking research groups on neuroscience, human vision and natural computation * ### **Large Scale Deep Learning and AI models on the Edge * deployment pipelines * there are several steps involved in the AI/ML life-cycle * several tools to help simplify the whole process * tensorflow extended (TFX): an end to end platform for deploying production ML pipelines * MLflow (other options michelangelo): an open source platform for the end to end machine learning life cycle * apache airflow (other options kubeflow): an open source workflow management platform * dataiku data science studio (DSS): collaborative data science software platform for teams of data scientist , data analysts, and engineers to explore prototype build and deliver * ### The Edge: The Hottest Market for AI Accelerator Chips - Introducing the Kisaco Leadership Chart on AI Hardware Accelerators 2020-21: Edge and Automotive * [https://www.kisacoresearch.com/#about-us](https://www.google.com/url?q=https%3A%2F%2Fwww.kisacoresearch.com%2F%23about-us&sa=D&sntz=1&usg=AOvVaw0nasAOo80KuyOwbm4OeiOb) [OpenHTF is a Python library that provides a set of convenient abstractions designed to remove as much boilerplate as possible from hardware test setup and execution, so test engineers can focus primarily on test logic.](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fgoogle%2Fopenhtf&sa=D&sntz=1&usg=AOvVaw0zU3RKntPn4N8JIkPvriIu) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/3LTMUMvLqIOxMLGRMkavoKhjR0yGpVpsaeb_NnspLybaPexjoQBnvVa2C2973HUQ30RiIqubg5B9F6eLjnxKXlE=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/3LTMUMvLqIOxMLGRMkavoKhjR0yGpVpsaeb_NnspLybaPexjoQBnvVa2C2973HUQ30RiIqubg5B9F6eLjnxKXlE=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Edge AI summit Short summary of the edge AI summit 18-20 November 2020 Wednesday, November 18, 2020 A Software Solution Enabling Predictive Maintenance at the Sensor Level Helping Fish Farmers Feed The World With Deep Learning tinyMLPerf: Benchmarking Ultra-low Power Machine Learning Systems Ultra-low power neuromorphic intelligence for the sensor edge * How is AI affecting hearables and sensors? Breaking the Barriers to Deploy DNNs on Low-Power Hardware Optimizing ML Models At The Edge Made Simple Thursday, November 19, 2020 Developing Edge AI Solutions For A Post-Pandemic Society The Evolving Landscape of Edge AI InferX X1, The Fastest and Most Efficient Edge Inference Accelerator Implementing Edge Technologies in Retail: Walmart Case Study The Era of Analog AI Compute Is Here Using Edge AI To Detect Repetitive Mot Friday, November 20, 2020 *Spatial Computing: A Collision of Edge and Cloud-Based Computing Building An Autonomous Network For IoT and Edge Applications Practical Edge Inferencing: Enabling fastest AI inferencing per Watt leveraging sparsity **Large Scale Deep Learning and AI models on the Edge The Edge: The Hottest Market for AI Accelerator Chips - Introducing the Kisaco Leadership Chart on AI Hardware Accelerators 2020-21: Edge and Automotive # Short summary of the edge AI summit 18-20 November 2020 Best of Wednesday, November 18, 2020; tinyMLPerf, Breaking the Barriers to Deploy DNNs on Low-Power Hardware, Optimizing ML Models At The Edge Made Simple **Thursday, November 19, 2020** * 8:00 AM - 8:30 AM (PST) KEYNOTE PRESENTATION: Developing Edge AI Solutions For A Post-Pandemic Society Sastry Malladi - FogHorn Systems * 8:35 AM - 9:05 AM (PST) PRESENTATION: The Evolving Landscape of Edge AI Ajay Nair - Google * 9:05 AM - 9:20 AM (PST) Comfort Break * 9:20 AM - 9:45 AM (PST) PRESENTATION: InferX X1, The Fastest and Most Efficient Edge Inference Accelerator Cheng Wang - Flex Logix Technologies Inc. * 9:50 AM - 10:20 AM (PST) PRESENTATION: Implementing Edge Technologies in Retail: Walmart Case Study Alex Sabatier - Nvidia * 10:20 AM - 10:35 AM (PST) Comfort Break * 10:35 AM - 11:20 AM (PST) Meet speaker! * **11:20 AM - 11:50 AM (PST) PRESENTATION: The Era of Analog AI Compute Is Here Mike Henry - Mythic** * 11:55 AM - 12:25 PM (PST) PRESENTATION: Using Edge AI To Detect Repetitive Mot Marcellino Gemelli - Bosch Sensortec * 12:30 PM - 2:30 PM (PST) NETWORKING - Dedicated Networking 2 hours for 1-2-1 Video Meetings **Friday, November 20, 2020** * 8:00 AM - 8:30 AM (PST) PRESENTATION: Spatial Computing: A Collision of Edge and Cloud-Based Computing Ashwin Swaminathan - Magic Leap * 8:35 AM - 9:05 AM (PST) PRESENTATION: Building An Autonomous Network For IoT and Edge Applications Anshul Bhatt - Rakuten Mobile * 9:05 AM - 9:20 AM (PST) Comfort Break * 9:20 AM - 9:45 AM (PST) PRESENTATION: Practical Edge Inferencing: Enabling fastest AI inferencing per Watt leveraging sparsity Mahesh Makhijani - GrAI Matter Labs * **9:50 AM - 10:20 AM (PST) PRESENTATION: Large Scale Deep Learning and AI models on the Edge Chandra Khatri - Got It AI** * 10:20 AM - 10:35 AM (PST) Comfort Break * 10:35 AM - 11:20 AM (PST) NETWORKING: Interest Groups (18 people per room, topic-specific discussions) * 11:20 AM - 11:50 AM (PST) PANEL DISCUSSION: The Symbiotic Relationship between 5G and Edge AI Sami Badri - Credit Suisse, Christos Kolias - Orange, Rima Raouda - Independent * 11:55 AM - 12:25 PM (PST) PANEL DISCUSSION: Investment Trends & Dynamics Panel Rashmi Gopinath - B Capital Group, Yvonne Lutsch - Bosch Venture Capital, Eileen Tanghal - In-Q-Tel, Albert Wang - Qualcomm Ventures * **12:30 PM - 12:50 PM (PST) PRESENTATION: The Edge: The Hottest Market for AI Accelerator Chips - Introducing the Kisaco Leadership Chart on AI Hardware Accelerators 2020-21: Edge and Automotive Michael Azoff - Kisaco Research** ## Wednesday, November 18, 2020 * ### A Software Solution Enabling Predictive Maintenance at the Sensor Level * SensiML Toolkit enables AI for a broad array of resource constrained time-series sensor endpoint applications. These include a wide range of consumer and industrial sensing applications. * The problem is machine learning engineer do not have experience with embedded system and moving model to embedded system takes long time. * AutoML for Embedded system usage. it is on the cloud. * using the compiler for that device for this tools * cost edge and cloud. easy to work on cloud. streaming data to cloud is difficult. faster if working on edge. * TinyML addresses problems, battery powered, limited internet connectivity, security/privacy, latency, economic * [https://sensiml.com/products/#process](https://www.google.com/url?q=https%3A%2F%2Fsensiml.com%2Fproducts%2F%23process&sa=D&sntz=1&usg=AOvVaw116wjx6mBnEo9x0htyL2pr) * ### Helping Fish Farmers Feed The World With Deep Learning * [https://s3-us-west-1.amazonaws.com/aquabyte-static/videos/welcome_to_aquabyte_subtitled.mp4](https://www.google.com/url?q=https%3A%2F%2Fs3-us-west-1.amazonaws.com%2Faquabyte-static%2Fvideos%2Fwelcome_to_aquabyte_subtitled.mp4&sa=D&sntz=1&usg=AOvVaw1osNtsRVM9TMCmKoPsTnj_) * Count sea lice and accurately measure biomass in real-time while reducing cage furniture. Our experts‑in‑the‑loop ensure that every single prediction is correct. * Aquabyte is seeking a Machine Learning Platform Engineer to drive the development, testing, and delivery of machine learning models that enable cutting-edge analytics and automation of fish farms around the world. * Aquabyte is on a mission to revolutionize the sustainability and efficiency of aquaculture. It is an audacious, and incredibly rewarding mission. By making fish farming cheaper and more viable than livestock production, we aim to mitigate one of the biggest causes of climate change and help prepare our planet for impending population growth. Aquaculture is the single fastest growing food-production sector in the world, and now is the time to define how technology is used to harvest the sea for generations to come. * We are currently focused on helping Norwegian salmon farmers better understand their fish populations and make environmentally-sound decisions. Through custom underwater cameras, computer vision, and machine learning we are able to quantify fish weights, detect sea lice infestations, and generate optimal feeding plans in real time. Our product operates at three levels: on-site hardware for image capture, cloud pipelines for data processing, and a user-facing web application. As a result, there are hundreds of moving pieces and no shortage of fascinating challenges across all levels of the stack. * * ### tinyMLPerf: Benchmarking Ultra-low Power Machine Learning Systems * [https://github.com/mlperf/tiny](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fmlperf%2Ftiny&sa=D&sntz=1&usg=AOvVaw1TgviYAuh83PMxDPYljOjs) * tinyMLPerf Deep Learning Benchmarks for Embedded Devices * The goal of TinyMLPerf is to provide a representative set of deep neural nets and benchmarking code to compare performance between embedded devices. Embedded devices include microcontrollers, DSPs, and tiny NN accelerators. These devics typically run at between 10MHz and 250MHz, and can perform inference using less then 50mW of power. TinyMLPerf submissions will allow device makers and researchers to choose the best hardware for their use case, and allows hardware vendors to showcase their offerings. TinyMLPerf is primarily intended to benchmark hardware rather than new network archietctures, or embedded neural net runtimes. The reference benchmarks are provided using TensorFlow Lite for Microcontrollers (TFLM). Submitters can directly use the TFLM, although submitters are encouraged to use the software stack that works best on thier hardware. * anomaly detection benchmark, visual wake words benchmark, * ### Ultra-low power neuromorphic intelligence for the sensor edge * Innatera Nanosystems BV (Innatera, (Innatera, innatera.com) is a rapidly-growing Dutch semiconductor company that develops ultra-efficient neuromorphic processors for AI at the edge. These microprocessors mimic the brain’s mechanisms for processing fast data streams from sensors, enabling complex turn-key sensor analytics functionalities, with 10,000x higher performance per watt than competing solutions. Innatera's technology serves as a critical enabler for next-generation use-cases in the IoT, wearable, embedded, and automotive domains. * ### * How is AI affecting hearables and sensors? * [https://github.com/greenwaves-technologies/nn_menu](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fgreenwaves-technologies%2Fnn_menu&sa=D&sntz=1&usg=AOvVaw2JKYYAPrnA9Mkalw2qenUQ) * The Neural Network Menu* is a collection of software that implements Neural Networks on Greenwaves Application Processors (GAP). This repository contains common mobile and edge NN archtecture examples, NN sample applications and full flagged reference designs. Our tools maps a TFLITE model (quantized or unquantized) onto gap. There is also a flow in the ingredients directory showing how to hand map from a Pytorch Model onto GAP. * [https://greenwaves-technologies.com/store/](https://www.google.com/url?q=https%3A%2F%2Fgreenwaves-technologies.com%2Fstore%2F&sa=D&sntz=1&usg=AOvVaw0Ya_w_NBAr4AbIxBe2j_YX) * GAPPoc-A is a Proof of Concept Board that can be used for demonstration of battery-operated, edge computer vision applications based on GAP8. * It incorporates GAPmod, a surface-mount module that implements all the layout sensitive portion of a GAP8 design, along with a VGA image sensor and a Bluetooth Low Energy radio. * The GAPPoc-A board enables battery-operated applications developed around algorithms such as people counting, face-identification and many others to be quickly assembled and evaluated in the field. * [https://riscv.org/blog/2019/08/risc-v-emea-roadshow-spotlight-greenwaves-technologies/](https://www.google.com/url?q=https%3A%2F%2Friscv.org%2Fblog%2F2019%2F08%2Frisc-v-emea-roadshow-spotlight-greenwaves-technologies%2F&sa=D&sntz=1&usg=AOvVaw1ikZjtEoYTgFb-S_eGEB3i) * ### Breaking the Barriers to Deploy DNNs on Low-Power Hardware * Deeplite, named to the 2020 CB Insights AI100 List of Most Innovative Artificial Intelligence Startups, is devoted to making fundamental advancements in accessible and efficient deep learning. Our solution helps deep learning engineers and experts automatically create faster, smaller and more energy-efficient deep neural networks. Industry leaders in computer vision, augmented reality and autonomous driving use our technology to unlock new possibilities for deep learning in the real world. At Deeplite, our vision is to create a lightweight intelligence that’s accessible for daily life. * [https://www.deeplite.ai/](https://www.google.com/url?q=https%3A%2F%2Fwww.deeplite.ai%2F&sa=D&sntz=1&usg=AOvVaw1r7NiQGt1hRi6S_xiJ522C) * At Deeplite, we are tackling inference optimization of deep neural networks, making them faster and energy-efficient from cloud to edge computing. Our solution leverages state-of-the-art technology from elite universities to make deep neural networks applicable for any device, and our team works hard on the iterative evolution of the science behind deep neural networks to directly improve daily life. * reduce the size of model 40x * ### Optimizing ML Models At The Edge Made Simple * [https://octoml.ai/](https://www.google.com/url?q=https%3A%2F%2Foctoml.ai%2F&sa=D&sntz=1&usg=AOvVaw2uXg6ESgQgVGrF9nQJKFve) * OctoML is an energetic new company changing how developers optimize and deploy machine learning models for their AI needs. We’re a team of machine learning systems leaders focused on making ML more efficient and easier to deploy by… applying machine learning to it! * OctoML is leveraging the power and traction of Apache TVM, an open source project originated by our founding team, to enable companies of every size to harness the power of deep learning without the expensive heavy lifting of tuning and securing models to each hardware configuration that a customer might need. * Apache TVM and Deep Learning Compilation Conference, Wed-Fri, December 2nd-4th 2020, Free Virtual Event. ## Thursday, November 19, 2020 * ### Developing Edge AI Solutions For A Post-Pandemic Society * [https://www.foghorn.io/](https://www.google.com/url?q=https%3A%2F%2Fwww.foghorn.io%2F&sa=D&sntz=1&usg=AOvVaw2aRKISC9BdrrriEej5Xb_I) * ogHorn’s Lightning™ Edge AI platform brings a groundbreaking dimension to IIoT and edge computing by embedding AI as close to the source of streaming sensor data as possible. The Edge AI software platform is a highly compact, advanced and feature-rich edge solution that delivers unprecedented low latency for onsite data processing, real-time analytics, ML and AI capabilities. It delivers the industry’s lowest total cost for computing requirements, communications services, and cloud processing and storage. * temperature detection, social distancing, cough detection, PPE/Mask detection * Flexible, customizable, integrated, actionable * ### The Evolving Landscape of Edge AI * * Coral’s local AI technology enables new possibilities across almost any kind of industry * The Coral Dev Board is a single-board computer that contains an Edge TPU coprocessor. It's ideal for prototyping new projects that demand fast on-device inferencing for machine learning models. This page is your guide to get started. The setup requires flashing Mendel Linux to the board, and then accessing the board's shell terminal. Once you have terminal access and update some of the software, we'll show you how to run an image classification model on the board. If you want to learn more about the hardware, see the Dev Board datasheet. * TPU v3, 32 to 512 TOPS, Q2 2021 * ### InferX X1, The Fastest and Most Efficient Edge Inference Accelerator * InferX X1: World's fastest and most efficient Edge Inference Accelerator. We have just launched our first inference chip and it is the best in the world for edge inference. We are bringing up neural network models now and moving forward on the steps required for Q2/2021 chip and board production and Inference Compiler availability. * mbedded FPGA, or eFPGA, enables your SoC to have flexibility in critical areas where algorithm, protocol or market needs are changing. FPGA can also accelerate many workloads faster than processors: Microsoft Azure uses one FPGA accelerator for every 2 Xeons.Flex Logix provides eFPGA cores which have density and performance similar to leading FPGAs in the same process node. Our EFLX eFPGA is silicon proven in 40nm, 28/22nm, 16nm and 12nm. 6/7nm EFLX eFPGA is planned. Our eFPGA is based on a “tile” called EFLX 4K, which comes in two versions: all logic or mostly logic with some MACs (multiply-accumulators). The programmable logic is called LUTs (look up tables) that can implement any Boolean function. EFLX 4K Logix has 4000 LUT4 equivalents, EFLX 4K DSP has 3000 LUT4s and 40 Multiplier-Accumulators (MACs): the MAC has a 22-bit pre-adder, a 22×22 multiple and a 48-bit post adder/accumulator. MACs can be combined or cascaded to form fast DSP functions. (For 40nm-180nm we offer an EFLX 1K tile). * depth-wise conv2d * ### Implementing Edge Technologies in Retail: Walmart Case Study * NVidia * ### The Era of Analog AI Compute Is Here * Mythic products are based on a unique tile-based AI compute architecture that features three fundamental hardware technologies – Compute-in-Memory, Dataflow Architecture, and Analog Computing. For AI developers, the Mythic SDK streamlines the preparation of trained neural networks for edge and low-latency datacenter deployments, and also performs automatic optimization and compilation of dataflow graphs for our unique architecture. * low power consumption, ultra-low latency, high ai performance, large weight capacity, small form factor, cost effective solution * ### **Us** ing Edge AI To Detect Repetitive Mot * Bosch Sensortec develops and markets a wide portfolio of MEMS sensors and solutions for applications in smartphones, tablets, wearables, AR/VR devices, drones, robots, smart home and the Internet of Things. Striving to meet the demanding requirements of the consumer electronics market, we provide best-in-class sensing solutions in terms of customer focus, quality and reliability, performance, sustainability and competitiveness. * [https://github.com/BoschSensortec](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FBoschSensortec&sa=D&sntz=1&usg=AOvVaw0Xr8dUHPERsj-rYH7ZAnP1) ## Friday, November 20, 2020 * ### *Spatial Computing: A Collision of Edge and Cloud-Based Computing * [https://github.com/magicleap](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fmagicleap&sa=D&sntz=1&usg=AOvVaw22R0IwwKYOdwv06BvMpvZF) * instance semantic segmentation contextual computing * spatial computing * SLAM: tracking/localization, mapping: * latency is critical for see through displays * weight is critical cannot compensate for lack of compute with more sensors * thermal is critical more sensors and more compute lead to heat * rigidity leads to weight our device should be light * very stringent requirements for MR * why build a map: drift correction, robustness (pose recovery), persistence * feature descriptors * matching across large baselines and illumination changes is challenging * most of the SOTA methods based on deep learning and not feasible withing compute budget * our deep descriptor is optimized for SLAM and provides the best trade off in terms of performance and compute * semantic segmentation 3d point cloud * ### Building An Autonomous Network For IoT and Edge Applications * 5G + AI * ### Practical Edge Inferencing: Enabling fastest AI inferencing per Watt leveraging sparsity * [https://www.graimatterlabs.ai/](https://www.google.com/url?q=https%3A%2F%2Fwww.graimatterlabs.ai%2F&sa=D&sntz=1&usg=AOvVaw1qLIEaRzrtXoYDAb_Y4tQb) * The world’s first sparsity-enabled AI processor optimized for ultra-low latency and low power processing at the edge. * GrAI One drastically reduces application latency, for instance, it reduces the end-to-end latencies for deep learning networks such as PilotNet to the order of milliseconds. The GrAI One chip is based on GML’s innovative NeuronFlow™ technology that combines the dynamic Dataflow paradigm with sparse computing to produce massively parallel in-network processing. * GrAI Matter Labs ([www.graimatterlabs.ai](http://www.google.com/url?q=http%3A%2F%2Fwww.graimatterlabs.ai%2F&sa=D&sntz=1&usg=AOvVaw3eJ9oROjswyCFHO-68LiDi)), a fabless semiconductor company specialized in brain-inspired technology, designs and develops fully programmable ultra-low power neuromorphic HW for sensor analytics and machine learning. The company has offices in Eindhoven (NL), Paris (FR) and San Jose (USA) and has strong relations with top-ranking research groups on neuroscience, human vision and natural computation * ### **Large Scale Deep Learning and AI models on the Edge * deployment pipelines * there are several steps involved in the AI/ML life-cycle * several tools to help simplify the whole process * tensorflow extended (TFX): an end to end platform for deploying production ML pipelines * MLflow (other options michelangelo): an open source platform for the end to end machine learning life cycle * apache airflow (other options kubeflow): an open source workflow management platform * dataiku data science studio (DSS): collaborative data science software platform for teams of data scientist , data analysts, and engineers to explore prototype build and deliver * ### The Edge: The Hottest Market for AI Accelerator Chips - Introducing the Kisaco Leadership Chart on AI Hardware Accelerators 2020-21: Edge and Automotive * [https://www.kisacoresearch.com/#about-us](https://www.google.com/url?q=https%3A%2F%2Fwww.kisacoresearch.com%2F%23about-us&sa=D&sntz=1&usg=AOvVaw0nasAOo80KuyOwbm4OeiOb) [OpenHTF is a Python library that provides a set of convenient abstractions designed to remove as much boilerplate as possible from hardware test setup and execution, so test engineers can focus primarily on test logic.](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fgoogle%2Fopenhtf&sa=D&sntz=1&usg=AOvVaw0zU3RKntPn4N8JIkPvriIu) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/3LTMUMvLqIOxMLGRMkavoKhjR0yGpVpsaeb_NnspLybaPexjoQBnvVa2C2973HUQ30RiIqubg5B9F6eLjnxKXlE=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/3LTMUMvLqIOxMLGRMkavoKhjR0yGpVpsaeb_NnspLybaPexjoQBnvVa2C2973HUQ30RiIqubg5B9F6eLjnxKXlE=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Edge AI summit Short summary of the edge AI summit 18-20 November 2020 Wednesday, November 18, 2020 A Software Solution Enabling Predictive Maintenance at the Sensor Level Helping Fish Farmers Feed The World With Deep Learning tinyMLPerf: Benchmarking Ultra-low Power Machine Learning Systems Ultra-low power neuromorphic intelligence for the sensor edge * How is AI affecting hearables and sensors? Breaking the Barriers to Deploy DNNs on Low-Power Hardware Optimizing ML Models At The Edge Made Simple Thursday, November 19, 2020 Developing Edge AI Solutions For A Post-Pandemic Society The Evolving Landscape of Edge AI InferX X1, The Fastest and Most Efficient Edge Inference Accelerator Implementing Edge Technologies in Retail: Walmart Case Study The Era of Analog AI Compute Is Here Using Edge AI To Detect Repetitive Mot Friday, November 20, 2020 *Spatial Computing: A Collision of Edge and Cloud-Based Computing Building An Autonomous Network For IoT and Edge Applications Practical Edge Inferencing: Enabling fastest AI inferencing per Watt leveraging sparsity **Large Scale Deep Learning and AI models on the Edge The Edge: The Hottest Market for AI Accelerator Chips - Introducing the Kisaco Leadership Chart on AI Hardware Accelerators 2020-21: Edge and Automotive # Short summary of the edge AI summit 18-20 November 2020 Best of Wednesday, November 18, 2020; tinyMLPerf, Breaking the Barriers to Deploy DNNs on Low-Power Hardware, Optimizing ML Models At The Edge Made Simple **Thursday, November 19, 2020** * 8:00 AM - 8:30 AM (PST) KEYNOTE PRESENTATION: Developing Edge AI Solutions For A Post-Pandemic Society Sastry Malladi - FogHorn Systems * 8:35 AM - 9:05 AM (PST) PRESENTATION: The Evolving Landscape of Edge AI Ajay Nair - Google * 9:05 AM - 9:20 AM (PST) Comfort Break * 9:20 AM - 9:45 AM (PST) PRESENTATION: InferX X1, The Fastest and Most Efficient Edge Inference Accelerator Cheng Wang - Flex Logix Technologies Inc. * 9:50 AM - 10:20 AM (PST) PRESENTATION: Implementing Edge Technologies in Retail: Walmart Case Study Alex Sabatier - Nvidia * 10:20 AM - 10:35 AM (PST) Comfort Break * 10:35 AM - 11:20 AM (PST) Meet speaker! * **11:20 AM - 11:50 AM (PST) PRESENTATION: The Era of Analog AI Compute Is Here Mike Henry - Mythic** * 11:55 AM - 12:25 PM (PST) PRESENTATION: Using Edge AI To Detect Repetitive Mot Marcellino Gemelli - Bosch Sensortec * 12:30 PM - 2:30 PM (PST) NETWORKING - Dedicated Networking 2 hours for 1-2-1 Video Meetings **Friday, November 20, 2020** * 8:00 AM - 8:30 AM (PST) PRESENTATION: Spatial Computing: A Collision of Edge and Cloud-Based Computing Ashwin Swaminathan - Magic Leap * 8:35 AM - 9:05 AM (PST) PRESENTATION: Building An Autonomous Network For IoT and Edge Applications Anshul Bhatt - Rakuten Mobile * 9:05 AM - 9:20 AM (PST) Comfort Break * 9:20 AM - 9:45 AM (PST) PRESENTATION: Practical Edge Inferencing: Enabling fastest AI inferencing per Watt leveraging sparsity Mahesh Makhijani - GrAI Matter Labs * **9:50 AM - 10:20 AM (PST) PRESENTATION: Large Scale Deep Learning and AI models on the Edge Chandra Khatri - Got It AI** * 10:20 AM - 10:35 AM (PST) Comfort Break * 10:35 AM - 11:20 AM (PST) NETWORKING: Interest Groups (18 people per room, topic-specific discussions) * 11:20 AM - 11:50 AM (PST) PANEL DISCUSSION: The Symbiotic Relationship between 5G and Edge AI Sami Badri - Credit Suisse, Christos Kolias - Orange, Rima Raouda - Independent * 11:55 AM - 12:25 PM (PST) PANEL DISCUSSION: Investment Trends & Dynamics Panel Rashmi Gopinath - B Capital Group, Yvonne Lutsch - Bosch Venture Capital, Eileen Tanghal - In-Q-Tel, Albert Wang - Qualcomm Ventures * **12:30 PM - 12:50 PM (PST) PRESENTATION: The Edge: The Hottest Market for AI Accelerator Chips - Introducing the Kisaco Leadership Chart on AI Hardware Accelerators 2020-21: Edge and Automotive Michael Azoff - Kisaco Research** ## Wednesday, November 18, 2020 * ### A Software Solution Enabling Predictive Maintenance at the Sensor Level * SensiML Toolkit enables AI for a broad array of resource constrained time-series sensor endpoint applications. These include a wide range of consumer and industrial sensing applications. * The problem is machine learning engineer do not have experience with embedded system and moving model to embedded system takes long time. * AutoML for Embedded system usage. it is on the cloud. * using the compiler for that device for this tools * cost edge and cloud. easy to work on cloud. streaming data to cloud is difficult. faster if working on edge. * TinyML addresses problems, battery powered, limited internet connectivity, security/privacy, latency, economic * [https://sensiml.com/products/#process](https://www.google.com/url?q=https%3A%2F%2Fsensiml.com%2Fproducts%2F%23process&sa=D&sntz=1&usg=AOvVaw116wjx6mBnEo9x0htyL2pr) * ### Helping Fish Farmers Feed The World With Deep Learning * [https://s3-us-west-1.amazonaws.com/aquabyte-static/videos/welcome_to_aquabyte_subtitled.mp4](https://www.google.com/url?q=https%3A%2F%2Fs3-us-west-1.amazonaws.com%2Faquabyte-static%2Fvideos%2Fwelcome_to_aquabyte_subtitled.mp4&sa=D&sntz=1&usg=AOvVaw1osNtsRVM9TMCmKoPsTnj_) * Count sea lice and accurately measure biomass in real-time while reducing cage furniture. Our experts‑in‑the‑loop ensure that every single prediction is correct. * Aquabyte is seeking a Machine Learning Platform Engineer to drive the development, testing, and delivery of machine learning models that enable cutting-edge analytics and automation of fish farms around the world. * Aquabyte is on a mission to revolutionize the sustainability and efficiency of aquaculture. It is an audacious, and incredibly rewarding mission. By making fish farming cheaper and more viable than livestock production, we aim to mitigate one of the biggest causes of climate change and help prepare our planet for impending population growth. Aquaculture is the single fastest growing food-production sector in the world, and now is the time to define how technology is used to harvest the sea for generations to come. * We are currently focused on helping Norwegian salmon farmers better understand their fish populations and make environmentally-sound decisions. Through custom underwater cameras, computer vision, and machine learning we are able to quantify fish weights, detect sea lice infestations, and generate optimal feeding plans in real time. Our product operates at three levels: on-site hardware for image capture, cloud pipelines for data processing, and a user-facing web application. As a result, there are hundreds of moving pieces and no shortage of fascinating challenges across all levels of the stack. * * ### tinyMLPerf: Benchmarking Ultra-low Power Machine Learning Systems * [https://github.com/mlperf/tiny](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fmlperf%2Ftiny&sa=D&sntz=1&usg=AOvVaw1TgviYAuh83PMxDPYljOjs) * tinyMLPerf Deep Learning Benchmarks for Embedded Devices * The goal of TinyMLPerf is to provide a representative set of deep neural nets and benchmarking code to compare performance between embedded devices. Embedded devices include microcontrollers, DSPs, and tiny NN accelerators. These devics typically run at between 10MHz and 250MHz, and can perform inference using less then 50mW of power. TinyMLPerf submissions will allow device makers and researchers to choose the best hardware for their use case, and allows hardware vendors to showcase their offerings. TinyMLPerf is primarily intended to benchmark hardware rather than new network archietctures, or embedded neural net runtimes. The reference benchmarks are provided using TensorFlow Lite for Microcontrollers (TFLM). Submitters can directly use the TFLM, although submitters are encouraged to use the software stack that works best on thier hardware. * anomaly detection benchmark, visual wake words benchmark, * ### Ultra-low power neuromorphic intelligence for the sensor edge * Innatera Nanosystems BV (Innatera, (Innatera, innatera.com) is a rapidly-growing Dutch semiconductor company that develops ultra-efficient neuromorphic processors for AI at the edge. These microprocessors mimic the brain’s mechanisms for processing fast data streams from sensors, enabling complex turn-key sensor analytics functionalities, with 10,000x higher performance per watt than competing solutions. Innatera's technology serves as a critical enabler for next-generation use-cases in the IoT, wearable, embedded, and automotive domains. * ### * How is AI affecting hearables and sensors? * [https://github.com/greenwaves-technologies/nn_menu](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fgreenwaves-technologies%2Fnn_menu&sa=D&sntz=1&usg=AOvVaw2JKYYAPrnA9Mkalw2qenUQ) * The Neural Network Menu* is a collection of software that implements Neural Networks on Greenwaves Application Processors (GAP). This repository contains common mobile and edge NN archtecture examples, NN sample applications and full flagged reference designs. Our tools maps a TFLITE model (quantized or unquantized) onto gap. There is also a flow in the ingredients directory showing how to hand map from a Pytorch Model onto GAP. * [https://greenwaves-technologies.com/store/](https://www.google.com/url?q=https%3A%2F%2Fgreenwaves-technologies.com%2Fstore%2F&sa=D&sntz=1&usg=AOvVaw0Ya_w_NBAr4AbIxBe2j_YX) * GAPPoc-A is a Proof of Concept Board that can be used for demonstration of battery-operated, edge computer vision applications based on GAP8. * It incorporates GAPmod, a surface-mount module that implements all the layout sensitive portion of a GAP8 design, along with a VGA image sensor and a Bluetooth Low Energy radio. * The GAPPoc-A board enables battery-operated applications developed around algorithms such as people counting, face-identification and many others to be quickly assembled and evaluated in the field. * [https://riscv.org/blog/2019/08/risc-v-emea-roadshow-spotlight-greenwaves-technologies/](https://www.google.com/url?q=https%3A%2F%2Friscv.org%2Fblog%2F2019%2F08%2Frisc-v-emea-roadshow-spotlight-greenwaves-technologies%2F&sa=D&sntz=1&usg=AOvVaw1ikZjtEoYTgFb-S_eGEB3i) * ### Breaking the Barriers to Deploy DNNs on Low-Power Hardware * Deeplite, named to the 2020 CB Insights AI100 List of Most Innovative Artificial Intelligence Startups, is devoted to making fundamental advancements in accessible and efficient deep learning. Our solution helps deep learning engineers and experts automatically create faster, smaller and more energy-efficient deep neural networks. Industry leaders in computer vision, augmented reality and autonomous driving use our technology to unlock new possibilities for deep learning in the real world. At Deeplite, our vision is to create a lightweight intelligence that’s accessible for daily life. * [https://www.deeplite.ai/](https://www.google.com/url?q=https%3A%2F%2Fwww.deeplite.ai%2F&sa=D&sntz=1&usg=AOvVaw1r7NiQGt1hRi6S_xiJ522C) * At Deeplite, we are tackling inference optimization of deep neural networks, making them faster and energy-efficient from cloud to edge computing. Our solution leverages state-of-the-art technology from elite universities to make deep neural networks applicable for any device, and our team works hard on the iterative evolution of the science behind deep neural networks to directly improve daily life. * reduce the size of model 40x * ### Optimizing ML Models At The Edge Made Simple * [https://octoml.ai/](https://www.google.com/url?q=https%3A%2F%2Foctoml.ai%2F&sa=D&sntz=1&usg=AOvVaw2uXg6ESgQgVGrF9nQJKFve) * OctoML is an energetic new company changing how developers optimize and deploy machine learning models for their AI needs. We’re a team of machine learning systems leaders focused on making ML more efficient and easier to deploy by… applying machine learning to it! * OctoML is leveraging the power and traction of Apache TVM, an open source project originated by our founding team, to enable companies of every size to harness the power of deep learning without the expensive heavy lifting of tuning and securing models to each hardware configuration that a customer might need. * Apache TVM and Deep Learning Compilation Conference, Wed-Fri, December 2nd-4th 2020, Free Virtual Event. ## Thursday, November 19, 2020 * ### Developing Edge AI Solutions For A Post-Pandemic Society * [https://www.foghorn.io/](https://www.google.com/url?q=https%3A%2F%2Fwww.foghorn.io%2F&sa=D&sntz=1&usg=AOvVaw2aRKISC9BdrrriEej5Xb_I) * ogHorn’s Lightning™ Edge AI platform brings a groundbreaking dimension to IIoT and edge computing by embedding AI as close to the source of streaming sensor data as possible. The Edge AI software platform is a highly compact, advanced and feature-rich edge solution that delivers unprecedented low latency for onsite data processing, real-time analytics, ML and AI capabilities. It delivers the industry’s lowest total cost for computing requirements, communications services, and cloud processing and storage. * temperature detection, social distancing, cough detection, PPE/Mask detection * Flexible, customizable, integrated, actionable * ### The Evolving Landscape of Edge AI * * Coral’s local AI technology enables new possibilities across almost any kind of industry * The Coral Dev Board is a single-board computer that contains an Edge TPU coprocessor. It's ideal for prototyping new projects that demand fast on-device inferencing for machine learning models. This page is your guide to get started. The setup requires flashing Mendel Linux to the board, and then accessing the board's shell terminal. Once you have terminal access and update some of the software, we'll show you how to run an image classification model on the board. If you want to learn more about the hardware, see the Dev Board datasheet. * TPU v3, 32 to 512 TOPS, Q2 2021 * ### InferX X1, The Fastest and Most Efficient Edge Inference Accelerator * InferX X1: World's fastest and most efficient Edge Inference Accelerator. We have just launched our first inference chip and it is the best in the world for edge inference. We are bringing up neural network models now and moving forward on the steps required for Q2/2021 chip and board production and Inference Compiler availability. * mbedded FPGA, or eFPGA, enables your SoC to have flexibility in critical areas where algorithm, protocol or market needs are changing. FPGA can also accelerate many workloads faster than processors: Microsoft Azure uses one FPGA accelerator for every 2 Xeons.Flex Logix provides eFPGA cores which have density and performance similar to leading FPGAs in the same process node. Our EFLX eFPGA is silicon proven in 40nm, 28/22nm, 16nm and 12nm. 6/7nm EFLX eFPGA is planned. Our eFPGA is based on a “tile” called EFLX 4K, which comes in two versions: all logic or mostly logic with some MACs (multiply-accumulators). The programmable logic is called LUTs (look up tables) that can implement any Boolean function. EFLX 4K Logix has 4000 LUT4 equivalents, EFLX 4K DSP has 3000 LUT4s and 40 Multiplier-Accumulators (MACs): the MAC has a 22-bit pre-adder, a 22×22 multiple and a 48-bit post adder/accumulator. MACs can be combined or cascaded to form fast DSP functions. (For 40nm-180nm we offer an EFLX 1K tile). * depth-wise conv2d * ### Implementing Edge Technologies in Retail: Walmart Case Study * NVidia * ### The Era of Analog AI Compute Is Here * Mythic products are based on a unique tile-based AI compute architecture that features three fundamental hardware technologies – Compute-in-Memory, Dataflow Architecture, and Analog Computing. For AI developers, the Mythic SDK streamlines the preparation of trained neural networks for edge and low-latency datacenter deployments, and also performs automatic optimization and compilation of dataflow graphs for our unique architecture. * low power consumption, ultra-low latency, high ai performance, large weight capacity, small form factor, cost effective solution * ### **Us** ing Edge AI To Detect Repetitive Mot * Bosch Sensortec develops and markets a wide portfolio of MEMS sensors and solutions for applications in smartphones, tablets, wearables, AR/VR devices, drones, robots, smart home and the Internet of Things. Striving to meet the demanding requirements of the consumer electronics market, we provide best-in-class sensing solutions in terms of customer focus, quality and reliability, performance, sustainability and competitiveness. * [https://github.com/BoschSensortec](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FBoschSensortec&sa=D&sntz=1&usg=AOvVaw0Xr8dUHPERsj-rYH7ZAnP1) ## Friday, November 20, 2020 * ### *Spatial Computing: A Collision of Edge and Cloud-Based Computing * [https://github.com/magicleap](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fmagicleap&sa=D&sntz=1&usg=AOvVaw22R0IwwKYOdwv06BvMpvZF) * instance semantic segmentation contextual computing * spatial computing * SLAM: tracking/localization, mapping: * latency is critical for see through displays * weight is critical cannot compensate for lack of compute with more sensors * thermal is critical more sensors and more compute lead to heat * rigidity leads to weight our device should be light * very stringent requirements for MR * why build a map: drift correction, robustness (pose recovery), persistence * feature descriptors * matching across large baselines and illumination changes is challenging * most of the SOTA methods based on deep learning and not feasible withing compute budget * our deep descriptor is optimized for SLAM and provides the best trade off in terms of performance and compute * semantic segmentation 3d point cloud * ### Building An Autonomous Network For IoT and Edge Applications * 5G + AI * ### Practical Edge Inferencing: Enabling fastest AI inferencing per Watt leveraging sparsity * [https://www.graimatterlabs.ai/](https://www.google.com/url?q=https%3A%2F%2Fwww.graimatterlabs.ai%2F&sa=D&sntz=1&usg=AOvVaw1qLIEaRzrtXoYDAb_Y4tQb) * The world’s first sparsity-enabled AI processor optimized for ultra-low latency and low power processing at the edge. * GrAI One drastically reduces application latency, for instance, it reduces the end-to-end latencies for deep learning networks such as PilotNet to the order of milliseconds. The GrAI One chip is based on GML’s innovative NeuronFlow™ technology that combines the dynamic Dataflow paradigm with sparse computing to produce massively parallel in-network processing. * GrAI Matter Labs ([www.graimatterlabs.ai](http://www.google.com/url?q=http%3A%2F%2Fwww.graimatterlabs.ai%2F&sa=D&sntz=1&usg=AOvVaw3eJ9oROjswyCFHO-68LiDi)), a fabless semiconductor company specialized in brain-inspired technology, designs and develops fully programmable ultra-low power neuromorphic HW for sensor analytics and machine learning. The company has offices in Eindhoven (NL), Paris (FR) and San Jose (USA) and has strong relations with top-ranking research groups on neuroscience, human vision and natural computation * ### **Large Scale Deep Learning and AI models on the Edge * deployment pipelines * there are several steps involved in the AI/ML life-cycle * several tools to help simplify the whole process * tensorflow extended (TFX): an end to end platform for deploying production ML pipelines * MLflow (other options michelangelo): an open source platform for the end to end machine learning life cycle * apache airflow (other options kubeflow): an open source workflow management platform * dataiku data science studio (DSS): collaborative data science software platform for teams of data scientist , data analysts, and engineers to explore prototype build and deliver * ### The Edge: The Hottest Market for AI Accelerator Chips - Introducing the Kisaco Leadership Chart on AI Hardware Accelerators 2020-21: Edge and Automotive * [https://www.kisacoresearch.com/#about-us](https://www.google.com/url?q=https%3A%2F%2Fwww.kisacoresearch.com%2F%23about-us&sa=D&sntz=1&usg=AOvVaw0nasAOo80KuyOwbm4OeiOb) [OpenHTF is a Python library that provides a set of convenient abstractions designed to remove as much boilerplate as possible from hardware test setup and execution, so test engineers can focus primarily on test logic.](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fgoogle%2Fopenhtf&sa=D&sntz=1&usg=AOvVaw0zU3RKntPn4N8JIkPvriIu) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/3LTMUMvLqIOxMLGRMkavoKhjR0yGpVpsaeb_NnspLybaPexjoQBnvVa2C2973HUQ30RiIqubg5B9F6eLjnxKXlE=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/3LTMUMvLqIOxMLGRMkavoKhjR0yGpVpsaeb_NnspLybaPexjoQBnvVa2C2973HUQ30RiIqubg5B9F6eLjnxKXlE=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Edge AI summit Short summary of the edge AI summit 18-20 November 2020 Wednesday, November 18, 2020 A Software Solution Enabling Predictive Maintenance at the Sensor Level Helping Fish Farmers Feed The World With Deep Learning tinyMLPerf: Benchmarking Ultra-low Power Machine Learning Systems Ultra-low power neuromorphic intelligence for the sensor edge * How is AI affecting hearables and sensors? Breaking the Barriers to Deploy DNNs on Low-Power Hardware Optimizing ML Models At The Edge Made Simple Thursday, November 19, 2020 Developing Edge AI Solutions For A Post-Pandemic Society The Evolving Landscape of Edge AI InferX X1, The Fastest and Most Efficient Edge Inference Accelerator Implementing Edge Technologies in Retail: Walmart Case Study The Era of Analog AI Compute Is Here Using Edge AI To Detect Repetitive Mot Friday, November 20, 2020 *Spatial Computing: A Collision of Edge and Cloud-Based Computing Building An Autonomous Network For IoT and Edge Applications Practical Edge Inferencing: Enabling fastest AI inferencing per Watt leveraging sparsity **Large Scale Deep Learning and AI models on the Edge The Edge: The Hottest Market for AI Accelerator Chips - Introducing the Kisaco Leadership Chart on AI Hardware Accelerators 2020-21: Edge and Automotive # Short summary of the edge AI summit 18-20 November 2020 Best of Wednesday, November 18, 2020; tinyMLPerf, Breaking the Barriers to Deploy DNNs on Low-Power Hardware, Optimizing ML Models At The Edge Made Simple **Thursday, November 19, 2020** * 8:00 AM - 8:30 AM (PST) KEYNOTE PRESENTATION: Developing Edge AI Solutions For A Post-Pandemic Society Sastry Malladi - FogHorn Systems * 8:35 AM - 9:05 AM (PST) PRESENTATION: The Evolving Landscape of Edge AI Ajay Nair - Google * 9:05 AM - 9:20 AM (PST) Comfort Break * 9:20 AM - 9:45 AM (PST) PRESENTATION: InferX X1, The Fastest and Most Efficient Edge Inference Accelerator Cheng Wang - Flex Logix Technologies Inc. * 9:50 AM - 10:20 AM (PST) PRESENTATION: Implementing Edge Technologies in Retail: Walmart Case Study Alex Sabatier - Nvidia * 10:20 AM - 10:35 AM (PST) Comfort Break * 10:35 AM - 11:20 AM (PST) Meet speaker! * **11:20 AM - 11:50 AM (PST) PRESENTATION: The Era of Analog AI Compute Is Here Mike Henry - Mythic** * 11:55 AM - 12:25 PM (PST) PRESENTATION: Using Edge AI To Detect Repetitive Mot Marcellino Gemelli - Bosch Sensortec * 12:30 PM - 2:30 PM (PST) NETWORKING - Dedicated Networking 2 hours for 1-2-1 Video Meetings **Friday, November 20, 2020** * 8:00 AM - 8:30 AM (PST) PRESENTATION: Spatial Computing: A Collision of Edge and Cloud-Based Computing Ashwin Swaminathan - Magic Leap * 8:35 AM - 9:05 AM (PST) PRESENTATION: Building An Autonomous Network For IoT and Edge Applications Anshul Bhatt - Rakuten Mobile * 9:05 AM - 9:20 AM (PST) Comfort Break * 9:20 AM - 9:45 AM (PST) PRESENTATION: Practical Edge Inferencing: Enabling fastest AI inferencing per Watt leveraging sparsity Mahesh Makhijani - GrAI Matter Labs * **9:50 AM - 10:20 AM (PST) PRESENTATION: Large Scale Deep Learning and AI models on the Edge Chandra Khatri - Got It AI** * 10:20 AM - 10:35 AM (PST) Comfort Break * 10:35 AM - 11:20 AM (PST) NETWORKING: Interest Groups (18 people per room, topic-specific discussions) * 11:20 AM - 11:50 AM (PST) PANEL DISCUSSION: The Symbiotic Relationship between 5G and Edge AI Sami Badri - Credit Suisse, Christos Kolias - Orange, Rima Raouda - Independent * 11:55 AM - 12:25 PM (PST) PANEL DISCUSSION: Investment Trends & Dynamics Panel Rashmi Gopinath - B Capital Group, Yvonne Lutsch - Bosch Venture Capital, Eileen Tanghal - In-Q-Tel, Albert Wang - Qualcomm Ventures * **12:30 PM - 12:50 PM (PST) PRESENTATION: The Edge: The Hottest Market for AI Accelerator Chips - Introducing the Kisaco Leadership Chart on AI Hardware Accelerators 2020-21: Edge and Automotive Michael Azoff - Kisaco Research** ## Wednesday, November 18, 2020 * ### A Software Solution Enabling Predictive Maintenance at the Sensor Level * SensiML Toolkit enables AI for a broad array of resource constrained time-series sensor endpoint applications. These include a wide range of consumer and industrial sensing applications. * The problem is machine learning engineer do not have experience with embedded system and moving model to embedded system takes long time. * AutoML for Embedded system usage. it is on the cloud. * using the compiler for that device for this tools * cost edge and cloud. easy to work on cloud. streaming data to cloud is difficult. faster if working on edge. * TinyML addresses problems, battery powered, limited internet connectivity, security/privacy, latency, economic * [https://sensiml.com/products/#process](https://www.google.com/url?q=https%3A%2F%2Fsensiml.com%2Fproducts%2F%23process&sa=D&sntz=1&usg=AOvVaw116wjx6mBnEo9x0htyL2pr) * ### Helping Fish Farmers Feed The World With Deep Learning * [https://s3-us-west-1.amazonaws.com/aquabyte-static/videos/welcome_to_aquabyte_subtitled.mp4](https://www.google.com/url?q=https%3A%2F%2Fs3-us-west-1.amazonaws.com%2Faquabyte-static%2Fvideos%2Fwelcome_to_aquabyte_subtitled.mp4&sa=D&sntz=1&usg=AOvVaw1osNtsRVM9TMCmKoPsTnj_) * Count sea lice and accurately measure biomass in real-time while reducing cage furniture. Our experts‑in‑the‑loop ensure that every single prediction is correct. * Aquabyte is seeking a Machine Learning Platform Engineer to drive the development, testing, and delivery of machine learning models that enable cutting-edge analytics and automation of fish farms around the world. * Aquabyte is on a mission to revolutionize the sustainability and efficiency of aquaculture. It is an audacious, and incredibly rewarding mission. By making fish farming cheaper and more viable than livestock production, we aim to mitigate one of the biggest causes of climate change and help prepare our planet for impending population growth. Aquaculture is the single fastest growing food-production sector in the world, and now is the time to define how technology is used to harvest the sea for generations to come. * We are currently focused on helping Norwegian salmon farmers better understand their fish populations and make environmentally-sound decisions. Through custom underwater cameras, computer vision, and machine learning we are able to quantify fish weights, detect sea lice infestations, and generate optimal feeding plans in real time. Our product operates at three levels: on-site hardware for image capture, cloud pipelines for data processing, and a user-facing web application. As a result, there are hundreds of moving pieces and no shortage of fascinating challenges across all levels of the stack. * * ### tinyMLPerf: Benchmarking Ultra-low Power Machine Learning Systems * [https://github.com/mlperf/tiny](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fmlperf%2Ftiny&sa=D&sntz=1&usg=AOvVaw1TgviYAuh83PMxDPYljOjs) * tinyMLPerf Deep Learning Benchmarks for Embedded Devices * The goal of TinyMLPerf is to provide a representative set of deep neural nets and benchmarking code to compare performance between embedded devices. Embedded devices include microcontrollers, DSPs, and tiny NN accelerators. These devics typically run at between 10MHz and 250MHz, and can perform inference using less then 50mW of power. TinyMLPerf submissions will allow device makers and researchers to choose the best hardware for their use case, and allows hardware vendors to showcase their offerings. TinyMLPerf is primarily intended to benchmark hardware rather than new network archietctures, or embedded neural net runtimes. The reference benchmarks are provided using TensorFlow Lite for Microcontrollers (TFLM). Submitters can directly use the TFLM, although submitters are encouraged to use the software stack that works best on thier hardware. * anomaly detection benchmark, visual wake words benchmark, * ### Ultra-low power neuromorphic intelligence for the sensor edge * Innatera Nanosystems BV (Innatera, (Innatera, innatera.com) is a rapidly-growing Dutch semiconductor company that develops ultra-efficient neuromorphic processors for AI at the edge. These microprocessors mimic the brain’s mechanisms for processing fast data streams from sensors, enabling complex turn-key sensor analytics functionalities, with 10,000x higher performance per watt than competing solutions. Innatera's technology serves as a critical enabler for next-generation use-cases in the IoT, wearable, embedded, and automotive domains. * ### * How is AI affecting hearables and sensors? * [https://github.com/greenwaves-technologies/nn_menu](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fgreenwaves-technologies%2Fnn_menu&sa=D&sntz=1&usg=AOvVaw2JKYYAPrnA9Mkalw2qenUQ) * The Neural Network Menu* is a collection of software that implements Neural Networks on Greenwaves Application Processors (GAP). This repository contains common mobile and edge NN archtecture examples, NN sample applications and full flagged reference designs. Our tools maps a TFLITE model (quantized or unquantized) onto gap. There is also a flow in the ingredients directory showing how to hand map from a Pytorch Model onto GAP. * [https://greenwaves-technologies.com/store/](https://www.google.com/url?q=https%3A%2F%2Fgreenwaves-technologies.com%2Fstore%2F&sa=D&sntz=1&usg=AOvVaw0Ya_w_NBAr4AbIxBe2j_YX) * GAPPoc-A is a Proof of Concept Board that can be used for demonstration of battery-operated, edge computer vision applications based on GAP8. * It incorporates GAPmod, a surface-mount module that implements all the layout sensitive portion of a GAP8 design, along with a VGA image sensor and a Bluetooth Low Energy radio. * The GAPPoc-A board enables battery-operated applications developed around algorithms such as people counting, face-identification and many others to be quickly assembled and evaluated in the field. * [https://riscv.org/blog/2019/08/risc-v-emea-roadshow-spotlight-greenwaves-technologies/](https://www.google.com/url?q=https%3A%2F%2Friscv.org%2Fblog%2F2019%2F08%2Frisc-v-emea-roadshow-spotlight-greenwaves-technologies%2F&sa=D&sntz=1&usg=AOvVaw1ikZjtEoYTgFb-S_eGEB3i) * ### Breaking the Barriers to Deploy DNNs on Low-Power Hardware * Deeplite, named to the 2020 CB Insights AI100 List of Most Innovative Artificial Intelligence Startups, is devoted to making fundamental advancements in accessible and efficient deep learning. Our solution helps deep learning engineers and experts automatically create faster, smaller and more energy-efficient deep neural networks. Industry leaders in computer vision, augmented reality and autonomous driving use our technology to unlock new possibilities for deep learning in the real world. At Deeplite, our vision is to create a lightweight intelligence that’s accessible for daily life. * [https://www.deeplite.ai/](https://www.google.com/url?q=https%3A%2F%2Fwww.deeplite.ai%2F&sa=D&sntz=1&usg=AOvVaw1r7NiQGt1hRi6S_xiJ522C) * At Deeplite, we are tackling inference optimization of deep neural networks, making them faster and energy-efficient from cloud to edge computing. Our solution leverages state-of-the-art technology from elite universities to make deep neural networks applicable for any device, and our team works hard on the iterative evolution of the science behind deep neural networks to directly improve daily life. * reduce the size of model 40x * ### Optimizing ML Models At The Edge Made Simple * [https://octoml.ai/](https://www.google.com/url?q=https%3A%2F%2Foctoml.ai%2F&sa=D&sntz=1&usg=AOvVaw2uXg6ESgQgVGrF9nQJKFve) * OctoML is an energetic new company changing how developers optimize and deploy machine learning models for their AI needs. We’re a team of machine learning systems leaders focused on making ML more efficient and easier to deploy by… applying machine learning to it! * OctoML is leveraging the power and traction of Apache TVM, an open source project originated by our founding team, to enable companies of every size to harness the power of deep learning without the expensive heavy lifting of tuning and securing models to each hardware configuration that a customer might need. * Apache TVM and Deep Learning Compilation Conference, Wed-Fri, December 2nd-4th 2020, Free Virtual Event. ## Thursday, November 19, 2020 * ### Developing Edge AI Solutions For A Post-Pandemic Society * [https://www.foghorn.io/](https://www.google.com/url?q=https%3A%2F%2Fwww.foghorn.io%2F&sa=D&sntz=1&usg=AOvVaw2aRKISC9BdrrriEej5Xb_I) * ogHorn’s Lightning™ Edge AI platform brings a groundbreaking dimension to IIoT and edge computing by embedding AI as close to the source of streaming sensor data as possible. The Edge AI software platform is a highly compact, advanced and feature-rich edge solution that delivers unprecedented low latency for onsite data processing, real-time analytics, ML and AI capabilities. It delivers the industry’s lowest total cost for computing requirements, communications services, and cloud processing and storage. * temperature detection, social distancing, cough detection, PPE/Mask detection * Flexible, customizable, integrated, actionable * ### The Evolving Landscape of Edge AI * * Coral’s local AI technology enables new possibilities across almost any kind of industry * The Coral Dev Board is a single-board computer that contains an Edge TPU coprocessor. It's ideal for prototyping new projects that demand fast on-device inferencing for machine learning models. This page is your guide to get started. The setup requires flashing Mendel Linux to the board, and then accessing the board's shell terminal. Once you have terminal access and update some of the software, we'll show you how to run an image classification model on the board. If you want to learn more about the hardware, see the Dev Board datasheet. * TPU v3, 32 to 512 TOPS, Q2 2021 * ### InferX X1, The Fastest and Most Efficient Edge Inference Accelerator * InferX X1: World's fastest and most efficient Edge Inference Accelerator. We have just launched our first inference chip and it is the best in the world for edge inference. We are bringing up neural network models now and moving forward on the steps required for Q2/2021 chip and board production and Inference Compiler availability. * mbedded FPGA, or eFPGA, enables your SoC to have flexibility in critical areas where algorithm, protocol or market needs are changing. FPGA can also accelerate many workloads faster than processors: Microsoft Azure uses one FPGA accelerator for every 2 Xeons.Flex Logix provides eFPGA cores which have density and performance similar to leading FPGAs in the same process node. Our EFLX eFPGA is silicon proven in 40nm, 28/22nm, 16nm and 12nm. 6/7nm EFLX eFPGA is planned. Our eFPGA is based on a “tile” called EFLX 4K, which comes in two versions: all logic or mostly logic with some MACs (multiply-accumulators). The programmable logic is called LUTs (look up tables) that can implement any Boolean function. EFLX 4K Logix has 4000 LUT4 equivalents, EFLX 4K DSP has 3000 LUT4s and 40 Multiplier-Accumulators (MACs): the MAC has a 22-bit pre-adder, a 22×22 multiple and a 48-bit post adder/accumulator. MACs can be combined or cascaded to form fast DSP functions. (For 40nm-180nm we offer an EFLX 1K tile). * depth-wise conv2d * ### Implementing Edge Technologies in Retail: Walmart Case Study * NVidia * ### The Era of Analog AI Compute Is Here * Mythic products are based on a unique tile-based AI compute architecture that features three fundamental hardware technologies – Compute-in-Memory, Dataflow Architecture, and Analog Computing. For AI developers, the Mythic SDK streamlines the preparation of trained neural networks for edge and low-latency datacenter deployments, and also performs automatic optimization and compilation of dataflow graphs for our unique architecture. * low power consumption, ultra-low latency, high ai performance, large weight capacity, small form factor, cost effective solution * ### **Us** ing Edge AI To Detect Repetitive Mot * Bosch Sensortec develops and markets a wide portfolio of MEMS sensors and solutions for applications in smartphones, tablets, wearables, AR/VR devices, drones, robots, smart home and the Internet of Things. Striving to meet the demanding requirements of the consumer electronics market, we provide best-in-class sensing solutions in terms of customer focus, quality and reliability, performance, sustainability and competitiveness. * [https://github.com/BoschSensortec](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FBoschSensortec&sa=D&sntz=1&usg=AOvVaw0Xr8dUHPERsj-rYH7ZAnP1) ## Friday, November 20, 2020 * ### *Spatial Computing: A Collision of Edge and Cloud-Based Computing * [https://github.com/magicleap](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fmagicleap&sa=D&sntz=1&usg=AOvVaw22R0IwwKYOdwv06BvMpvZF) * instance semantic segmentation contextual computing * spatial computing * SLAM: tracking/localization, mapping: * latency is critical for see through displays * weight is critical cannot compensate for lack of compute with more sensors * thermal is critical more sensors and more compute lead to heat * rigidity leads to weight our device should be light * very stringent requirements for MR * why build a map: drift correction, robustness (pose recovery), persistence * feature descriptors * matching across large baselines and illumination changes is challenging * most of the SOTA methods based on deep learning and not feasible withing compute budget * our deep descriptor is optimized for SLAM and provides the best trade off in terms of performance and compute * semantic segmentation 3d point cloud * ### Building An Autonomous Network For IoT and Edge Applications * 5G + AI * ### Practical Edge Inferencing: Enabling fastest AI inferencing per Watt leveraging sparsity * [https://www.graimatterlabs.ai/](https://www.google.com/url?q=https%3A%2F%2Fwww.graimatterlabs.ai%2F&sa=D&sntz=1&usg=AOvVaw1qLIEaRzrtXoYDAb_Y4tQb) * The world’s first sparsity-enabled AI processor optimized for ultra-low latency and low power processing at the edge. * GrAI One drastically reduces application latency, for instance, it reduces the end-to-end latencies for deep learning networks such as PilotNet to the order of milliseconds. The GrAI One chip is based on GML’s innovative NeuronFlow™ technology that combines the dynamic Dataflow paradigm with sparse computing to produce massively parallel in-network processing. * GrAI Matter Labs ([www.graimatterlabs.ai](http://www.google.com/url?q=http%3A%2F%2Fwww.graimatterlabs.ai%2F&sa=D&sntz=1&usg=AOvVaw3eJ9oROjswyCFHO-68LiDi)), a fabless semiconductor company specialized in brain-inspired technology, designs and develops fully programmable ultra-low power neuromorphic HW for sensor analytics and machine learning. The company has offices in Eindhoven (NL), Paris (FR) and San Jose (USA) and has strong relations with top-ranking research groups on neuroscience, human vision and natural computation * ### **Large Scale Deep Learning and AI models on the Edge * deployment pipelines * there are several steps involved in the AI/ML life-cycle * several tools to help simplify the whole process * tensorflow extended (TFX): an end to end platform for deploying production ML pipelines * MLflow (other options michelangelo): an open source platform for the end to end machine learning life cycle * apache airflow (other options kubeflow): an open source workflow management platform * dataiku data science studio (DSS): collaborative data science software platform for teams of data scientist , data analysts, and engineers to explore prototype build and deliver * ### The Edge: The Hottest Market for AI Accelerator Chips - Introducing the Kisaco Leadership Chart on AI Hardware Accelerators 2020-21: Edge and Automotive * [https://www.kisacoresearch.com/#about-us](https://www.google.com/url?q=https%3A%2F%2Fwww.kisacoresearch.com%2F%23about-us&sa=D&sntz=1&usg=AOvVaw0nasAOo80KuyOwbm4OeiOb) [OpenHTF is a Python library that provides a set of convenient abstractions designed to remove as much boilerplate as possible from hardware test setup and execution, so test engineers can focus primarily on test logic.](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fgoogle%2Fopenhtf&sa=D&sntz=1&usg=AOvVaw0zU3RKntPn4N8JIkPvriIu) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/3LTMUMvLqIOxMLGRMkavoKhjR0yGpVpsaeb_NnspLybaPexjoQBnvVa2C2973HUQ30RiIqubg5B9F6eLjnxKXlE=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/3LTMUMvLqIOxMLGRMkavoKhjR0yGpVpsaeb_NnspLybaPexjoQBnvVa2C2973HUQ30RiIqubg5B9F6eLjnxKXlE=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Edge AI summit Short summary of the edge AI summit 18-20 November 2020 Wednesday, November 18, 2020 A Software Solution Enabling Predictive Maintenance at the Sensor Level Helping Fish Farmers Feed The World With Deep Learning tinyMLPerf: Benchmarking Ultra-low Power Machine Learning Systems Ultra-low power neuromorphic intelligence for the sensor edge * How is AI affecting hearables and sensors? Breaking the Barriers to Deploy DNNs on Low-Power Hardware Optimizing ML Models At The Edge Made Simple Thursday, November 19, 2020 Developing Edge AI Solutions For A Post-Pandemic Society The Evolving Landscape of Edge AI InferX X1, The Fastest and Most Efficient Edge Inference Accelerator Implementing Edge Technologies in Retail: Walmart Case Study The Era of Analog AI Compute Is Here Using Edge AI To Detect Repetitive Mot Friday, November 20, 2020 *Spatial Computing: A Collision of Edge and Cloud-Based Computing Building An Autonomous Network For IoT and Edge Applications Practical Edge Inferencing: Enabling fastest AI inferencing per Watt leveraging sparsity **Large Scale Deep Learning and AI models on the Edge The Edge: The Hottest Market for AI Accelerator Chips - Introducing the Kisaco Leadership Chart on AI Hardware Accelerators 2020-21: Edge and Automotive # Short summary of the edge AI summit 18-20 November 2020 Best of Wednesday, November 18, 2020; tinyMLPerf, Breaking the Barriers to Deploy DNNs on Low-Power Hardware, Optimizing ML Models At The Edge Made Simple **Thursday, November 19, 2020** * 8:00 AM - 8:30 AM (PST) KEYNOTE PRESENTATION: Developing Edge AI Solutions For A Post-Pandemic Society Sastry Malladi - FogHorn Systems * 8:35 AM - 9:05 AM (PST) PRESENTATION: The Evolving Landscape of Edge AI Ajay Nair - Google * 9:05 AM - 9:20 AM (PST) Comfort Break * 9:20 AM - 9:45 AM (PST) PRESENTATION: InferX X1, The Fastest and Most Efficient Edge Inference Accelerator Cheng Wang - Flex Logix Technologies Inc. * 9:50 AM - 10:20 AM (PST) PRESENTATION: Implementing Edge Technologies in Retail: Walmart Case Study Alex Sabatier - Nvidia * 10:20 AM - 10:35 AM (PST) Comfort Break * 10:35 AM - 11:20 AM (PST) Meet speaker! * **11:20 AM - 11:50 AM (PST) PRESENTATION: The Era of Analog AI Compute Is Here Mike Henry - Mythic** * 11:55 AM - 12:25 PM (PST) PRESENTATION: Using Edge AI To Detect Repetitive Mot Marcellino Gemelli - Bosch Sensortec * 12:30 PM - 2:30 PM (PST) NETWORKING - Dedicated Networking 2 hours for 1-2-1 Video Meetings **Friday, November 20, 2020** * 8:00 AM - 8:30 AM (PST) PRESENTATION: Spatial Computing: A Collision of Edge and Cloud-Based Computing Ashwin Swaminathan - Magic Leap * 8:35 AM - 9:05 AM (PST) PRESENTATION: Building An Autonomous Network For IoT and Edge Applications Anshul Bhatt - Rakuten Mobile * 9:05 AM - 9:20 AM (PST) Comfort Break * 9:20 AM - 9:45 AM (PST) PRESENTATION: Practical Edge Inferencing: Enabling fastest AI inferencing per Watt leveraging sparsity Mahesh Makhijani - GrAI Matter Labs * **9:50 AM - 10:20 AM (PST) PRESENTATION: Large Scale Deep Learning and AI models on the Edge Chandra Khatri - Got It AI** * 10:20 AM - 10:35 AM (PST) Comfort Break * 10:35 AM - 11:20 AM (PST) NETWORKING: Interest Groups (18 people per room, topic-specific discussions) * 11:20 AM - 11:50 AM (PST) PANEL DISCUSSION: The Symbiotic Relationship between 5G and Edge AI Sami Badri - Credit Suisse, Christos Kolias - Orange, Rima Raouda - Independent * 11:55 AM - 12:25 PM (PST) PANEL DISCUSSION: Investment Trends & Dynamics Panel Rashmi Gopinath - B Capital Group, Yvonne Lutsch - Bosch Venture Capital, Eileen Tanghal - In-Q-Tel, Albert Wang - Qualcomm Ventures * **12:30 PM - 12:50 PM (PST) PRESENTATION: The Edge: The Hottest Market for AI Accelerator Chips - Introducing the Kisaco Leadership Chart on AI Hardware Accelerators 2020-21: Edge and Automotive Michael Azoff - Kisaco Research** ## Wednesday, November 18, 2020 * ### A Software Solution Enabling Predictive Maintenance at the Sensor Level * SensiML Toolkit enables AI for a broad array of resource constrained time-series sensor endpoint applications. These include a wide range of consumer and industrial sensing applications. * The problem is machine learning engineer do not have experience with embedded system and moving model to embedded system takes long time. * AutoML for Embedded system usage. it is on the cloud. * using the compiler for that device for this tools * cost edge and cloud. easy to work on cloud. streaming data to cloud is difficult. faster if working on edge. * TinyML addresses problems, battery powered, limited internet connectivity, security/privacy, latency, economic * [https://sensiml.com/products/#process](https://www.google.com/url?q=https%3A%2F%2Fsensiml.com%2Fproducts%2F%23process&sa=D&sntz=1&usg=AOvVaw116wjx6mBnEo9x0htyL2pr) * ### Helping Fish Farmers Feed The World With Deep Learning * [https://s3-us-west-1.amazonaws.com/aquabyte-static/videos/welcome_to_aquabyte_subtitled.mp4](https://www.google.com/url?q=https%3A%2F%2Fs3-us-west-1.amazonaws.com%2Faquabyte-static%2Fvideos%2Fwelcome_to_aquabyte_subtitled.mp4&sa=D&sntz=1&usg=AOvVaw1osNtsRVM9TMCmKoPsTnj_) * Count sea lice and accurately measure biomass in real-time while reducing cage furniture. Our experts‑in‑the‑loop ensure that every single prediction is correct. * Aquabyte is seeking a Machine Learning Platform Engineer to drive the development, testing, and delivery of machine learning models that enable cutting-edge analytics and automation of fish farms around the world. * Aquabyte is on a mission to revolutionize the sustainability and efficiency of aquaculture. It is an audacious, and incredibly rewarding mission. By making fish farming cheaper and more viable than livestock production, we aim to mitigate one of the biggest causes of climate change and help prepare our planet for impending population growth. Aquaculture is the single fastest growing food-production sector in the world, and now is the time to define how technology is used to harvest the sea for generations to come. * We are currently focused on helping Norwegian salmon farmers better understand their fish populations and make environmentally-sound decisions. Through custom underwater cameras, computer vision, and machine learning we are able to quantify fish weights, detect sea lice infestations, and generate optimal feeding plans in real time. Our product operates at three levels: on-site hardware for image capture, cloud pipelines for data processing, and a user-facing web application. As a result, there are hundreds of moving pieces and no shortage of fascinating challenges across all levels of the stack. * * ### tinyMLPerf: Benchmarking Ultra-low Power Machine Learning Systems * [https://github.com/mlperf/tiny](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fmlperf%2Ftiny&sa=D&sntz=1&usg=AOvVaw1TgviYAuh83PMxDPYljOjs) * tinyMLPerf Deep Learning Benchmarks for Embedded Devices * The goal of TinyMLPerf is to provide a representative set of deep neural nets and benchmarking code to compare performance between embedded devices. Embedded devices include microcontrollers, DSPs, and tiny NN accelerators. These devics typically run at between 10MHz and 250MHz, and can perform inference using less then 50mW of power. TinyMLPerf submissions will allow device makers and researchers to choose the best hardware for their use case, and allows hardware vendors to showcase their offerings. TinyMLPerf is primarily intended to benchmark hardware rather than new network archietctures, or embedded neural net runtimes. The reference benchmarks are provided using TensorFlow Lite for Microcontrollers (TFLM). Submitters can directly use the TFLM, although submitters are encouraged to use the software stack that works best on thier hardware. * anomaly detection benchmark, visual wake words benchmark, * ### Ultra-low power neuromorphic intelligence for the sensor edge * Innatera Nanosystems BV (Innatera, (Innatera, innatera.com) is a rapidly-growing Dutch semiconductor company that develops ultra-efficient neuromorphic processors for AI at the edge. These microprocessors mimic the brain’s mechanisms for processing fast data streams from sensors, enabling complex turn-key sensor analytics functionalities, with 10,000x higher performance per watt than competing solutions. Innatera's technology serves as a critical enabler for next-generation use-cases in the IoT, wearable, embedded, and automotive domains. * ### * How is AI affecting hearables and sensors? * [https://github.com/greenwaves-technologies/nn_menu](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fgreenwaves-technologies%2Fnn_menu&sa=D&sntz=1&usg=AOvVaw2JKYYAPrnA9Mkalw2qenUQ) * The Neural Network Menu* is a collection of software that implements Neural Networks on Greenwaves Application Processors (GAP). This repository contains common mobile and edge NN archtecture examples, NN sample applications and full flagged reference designs. Our tools maps a TFLITE model (quantized or unquantized) onto gap. There is also a flow in the ingredients directory showing how to hand map from a Pytorch Model onto GAP. * [https://greenwaves-technologies.com/store/](https://www.google.com/url?q=https%3A%2F%2Fgreenwaves-technologies.com%2Fstore%2F&sa=D&sntz=1&usg=AOvVaw0Ya_w_NBAr4AbIxBe2j_YX) * GAPPoc-A is a Proof of Concept Board that can be used for demonstration of battery-operated, edge computer vision applications based on GAP8. * It incorporates GAPmod, a surface-mount module that implements all the layout sensitive portion of a GAP8 design, along with a VGA image sensor and a Bluetooth Low Energy radio. * The GAPPoc-A board enables battery-operated applications developed around algorithms such as people counting, face-identification and many others to be quickly assembled and evaluated in the field. * [https://riscv.org/blog/2019/08/risc-v-emea-roadshow-spotlight-greenwaves-technologies/](https://www.google.com/url?q=https%3A%2F%2Friscv.org%2Fblog%2F2019%2F08%2Frisc-v-emea-roadshow-spotlight-greenwaves-technologies%2F&sa=D&sntz=1&usg=AOvVaw1ikZjtEoYTgFb-S_eGEB3i) * ### Breaking the Barriers to Deploy DNNs on Low-Power Hardware * Deeplite, named to the 2020 CB Insights AI100 List of Most Innovative Artificial Intelligence Startups, is devoted to making fundamental advancements in accessible and efficient deep learning. Our solution helps deep learning engineers and experts automatically create faster, smaller and more energy-efficient deep neural networks. Industry leaders in computer vision, augmented reality and autonomous driving use our technology to unlock new possibilities for deep learning in the real world. At Deeplite, our vision is to create a lightweight intelligence that’s accessible for daily life. * [https://www.deeplite.ai/](https://www.google.com/url?q=https%3A%2F%2Fwww.deeplite.ai%2F&sa=D&sntz=1&usg=AOvVaw1r7NiQGt1hRi6S_xiJ522C) * At Deeplite, we are tackling inference optimization of deep neural networks, making them faster and energy-efficient from cloud to edge computing. Our solution leverages state-of-the-art technology from elite universities to make deep neural networks applicable for any device, and our team works hard on the iterative evolution of the science behind deep neural networks to directly improve daily life. * reduce the size of model 40x * ### Optimizing ML Models At The Edge Made Simple * [https://octoml.ai/](https://www.google.com/url?q=https%3A%2F%2Foctoml.ai%2F&sa=D&sntz=1&usg=AOvVaw2uXg6ESgQgVGrF9nQJKFve) * OctoML is an energetic new company changing how developers optimize and deploy machine learning models for their AI needs. We’re a team of machine learning systems leaders focused on making ML more efficient and easier to deploy by… applying machine learning to it! * OctoML is leveraging the power and traction of Apache TVM, an open source project originated by our founding team, to enable companies of every size to harness the power of deep learning without the expensive heavy lifting of tuning and securing models to each hardware configuration that a customer might need. * Apache TVM and Deep Learning Compilation Conference, Wed-Fri, December 2nd-4th 2020, Free Virtual Event. ## Thursday, November 19, 2020 * ### Developing Edge AI Solutions For A Post-Pandemic Society * [https://www.foghorn.io/](https://www.google.com/url?q=https%3A%2F%2Fwww.foghorn.io%2F&sa=D&sntz=1&usg=AOvVaw2aRKISC9BdrrriEej5Xb_I) * ogHorn’s Lightning™ Edge AI platform brings a groundbreaking dimension to IIoT and edge computing by embedding AI as close to the source of streaming sensor data as possible. The Edge AI software platform is a highly compact, advanced and feature-rich edge solution that delivers unprecedented low latency for onsite data processing, real-time analytics, ML and AI capabilities. It delivers the industry’s lowest total cost for computing requirements, communications services, and cloud processing and storage. * temperature detection, social distancing, cough detection, PPE/Mask detection * Flexible, customizable, integrated, actionable * ### The Evolving Landscape of Edge AI * * Coral’s local AI technology enables new possibilities across almost any kind of industry * The Coral Dev Board is a single-board computer that contains an Edge TPU coprocessor. It's ideal for prototyping new projects that demand fast on-device inferencing for machine learning models. This page is your guide to get started. The setup requires flashing Mendel Linux to the board, and then accessing the board's shell terminal. Once you have terminal access and update some of the software, we'll show you how to run an image classification model on the board. If you want to learn more about the hardware, see the Dev Board datasheet. * TPU v3, 32 to 512 TOPS, Q2 2021 * ### InferX X1, The Fastest and Most Efficient Edge Inference Accelerator * InferX X1: World's fastest and most efficient Edge Inference Accelerator. We have just launched our first inference chip and it is the best in the world for edge inference. We are bringing up neural network models now and moving forward on the steps required for Q2/2021 chip and board production and Inference Compiler availability. * mbedded FPGA, or eFPGA, enables your SoC to have flexibility in critical areas where algorithm, protocol or market needs are changing. FPGA can also accelerate many workloads faster than processors: Microsoft Azure uses one FPGA accelerator for every 2 Xeons.Flex Logix provides eFPGA cores which have density and performance similar to leading FPGAs in the same process node. Our EFLX eFPGA is silicon proven in 40nm, 28/22nm, 16nm and 12nm. 6/7nm EFLX eFPGA is planned. Our eFPGA is based on a “tile” called EFLX 4K, which comes in two versions: all logic or mostly logic with some MACs (multiply-accumulators). The programmable logic is called LUTs (look up tables) that can implement any Boolean function. EFLX 4K Logix has 4000 LUT4 equivalents, EFLX 4K DSP has 3000 LUT4s and 40 Multiplier-Accumulators (MACs): the MAC has a 22-bit pre-adder, a 22×22 multiple and a 48-bit post adder/accumulator. MACs can be combined or cascaded to form fast DSP functions. (For 40nm-180nm we offer an EFLX 1K tile). * depth-wise conv2d * ### Implementing Edge Technologies in Retail: Walmart Case Study * NVidia * ### The Era of Analog AI Compute Is Here * Mythic products are based on a unique tile-based AI compute architecture that features three fundamental hardware technologies – Compute-in-Memory, Dataflow Architecture, and Analog Computing. For AI developers, the Mythic SDK streamlines the preparation of trained neural networks for edge and low-latency datacenter deployments, and also performs automatic optimization and compilation of dataflow graphs for our unique architecture. * low power consumption, ultra-low latency, high ai performance, large weight capacity, small form factor, cost effective solution * ### **Us** ing Edge AI To Detect Repetitive Mot * Bosch Sensortec develops and markets a wide portfolio of MEMS sensors and solutions for applications in smartphones, tablets, wearables, AR/VR devices, drones, robots, smart home and the Internet of Things. Striving to meet the demanding requirements of the consumer electronics market, we provide best-in-class sensing solutions in terms of customer focus, quality and reliability, performance, sustainability and competitiveness. * [https://github.com/BoschSensortec](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FBoschSensortec&sa=D&sntz=1&usg=AOvVaw0Xr8dUHPERsj-rYH7ZAnP1) ## Friday, November 20, 2020 * ### *Spatial Computing: A Collision of Edge and Cloud-Based Computing * [https://github.com/magicleap](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fmagicleap&sa=D&sntz=1&usg=AOvVaw22R0IwwKYOdwv06BvMpvZF) * instance semantic segmentation contextual computing * spatial computing * SLAM: tracking/localization, mapping: * latency is critical for see through displays * weight is critical cannot compensate for lack of compute with more sensors * thermal is critical more sensors and more compute lead to heat * rigidity leads to weight our device should be light * very stringent requirements for MR * why build a map: drift correction, robustness (pose recovery), persistence * feature descriptors * matching across large baselines and illumination changes is challenging * most of the SOTA methods based on deep learning and not feasible withing compute budget * our deep descriptor is optimized for SLAM and provides the best trade off in terms of performance and compute * semantic segmentation 3d point cloud * ### Building An Autonomous Network For IoT and Edge Applications * 5G + AI * ### Practical Edge Inferencing: Enabling fastest AI inferencing per Watt leveraging sparsity * [https://www.graimatterlabs.ai/](https://www.google.com/url?q=https%3A%2F%2Fwww.graimatterlabs.ai%2F&sa=D&sntz=1&usg=AOvVaw1qLIEaRzrtXoYDAb_Y4tQb) * The world’s first sparsity-enabled AI processor optimized for ultra-low latency and low power processing at the edge. * GrAI One drastically reduces application latency, for instance, it reduces the end-to-end latencies for deep learning networks such as PilotNet to the order of milliseconds. The GrAI One chip is based on GML’s innovative NeuronFlow™ technology that combines the dynamic Dataflow paradigm with sparse computing to produce massively parallel in-network processing. * GrAI Matter Labs ([www.graimatterlabs.ai](http://www.google.com/url?q=http%3A%2F%2Fwww.graimatterlabs.ai%2F&sa=D&sntz=1&usg=AOvVaw3eJ9oROjswyCFHO-68LiDi)), a fabless semiconductor company specialized in brain-inspired technology, designs and develops fully programmable ultra-low power neuromorphic HW for sensor analytics and machine learning. The company has offices in Eindhoven (NL), Paris (FR) and San Jose (USA) and has strong relations with top-ranking research groups on neuroscience, human vision and natural computation * ### **Large Scale Deep Learning and AI models on the Edge * deployment pipelines * there are several steps involved in the AI/ML life-cycle * several tools to help simplify the whole process * tensorflow extended (TFX): an end to end platform for deploying production ML pipelines * MLflow (other options michelangelo): an open source platform for the end to end machine learning life cycle * apache airflow (other options kubeflow): an open source workflow management platform * dataiku data science studio (DSS): collaborative data science software platform for teams of data scientist , data analysts, and engineers to explore prototype build and deliver * ### The Edge: The Hottest Market for AI Accelerator Chips - Introducing the Kisaco Leadership Chart on AI Hardware Accelerators 2020-21: Edge and Automotive * [https://www.kisacoresearch.com/#about-us](https://www.google.com/url?q=https%3A%2F%2Fwww.kisacoresearch.com%2F%23about-us&sa=D&sntz=1&usg=AOvVaw0nasAOo80KuyOwbm4OeiOb) [OpenHTF is a Python library that provides a set of convenient abstractions designed to remove as much boilerplate as possible from hardware test setup and execution, so test engineers can focus primarily on test logic.](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fgoogle%2Fopenhtf&sa=D&sntz=1&usg=AOvVaw0zU3RKntPn4N8JIkPvriIu) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/3LTMUMvLqIOxMLGRMkavoKhjR0yGpVpsaeb_NnspLybaPexjoQBnvVa2C2973HUQ30RiIqubg5B9F6eLjnxKXlE=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/3LTMUMvLqIOxMLGRMkavoKhjR0yGpVpsaeb_NnspLybaPexjoQBnvVa2C2973HUQ30RiIqubg5B9F6eLjnxKXlE=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Edge AI summit Short summary of the edge AI summit 18-20 November 2020 Wednesday, November 18, 2020 A Software Solution Enabling Predictive Maintenance at the Sensor Level Helping Fish Farmers Feed The World With Deep Learning tinyMLPerf: Benchmarking Ultra-low Power Machine Learning Systems Ultra-low power neuromorphic intelligence for the sensor edge * How is AI affecting hearables and sensors? Breaking the Barriers to Deploy DNNs on Low-Power Hardware Optimizing ML Models At The Edge Made Simple Thursday, November 19, 2020 Developing Edge AI Solutions For A Post-Pandemic Society The Evolving Landscape of Edge AI InferX X1, The Fastest and Most Efficient Edge Inference Accelerator Implementing Edge Technologies in Retail: Walmart Case Study The Era of Analog AI Compute Is Here Using Edge AI To Detect Repetitive Mot Friday, November 20, 2020 *Spatial Computing: A Collision of Edge and Cloud-Based Computing Building An Autonomous Network For IoT and Edge Applications Practical Edge Inferencing: Enabling fastest AI inferencing per Watt leveraging sparsity **Large Scale Deep Learning and AI models on the Edge The Edge: The Hottest Market for AI Accelerator Chips - Introducing the Kisaco Leadership Chart on AI Hardware Accelerators 2020-21: Edge and Automotive # Short summary of the edge AI summit 18-20 November 2020 Best of Wednesday, November 18, 2020; tinyMLPerf, Breaking the Barriers to Deploy DNNs on Low-Power Hardware, Optimizing ML Models At The Edge Made Simple **Thursday, November 19, 2020** * 8:00 AM - 8:30 AM (PST) KEYNOTE PRESENTATION: Developing Edge AI Solutions For A Post-Pandemic Society Sastry Malladi - FogHorn Systems * 8:35 AM - 9:05 AM (PST) PRESENTATION: The Evolving Landscape of Edge AI Ajay Nair - Google * 9:05 AM - 9:20 AM (PST) Comfort Break * 9:20 AM - 9:45 AM (PST) PRESENTATION: InferX X1, The Fastest and Most Efficient Edge Inference Accelerator Cheng Wang - Flex Logix Technologies Inc. * 9:50 AM - 10:20 AM (PST) PRESENTATION: Implementing Edge Technologies in Retail: Walmart Case Study Alex Sabatier - Nvidia * 10:20 AM - 10:35 AM (PST) Comfort Break * 10:35 AM - 11:20 AM (PST) Meet speaker! * **11:20 AM - 11:50 AM (PST) PRESENTATION: The Era of Analog AI Compute Is Here Mike Henry - Mythic** * 11:55 AM - 12:25 PM (PST) PRESENTATION: Using Edge AI To Detect Repetitive Mot Marcellino Gemelli - Bosch Sensortec * 12:30 PM - 2:30 PM (PST) NETWORKING - Dedicated Networking 2 hours for 1-2-1 Video Meetings **Friday, November 20, 2020** * 8:00 AM - 8:30 AM (PST) PRESENTATION: Spatial Computing: A Collision of Edge and Cloud-Based Computing Ashwin Swaminathan - Magic Leap * 8:35 AM - 9:05 AM (PST) PRESENTATION: Building An Autonomous Network For IoT and Edge Applications Anshul Bhatt - Rakuten Mobile * 9:05 AM - 9:20 AM (PST) Comfort Break * 9:20 AM - 9:45 AM (PST) PRESENTATION: Practical Edge Inferencing: Enabling fastest AI inferencing per Watt leveraging sparsity Mahesh Makhijani - GrAI Matter Labs * **9:50 AM - 10:20 AM (PST) PRESENTATION: Large Scale Deep Learning and AI models on the Edge Chandra Khatri - Got It AI** * 10:20 AM - 10:35 AM (PST) Comfort Break * 10:35 AM - 11:20 AM (PST) NETWORKING: Interest Groups (18 people per room, topic-specific discussions) * 11:20 AM - 11:50 AM (PST) PANEL DISCUSSION: The Symbiotic Relationship between 5G and Edge AI Sami Badri - Credit Suisse, Christos Kolias - Orange, Rima Raouda - Independent * 11:55 AM - 12:25 PM (PST) PANEL DISCUSSION: Investment Trends & Dynamics Panel Rashmi Gopinath - B Capital Group, Yvonne Lutsch - Bosch Venture Capital, Eileen Tanghal - In-Q-Tel, Albert Wang - Qualcomm Ventures * **12:30 PM - 12:50 PM (PST) PRESENTATION: The Edge: The Hottest Market for AI Accelerator Chips - Introducing the Kisaco Leadership Chart on AI Hardware Accelerators 2020-21: Edge and Automotive Michael Azoff - Kisaco Research** ## Wednesday, November 18, 2020 * ### A Software Solution Enabling Predictive Maintenance at the Sensor Level * SensiML Toolkit enables AI for a broad array of resource constrained time-series sensor endpoint applications. These include a wide range of consumer and industrial sensing applications. * The problem is machine learning engineer do not have experience with embedded system and moving model to embedded system takes long time. * AutoML for Embedded system usage. it is on the cloud. * using the compiler for that device for this tools * cost edge and cloud. easy to work on cloud. streaming data to cloud is difficult. faster if working on edge. * TinyML addresses problems, battery powered, limited internet connectivity, security/privacy, latency, economic * [https://sensiml.com/products/#process](https://www.google.com/url?q=https%3A%2F%2Fsensiml.com%2Fproducts%2F%23process&sa=D&sntz=1&usg=AOvVaw116wjx6mBnEo9x0htyL2pr) * ### Helping Fish Farmers Feed The World With Deep Learning * [https://s3-us-west-1.amazonaws.com/aquabyte-static/videos/welcome_to_aquabyte_subtitled.mp4](https://www.google.com/url?q=https%3A%2F%2Fs3-us-west-1.amazonaws.com%2Faquabyte-static%2Fvideos%2Fwelcome_to_aquabyte_subtitled.mp4&sa=D&sntz=1&usg=AOvVaw1osNtsRVM9TMCmKoPsTnj_) * Count sea lice and accurately measure biomass in real-time while reducing cage furniture. Our experts‑in‑the‑loop ensure that every single prediction is correct. * Aquabyte is seeking a Machine Learning Platform Engineer to drive the development, testing, and delivery of machine learning models that enable cutting-edge analytics and automation of fish farms around the world. * Aquabyte is on a mission to revolutionize the sustainability and efficiency of aquaculture. It is an audacious, and incredibly rewarding mission. By making fish farming cheaper and more viable than livestock production, we aim to mitigate one of the biggest causes of climate change and help prepare our planet for impending population growth. Aquaculture is the single fastest growing food-production sector in the world, and now is the time to define how technology is used to harvest the sea for generations to come. * We are currently focused on helping Norwegian salmon farmers better understand their fish populations and make environmentally-sound decisions. Through custom underwater cameras, computer vision, and machine learning we are able to quantify fish weights, detect sea lice infestations, and generate optimal feeding plans in real time. Our product operates at three levels: on-site hardware for image capture, cloud pipelines for data processing, and a user-facing web application. As a result, there are hundreds of moving pieces and no shortage of fascinating challenges across all levels of the stack. * * ### tinyMLPerf: Benchmarking Ultra-low Power Machine Learning Systems * [https://github.com/mlperf/tiny](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fmlperf%2Ftiny&sa=D&sntz=1&usg=AOvVaw1TgviYAuh83PMxDPYljOjs) * tinyMLPerf Deep Learning Benchmarks for Embedded Devices * The goal of TinyMLPerf is to provide a representative set of deep neural nets and benchmarking code to compare performance between embedded devices. Embedded devices include microcontrollers, DSPs, and tiny NN accelerators. These devics typically run at between 10MHz and 250MHz, and can perform inference using less then 50mW of power. TinyMLPerf submissions will allow device makers and researchers to choose the best hardware for their use case, and allows hardware vendors to showcase their offerings. TinyMLPerf is primarily intended to benchmark hardware rather than new network archietctures, or embedded neural net runtimes. The reference benchmarks are provided using TensorFlow Lite for Microcontrollers (TFLM). Submitters can directly use the TFLM, although submitters are encouraged to use the software stack that works best on thier hardware. * anomaly detection benchmark, visual wake words benchmark, * ### Ultra-low power neuromorphic intelligence for the sensor edge * Innatera Nanosystems BV (Innatera, (Innatera, innatera.com) is a rapidly-growing Dutch semiconductor company that develops ultra-efficient neuromorphic processors for AI at the edge. These microprocessors mimic the brain’s mechanisms for processing fast data streams from sensors, enabling complex turn-key sensor analytics functionalities, with 10,000x higher performance per watt than competing solutions. Innatera's technology serves as a critical enabler for next-generation use-cases in the IoT, wearable, embedded, and automotive domains. * ### * How is AI affecting hearables and sensors? * [https://github.com/greenwaves-technologies/nn_menu](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fgreenwaves-technologies%2Fnn_menu&sa=D&sntz=1&usg=AOvVaw2JKYYAPrnA9Mkalw2qenUQ) * The Neural Network Menu* is a collection of software that implements Neural Networks on Greenwaves Application Processors (GAP). This repository contains common mobile and edge NN archtecture examples, NN sample applications and full flagged reference designs. Our tools maps a TFLITE model (quantized or unquantized) onto gap. There is also a flow in the ingredients directory showing how to hand map from a Pytorch Model onto GAP. * [https://greenwaves-technologies.com/store/](https://www.google.com/url?q=https%3A%2F%2Fgreenwaves-technologies.com%2Fstore%2F&sa=D&sntz=1&usg=AOvVaw0Ya_w_NBAr4AbIxBe2j_YX) * GAPPoc-A is a Proof of Concept Board that can be used for demonstration of battery-operated, edge computer vision applications based on GAP8. * It incorporates GAPmod, a surface-mount module that implements all the layout sensitive portion of a GAP8 design, along with a VGA image sensor and a Bluetooth Low Energy radio. * The GAPPoc-A board enables battery-operated applications developed around algorithms such as people counting, face-identification and many others to be quickly assembled and evaluated in the field. * [https://riscv.org/blog/2019/08/risc-v-emea-roadshow-spotlight-greenwaves-technologies/](https://www.google.com/url?q=https%3A%2F%2Friscv.org%2Fblog%2F2019%2F08%2Frisc-v-emea-roadshow-spotlight-greenwaves-technologies%2F&sa=D&sntz=1&usg=AOvVaw1ikZjtEoYTgFb-S_eGEB3i) * ### Breaking the Barriers to Deploy DNNs on Low-Power Hardware * Deeplite, named to the 2020 CB Insights AI100 List of Most Innovative Artificial Intelligence Startups, is devoted to making fundamental advancements in accessible and efficient deep learning. Our solution helps deep learning engineers and experts automatically create faster, smaller and more energy-efficient deep neural networks. Industry leaders in computer vision, augmented reality and autonomous driving use our technology to unlock new possibilities for deep learning in the real world. At Deeplite, our vision is to create a lightweight intelligence that’s accessible for daily life. * [https://www.deeplite.ai/](https://www.google.com/url?q=https%3A%2F%2Fwww.deeplite.ai%2F&sa=D&sntz=1&usg=AOvVaw1r7NiQGt1hRi6S_xiJ522C) * At Deeplite, we are tackling inference optimization of deep neural networks, making them faster and energy-efficient from cloud to edge computing. Our solution leverages state-of-the-art technology from elite universities to make deep neural networks applicable for any device, and our team works hard on the iterative evolution of the science behind deep neural networks to directly improve daily life. * reduce the size of model 40x * ### Optimizing ML Models At The Edge Made Simple * [https://octoml.ai/](https://www.google.com/url?q=https%3A%2F%2Foctoml.ai%2F&sa=D&sntz=1&usg=AOvVaw2uXg6ESgQgVGrF9nQJKFve) * OctoML is an energetic new company changing how developers optimize and deploy machine learning models for their AI needs. We’re a team of machine learning systems leaders focused on making ML more efficient and easier to deploy by… applying machine learning to it! * OctoML is leveraging the power and traction of Apache TVM, an open source project originated by our founding team, to enable companies of every size to harness the power of deep learning without the expensive heavy lifting of tuning and securing models to each hardware configuration that a customer might need. * Apache TVM and Deep Learning Compilation Conference, Wed-Fri, December 2nd-4th 2020, Free Virtual Event. ## Thursday, November 19, 2020 * ### Developing Edge AI Solutions For A Post-Pandemic Society * [https://www.foghorn.io/](https://www.google.com/url?q=https%3A%2F%2Fwww.foghorn.io%2F&sa=D&sntz=1&usg=AOvVaw2aRKISC9BdrrriEej5Xb_I) * ogHorn’s Lightning™ Edge AI platform brings a groundbreaking dimension to IIoT and edge computing by embedding AI as close to the source of streaming sensor data as possible. The Edge AI software platform is a highly compact, advanced and feature-rich edge solution that delivers unprecedented low latency for onsite data processing, real-time analytics, ML and AI capabilities. It delivers the industry’s lowest total cost for computing requirements, communications services, and cloud processing and storage. * temperature detection, social distancing, cough detection, PPE/Mask detection * Flexible, customizable, integrated, actionable * ### The Evolving Landscape of Edge AI * * Coral’s local AI technology enables new possibilities across almost any kind of industry * The Coral Dev Board is a single-board computer that contains an Edge TPU coprocessor. It's ideal for prototyping new projects that demand fast on-device inferencing for machine learning models. This page is your guide to get started. The setup requires flashing Mendel Linux to the board, and then accessing the board's shell terminal. Once you have terminal access and update some of the software, we'll show you how to run an image classification model on the board. If you want to learn more about the hardware, see the Dev Board datasheet. * TPU v3, 32 to 512 TOPS, Q2 2021 * ### InferX X1, The Fastest and Most Efficient Edge Inference Accelerator * InferX X1: World's fastest and most efficient Edge Inference Accelerator. We have just launched our first inference chip and it is the best in the world for edge inference. We are bringing up neural network models now and moving forward on the steps required for Q2/2021 chip and board production and Inference Compiler availability. * mbedded FPGA, or eFPGA, enables your SoC to have flexibility in critical areas where algorithm, protocol or market needs are changing. FPGA can also accelerate many workloads faster than processors: Microsoft Azure uses one FPGA accelerator for every 2 Xeons.Flex Logix provides eFPGA cores which have density and performance similar to leading FPGAs in the same process node. Our EFLX eFPGA is silicon proven in 40nm, 28/22nm, 16nm and 12nm. 6/7nm EFLX eFPGA is planned. Our eFPGA is based on a “tile” called EFLX 4K, which comes in two versions: all logic or mostly logic with some MACs (multiply-accumulators). The programmable logic is called LUTs (look up tables) that can implement any Boolean function. EFLX 4K Logix has 4000 LUT4 equivalents, EFLX 4K DSP has 3000 LUT4s and 40 Multiplier-Accumulators (MACs): the MAC has a 22-bit pre-adder, a 22×22 multiple and a 48-bit post adder/accumulator. MACs can be combined or cascaded to form fast DSP functions. (For 40nm-180nm we offer an EFLX 1K tile). * depth-wise conv2d * ### Implementing Edge Technologies in Retail: Walmart Case Study * NVidia * ### The Era of Analog AI Compute Is Here * Mythic products are based on a unique tile-based AI compute architecture that features three fundamental hardware technologies – Compute-in-Memory, Dataflow Architecture, and Analog Computing. For AI developers, the Mythic SDK streamlines the preparation of trained neural networks for edge and low-latency datacenter deployments, and also performs automatic optimization and compilation of dataflow graphs for our unique architecture. * low power consumption, ultra-low latency, high ai performance, large weight capacity, small form factor, cost effective solution * ### **Us** ing Edge AI To Detect Repetitive Mot * Bosch Sensortec develops and markets a wide portfolio of MEMS sensors and solutions for applications in smartphones, tablets, wearables, AR/VR devices, drones, robots, smart home and the Internet of Things. Striving to meet the demanding requirements of the consumer electronics market, we provide best-in-class sensing solutions in terms of customer focus, quality and reliability, performance, sustainability and competitiveness. * [https://github.com/BoschSensortec](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FBoschSensortec&sa=D&sntz=1&usg=AOvVaw0Xr8dUHPERsj-rYH7ZAnP1) ## Friday, November 20, 2020 * ### *Spatial Computing: A Collision of Edge and Cloud-Based Computing * [https://github.com/magicleap](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fmagicleap&sa=D&sntz=1&usg=AOvVaw22R0IwwKYOdwv06BvMpvZF) * instance semantic segmentation contextual computing * spatial computing * SLAM: tracking/localization, mapping: * latency is critical for see through displays * weight is critical cannot compensate for lack of compute with more sensors * thermal is critical more sensors and more compute lead to heat * rigidity leads to weight our device should be light * very stringent requirements for MR * why build a map: drift correction, robustness (pose recovery), persistence * feature descriptors * matching across large baselines and illumination changes is challenging * most of the SOTA methods based on deep learning and not feasible withing compute budget * our deep descriptor is optimized for SLAM and provides the best trade off in terms of performance and compute * semantic segmentation 3d point cloud * ### Building An Autonomous Network For IoT and Edge Applications * 5G + AI * ### Practical Edge Inferencing: Enabling fastest AI inferencing per Watt leveraging sparsity * [https://www.graimatterlabs.ai/](https://www.google.com/url?q=https%3A%2F%2Fwww.graimatterlabs.ai%2F&sa=D&sntz=1&usg=AOvVaw1qLIEaRzrtXoYDAb_Y4tQb) * The world’s first sparsity-enabled AI processor optimized for ultra-low latency and low power processing at the edge. * GrAI One drastically reduces application latency, for instance, it reduces the end-to-end latencies for deep learning networks such as PilotNet to the order of milliseconds. The GrAI One chip is based on GML’s innovative NeuronFlow™ technology that combines the dynamic Dataflow paradigm with sparse computing to produce massively parallel in-network processing. * GrAI Matter Labs ([www.graimatterlabs.ai](http://www.google.com/url?q=http%3A%2F%2Fwww.graimatterlabs.ai%2F&sa=D&sntz=1&usg=AOvVaw3eJ9oROjswyCFHO-68LiDi)), a fabless semiconductor company specialized in brain-inspired technology, designs and develops fully programmable ultra-low power neuromorphic HW for sensor analytics and machine learning. The company has offices in Eindhoven (NL), Paris (FR) and San Jose (USA) and has strong relations with top-ranking research groups on neuroscience, human vision and natural computation * ### **Large Scale Deep Learning and AI models on the Edge * deployment pipelines * there are several steps involved in the AI/ML life-cycle * several tools to help simplify the whole process * tensorflow extended (TFX): an end to end platform for deploying production ML pipelines * MLflow (other options michelangelo): an open source platform for the end to end machine learning life cycle * apache airflow (other options kubeflow): an open source workflow management platform * dataiku data science studio (DSS): collaborative data science software platform for teams of data scientist , data analysts, and engineers to explore prototype build and deliver * ### The Edge: The Hottest Market for AI Accelerator Chips - Introducing the Kisaco Leadership Chart on AI Hardware Accelerators 2020-21: Edge and Automotive * [https://www.kisacoresearch.com/#about-us](https://www.google.com/url?q=https%3A%2F%2Fwww.kisacoresearch.com%2F%23about-us&sa=D&sntz=1&usg=AOvVaw0nasAOo80KuyOwbm4OeiOb) [OpenHTF is a Python library that provides a set of convenient abstractions designed to remove as much boilerplate as possible from hardware test setup and execution, so test engineers can focus primarily on test logic.](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fgoogle%2Fopenhtf&sa=D&sntz=1&usg=AOvVaw0zU3RKntPn4N8JIkPvriIu) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/3LTMUMvLqIOxMLGRMkavoKhjR0yGpVpsaeb_NnspLybaPexjoQBnvVa2C2973HUQ30RiIqubg5B9F6eLjnxKXlE=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/3LTMUMvLqIOxMLGRMkavoKhjR0yGpVpsaeb_NnspLybaPexjoQBnvVa2C2973HUQ30RiIqubg5B9F6eLjnxKXlE=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Edge AI summit Short summary of the edge AI summit 18-20 November 2020 Wednesday, November 18, 2020 A Software Solution Enabling Predictive Maintenance at the Sensor Level Helping Fish Farmers Feed The World With Deep Learning tinyMLPerf: Benchmarking Ultra-low Power Machine Learning Systems Ultra-low power neuromorphic intelligence for the sensor edge * How is AI affecting hearables and sensors? Breaking the Barriers to Deploy DNNs on Low-Power Hardware Optimizing ML Models At The Edge Made Simple Thursday, November 19, 2020 Developing Edge AI Solutions For A Post-Pandemic Society The Evolving Landscape of Edge AI InferX X1, The Fastest and Most Efficient Edge Inference Accelerator Implementing Edge Technologies in Retail: Walmart Case Study The Era of Analog AI Compute Is Here Using Edge AI To Detect Repetitive Mot Friday, November 20, 2020 *Spatial Computing: A Collision of Edge and Cloud-Based Computing Building An Autonomous Network For IoT and Edge Applications Practical Edge Inferencing: Enabling fastest AI inferencing per Watt leveraging sparsity **Large Scale Deep Learning and AI models on the Edge The Edge: The Hottest Market for AI Accelerator Chips - Introducing the Kisaco Leadership Chart on AI Hardware Accelerators 2020-21: Edge and Automotive # Short summary of the edge AI summit 18-20 November 2020 Best of Wednesday, November 18, 2020; tinyMLPerf, Breaking the Barriers to Deploy DNNs on Low-Power Hardware, Optimizing ML Models At The Edge Made Simple **Thursday, November 19, 2020** * 8:00 AM - 8:30 AM (PST) KEYNOTE PRESENTATION: Developing Edge AI Solutions For A Post-Pandemic Society Sastry Malladi - FogHorn Systems * 8:35 AM - 9:05 AM (PST) PRESENTATION: The Evolving Landscape of Edge AI Ajay Nair - Google * 9:05 AM - 9:20 AM (PST) Comfort Break * 9:20 AM - 9:45 AM (PST) PRESENTATION: InferX X1, The Fastest and Most Efficient Edge Inference Accelerator Cheng Wang - Flex Logix Technologies Inc. * 9:50 AM - 10:20 AM (PST) PRESENTATION: Implementing Edge Technologies in Retail: Walmart Case Study Alex Sabatier - Nvidia * 10:20 AM - 10:35 AM (PST) Comfort Break * 10:35 AM - 11:20 AM (PST) Meet speaker! * **11:20 AM - 11:50 AM (PST) PRESENTATION: The Era of Analog AI Compute Is Here Mike Henry - Mythic** * 11:55 AM - 12:25 PM (PST) PRESENTATION: Using Edge AI To Detect Repetitive Mot Marcellino Gemelli - Bosch Sensortec * 12:30 PM - 2:30 PM (PST) NETWORKING - Dedicated Networking 2 hours for 1-2-1 Video Meetings **Friday, November 20, 2020** * 8:00 AM - 8:30 AM (PST) PRESENTATION: Spatial Computing: A Collision of Edge and Cloud-Based Computing Ashwin Swaminathan - Magic Leap * 8:35 AM - 9:05 AM (PST) PRESENTATION: Building An Autonomous Network For IoT and Edge Applications Anshul Bhatt - Rakuten Mobile * 9:05 AM - 9:20 AM (PST) Comfort Break * 9:20 AM - 9:45 AM (PST) PRESENTATION: Practical Edge Inferencing: Enabling fastest AI inferencing per Watt leveraging sparsity Mahesh Makhijani - GrAI Matter Labs * **9:50 AM - 10:20 AM (PST) PRESENTATION: Large Scale Deep Learning and AI models on the Edge Chandra Khatri - Got It AI** * 10:20 AM - 10:35 AM (PST) Comfort Break * 10:35 AM - 11:20 AM (PST) NETWORKING: Interest Groups (18 people per room, topic-specific discussions) * 11:20 AM - 11:50 AM (PST) PANEL DISCUSSION: The Symbiotic Relationship between 5G and Edge AI Sami Badri - Credit Suisse, Christos Kolias - Orange, Rima Raouda - Independent * 11:55 AM - 12:25 PM (PST) PANEL DISCUSSION: Investment Trends & Dynamics Panel Rashmi Gopinath - B Capital Group, Yvonne Lutsch - Bosch Venture Capital, Eileen Tanghal - In-Q-Tel, Albert Wang - Qualcomm Ventures * **12:30 PM - 12:50 PM (PST) PRESENTATION: The Edge: The Hottest Market for AI Accelerator Chips - Introducing the Kisaco Leadership Chart on AI Hardware Accelerators 2020-21: Edge and Automotive Michael Azoff - Kisaco Research** ## Wednesday, November 18, 2020 * ### A Software Solution Enabling Predictive Maintenance at the Sensor Level * SensiML Toolkit enables AI for a broad array of resource constrained time-series sensor endpoint applications. These include a wide range of consumer and industrial sensing applications. * The problem is machine learning engineer do not have experience with embedded system and moving model to embedded system takes long time. * AutoML for Embedded system usage. it is on the cloud. * using the compiler for that device for this tools * cost edge and cloud. easy to work on cloud. streaming data to cloud is difficult. faster if working on edge. * TinyML addresses problems, battery powered, limited internet connectivity, security/privacy, latency, economic * [https://sensiml.com/products/#process](https://www.google.com/url?q=https%3A%2F%2Fsensiml.com%2Fproducts%2F%23process&sa=D&sntz=1&usg=AOvVaw116wjx6mBnEo9x0htyL2pr) * ### Helping Fish Farmers Feed The World With Deep Learning * [https://s3-us-west-1.amazonaws.com/aquabyte-static/videos/welcome_to_aquabyte_subtitled.mp4](https://www.google.com/url?q=https%3A%2F%2Fs3-us-west-1.amazonaws.com%2Faquabyte-static%2Fvideos%2Fwelcome_to_aquabyte_subtitled.mp4&sa=D&sntz=1&usg=AOvVaw1osNtsRVM9TMCmKoPsTnj_) * Count sea lice and accurately measure biomass in real-time while reducing cage furniture. Our experts‑in‑the‑loop ensure that every single prediction is correct. * Aquabyte is seeking a Machine Learning Platform Engineer to drive the development, testing, and delivery of machine learning models that enable cutting-edge analytics and automation of fish farms around the world. * Aquabyte is on a mission to revolutionize the sustainability and efficiency of aquaculture. It is an audacious, and incredibly rewarding mission. By making fish farming cheaper and more viable than livestock production, we aim to mitigate one of the biggest causes of climate change and help prepare our planet for impending population growth. Aquaculture is the single fastest growing food-production sector in the world, and now is the time to define how technology is used to harvest the sea for generations to come. * We are currently focused on helping Norwegian salmon farmers better understand their fish populations and make environmentally-sound decisions. Through custom underwater cameras, computer vision, and machine learning we are able to quantify fish weights, detect sea lice infestations, and generate optimal feeding plans in real time. Our product operates at three levels: on-site hardware for image capture, cloud pipelines for data processing, and a user-facing web application. As a result, there are hundreds of moving pieces and no shortage of fascinating challenges across all levels of the stack. * * ### tinyMLPerf: Benchmarking Ultra-low Power Machine Learning Systems * [https://github.com/mlperf/tiny](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fmlperf%2Ftiny&sa=D&sntz=1&usg=AOvVaw1TgviYAuh83PMxDPYljOjs) * tinyMLPerf Deep Learning Benchmarks for Embedded Devices * The goal of TinyMLPerf is to provide a representative set of deep neural nets and benchmarking code to compare performance between embedded devices. Embedded devices include microcontrollers, DSPs, and tiny NN accelerators. These devics typically run at between 10MHz and 250MHz, and can perform inference using less then 50mW of power. TinyMLPerf submissions will allow device makers and researchers to choose the best hardware for their use case, and allows hardware vendors to showcase their offerings. TinyMLPerf is primarily intended to benchmark hardware rather than new network archietctures, or embedded neural net runtimes. The reference benchmarks are provided using TensorFlow Lite for Microcontrollers (TFLM). Submitters can directly use the TFLM, although submitters are encouraged to use the software stack that works best on thier hardware. * anomaly detection benchmark, visual wake words benchmark, * ### Ultra-low power neuromorphic intelligence for the sensor edge * Innatera Nanosystems BV (Innatera, (Innatera, innatera.com) is a rapidly-growing Dutch semiconductor company that develops ultra-efficient neuromorphic processors for AI at the edge. These microprocessors mimic the brain’s mechanisms for processing fast data streams from sensors, enabling complex turn-key sensor analytics functionalities, with 10,000x higher performance per watt than competing solutions. Innatera's technology serves as a critical enabler for next-generation use-cases in the IoT, wearable, embedded, and automotive domains. * ### * How is AI affecting hearables and sensors? * [https://github.com/greenwaves-technologies/nn_menu](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fgreenwaves-technologies%2Fnn_menu&sa=D&sntz=1&usg=AOvVaw2JKYYAPrnA9Mkalw2qenUQ) * The Neural Network Menu* is a collection of software that implements Neural Networks on Greenwaves Application Processors (GAP). This repository contains common mobile and edge NN archtecture examples, NN sample applications and full flagged reference designs. Our tools maps a TFLITE model (quantized or unquantized) onto gap. There is also a flow in the ingredients directory showing how to hand map from a Pytorch Model onto GAP. * [https://greenwaves-technologies.com/store/](https://www.google.com/url?q=https%3A%2F%2Fgreenwaves-technologies.com%2Fstore%2F&sa=D&sntz=1&usg=AOvVaw0Ya_w_NBAr4AbIxBe2j_YX) * GAPPoc-A is a Proof of Concept Board that can be used for demonstration of battery-operated, edge computer vision applications based on GAP8. * It incorporates GAPmod, a surface-mount module that implements all the layout sensitive portion of a GAP8 design, along with a VGA image sensor and a Bluetooth Low Energy radio. * The GAPPoc-A board enables battery-operated applications developed around algorithms such as people counting, face-identification and many others to be quickly assembled and evaluated in the field. * [https://riscv.org/blog/2019/08/risc-v-emea-roadshow-spotlight-greenwaves-technologies/](https://www.google.com/url?q=https%3A%2F%2Friscv.org%2Fblog%2F2019%2F08%2Frisc-v-emea-roadshow-spotlight-greenwaves-technologies%2F&sa=D&sntz=1&usg=AOvVaw1ikZjtEoYTgFb-S_eGEB3i) * ### Breaking the Barriers to Deploy DNNs on Low-Power Hardware * Deeplite, named to the 2020 CB Insights AI100 List of Most Innovative Artificial Intelligence Startups, is devoted to making fundamental advancements in accessible and efficient deep learning. Our solution helps deep learning engineers and experts automatically create faster, smaller and more energy-efficient deep neural networks. Industry leaders in computer vision, augmented reality and autonomous driving use our technology to unlock new possibilities for deep learning in the real world. At Deeplite, our vision is to create a lightweight intelligence that’s accessible for daily life. * [https://www.deeplite.ai/](https://www.google.com/url?q=https%3A%2F%2Fwww.deeplite.ai%2F&sa=D&sntz=1&usg=AOvVaw1r7NiQGt1hRi6S_xiJ522C) * At Deeplite, we are tackling inference optimization of deep neural networks, making them faster and energy-efficient from cloud to edge computing. Our solution leverages state-of-the-art technology from elite universities to make deep neural networks applicable for any device, and our team works hard on the iterative evolution of the science behind deep neural networks to directly improve daily life. * reduce the size of model 40x * ### Optimizing ML Models At The Edge Made Simple * [https://octoml.ai/](https://www.google.com/url?q=https%3A%2F%2Foctoml.ai%2F&sa=D&sntz=1&usg=AOvVaw2uXg6ESgQgVGrF9nQJKFve) * OctoML is an energetic new company changing how developers optimize and deploy machine learning models for their AI needs. We’re a team of machine learning systems leaders focused on making ML more efficient and easier to deploy by… applying machine learning to it! * OctoML is leveraging the power and traction of Apache TVM, an open source project originated by our founding team, to enable companies of every size to harness the power of deep learning without the expensive heavy lifting of tuning and securing models to each hardware configuration that a customer might need. * Apache TVM and Deep Learning Compilation Conference, Wed-Fri, December 2nd-4th 2020, Free Virtual Event. ## Thursday, November 19, 2020 * ### Developing Edge AI Solutions For A Post-Pandemic Society * [https://www.foghorn.io/](https://www.google.com/url?q=https%3A%2F%2Fwww.foghorn.io%2F&sa=D&sntz=1&usg=AOvVaw2aRKISC9BdrrriEej5Xb_I) * ogHorn’s Lightning™ Edge AI platform brings a groundbreaking dimension to IIoT and edge computing by embedding AI as close to the source of streaming sensor data as possible. The Edge AI software platform is a highly compact, advanced and feature-rich edge solution that delivers unprecedented low latency for onsite data processing, real-time analytics, ML and AI capabilities. It delivers the industry’s lowest total cost for computing requirements, communications services, and cloud processing and storage. * temperature detection, social distancing, cough detection, PPE/Mask detection * Flexible, customizable, integrated, actionable * ### The Evolving Landscape of Edge AI * * Coral’s local AI technology enables new possibilities across almost any kind of industry * The Coral Dev Board is a single-board computer that contains an Edge TPU coprocessor. It's ideal for prototyping new projects that demand fast on-device inferencing for machine learning models. This page is your guide to get started. The setup requires flashing Mendel Linux to the board, and then accessing the board's shell terminal. Once you have terminal access and update some of the software, we'll show you how to run an image classification model on the board. If you want to learn more about the hardware, see the Dev Board datasheet. * TPU v3, 32 to 512 TOPS, Q2 2021 * ### InferX X1, The Fastest and Most Efficient Edge Inference Accelerator * InferX X1: World's fastest and most efficient Edge Inference Accelerator. We have just launched our first inference chip and it is the best in the world for edge inference. We are bringing up neural network models now and moving forward on the steps required for Q2/2021 chip and board production and Inference Compiler availability. * mbedded FPGA, or eFPGA, enables your SoC to have flexibility in critical areas where algorithm, protocol or market needs are changing. FPGA can also accelerate many workloads faster than processors: Microsoft Azure uses one FPGA accelerator for every 2 Xeons.Flex Logix provides eFPGA cores which have density and performance similar to leading FPGAs in the same process node. Our EFLX eFPGA is silicon proven in 40nm, 28/22nm, 16nm and 12nm. 6/7nm EFLX eFPGA is planned. Our eFPGA is based on a “tile” called EFLX 4K, which comes in two versions: all logic or mostly logic with some MACs (multiply-accumulators). The programmable logic is called LUTs (look up tables) that can implement any Boolean function. EFLX 4K Logix has 4000 LUT4 equivalents, EFLX 4K DSP has 3000 LUT4s and 40 Multiplier-Accumulators (MACs): the MAC has a 22-bit pre-adder, a 22×22 multiple and a 48-bit post adder/accumulator. MACs can be combined or cascaded to form fast DSP functions. (For 40nm-180nm we offer an EFLX 1K tile). * depth-wise conv2d * ### Implementing Edge Technologies in Retail: Walmart Case Study * NVidia * ### The Era of Analog AI Compute Is Here * Mythic products are based on a unique tile-based AI compute architecture that features three fundamental hardware technologies – Compute-in-Memory, Dataflow Architecture, and Analog Computing. For AI developers, the Mythic SDK streamlines the preparation of trained neural networks for edge and low-latency datacenter deployments, and also performs automatic optimization and compilation of dataflow graphs for our unique architecture. * low power consumption, ultra-low latency, high ai performance, large weight capacity, small form factor, cost effective solution * ### **Us** ing Edge AI To Detect Repetitive Mot * Bosch Sensortec develops and markets a wide portfolio of MEMS sensors and solutions for applications in smartphones, tablets, wearables, AR/VR devices, drones, robots, smart home and the Internet of Things. Striving to meet the demanding requirements of the consumer electronics market, we provide best-in-class sensing solutions in terms of customer focus, quality and reliability, performance, sustainability and competitiveness. * [https://github.com/BoschSensortec](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FBoschSensortec&sa=D&sntz=1&usg=AOvVaw0Xr8dUHPERsj-rYH7ZAnP1) ## Friday, November 20, 2020 * ### *Spatial Computing: A Collision of Edge and Cloud-Based Computing * [https://github.com/magicleap](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fmagicleap&sa=D&sntz=1&usg=AOvVaw22R0IwwKYOdwv06BvMpvZF) * instance semantic segmentation contextual computing * spatial computing * SLAM: tracking/localization, mapping: * latency is critical for see through displays * weight is critical cannot compensate for lack of compute with more sensors * thermal is critical more sensors and more compute lead to heat * rigidity leads to weight our device should be light * very stringent requirements for MR * why build a map: drift correction, robustness (pose recovery), persistence * feature descriptors * matching across large baselines and illumination changes is challenging * most of the SOTA methods based on deep learning and not feasible withing compute budget * our deep descriptor is optimized for SLAM and provides the best trade off in terms of performance and compute * semantic segmentation 3d point cloud * ### Building An Autonomous Network For IoT and Edge Applications * 5G + AI * ### Practical Edge Inferencing: Enabling fastest AI inferencing per Watt leveraging sparsity * [https://www.graimatterlabs.ai/](https://www.google.com/url?q=https%3A%2F%2Fwww.graimatterlabs.ai%2F&sa=D&sntz=1&usg=AOvVaw1qLIEaRzrtXoYDAb_Y4tQb) * The world’s first sparsity-enabled AI processor optimized for ultra-low latency and low power processing at the edge. * GrAI One drastically reduces application latency, for instance, it reduces the end-to-end latencies for deep learning networks such as PilotNet to the order of milliseconds. The GrAI One chip is based on GML’s innovative NeuronFlow™ technology that combines the dynamic Dataflow paradigm with sparse computing to produce massively parallel in-network processing. * GrAI Matter Labs ([www.graimatterlabs.ai](http://www.google.com/url?q=http%3A%2F%2Fwww.graimatterlabs.ai%2F&sa=D&sntz=1&usg=AOvVaw3eJ9oROjswyCFHO-68LiDi)), a fabless semiconductor company specialized in brain-inspired technology, designs and develops fully programmable ultra-low power neuromorphic HW for sensor analytics and machine learning. The company has offices in Eindhoven (NL), Paris (FR) and San Jose (USA) and has strong relations with top-ranking research groups on neuroscience, human vision and natural computation * ### **Large Scale Deep Learning and AI models on the Edge * deployment pipelines * there are several steps involved in the AI/ML life-cycle * several tools to help simplify the whole process * tensorflow extended (TFX): an end to end platform for deploying production ML pipelines * MLflow (other options michelangelo): an open source platform for the end to end machine learning life cycle * apache airflow (other options kubeflow): an open source workflow management platform * dataiku data science studio (DSS): collaborative data science software platform for teams of data scientist , data analysts, and engineers to explore prototype build and deliver * ### The Edge: The Hottest Market for AI Accelerator Chips - Introducing the Kisaco Leadership Chart on AI Hardware Accelerators 2020-21: Edge and Automotive * [https://www.kisacoresearch.com/#about-us](https://www.google.com/url?q=https%3A%2F%2Fwww.kisacoresearch.com%2F%23about-us&sa=D&sntz=1&usg=AOvVaw0nasAOo80KuyOwbm4OeiOb) [OpenHTF is a Python library that provides a set of convenient abstractions designed to remove as much boilerplate as possible from hardware test setup and execution, so test engineers can focus primarily on test logic.](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fgoogle%2Fopenhtf&sa=D&sntz=1&usg=AOvVaw0zU3RKntPn4N8JIkPvriIu) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/3LTMUMvLqIOxMLGRMkavoKhjR0yGpVpsaeb_NnspLybaPexjoQBnvVa2C2973HUQ30RiIqubg5B9F6eLjnxKXlE=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/3LTMUMvLqIOxMLGRMkavoKhjR0yGpVpsaeb_NnspLybaPexjoQBnvVa2C2973HUQ30RiIqubg5B9F6eLjnxKXlE=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Edge AI summit Short summary of the edge AI summit 18-20 November 2020 Wednesday, November 18, 2020 A Software Solution Enabling Predictive Maintenance at the Sensor Level Helping Fish Farmers Feed The World With Deep Learning tinyMLPerf: Benchmarking Ultra-low Power Machine Learning Systems Ultra-low power neuromorphic intelligence for the sensor edge * How is AI affecting hearables and sensors? Breaking the Barriers to Deploy DNNs on Low-Power Hardware Optimizing ML Models At The Edge Made Simple Thursday, November 19, 2020 Developing Edge AI Solutions For A Post-Pandemic Society The Evolving Landscape of Edge AI InferX X1, The Fastest and Most Efficient Edge Inference Accelerator Implementing Edge Technologies in Retail: Walmart Case Study The Era of Analog AI Compute Is Here Using Edge AI To Detect Repetitive Mot Friday, November 20, 2020 *Spatial Computing: A Collision of Edge and Cloud-Based Computing Building An Autonomous Network For IoT and Edge Applications Practical Edge Inferencing: Enabling fastest AI inferencing per Watt leveraging sparsity **Large Scale Deep Learning and AI models on the Edge The Edge: The Hottest Market for AI Accelerator Chips - Introducing the Kisaco Leadership Chart on AI Hardware Accelerators 2020-21: Edge and Automotive # Short summary of the edge AI summit 18-20 November 2020 Best of Wednesday, November 18, 2020; tinyMLPerf, Breaking the Barriers to Deploy DNNs on Low-Power Hardware, Optimizing ML Models At The Edge Made Simple **Thursday, November 19, 2020** * 8:00 AM - 8:30 AM (PST) KEYNOTE PRESENTATION: Developing Edge AI Solutions For A Post-Pandemic Society Sastry Malladi - FogHorn Systems * 8:35 AM - 9:05 AM (PST) PRESENTATION: The Evolving Landscape of Edge AI Ajay Nair - Google * 9:05 AM - 9:20 AM (PST) Comfort Break * 9:20 AM - 9:45 AM (PST) PRESENTATION: InferX X1, The Fastest and Most Efficient Edge Inference Accelerator Cheng Wang - Flex Logix Technologies Inc. * 9:50 AM - 10:20 AM (PST) PRESENTATION: Implementing Edge Technologies in Retail: Walmart Case Study Alex Sabatier - Nvidia * 10:20 AM - 10:35 AM (PST) Comfort Break * 10:35 AM - 11:20 AM (PST) Meet speaker! * **11:20 AM - 11:50 AM (PST) PRESENTATION: The Era of Analog AI Compute Is Here Mike Henry - Mythic** * 11:55 AM - 12:25 PM (PST) PRESENTATION: Using Edge AI To Detect Repetitive Mot Marcellino Gemelli - Bosch Sensortec * 12:30 PM - 2:30 PM (PST) NETWORKING - Dedicated Networking 2 hours for 1-2-1 Video Meetings **Friday, November 20, 2020** * 8:00 AM - 8:30 AM (PST) PRESENTATION: Spatial Computing: A Collision of Edge and Cloud-Based Computing Ashwin Swaminathan - Magic Leap * 8:35 AM - 9:05 AM (PST) PRESENTATION: Building An Autonomous Network For IoT and Edge Applications Anshul Bhatt - Rakuten Mobile * 9:05 AM - 9:20 AM (PST) Comfort Break * 9:20 AM - 9:45 AM (PST) PRESENTATION: Practical Edge Inferencing: Enabling fastest AI inferencing per Watt leveraging sparsity Mahesh Makhijani - GrAI Matter Labs * **9:50 AM - 10:20 AM (PST) PRESENTATION: Large Scale Deep Learning and AI models on the Edge Chandra Khatri - Got It AI** * 10:20 AM - 10:35 AM (PST) Comfort Break * 10:35 AM - 11:20 AM (PST) NETWORKING: Interest Groups (18 people per room, topic-specific discussions) * 11:20 AM - 11:50 AM (PST) PANEL DISCUSSION: The Symbiotic Relationship between 5G and Edge AI Sami Badri - Credit Suisse, Christos Kolias - Orange, Rima Raouda - Independent * 11:55 AM - 12:25 PM (PST) PANEL DISCUSSION: Investment Trends & Dynamics Panel Rashmi Gopinath - B Capital Group, Yvonne Lutsch - Bosch Venture Capital, Eileen Tanghal - In-Q-Tel, Albert Wang - Qualcomm Ventures * **12:30 PM - 12:50 PM (PST) PRESENTATION: The Edge: The Hottest Market for AI Accelerator Chips - Introducing the Kisaco Leadership Chart on AI Hardware Accelerators 2020-21: Edge and Automotive Michael Azoff - Kisaco Research** ## Wednesday, November 18, 2020 * ### A Software Solution Enabling Predictive Maintenance at the Sensor Level * SensiML Toolkit enables AI for a broad array of resource constrained time-series sensor endpoint applications. These include a wide range of consumer and industrial sensing applications. * The problem is machine learning engineer do not have experience with embedded system and moving model to embedded system takes long time. * AutoML for Embedded system usage. it is on the cloud. * using the compiler for that device for this tools * cost edge and cloud. easy to work on cloud. streaming data to cloud is difficult. faster if working on edge. * TinyML addresses problems, battery powered, limited internet connectivity, security/privacy, latency, economic * [https://sensiml.com/products/#process](https://www.google.com/url?q=https%3A%2F%2Fsensiml.com%2Fproducts%2F%23process&sa=D&sntz=1&usg=AOvVaw116wjx6mBnEo9x0htyL2pr) * ### Helping Fish Farmers Feed The World With Deep Learning * [https://s3-us-west-1.amazonaws.com/aquabyte-static/videos/welcome_to_aquabyte_subtitled.mp4](https://www.google.com/url?q=https%3A%2F%2Fs3-us-west-1.amazonaws.com%2Faquabyte-static%2Fvideos%2Fwelcome_to_aquabyte_subtitled.mp4&sa=D&sntz=1&usg=AOvVaw1osNtsRVM9TMCmKoPsTnj_) * Count sea lice and accurately measure biomass in real-time while reducing cage furniture. Our experts‑in‑the‑loop ensure that every single prediction is correct. * Aquabyte is seeking a Machine Learning Platform Engineer to drive the development, testing, and delivery of machine learning models that enable cutting-edge analytics and automation of fish farms around the world. * Aquabyte is on a mission to revolutionize the sustainability and efficiency of aquaculture. It is an audacious, and incredibly rewarding mission. By making fish farming cheaper and more viable than livestock production, we aim to mitigate one of the biggest causes of climate change and help prepare our planet for impending population growth. Aquaculture is the single fastest growing food-production sector in the world, and now is the time to define how technology is used to harvest the sea for generations to come. * We are currently focused on helping Norwegian salmon farmers better understand their fish populations and make environmentally-sound decisions. Through custom underwater cameras, computer vision, and machine learning we are able to quantify fish weights, detect sea lice infestations, and generate optimal feeding plans in real time. Our product operates at three levels: on-site hardware for image capture, cloud pipelines for data processing, and a user-facing web application. As a result, there are hundreds of moving pieces and no shortage of fascinating challenges across all levels of the stack. * * ### tinyMLPerf: Benchmarking Ultra-low Power Machine Learning Systems * [https://github.com/mlperf/tiny](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fmlperf%2Ftiny&sa=D&sntz=1&usg=AOvVaw1TgviYAuh83PMxDPYljOjs) * tinyMLPerf Deep Learning Benchmarks for Embedded Devices * The goal of TinyMLPerf is to provide a representative set of deep neural nets and benchmarking code to compare performance between embedded devices. Embedded devices include microcontrollers, DSPs, and tiny NN accelerators. These devics typically run at between 10MHz and 250MHz, and can perform inference using less then 50mW of power. TinyMLPerf submissions will allow device makers and researchers to choose the best hardware for their use case, and allows hardware vendors to showcase their offerings. TinyMLPerf is primarily intended to benchmark hardware rather than new network archietctures, or embedded neural net runtimes. The reference benchmarks are provided using TensorFlow Lite for Microcontrollers (TFLM). Submitters can directly use the TFLM, although submitters are encouraged to use the software stack that works best on thier hardware. * anomaly detection benchmark, visual wake words benchmark, * ### Ultra-low power neuromorphic intelligence for the sensor edge * Innatera Nanosystems BV (Innatera, (Innatera, innatera.com) is a rapidly-growing Dutch semiconductor company that develops ultra-efficient neuromorphic processors for AI at the edge. These microprocessors mimic the brain’s mechanisms for processing fast data streams from sensors, enabling complex turn-key sensor analytics functionalities, with 10,000x higher performance per watt than competing solutions. Innatera's technology serves as a critical enabler for next-generation use-cases in the IoT, wearable, embedded, and automotive domains. * ### * How is AI affecting hearables and sensors? * [https://github.com/greenwaves-technologies/nn_menu](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fgreenwaves-technologies%2Fnn_menu&sa=D&sntz=1&usg=AOvVaw2JKYYAPrnA9Mkalw2qenUQ) * The Neural Network Menu* is a collection of software that implements Neural Networks on Greenwaves Application Processors (GAP). This repository contains common mobile and edge NN archtecture examples, NN sample applications and full flagged reference designs. Our tools maps a TFLITE model (quantized or unquantized) onto gap. There is also a flow in the ingredients directory showing how to hand map from a Pytorch Model onto GAP. * [https://greenwaves-technologies.com/store/](https://www.google.com/url?q=https%3A%2F%2Fgreenwaves-technologies.com%2Fstore%2F&sa=D&sntz=1&usg=AOvVaw0Ya_w_NBAr4AbIxBe2j_YX) * GAPPoc-A is a Proof of Concept Board that can be used for demonstration of battery-operated, edge computer vision applications based on GAP8. * It incorporates GAPmod, a surface-mount module that implements all the layout sensitive portion of a GAP8 design, along with a VGA image sensor and a Bluetooth Low Energy radio. * The GAPPoc-A board enables battery-operated applications developed around algorithms such as people counting, face-identification and many others to be quickly assembled and evaluated in the field. * [https://riscv.org/blog/2019/08/risc-v-emea-roadshow-spotlight-greenwaves-technologies/](https://www.google.com/url?q=https%3A%2F%2Friscv.org%2Fblog%2F2019%2F08%2Frisc-v-emea-roadshow-spotlight-greenwaves-technologies%2F&sa=D&sntz=1&usg=AOvVaw1ikZjtEoYTgFb-S_eGEB3i) * ### Breaking the Barriers to Deploy DNNs on Low-Power Hardware * Deeplite, named to the 2020 CB Insights AI100 List of Most Innovative Artificial Intelligence Startups, is devoted to making fundamental advancements in accessible and efficient deep learning. Our solution helps deep learning engineers and experts automatically create faster, smaller and more energy-efficient deep neural networks. Industry leaders in computer vision, augmented reality and autonomous driving use our technology to unlock new possibilities for deep learning in the real world. At Deeplite, our vision is to create a lightweight intelligence that’s accessible for daily life. * [https://www.deeplite.ai/](https://www.google.com/url?q=https%3A%2F%2Fwww.deeplite.ai%2F&sa=D&sntz=1&usg=AOvVaw1r7NiQGt1hRi6S_xiJ522C) * At Deeplite, we are tackling inference optimization of deep neural networks, making them faster and energy-efficient from cloud to edge computing. Our solution leverages state-of-the-art technology from elite universities to make deep neural networks applicable for any device, and our team works hard on the iterative evolution of the science behind deep neural networks to directly improve daily life. * reduce the size of model 40x * ### Optimizing ML Models At The Edge Made Simple * [https://octoml.ai/](https://www.google.com/url?q=https%3A%2F%2Foctoml.ai%2F&sa=D&sntz=1&usg=AOvVaw2uXg6ESgQgVGrF9nQJKFve) * OctoML is an energetic new company changing how developers optimize and deploy machine learning models for their AI needs. We’re a team of machine learning systems leaders focused on making ML more efficient and easier to deploy by… applying machine learning to it! * OctoML is leveraging the power and traction of Apache TVM, an open source project originated by our founding team, to enable companies of every size to harness the power of deep learning without the expensive heavy lifting of tuning and securing models to each hardware configuration that a customer might need. * Apache TVM and Deep Learning Compilation Conference, Wed-Fri, December 2nd-4th 2020, Free Virtual Event. ## Thursday, November 19, 2020 * ### Developing Edge AI Solutions For A Post-Pandemic Society * [https://www.foghorn.io/](https://www.google.com/url?q=https%3A%2F%2Fwww.foghorn.io%2F&sa=D&sntz=1&usg=AOvVaw2aRKISC9BdrrriEej5Xb_I) * ogHorn’s Lightning™ Edge AI platform brings a groundbreaking dimension to IIoT and edge computing by embedding AI as close to the source of streaming sensor data as possible. The Edge AI software platform is a highly compact, advanced and feature-rich edge solution that delivers unprecedented low latency for onsite data processing, real-time analytics, ML and AI capabilities. It delivers the industry’s lowest total cost for computing requirements, communications services, and cloud processing and storage. * temperature detection, social distancing, cough detection, PPE/Mask detection * Flexible, customizable, integrated, actionable * ### The Evolving Landscape of Edge AI * * Coral’s local AI technology enables new possibilities across almost any kind of industry * The Coral Dev Board is a single-board computer that contains an Edge TPU coprocessor. It's ideal for prototyping new projects that demand fast on-device inferencing for machine learning models. This page is your guide to get started. The setup requires flashing Mendel Linux to the board, and then accessing the board's shell terminal. Once you have terminal access and update some of the software, we'll show you how to run an image classification model on the board. If you want to learn more about the hardware, see the Dev Board datasheet. * TPU v3, 32 to 512 TOPS, Q2 2021 * ### InferX X1, The Fastest and Most Efficient Edge Inference Accelerator * InferX X1: World's fastest and most efficient Edge Inference Accelerator. We have just launched our first inference chip and it is the best in the world for edge inference. We are bringing up neural network models now and moving forward on the steps required for Q2/2021 chip and board production and Inference Compiler availability. * mbedded FPGA, or eFPGA, enables your SoC to have flexibility in critical areas where algorithm, protocol or market needs are changing. FPGA can also accelerate many workloads faster than processors: Microsoft Azure uses one FPGA accelerator for every 2 Xeons.Flex Logix provides eFPGA cores which have density and performance similar to leading FPGAs in the same process node. Our EFLX eFPGA is silicon proven in 40nm, 28/22nm, 16nm and 12nm. 6/7nm EFLX eFPGA is planned. Our eFPGA is based on a “tile” called EFLX 4K, which comes in two versions: all logic or mostly logic with some MACs (multiply-accumulators). The programmable logic is called LUTs (look up tables) that can implement any Boolean function. EFLX 4K Logix has 4000 LUT4 equivalents, EFLX 4K DSP has 3000 LUT4s and 40 Multiplier-Accumulators (MACs): the MAC has a 22-bit pre-adder, a 22×22 multiple and a 48-bit post adder/accumulator. MACs can be combined or cascaded to form fast DSP functions. (For 40nm-180nm we offer an EFLX 1K tile). * depth-wise conv2d * ### Implementing Edge Technologies in Retail: Walmart Case Study * NVidia * ### The Era of Analog AI Compute Is Here * Mythic products are based on a unique tile-based AI compute architecture that features three fundamental hardware technologies – Compute-in-Memory, Dataflow Architecture, and Analog Computing. For AI developers, the Mythic SDK streamlines the preparation of trained neural networks for edge and low-latency datacenter deployments, and also performs automatic optimization and compilation of dataflow graphs for our unique architecture. * low power consumption, ultra-low latency, high ai performance, large weight capacity, small form factor, cost effective solution * ### **Us** ing Edge AI To Detect Repetitive Mot * Bosch Sensortec develops and markets a wide portfolio of MEMS sensors and solutions for applications in smartphones, tablets, wearables, AR/VR devices, drones, robots, smart home and the Internet of Things. Striving to meet the demanding requirements of the consumer electronics market, we provide best-in-class sensing solutions in terms of customer focus, quality and reliability, performance, sustainability and competitiveness. * [https://github.com/BoschSensortec](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FBoschSensortec&sa=D&sntz=1&usg=AOvVaw0Xr8dUHPERsj-rYH7ZAnP1) ## Friday, November 20, 2020 * ### *Spatial Computing: A Collision of Edge and Cloud-Based Computing * [https://github.com/magicleap](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fmagicleap&sa=D&sntz=1&usg=AOvVaw22R0IwwKYOdwv06BvMpvZF) * instance semantic segmentation contextual computing * spatial computing * SLAM: tracking/localization, mapping: * latency is critical for see through displays * weight is critical cannot compensate for lack of compute with more sensors * thermal is critical more sensors and more compute lead to heat * rigidity leads to weight our device should be light * very stringent requirements for MR * why build a map: drift correction, robustness (pose recovery), persistence * feature descriptors * matching across large baselines and illumination changes is challenging * most of the SOTA methods based on deep learning and not feasible withing compute budget * our deep descriptor is optimized for SLAM and provides the best trade off in terms of performance and compute * semantic segmentation 3d point cloud * ### Building An Autonomous Network For IoT and Edge Applications * 5G + AI * ### Practical Edge Inferencing: Enabling fastest AI inferencing per Watt leveraging sparsity * [https://www.graimatterlabs.ai/](https://www.google.com/url?q=https%3A%2F%2Fwww.graimatterlabs.ai%2F&sa=D&sntz=1&usg=AOvVaw1qLIEaRzrtXoYDAb_Y4tQb) * The world’s first sparsity-enabled AI processor optimized for ultra-low latency and low power processing at the edge. * GrAI One drastically reduces application latency, for instance, it reduces the end-to-end latencies for deep learning networks such as PilotNet to the order of milliseconds. The GrAI One chip is based on GML’s innovative NeuronFlow™ technology that combines the dynamic Dataflow paradigm with sparse computing to produce massively parallel in-network processing. * GrAI Matter Labs ([www.graimatterlabs.ai](http://www.google.com/url?q=http%3A%2F%2Fwww.graimatterlabs.ai%2F&sa=D&sntz=1&usg=AOvVaw3eJ9oROjswyCFHO-68LiDi)), a fabless semiconductor company specialized in brain-inspired technology, designs and develops fully programmable ultra-low power neuromorphic HW for sensor analytics and machine learning. The company has offices in Eindhoven (NL), Paris (FR) and San Jose (USA) and has strong relations with top-ranking research groups on neuroscience, human vision and natural computation * ### **Large Scale Deep Learning and AI models on the Edge * deployment pipelines * there are several steps involved in the AI/ML life-cycle * several tools to help simplify the whole process * tensorflow extended (TFX): an end to end platform for deploying production ML pipelines * MLflow (other options michelangelo): an open source platform for the end to end machine learning life cycle * apache airflow (other options kubeflow): an open source workflow management platform * dataiku data science studio (DSS): collaborative data science software platform for teams of data scientist , data analysts, and engineers to explore prototype build and deliver * ### The Edge: The Hottest Market for AI Accelerator Chips - Introducing the Kisaco Leadership Chart on AI Hardware Accelerators 2020-21: Edge and Automotive * [https://www.kisacoresearch.com/#about-us](https://www.google.com/url?q=https%3A%2F%2Fwww.kisacoresearch.com%2F%23about-us&sa=D&sntz=1&usg=AOvVaw0nasAOo80KuyOwbm4OeiOb) [OpenHTF is a Python library that provides a set of convenient abstractions designed to remove as much boilerplate as possible from hardware test setup and execution, so test engineers can focus primarily on test logic.](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fgoogle%2Fopenhtf&sa=D&sntz=1&usg=AOvVaw0zU3RKntPn4N8JIkPvriIu) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/G7JKnoBc5s5bG3WK7alpRCEIdQazcLj2L1DLGACGDrsMeHOK9CTS5fh5v74shZzmMJ8YN6hl77hXFxOIDH_8b3M=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/G7JKnoBc5s5bG3WK7alpRCEIdQazcLj2L1DLGACGDrsMeHOK9CTS5fh5v74shZzmMJ8YN6hl77hXFxOIDH_8b3M=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Edge AI summit Short summary of the edge AI summit 18-20 November 2020 Wednesday, November 18, 2020 A Software Solution Enabling Predictive Maintenance at the Sensor Level Helping Fish Farmers Feed The World With Deep Learning tinyMLPerf: Benchmarking Ultra-low Power Machine Learning Systems Ultra-low power neuromorphic intelligence for the sensor edge * How is AI affecting hearables and sensors? Breaking the Barriers to Deploy DNNs on Low-Power Hardware Optimizing ML Models At The Edge Made Simple Thursday, November 19, 2020 Developing Edge AI Solutions For A Post-Pandemic Society The Evolving Landscape of Edge AI InferX X1, The Fastest and Most Efficient Edge Inference Accelerator Implementing Edge Technologies in Retail: Walmart Case Study The Era of Analog AI Compute Is Here Using Edge AI To Detect Repetitive Mot Friday, November 20, 2020 *Spatial Computing: A Collision of Edge and Cloud-Based Computing Building An Autonomous Network For IoT and Edge Applications Practical Edge Inferencing: Enabling fastest AI inferencing per Watt leveraging sparsity **Large Scale Deep Learning and AI models on the Edge The Edge: The Hottest Market for AI Accelerator Chips - Introducing the Kisaco Leadership Chart on AI Hardware Accelerators 2020-21: Edge and Automotive # Short summary of the edge AI summit 18-20 November 2020 Best of Wednesday, November 18, 2020; tinyMLPerf, Breaking the Barriers to Deploy DNNs on Low-Power Hardware, Optimizing ML Models At The Edge Made Simple **Thursday, November 19, 2020** * 8:00 AM - 8:30 AM (PST) KEYNOTE PRESENTATION: Developing Edge AI Solutions For A Post-Pandemic Society Sastry Malladi - FogHorn Systems * 8:35 AM - 9:05 AM (PST) PRESENTATION: The Evolving Landscape of Edge AI Ajay Nair - Google * 9:05 AM - 9:20 AM (PST) Comfort Break * 9:20 AM - 9:45 AM (PST) PRESENTATION: InferX X1, The Fastest and Most Efficient Edge Inference Accelerator Cheng Wang - Flex Logix Technologies Inc. * 9:50 AM - 10:20 AM (PST) PRESENTATION: Implementing Edge Technologies in Retail: Walmart Case Study Alex Sabatier - Nvidia * 10:20 AM - 10:35 AM (PST) Comfort Break * 10:35 AM - 11:20 AM (PST) Meet speaker! * **11:20 AM - 11:50 AM (PST) PRESENTATION: The Era of Analog AI Compute Is Here Mike Henry - Mythic** * 11:55 AM - 12:25 PM (PST) PRESENTATION: Using Edge AI To Detect Repetitive Mot Marcellino Gemelli - Bosch Sensortec * 12:30 PM - 2:30 PM (PST) NETWORKING - Dedicated Networking 2 hours for 1-2-1 Video Meetings **Friday, November 20, 2020** * 8:00 AM - 8:30 AM (PST) PRESENTATION: Spatial Computing: A Collision of Edge and Cloud-Based Computing Ashwin Swaminathan - Magic Leap * 8:35 AM - 9:05 AM (PST) PRESENTATION: Building An Autonomous Network For IoT and Edge Applications Anshul Bhatt - Rakuten Mobile * 9:05 AM - 9:20 AM (PST) Comfort Break * 9:20 AM - 9:45 AM (PST) PRESENTATION: Practical Edge Inferencing: Enabling fastest AI inferencing per Watt leveraging sparsity Mahesh Makhijani - GrAI Matter Labs * **9:50 AM - 10:20 AM (PST) PRESENTATION: Large Scale Deep Learning and AI models on the Edge Chandra Khatri - Got It AI** * 10:20 AM - 10:35 AM (PST) Comfort Break * 10:35 AM - 11:20 AM (PST) NETWORKING: Interest Groups (18 people per room, topic-specific discussions) * 11:20 AM - 11:50 AM (PST) PANEL DISCUSSION: The Symbiotic Relationship between 5G and Edge AI Sami Badri - Credit Suisse, Christos Kolias - Orange, Rima Raouda - Independent * 11:55 AM - 12:25 PM (PST) PANEL DISCUSSION: Investment Trends & Dynamics Panel Rashmi Gopinath - B Capital Group, Yvonne Lutsch - Bosch Venture Capital, Eileen Tanghal - In-Q-Tel, Albert Wang - Qualcomm Ventures * **12:30 PM - 12:50 PM (PST) PRESENTATION: The Edge: The Hottest Market for AI Accelerator Chips - Introducing the Kisaco Leadership Chart on AI Hardware Accelerators 2020-21: Edge and Automotive Michael Azoff - Kisaco Research** ## Wednesday, November 18, 2020 * ### A Software Solution Enabling Predictive Maintenance at the Sensor Level * SensiML Toolkit enables AI for a broad array of resource constrained time-series sensor endpoint applications. These include a wide range of consumer and industrial sensing applications. * The problem is machine learning engineer do not have experience with embedded system and moving model to embedded system takes long time. * AutoML for Embedded system usage. it is on the cloud. * using the compiler for that device for this tools * cost edge and cloud. easy to work on cloud. streaming data to cloud is difficult. faster if working on edge. * TinyML addresses problems, battery powered, limited internet connectivity, security/privacy, latency, economic * [https://sensiml.com/products/#process](https://www.google.com/url?q=https%3A%2F%2Fsensiml.com%2Fproducts%2F%23process&sa=D&sntz=1&usg=AOvVaw116wjx6mBnEo9x0htyL2pr) * ### Helping Fish Farmers Feed The World With Deep Learning * [https://s3-us-west-1.amazonaws.com/aquabyte-static/videos/welcome_to_aquabyte_subtitled.mp4](https://www.google.com/url?q=https%3A%2F%2Fs3-us-west-1.amazonaws.com%2Faquabyte-static%2Fvideos%2Fwelcome_to_aquabyte_subtitled.mp4&sa=D&sntz=1&usg=AOvVaw1osNtsRVM9TMCmKoPsTnj_) * Count sea lice and accurately measure biomass in real-time while reducing cage furniture. Our experts‑in‑the‑loop ensure that every single prediction is correct. * Aquabyte is seeking a Machine Learning Platform Engineer to drive the development, testing, and delivery of machine learning models that enable cutting-edge analytics and automation of fish farms around the world. * Aquabyte is on a mission to revolutionize the sustainability and efficiency of aquaculture. It is an audacious, and incredibly rewarding mission. By making fish farming cheaper and more viable than livestock production, we aim to mitigate one of the biggest causes of climate change and help prepare our planet for impending population growth. Aquaculture is the single fastest growing food-production sector in the world, and now is the time to define how technology is used to harvest the sea for generations to come. * We are currently focused on helping Norwegian salmon farmers better understand their fish populations and make environmentally-sound decisions. Through custom underwater cameras, computer vision, and machine learning we are able to quantify fish weights, detect sea lice infestations, and generate optimal feeding plans in real time. Our product operates at three levels: on-site hardware for image capture, cloud pipelines for data processing, and a user-facing web application. As a result, there are hundreds of moving pieces and no shortage of fascinating challenges across all levels of the stack. * * ### tinyMLPerf: Benchmarking Ultra-low Power Machine Learning Systems * [https://github.com/mlperf/tiny](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fmlperf%2Ftiny&sa=D&sntz=1&usg=AOvVaw1TgviYAuh83PMxDPYljOjs) * tinyMLPerf Deep Learning Benchmarks for Embedded Devices * The goal of TinyMLPerf is to provide a representative set of deep neural nets and benchmarking code to compare performance between embedded devices. Embedded devices include microcontrollers, DSPs, and tiny NN accelerators. These devics typically run at between 10MHz and 250MHz, and can perform inference using less then 50mW of power. TinyMLPerf submissions will allow device makers and researchers to choose the best hardware for their use case, and allows hardware vendors to showcase their offerings. TinyMLPerf is primarily intended to benchmark hardware rather than new network archietctures, or embedded neural net runtimes. The reference benchmarks are provided using TensorFlow Lite for Microcontrollers (TFLM). Submitters can directly use the TFLM, although submitters are encouraged to use the software stack that works best on thier hardware. * anomaly detection benchmark, visual wake words benchmark, * ### Ultra-low power neuromorphic intelligence for the sensor edge * Innatera Nanosystems BV (Innatera, (Innatera, innatera.com) is a rapidly-growing Dutch semiconductor company that develops ultra-efficient neuromorphic processors for AI at the edge. These microprocessors mimic the brain’s mechanisms for processing fast data streams from sensors, enabling complex turn-key sensor analytics functionalities, with 10,000x higher performance per watt than competing solutions. Innatera's technology serves as a critical enabler for next-generation use-cases in the IoT, wearable, embedded, and automotive domains. * ### * How is AI affecting hearables and sensors? * [https://github.com/greenwaves-technologies/nn_menu](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fgreenwaves-technologies%2Fnn_menu&sa=D&sntz=1&usg=AOvVaw2JKYYAPrnA9Mkalw2qenUQ) * The Neural Network Menu* is a collection of software that implements Neural Networks on Greenwaves Application Processors (GAP). This repository contains common mobile and edge NN archtecture examples, NN sample applications and full flagged reference designs. Our tools maps a TFLITE model (quantized or unquantized) onto gap. There is also a flow in the ingredients directory showing how to hand map from a Pytorch Model onto GAP. * [https://greenwaves-technologies.com/store/](https://www.google.com/url?q=https%3A%2F%2Fgreenwaves-technologies.com%2Fstore%2F&sa=D&sntz=1&usg=AOvVaw0Ya_w_NBAr4AbIxBe2j_YX) * GAPPoc-A is a Proof of Concept Board that can be used for demonstration of battery-operated, edge computer vision applications based on GAP8. * It incorporates GAPmod, a surface-mount module that implements all the layout sensitive portion of a GAP8 design, along with a VGA image sensor and a Bluetooth Low Energy radio. * The GAPPoc-A board enables battery-operated applications developed around algorithms such as people counting, face-identification and many others to be quickly assembled and evaluated in the field. * [https://riscv.org/blog/2019/08/risc-v-emea-roadshow-spotlight-greenwaves-technologies/](https://www.google.com/url?q=https%3A%2F%2Friscv.org%2Fblog%2F2019%2F08%2Frisc-v-emea-roadshow-spotlight-greenwaves-technologies%2F&sa=D&sntz=1&usg=AOvVaw1ikZjtEoYTgFb-S_eGEB3i) * ### Breaking the Barriers to Deploy DNNs on Low-Power Hardware * Deeplite, named to the 2020 CB Insights AI100 List of Most Innovative Artificial Intelligence Startups, is devoted to making fundamental advancements in accessible and efficient deep learning. Our solution helps deep learning engineers and experts automatically create faster, smaller and more energy-efficient deep neural networks. Industry leaders in computer vision, augmented reality and autonomous driving use our technology to unlock new possibilities for deep learning in the real world. At Deeplite, our vision is to create a lightweight intelligence that’s accessible for daily life. * [https://www.deeplite.ai/](https://www.google.com/url?q=https%3A%2F%2Fwww.deeplite.ai%2F&sa=D&sntz=1&usg=AOvVaw1r7NiQGt1hRi6S_xiJ522C) * At Deeplite, we are tackling inference optimization of deep neural networks, making them faster and energy-efficient from cloud to edge computing. Our solution leverages state-of-the-art technology from elite universities to make deep neural networks applicable for any device, and our team works hard on the iterative evolution of the science behind deep neural networks to directly improve daily life. * reduce the size of model 40x * ### Optimizing ML Models At The Edge Made Simple * [https://octoml.ai/](https://www.google.com/url?q=https%3A%2F%2Foctoml.ai%2F&sa=D&sntz=1&usg=AOvVaw2uXg6ESgQgVGrF9nQJKFve) * OctoML is an energetic new company changing how developers optimize and deploy machine learning models for their AI needs. We’re a team of machine learning systems leaders focused on making ML more efficient and easier to deploy by… applying machine learning to it! * OctoML is leveraging the power and traction of Apache TVM, an open source project originated by our founding team, to enable companies of every size to harness the power of deep learning without the expensive heavy lifting of tuning and securing models to each hardware configuration that a customer might need. * Apache TVM and Deep Learning Compilation Conference, Wed-Fri, December 2nd-4th 2020, Free Virtual Event. ## Thursday, November 19, 2020 * ### Developing Edge AI Solutions For A Post-Pandemic Society * [https://www.foghorn.io/](https://www.google.com/url?q=https%3A%2F%2Fwww.foghorn.io%2F&sa=D&sntz=1&usg=AOvVaw2aRKISC9BdrrriEej5Xb_I) * ogHorn’s Lightning™ Edge AI platform brings a groundbreaking dimension to IIoT and edge computing by embedding AI as close to the source of streaming sensor data as possible. The Edge AI software platform is a highly compact, advanced and feature-rich edge solution that delivers unprecedented low latency for onsite data processing, real-time analytics, ML and AI capabilities. It delivers the industry’s lowest total cost for computing requirements, communications services, and cloud processing and storage. * temperature detection, social distancing, cough detection, PPE/Mask detection * Flexible, customizable, integrated, actionable * ### The Evolving Landscape of Edge AI * * Coral’s local AI technology enables new possibilities across almost any kind of industry * The Coral Dev Board is a single-board computer that contains an Edge TPU coprocessor. It's ideal for prototyping new projects that demand fast on-device inferencing for machine learning models. This page is your guide to get started. The setup requires flashing Mendel Linux to the board, and then accessing the board's shell terminal. Once you have terminal access and update some of the software, we'll show you how to run an image classification model on the board. If you want to learn more about the hardware, see the Dev Board datasheet. * TPU v3, 32 to 512 TOPS, Q2 2021 * ### InferX X1, The Fastest and Most Efficient Edge Inference Accelerator * InferX X1: World's fastest and most efficient Edge Inference Accelerator. We have just launched our first inference chip and it is the best in the world for edge inference. We are bringing up neural network models now and moving forward on the steps required for Q2/2021 chip and board production and Inference Compiler availability. * mbedded FPGA, or eFPGA, enables your SoC to have flexibility in critical areas where algorithm, protocol or market needs are changing. FPGA can also accelerate many workloads faster than processors: Microsoft Azure uses one FPGA accelerator for every 2 Xeons.Flex Logix provides eFPGA cores which have density and performance similar to leading FPGAs in the same process node. Our EFLX eFPGA is silicon proven in 40nm, 28/22nm, 16nm and 12nm. 6/7nm EFLX eFPGA is planned. Our eFPGA is based on a “tile” called EFLX 4K, which comes in two versions: all logic or mostly logic with some MACs (multiply-accumulators). The programmable logic is called LUTs (look up tables) that can implement any Boolean function. EFLX 4K Logix has 4000 LUT4 equivalents, EFLX 4K DSP has 3000 LUT4s and 40 Multiplier-Accumulators (MACs): the MAC has a 22-bit pre-adder, a 22×22 multiple and a 48-bit post adder/accumulator. MACs can be combined or cascaded to form fast DSP functions. (For 40nm-180nm we offer an EFLX 1K tile). * depth-wise conv2d * ### Implementing Edge Technologies in Retail: Walmart Case Study * NVidia * ### The Era of Analog AI Compute Is Here * Mythic products are based on a unique tile-based AI compute architecture that features three fundamental hardware technologies – Compute-in-Memory, Dataflow Architecture, and Analog Computing. For AI developers, the Mythic SDK streamlines the preparation of trained neural networks for edge and low-latency datacenter deployments, and also performs automatic optimization and compilation of dataflow graphs for our unique architecture. * low power consumption, ultra-low latency, high ai performance, large weight capacity, small form factor, cost effective solution * ### **Us** ing Edge AI To Detect Repetitive Mot * Bosch Sensortec develops and markets a wide portfolio of MEMS sensors and solutions for applications in smartphones, tablets, wearables, AR/VR devices, drones, robots, smart home and the Internet of Things. Striving to meet the demanding requirements of the consumer electronics market, we provide best-in-class sensing solutions in terms of customer focus, quality and reliability, performance, sustainability and competitiveness. * [https://github.com/BoschSensortec](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2FBoschSensortec&sa=D&sntz=1&usg=AOvVaw0Xr8dUHPERsj-rYH7ZAnP1) ## Friday, November 20, 2020 * ### *Spatial Computing: A Collision of Edge and Cloud-Based Computing * [https://github.com/magicleap](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fmagicleap&sa=D&sntz=1&usg=AOvVaw22R0IwwKYOdwv06BvMpvZF) * instance semantic segmentation contextual computing * spatial computing * SLAM: tracking/localization, mapping: * latency is critical for see through displays * weight is critical cannot compensate for lack of compute with more sensors * thermal is critical more sensors and more compute lead to heat * rigidity leads to weight our device should be light * very stringent requirements for MR * why build a map: drift correction, robustness (pose recovery), persistence * feature descriptors * matching across large baselines and illumination changes is challenging * most of the SOTA methods based on deep learning and not feasible withing compute budget * our deep descriptor is optimized for SLAM and provides the best trade off in terms of performance and compute * semantic segmentation 3d point cloud * ### Building An Autonomous Network For IoT and Edge Applications * 5G + AI * ### Practical Edge Inferencing: Enabling fastest AI inferencing per Watt leveraging sparsity * [https://www.graimatterlabs.ai/](https://www.google.com/url?q=https%3A%2F%2Fwww.graimatterlabs.ai%2F&sa=D&sntz=1&usg=AOvVaw1qLIEaRzrtXoYDAb_Y4tQb) * The world’s first sparsity-enabled AI processor optimized for ultra-low latency and low power processing at the edge. * GrAI One drastically reduces application latency, for instance, it reduces the end-to-end latencies for deep learning networks such as PilotNet to the order of milliseconds. The GrAI One chip is based on GML’s innovative NeuronFlow™ technology that combines the dynamic Dataflow paradigm with sparse computing to produce massively parallel in-network processing. * GrAI Matter Labs ([www.graimatterlabs.ai](http://www.google.com/url?q=http%3A%2F%2Fwww.graimatterlabs.ai%2F&sa=D&sntz=1&usg=AOvVaw3eJ9oROjswyCFHO-68LiDi)), a fabless semiconductor company specialized in brain-inspired technology, designs and develops fully programmable ultra-low power neuromorphic HW for sensor analytics and machine learning. The company has offices in Eindhoven (NL), Paris (FR) and San Jose (USA) and has strong relations with top-ranking research groups on neuroscience, human vision and natural computation * ### **Large Scale Deep Learning and AI models on the Edge * deployment pipelines * there are several steps involved in the AI/ML life-cycle * several tools to help simplify the whole process * tensorflow extended (TFX): an end to end platform for deploying production ML pipelines * MLflow (other options michelangelo): an open source platform for the end to end machine learning life cycle * apache airflow (other options kubeflow): an open source workflow management platform * dataiku data science studio (DSS): collaborative data science software platform for teams of data scientist , data analysts, and engineers to explore prototype build and deliver * ### The Edge: The Hottest Market for AI Accelerator Chips - Introducing the Kisaco Leadership Chart on AI Hardware Accelerators 2020-21: Edge and Automotive * [https://www.kisacoresearch.com/#about-us](https://www.google.com/url?q=https%3A%2F%2Fwww.kisacoresearch.com%2F%23about-us&sa=D&sntz=1&usg=AOvVaw0nasAOo80KuyOwbm4OeiOb) [OpenHTF is a Python library that provides a set of convenient abstractions designed to remove as much boilerplate as possible from hardware test setup and execution, so test engineers can focus primarily on test logic.](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fgoogle%2Fopenhtf&sa=D&sntz=1&usg=AOvVaw0zU3RKntPn4N8JIkPvriIu) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/ssWXVQN4wWnp524T8UjHyQEdArMpTIaM4OalIHhpfDv0Sv6HC6tLnwxjfrdaemWa002sbPUNKYrtuvBhEcn6DIQ=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/ssWXVQN4wWnp524T8UjHyQEdArMpTIaM4OalIHhpfDv0Sv6HC6tLnwxjfrdaemWa002sbPUNKYrtuvBhEcn6DIQ=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # RISC-V for AI Reference RISC-V Magazine ( December 2020) RISC-V Summit 2020; The third annual RISC-V Summit will highlight the continued rapid expansion of the RISC-V ecosystem, presenting both commercial offerings and exciting open-source developments. # Reference * [https://riscv.org/](https://www.google.com/url?q=https%3A%2F%2Friscv.org%2F&sa=D&sntz=1&usg=AOvVaw266IPHB04l6cEz7QBa8q_k) * [https://www.eetimes.com/micro-magic-risc-v-core-claims-to-beat-apple-m1-and-arm-cortex-a9/](https://www.google.com/url?q=https%3A%2F%2Fwww.eetimes.com%2Fmicro-magic-risc-v-core-claims-to-beat-apple-m1-and-arm-cortex-a9%2F&sa=D&sntz=1&usg=AOvVaw1RVBM0YEksAO_iIopOBghj) * HiFive1 Rev B. * [https://www.amazon.de/-/en/HiFive1-Rev-B/dp/B086RGFS5N/](https://www.google.com/url?q=https%3A%2F%2Fwww.amazon.de%2F-%2Fen%2FHiFive1-Rev-B%2Fdp%2FB086RGFS5N%2F&sa=D&sntz=1&usg=AOvVaw0wuUWI1Xp4yt4ddM9my8BA) * [https://www.benchcouncil.org/competition/papers/YangyangKong.pdf](https://www.google.com/url?q=https%3A%2F%2Fwww.benchcouncil.org%2Fcompetition%2Fpapers%2FYangyangKong.pdf&sa=D&sntz=1&usg=AOvVaw2zThQ8r3_ymT70p4Sf_be-) * [http://people.bu.edu/joshi/files/rvmlpu-barc-2020.pdf](http://www.google.com/url?q=http%3A%2F%2Fpeople.bu.edu%2Fjoshi%2Ffiles%2Frvmlpu-barc-2020.pdf&sa=D&sntz=1&usg=AOvVaw2Sm9ZtzbEpBJIscCdXO1M0) * [RISC V 15 minute sample course](https://www.youtube.com/watch?v=KBvAKHsHBW4&ab_channel=BuzzTeam) * [https://www.eenewseurope.com/news/risc-v-boom-edge-ai-says-facebooks-chief-ai-scientist?fbclid=IwAR3-Xw_g3jSTUJEl_CGoFYwSNI5XMA608MrpGMP8R1aDLSo8KldZU5ybt7I](https://www.google.com/url?q=https%3A%2F%2Fwww.eenewseurope.com%2Fnews%2Frisc-v-boom-edge-ai-says-facebooks-chief-ai-scientist%3Ffbclid%3DIwAR3-Xw_g3jSTUJEl_CGoFYwSNI5XMA608MrpGMP8R1aDLSo8KldZU5ybt7I&sa=D&sntz=1&usg=AOvVaw2V1Lf8BI7MozXDI6uKlsWL) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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The third annual RISC-V Summit will highlight the continued rapid expansion of the RISC-V ecosystem, presenting both commercial offerings and exciting open-source developments. # Reference * [https://riscv.org/](https://www.google.com/url?q=https%3A%2F%2Friscv.org%2F&sa=D&sntz=1&usg=AOvVaw266IPHB04l6cEz7QBa8q_k) * [https://www.eetimes.com/micro-magic-risc-v-core-claims-to-beat-apple-m1-and-arm-cortex-a9/](https://www.google.com/url?q=https%3A%2F%2Fwww.eetimes.com%2Fmicro-magic-risc-v-core-claims-to-beat-apple-m1-and-arm-cortex-a9%2F&sa=D&sntz=1&usg=AOvVaw1RVBM0YEksAO_iIopOBghj) * HiFive1 Rev B. * [https://www.amazon.de/-/en/HiFive1-Rev-B/dp/B086RGFS5N/](https://www.google.com/url?q=https%3A%2F%2Fwww.amazon.de%2F-%2Fen%2FHiFive1-Rev-B%2Fdp%2FB086RGFS5N%2F&sa=D&sntz=1&usg=AOvVaw0wuUWI1Xp4yt4ddM9my8BA) * [https://www.benchcouncil.org/competition/papers/YangyangKong.pdf](https://www.google.com/url?q=https%3A%2F%2Fwww.benchcouncil.org%2Fcompetition%2Fpapers%2FYangyangKong.pdf&sa=D&sntz=1&usg=AOvVaw2zThQ8r3_ymT70p4Sf_be-) * [http://people.bu.edu/joshi/files/rvmlpu-barc-2020.pdf](http://www.google.com/url?q=http%3A%2F%2Fpeople.bu.edu%2Fjoshi%2Ffiles%2Frvmlpu-barc-2020.pdf&sa=D&sntz=1&usg=AOvVaw2Sm9ZtzbEpBJIscCdXO1M0) * [RISC V 15 minute sample course](https://www.youtube.com/watch?v=KBvAKHsHBW4&ab_channel=BuzzTeam) * [https://www.eenewseurope.com/news/risc-v-boom-edge-ai-says-facebooks-chief-ai-scientist?fbclid=IwAR3-Xw_g3jSTUJEl_CGoFYwSNI5XMA608MrpGMP8R1aDLSo8KldZU5ybt7I](https://www.google.com/url?q=https%3A%2F%2Fwww.eenewseurope.com%2Fnews%2Frisc-v-boom-edge-ai-says-facebooks-chief-ai-scientist%3Ffbclid%3DIwAR3-Xw_g3jSTUJEl_CGoFYwSNI5XMA608MrpGMP8R1aDLSo8KldZU5ybt7I&sa=D&sntz=1&usg=AOvVaw2V1Lf8BI7MozXDI6uKlsWL) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/G7JKnoBc5s5bG3WK7alpRCEIdQazcLj2L1DLGACGDrsMeHOK9CTS5fh5v74shZzmMJ8YN6hl77hXFxOIDH_8b3M=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/G7JKnoBc5s5bG3WK7alpRCEIdQazcLj2L1DLGACGDrsMeHOK9CTS5fh5v74shZzmMJ8YN6hl77hXFxOIDH_8b3M=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # TensorFlow: Data and Deployment Specialization 1\. Browser-based Models with TensorFlow.js 2\. Device-based Models with TensorFlow Lite 3\. Data Pipelines with TensorFlow Data Services 4\. Advanced Deployment Scenarios with TensorFlow 6 hours to complete Device-based models with TensorFlow Lite 1 hour to complete Running a TF model in an Android App 2 hours to complete Building the TensorFLow model on IOS 2 hours to complete TensorFlow Lite on devices basic understanding of Kotlin and/or Swift, as well as Android Studio and/or Xcode, will help you follow along. **W1** : lightweight+low-latency+privacy+improved power consumption+efficient model ready to used Quantization * All available CPU platforms are supported * Reducing latency and inference cost * Low memory footprint * Allow execution on hardware restricted-to or optimize for fixed-point operations * Optimized models for special purpose HW accelerator TUP Weight pruning Model topology transforms Tensor decomposition Distillation [Chrome](https://www.google.com/chrome/) as our internet browser,[ ](http://www.google.com/url?q=http%3A%2F%2Fbrackets.io%2F&sa=D&sntz=1&usg=AOvVaw2oTaITwFYLqoanhau5XDPN)[Brackets](http://www.google.com/url?q=http%3A%2F%2Fbrackets.io%2F&sa=D&sntz=1&usg=AOvVaw2oTaITwFYLqoanhau5XDPN) as our HTML editor and the[ ](https://chrome.google.com/webstore/detail/web- server-for-chrome/ofhbbkphhbklhfoeikjpcbhemlocgigb?hl=en)[Web Server for Chrome App](https://chrome.google.com/webstore/detail/web-server-for- chrome/ofhbbkphhbklhfoeikjpcbhemlocgigb?hl=en) as our web server. TF.js: training and inference on browser 1. Chrome 2. 3. [http://brackets.io/](http://www.google.com/url?q=http%3A%2F%2Fbrackets.io%2F&sa=D&sntz=1&usg=AOvVaw2oTaITwFYLqoanhau5XDPN) 4. [https://github.com/lmoroney/dlaicourse](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Flmoroney%2Fdlaicourse&sa=D&sntz=1&usg=AOvVaw2FJMlsF8wydBRUXwo_iXs8) CSV: **W2:** Android (cat and dog) mobileNet classification android MobileNet ssd up to 10 objects from 80 classes CNN+javascript Tf-vis Tf.tidy() -> save memory **W3:** [https://github.com/tensorflow/tfjs- models](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Ftensorflow%2Ftfjs- models&sa=D&sntz=1&usg=AOvVaw2u9Kt8FhmMnVqIJwHDcjC5) [http://www.laurencemoroney.com/wp- content/uploads/2019/04/labels.txt](http://www.google.com/url?q=http%3A%2F%2Fwww.laurencemoroney.com%2Fwp- content%2Fuploads%2F2019%2F04%2Flabels.txt&sa=D&sntz=1&usg=AOvVaw1XAGoF6AY1Q1_PMgOLvKdW) **Converting Models to JavaScript** **Install Wget on Mac/Linux** 1\. Install[ ](https://www.google.com/url?q=https%3A%2F%2Fbrew.sh%2F&sa=D&sntz=1&usg=AOvVaw1HyZ0N9oSDEmaJ6fVxOggU)[Homebrew](https://www.google.com/url?q=https%3A%2F%2Fbrew.sh%2F&sa=D&sntz=1&usg=AOvVaw1HyZ0N9oSDEmaJ6fVxOggU) by running the following command in your terminal: $ /usr/bin/ruby -e "$(curl -fsSL[ ](https://www.google.com/url?q=https%3A%2F%2Fraw.githubusercontent.com%2FHomebrew%2Finstall%2Fmaster%2Finstall&sa=D&sntz=1&usg=AOvVaw3Kxs1AQuw1HsvcWdHSLw8Y)[https://raw.githubusercontent.com/Homebrew/install/master/install](https://www.google.com/url?q=https%3A%2F%2Fraw.githubusercontent.com%2FHomebrew%2Finstall%2Fmaster%2Finstall&sa=D&sntz=1&usg=AOvVaw3Kxs1AQuw1HsvcWdHSLw8Y))" 2\. Install **wget** byrunning the following command in your terminal: $ brew install wget **Install Wget on Windows** 1. Go to[ ](https://www.google.com/url?q=https%3A%2F%2Feternallybored.org%2Fmisc%2Fwget%2F&sa=D&sntz=1&usg=AOvVaw0MxY9md-TfFCFPF6MKq6zi)[https://eternallybored.org/misc/wget/](https://www.google.com/url?q=https%3A%2F%2Feternallybored.org%2Fmisc%2Fwget%2F&sa=D&sntz=1&usg=AOvVaw0MxY9md-TfFCFPF6MKq6zi) 2. Download the **wget.exe** file from the links provided. You can download the latest version of **wget** for either 32-bit or 64-bit systems. 3. If prompted, click **Run** or **Save**. 4. If you chose **Save** , double-click the downloaded file to start installing. **W4** : Transfer learning mobileNet Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/G7JKnoBc5s5bG3WK7alpRCEIdQazcLj2L1DLGACGDrsMeHOK9CTS5fh5v74shZzmMJ8YN6hl77hXFxOIDH_8b3M=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/G7JKnoBc5s5bG3WK7alpRCEIdQazcLj2L1DLGACGDrsMeHOK9CTS5fh5v74shZzmMJ8YN6hl77hXFxOIDH_8b3M=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # IoT Scholarship Foundation Face recognition by using OpenCV 4.1.1 is face but not accurate Image classification by deep learning is very good with high accuracy Object detection is around 5 frame per second: MobileNet SSD Using Coral is very good - need some modification to installing - pose estimation is very face and accurate Using Intel Movidius Stick 2 is good because we can use OpenCV and Python and different range of deep learning frameworks Asfd [1] Y. LeCun, “Generalization and network design strategies,” in _Connectionism in perspective_ , 1989, pp. 143–155. [1] S. Patrice, V. Bernard, L. Yann, and D. John S, “Tangent Prop: a formalism for specifying selected invariances in adaptive networks,” in _Nips_ , 1991, vol. 4, pp. 895–903. [1] Y. Le Cun, J. S. Denker, and S. a Solla, “Optimal Brain Damage,” in _Advances in Neural Information Processing Systems_ , 1990, vol. 2, no. 1, pp. 598–605. ===================================================== * OpenCV: A computer vision (CV) library filled with many different computer vision functions and other useful image and video processing and handling capabilities. * MQTT: A publisher-subscriber protocol often used for IoT devices due to its lightweight nature. The paho-mqtt library is a common way of working with MQTT in Python. * Publish-Subscribe Architecture: A messaging architecture whereby it is made up of publishers, that send messages to some central broker, without knowing of the subscribers themselves. These messages can be posted on some given “topic”, which the subscribers can then listen to without having to know the publisher itself, just the “topic”. * Publisher: In a publish-subscribe architecture, the entity that is sending data to a broker on a certain “topic”. * Subscriber: In a publish-subscribe architecture, the entity that is listening to data on a certain “topic” from a broker. * Topic: In a publish-subscribe architecture, data is published to a given topic, and subscribers to that topic can then receive that data. * FFmpeg: Software that can help convert or stream audio and video. In the course, the related ffserver software is used to stream to a web server, which can then be queried by a Node server for viewing in a web browser. * Flask: A[ ](https://www.google.com/url?q=https%3A%2F%2Fwww.fullstackpython.com%2Fflask.html&sa=D&sntz=1&usg=AOvVaw0RsnQtaW38wrzRz9BN0Ud_)[Python framework](https://www.google.com/url?q=https%3A%2F%2Fwww.fullstackpython.com%2Fflask.html&sa=D&sntz=1&usg=AOvVaw0RsnQtaW38wrzRz9BN0Ud_) useful for web development and another potential option for video streaming to a web browser. * Node Server: A web server built with Node.js that can handle HTTP requests and/or serve up a webpage for viewing in a browser. The "edge" means local (or near local) processing NOT just anywhere in the cloud Edge applications are often used where low latency is necessary Also used where a network may not always be available Can come from a desire for real-time decision making Lesson 1: introduction to AI at the Edge Lesson 2: Leveraging Pre-Trained Models Lesson 3: The model Optimizer Lesson 4: The inference engine Lesson 5: Deploying an Edge App No need to send to the cloud-> secure, less impact on network Which of these are reasons for development of the Edge? Proliferation of devices, need for low-latency compute, need for disconnected devices. * In this course, we’ll largely focus on AI at the Edge using the[ ](https://www.google.com/url?q=https%3A%2F%2Fsoftware.intel.com%2Fen-us%2Fopenvino-toolkit&sa=D&sntz=1&usg=AOvVaw2XaqrILHM8dGo3eSDuFR7r)[Intel® Distribution of OpenVINO™ Toolkit](https://www.google.com/url?q=https%3A%2F%2Fsoftware.intel.com%2Fen-us%2Fopenvino-toolkit&sa=D&sntz=1&usg=AOvVaw2XaqrILHM8dGo3eSDuFR7r). * First, we’ll start off with pre-trained models available in the OpenVINO™ Open Model Zoo. Even without needing huge amounts of your own data and costly training, you can deploy powerful models already created for many applications. * Next, you’ll learn about the Model Optimizer, which can take a model you trained in frameworks such as TensorFlow, PyTorch, Caffe and more, and create an Intermediate Representation (IR) optimized for inference with OpenVINO™ and Intel® hardware. * Third, you’ll learn about the Inference Engine, where the actual inference is performed on the IR model. * Lastly, we'll hit some more topics on deploying at the edge, including things like handling input streams, processing model outputs, and the lightweight MQTT architecture used to publish data from your edge models to the web. Very important listen again first two video : Classification Yes/no Classes (1000 competition) 20 000 classes ImageNet Detection Find objects and location Bounding boxes where object is Combined with some form of classification Segmentation Classification segment of images (classify each and every pixel) Semantic segmentation All objects of the same class are one Instance segmentation Each object of a class is separate (two cats will different color) Pose estimation Text recognition GANs sudo ./downloader --name vehicle-attributes-recognition-barrier-0039 --precisions INT8 -o /home/workspace Pre-processing: Varies by model Color channel order matters (RGB vs. BGR) Image resizing Normalization def preprocessing(input_image, height, width): image = cv2.resize(input_image, (width, height)) image = image.transpose((2,0,1)) image = image.reshape(1, 3, height, width) return image python app.py -i "images/blue-car.jpg" -t "CAR_META" -m "/home/workspace/models/vehicle-attributes-recognition-barrier-0039.xml" -c "/opt/intel/openvino/deployment_tools/inference_engine/lib/intel64/libcpu_extension_sse4.so" python app.py -i "images/sitting-on-car.jpg" -t "POSE" -m "/home/workspace/models/human-pose-estimation-0001.xml" -c "/opt/intel/openvino/deployment_tools/inference_engine/lib/intel64/libcpu_extension_sse4.so" python app.py -i "images/sign.jpg" -t "TEXT" -m "/home/workspace/models/text- detection-0004.xml" -c "/opt/intel/openvino/deployment_tools/inference_engine/lib/intel64/libcpu_extension_sse4.so" source /opt/intel/openvino/bin/setupvars.sh -pyver 3.5 python /opt/intel/openvino/deployment_tools/model_optimizer/mo.py --input_model frozen_inference_graph.pb --tensorflow_object_detection_api_pipeline_config pipeline.config --reverse_input_channels --tensorflow_use_custom_operations_config /opt/intel/openvino/deployment_tools/model_optimizer/extensions/front/tf/ssd_v2_support.json export MOD_OPT=/opt/intel/openvino/deployment_tools/model_optimizer python /opt/intel/openvino/deployment_tools/model_optimizer/mo.py --input_model squeezenet_v1.1.caffemodel --input_proto deploy.prototxt python /opt/intel/openvino/deployment_tools/model_optimizer/mo.py --input_model model.onnx There’s two main command line arguments to use for cutting a model with the Model Optimizer, named intuitively as --input and --output, where they are used to feed in the layer names that should be either the new entry or exit points of the model. -l $CLWS/cl_cosh/user_ie_extensions/cpu/build/libcosh_cpu_extension.so ~/inference_engine_samples_build/intel64/Release/classification_sample_async -i $CLT/pics/dog.bmp -m $CLWS/cl_ext_cosh/model.ckpt.xml -d CPU -l $CLWS/cl_cosh/user_ie_extensions/cpu/build/libcosh_cpu_extension.so import argparse import cv2 from helpers import load_to_IE, preprocessing CPU_EXTENSION = "/opt/intel/openvino/deployment_tools/inference_engine/lib/intel64/libcpu_extension_sse4.so" def get_args(): ''' Gets the arguments from the command line. ''' parser = argparse.ArgumentParser("Load an IR into the Inference Engine") # -- Create the descriptions for the commands m_desc = "The location of the model XML file" i_desc = "The location of the image input" r_desc = "The type of inference request: Async ('A') or Sync ('S')" # -- Create the arguments parser.add_argument("-m", help=m_desc) parser.add_argument("-i", help=i_desc) parser.add_argument("-r", help=i_desc) args = parser.parse_args() return args def async_inference(exec_net, input_blob, image): ### TODO: Add code to perform asynchronous inference ### Note: Return the exec_net exec_net.start_async(request_id=0, inputs={input_blob: image}) while True: status = exec_net.requests[0].wait(-1) if status == 0: break else: time.sleep(1) return exec_net def sync_inference(exec_net, input_blob, image): ### TODO: Add code to perform synchronous inference ### Note: Return the result of inference result = exec_net.infer({input_blob: image}) return result def perform_inference(exec_net, request_type, input_image, input_shape): ''' Performs inference on an input image, given an ExecutableNetwork ''' # Get input image image = cv2.imread(input_image) # Extract the input shape n, c, h, w = input_shape # Preprocess it (applies for the IRs from the Pre-Trained Models lesson) preprocessed_image = preprocessing(image, h, w) # Get the input blob for the inference request input_blob = next(iter(exec_net.inputs)) # Perform either synchronous or asynchronous inference request_type = request_type.lower() if request_type == 'a': output = async_inference(exec_net, input_blob, preprocessed_image) elif request_type == 's': output = sync_inference(exec_net, input_blob, preprocessed_image) else: print("Unknown inference request type, should be 'A' or 'S'.") exit(1) # Return the exec_net for testing purposes return output def main(): args = get_args() exec_net, input_shape = load_to_IE(args.m, CPU_EXTENSION) perform_inference(exec_net, args.r, args.i, input_shape) if __name__ == "__main__": main() [https://docs.openvinotoolkit.org/latest/classInferenceEngine_1_1Blob.html](https://www.google.com/url?q=https%3A%2F%2Fdocs.openvinotoolkit.org%2Flatest%2FclassInferenceEngine_1_1Blob.html&sa=D&sntz=1&usg=AOvVaw2YiI0Ocl1r6aXLMNeOCjfc) Note: There is one small change from the code on-screen for running on Linux machines versus Mac. On Mac, cv2.VideoWriter uses cv2.VideoWriter_fourcc('M','J','P','G') to write an .mp4 file, while Linux uses 0x00000021. import argparse import cv2 import numpy as np def get_args(): ''' Gets the arguments from the command line. ''' parser = argparse.ArgumentParser("Handle an input stream") # -- Create the descriptions for the commands i_desc = "The location of the input file" # -- Create the arguments parser.add_argument("-i", help=i_desc) args = parser.parse_args() return args def capture_stream(args): ### TODO: Handle image, video or webcam image_flag=False if args.i=='CAM': args.i=0 elif args.i.endswith('.jpg') or args.i.endswith('.bmp'): image_flag=True capture = cv2.VideoCapture(args.i) capture.open(args.i) if not image_flag: out=cv2.VideoWriter('out.mp4',cv2.VideoWriter_fourcc('M','J','P','G'),30,(100,100)) else: out=None while capture.isOpened(): flag, frame = capture.read() if not flag: break ### TODO: Get and open video capture key_pressed=cv2.waitKey(60) if key_pressed == 27: break ### TODO: Re-size the frame to 100x100 image = cv2.resize(frame, (100, 100)) edges = cv2.Canny(image,100,200) if image_flag: cv2.imwrite("out.jpg",edges) else: out.write(edges) if not image_flag: out.release cv2.imshow('display', edges) #cv2.imwrite('output.jpg', edges) ### TODO: Add Canny Edge Detection to the frame, ### with min & max values of 100 and 200 ### Make sure to use np.dstack after to make a 3-channel image ### TODO: Write out the frame, depending on image or video ### TODO: Close the stream and any windows at the end of the application #capture.release() #cv2.destroyAllWindows() def main(): args = get_args() capture_stream(args) if __name__ == "__main__": main() =============== import argparse import cv2 from inference import Network INPUT_STREAM = "pets.mp4" CPU_EXTENSION = "/opt/intel/openvino/deployment_tools/inference_engine/lib/intel64/libcpu_extension_sse4.so" def get_args(): ''' Gets the arguments from the command line. ''' parser = argparse.ArgumentParser("Run inference on an input video") # -- Create the descriptions for the commands m_desc = "The location of the model XML file" i_desc = "The location of the input file" d_desc = "The device name, if not 'CPU'" # -- Add required and optional groups parser._action_groups.pop() required = parser.add_argument_group('required arguments') optional = parser.add_argument_group('optional arguments') # -- Create the arguments required.add_argument("-m", help=m_desc, required=True) optional.add_argument("-i", help=i_desc, default=INPUT_STREAM) optional.add_argument("-d", help=d_desc, default='CPU') args = parser.parse_args() return args def assess_scene(result, counter, incident_flag): ''' Based on the determined situation, potentially send a message to the pets to break it up. ''' if result[0][1] == 1 and not incident_flag: timestamp = counter / 30 print("Log: Incident at {:.3f} seconds.".format(timestamp)) print("Break it up!") incident_flag = True elif result[0][1] != 1: incident_flag = False return incident_flag def infer_on_video(args): # Initialize the Inference Engine plugin = Network() # Load the network model into the IE plugin.load_model(args.m, args.d, CPU_EXTENSION) net_input_shape = plugin.get_input_shape() # Get and open video capture cap = cv2.VideoCapture(args.i) cap.open(args.i) incident_flag=False counter=0 # Process frames until the video ends, or process is exited while cap.isOpened(): # Read the next frame counter+=1 flag, frame = cap.read() if not flag: break key_pressed = cv2.waitKey(60) #print(flag) # Pre-process the frame p_frame = cv2.resize(frame, (net_input_shape[3], net_input_shape[2])) p_frame = p_frame.transpose((2,0,1)) p_frame = p_frame.reshape(1, *p_frame.shape) # Perform inference on the frame plugin.async_inference(p_frame) # Get the output of inference if plugin.wait() == 0: result = plugin.extract_output() ### TODO: Process the output incident_flag=assess_scene(result,counter,incident_flag) # Break if escape key pressed if key_pressed == 27: break # Release the capture and destroy any OpenCV windows cap.release() cv2.destroyAllWindows() def main(): args = get_args() infer_on_video(args) if __name__ == "__main__": main() ************* ************************************ import argparse import cv2 import numpy as np import socket import json from random import randint from inference import Network ### TODO: Import any libraries for MQTT and FFmpeg import paho.mqtt.client as mqtt INPUT_STREAM = "test_video.mp4" CPU_EXTENSION = "/opt/intel/openvino/deployment_tools/inference_engine/lib/intel64/libcpu_extension_sse4.so" ADAS_MODEL = "/home/workspace/models/semantic-segmentation-adas-0001.xml" CLASSES = ['road', 'sidewalk', 'building', 'wall', 'fence', 'pole', 'traffic_light', 'traffic_sign', 'vegetation', 'terrain', 'sky', 'person', 'rider', 'car', 'truck', 'bus', 'train', 'motorcycle', 'bicycle', 'ego- vehicle'] # MQTT server environment variables HOSTNAME = socket.gethostname() IPADDRESS = socket.gethostbyname(HOSTNAME) MQTT_HOST = IPADDRESS MQTT_PORT = 3004 #None ### TODO: Set the Port for MQTT MQTT_KEEPALIVE_INTERVAL = 60 def get_args(): ''' Gets the arguments from the command line. ''' parser = argparse.ArgumentParser("Run inference on an input video") # -- Create the descriptions for the commands i_desc = "The location of the input file" d_desc = "The device name, if not 'CPU'" # -- Create the arguments parser.add_argument("-i", help=i_desc, default=INPUT_STREAM) parser.add_argument("-d", help=d_desc, default='CPU') args = parser.parse_args() return args def draw_masks(result, width, height): ''' Draw semantic mask classes onto the frame. ''' # Create a mask with color by class classes = cv2.resize(result[0].transpose((1,2,0)), (width,height), interpolation=cv2.INTER_NEAREST) unique_classes = np.unique(classes) out_mask = classes * (255/20) # Stack the mask so FFmpeg understands it out_mask = np.dstack((out_mask, out_mask, out_mask)) out_mask = np.uint8(out_mask) return out_mask, unique_classes def get_class_names(class_nums): class_names= [] for i in class_nums: class_names.append(CLASSES[int(i)]) return class_names def infer_on_video(args, model): ### TODO: Connect to the MQTT server client = mqtt.Client() client.connect(MQTT_HOST, MQTT_PORT, MQTT_KEEPALIVE_INTERVAL) # Initialize the Inference Engine plugin = Network() # Load the network model into the IE plugin.load_model(model, args.d, CPU_EXTENSION) net_input_shape = plugin.get_input_shape() # Get and open video capture cap = cv2.VideoCapture(args.i) cap.open(args.i) # Grab the shape of the input width = int(cap.get(3)) height = int(cap.get(4)) # Process frames until the video ends, or process is exited while cap.isOpened(): # Read the next frame flag, frame = cap.read() if not flag: break key_pressed = cv2.waitKey(60) # Pre-process the frame p_frame = cv2.resize(frame, (net_input_shape[3], net_input_shape[2])) p_frame = p_frame.transpose((2,0,1)) p_frame = p_frame.reshape(1, *p_frame.shape) # Perform inference on the frame plugin.async_inference(p_frame) # Get the output of inference if plugin.wait() == 0: result = plugin.extract_output() # Draw the output mask onto the input out_frame, classes = draw_masks(result, width, height) class_names = get_class_names(classes) speed = randint(50,70) ### TODO: Send the class names and speed to the MQTT server client.publish("class", json.dumps({"class_names": class_names})) client.publish("speedometer", json.dumps({"speed": speed})) ### Hint: The UI web server will check for a "class" and ### "speedometer" topic. Additionally, it expects "class_names" ### and "speed" as the json keys of the data, respectively. ### TODO: Send frame to the ffmpeg server sys.stdout.buffer.write(frame) sys.stdout.flush() # Break if escape key pressed if key_pressed == 27: break # Release the capture and destroy any OpenCV windows cap.release() cv2.destroyAllWindows() ### TODO: Disconnect from MQTT client.disconnect() def main(): args = get_args() model = ADAS_MODEL infer_on_video(args, model) if __name__ == "__main__": main() #1 #wget[ ](http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v2_coco_2018_03_29.tar.gz) #2 #tar -xvf ssd_mobilenet_v2_coco_2018_03_29.tar.gz #3 # python /opt/intel/openvino/deployment_tools/model_optimizer/mo.py --input_model /home/workspace/ssd_mobilenet_v2_coco_2018_03_29/frozen_inference_graph.pb --tensorflow_object_detection_api_pipeline_config /home/workspace/ssd_mobilenet_v2_coco_2018_03_29/pipeline.config --reverse_input_channels --tensorflow_use_custom_operations_config /opt/intel/openvino/deployment_tools/model_optimizer/extensions/front/tf/ssd_v2_support.json # 4 # #python app.py -m frozen_inference_graph.xml -ct 0.6 -c BLUE python app.py | ffmpeg -v warning -f rawvideo -pixel_format bgr24 -video_size 1280x720 -framerate 24 -i -[ ](http://www.google.com/url?q=http%3A%2F%2F0.0.0.0%3A3004%2Ffac.ffm&sa=D&sntz=1&usg=AOvVaw1igbX4z68nKAHZDP9jeLWb)[http://0.0.0.0:3004/fac.ffm](http://www.google.com/url?q=http%3A%2F%2F0.0.0.0%3A3004%2Ffac.ffm&sa=D&sntz=1&usg=AOvVaw1igbX4z68nKAHZDP9jeLWb) E4: import argparse import cv2 import numpy as np def get_args(): ''' Gets the arguments from the command line. ''' parser = argparse.ArgumentParser("Handle an input stream") # -- Create the descriptions for the commands i_desc = "The location of the input file" # -- Create the arguments parser.add_argument("-i", help=i_desc) args = parser.parse_args() return args def capture_stream(args): ### TODO: Handle image, video or webcam image_flag=False if args.i=='CAM': args.i=0 elif args.i.endswith('.jpg') or args.i.endswith('.bmp'): image_flag=True capture = cv2.VideoCapture(args.i) capture.open(args.i) if not image_flag: #out=cv2.VideoWriter('out.mp4',cv2.VideoWriter_fourcc('M','J','P','G'),30,(100,100)) # if Linux #fourcc=0x00000021 out=cv2.VideoWriter('out.mp4',0x00000021,30,(100,100)) else: out=None while capture.isOpened(): flag, frame = capture.read() if not flag: break ### TODO: Get and open video capture key_pressed=cv2.waitKey(60) if key_pressed == 27: break ### TODO: Re-size the frame to 100x100 image = cv2.resize(frame, (100, 100)) ### TODO: Add Canny Edge Detection to the frame, ### with min & max values of 100 and 200 edges = cv2.Canny(image,100,200) ### Make sure to use np.dstack after to make a 3-channel image edges = np.dstack((edges, edges, edges)) ### TODO: Write out the frame, depending on image or video if image_flag: cv2.imwrite("out.jpg",edges) else: out.write(edges) if not image_flag: out.release # (display:144): Gtk-WARNING **: cannot open display: :1 #cv2.imshow('display', edges) ### TODO: Close the stream and any windows at the end of the application capture.release() cv2.destroyAllWindows() def main(): args = get_args() capture_stream(args) if __name__ == "__main__": main() Let's say you have a cat and two dogs at your house. import argparse import cv2 from inference import Network INPUT_STREAM = "pets.mp4" CPU_EXTENSION = "/opt/intel/openvino/deployment_tools/inference_engine/lib/intel64/libcpu_extension_sse4.so" def get_args(): ''' Gets the arguments from the command line. ''' parser = argparse.ArgumentParser("Run inference on an input video") # -- Create the descriptions for the commands m_desc = "The location of the model XML file" i_desc = "The location of the input file" d_desc = "The device name, if not 'CPU'" # -- Add required and optional groups parser._action_groups.pop() required = parser.add_argument_group('required arguments') optional = parser.add_argument_group('optional arguments') # -- Create the arguments required.add_argument("-m", help=m_desc, required=True) optional.add_argument("-i", help=i_desc, default=INPUT_STREAM) optional.add_argument("-d", help=d_desc, default='CPU') args = parser.parse_args() return args def assess_scene(result, counter, incident_flag): ''' Based on the determined situation, potentially send a message to the pets to break it up. ''' if result[0][1] == 1 and not incident_flag: timestamp = counter / 30 print("Log: Incident at {:.3f} seconds.".format(timestamp)) print("Break it up!") incident_flag = True elif result[0][1] != 1: incident_flag = False return incident_flag def infer_on_video(args): # Initialize the Inference Engine plugin = Network() # Load the network model into the IE plugin.load_model(args.m, args.d, CPU_EXTENSION) net_input_shape = plugin.get_input_shape() # Get and open video capture cap = cv2.VideoCapture(args.i) cap.open(args.i) incident_flag=False counter=0 # Process frames until the video ends, or process is exited while cap.isOpened(): # Read the next frame counter+=1 flag, frame = cap.read() if not flag: break key_pressed = cv2.waitKey(60) #print(flag) # Pre-process the frame p_frame = cv2.resize(frame, (net_input_shape[3], net_input_shape[2])) p_frame = p_frame.transpose((2,0,1)) p_frame = p_frame.reshape(1, *p_frame.shape) # Perform inference on the frame plugin.async_inference(p_frame) # Get the output of inference if plugin.wait() == 0: result = plugin.extract_output() ### TODO: Process the output incident_flag=assess_scene(result,counter,incident_flag) # Break if escape key pressed if key_pressed == 27: break # Release the capture and destroy any OpenCV windows cap.release() cv2.destroyAllWindows() def main(): args = get_args() infer_on_video(args) if __name__ == "__main__": main() # Integrate the Inference Engine - Solution Let's step through the tasks one by one, with a potential approach for each. > Convert a bounding box model to an IR with the Model Optimizer. I used the SSD Mobilenet V2 architecture from TensorFlow from the earlier lesson here. Note that the original was downloaded in a separate workspace, so I needed to download it again and then convert it. ``` python /opt/intel/openvino/deployment_tools/model_optimizer/mo.py --input_model frozen_inference_graph.pb --tensorflow_object_detection_api_pipeline_config pipeline.config --reverse_input_channels --tensorflow_use_custom_operations_config /opt/intel/openvino/deployment_tools/model_optimizer/extensions/front/tf/ssd_v2_support.json ``` > Extract the results from the inference request ``` self.exec_network.requests[0].outputs[self.output_blob] ``` > Add code to make the requests and feed back the results within the > application ``` self.exec_network.start_async(request_id=0, inputs={self.input_blob: image}) ... status = self.exec_network.requests[0].wait(-1) ``` > Add a command line argument to allow for different confidence thresholds for > the model I chose to use `-ct` as the argument name here, and added it to the existing arguments. ``` optional.add_argument("-ct", help="The confidence threshold to use with the bounding boxes", default=0.5) ``` I set a default of 0.5, so it does not need to be input by the user every time. > Add a command line argument to allow for different bounding box colors for > the output Similarly, I added the `-c` argument for inputting a bounding box color. Note that in my approach, I chose to only allow "RED", "GREEN" and "BLUE", which also impacts what I'll do in the next step; there are many possible approaches here. ``` optional.add_argument("-c", help="The color of the bounding boxes to draw; RED, GREEN or BLUE", default='BLUE') ``` > Correctly utilize the command line arguments in #3 and #4 within the > application Both of these will come into play within the `draw_boxes` function. For the first, a new line should be added before extracting the bounding box points that check whether `box[2]` (e.g. the probability of a given box) is above `args.ct` - assuming you have added `args.ct` as an argument passed to the `draw_boxes` function. If not, the box should not be drawn. Without this, any random box will be drawn, which could be a ton of very unlikely bounding box detections. ========== app.py import argparse import cv2 from inference import Network INPUT_STREAM = "test_video.mp4" CPU_EXTENSION = "/opt/intel/openvino/deployment_tools/inference_engine/lib/intel64/libcpu_extension_sse4.so" def get_args(): ''' Gets the arguments from the command line. ''' parser = argparse.ArgumentParser("Run inference on an input video") # -- Create the descriptions for the commands m_desc = "The location of the model XML file" i_desc = "The location of the input file" d_desc = "The device name, if not 'CPU'" ### TODO: Add additional arguments and descriptions for: ### 1) Different confidence thresholds used to draw bounding boxes ### 2) The user choosing the color of the bounding boxes # -- Add required and optional groups parser._action_groups.pop() required = parser.add_argument_group('required arguments') optional = parser.add_argument_group('optional arguments') # -- Create the arguments required.add_argument("-m", help=m_desc, required=True) optional.add_argument("-i", help=i_desc, default=INPUT_STREAM) optional.add_argument("-d", help=d_desc, default='CPU') args = parser.parse_args() return args def draw_boxes(frame, result, args, width, height): ''' Draw bounding boxes onto the frame. ''' for box in result[0][0]: # Output shape is 1x1x100x7 conf = box[2] if conf >= 0.5: xmin = int(box[3] * width) ymin = int(box[4] * height) xmax = int(box[5] * width) ymax = int(box[6] * height) cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), (0, 0, 255), 1) return frame def infer_on_video(args): ### TODO: Initialize the Inference Engine plugin = Network() ### TODO: Load the network model into the IE plugin.load_model(args.m, args.d, CPU_EXTENSION) net_input_shape = plugin.get_input_shape() # Get and open video capture cap = cv2.VideoCapture(args.i) cap.open(args.i) # Grab the shape of the input width = int(cap.get(3)) height = int(cap.get(4)) # Create a video writer for the output video # The second argument should be `cv2.VideoWriter_fourcc('M','J','P','G')` # on Mac, and `0x00000021` on Linux out = cv2.VideoWriter('out.mp4', 0x00000021, 30, (width,height)) # Process frames until the video ends, or process is exited while cap.isOpened(): # Read the next frame flag, frame = cap.read() if not flag: break key_pressed = cv2.waitKey(60) ### TODO: Pre-process the frame p_frame = cv2.resize(frame, (net_input_shape[3], net_input_shape[2])) p_frame = p_frame.transpose((2,0,1)) p_frame = p_frame.reshape(1, *p_frame.shape) ### TODO: Perform inference on the frame plugin.async_inference(p_frame) ### TODO: Get the output of inference if plugin.wait() == 0: result = plugin.extract_output() ### TODO: Update the frame to include detected bounding boxes frame = draw_boxes(frame, result, args, width, height) # Write out the frame out.write(frame) # Break if escape key pressed if key_pressed == 27: break # Release the out writer, capture, and destroy any OpenCV windows out.release() cap.release() cv2.destroyAllWindows() def main(): args = get_args() infer_on_video(args) if __name__ == "__main__": main() ========================================================app-sustom.py import argparse import cv2 from inference import Network INPUT_STREAM = "test_video.mp4" CPU_EXTENSION = "/opt/intel/openvino/deployment_tools/inference_engine/lib/intel64/libcpu_extension_sse4.so" def get_args(): ''' Gets the arguments from the command line. ''' parser = argparse.ArgumentParser("Run inference on an input video") # -- Create the descriptions for the commands m_desc = "The location of the model XML file" i_desc = "The location of the input file" d_desc = "The device name, if not 'CPU'" ### TODO: Add additional arguments and descriptions for: ### 1) Different confidence thresholds used to draw bounding boxes ### 2) The user choosing the color of the bounding boxes c_desc = "The color of the bounding boxes to draw; RED, GREEN or BLUE" ct_desc = "The confidence threshold to use with the bounding boxes" # -- Add required and optional groups parser._action_groups.pop() required = parser.add_argument_group('required arguments') optional = parser.add_argument_group('optional arguments') # -- Create the arguments required.add_argument("-m", help=m_desc, required=True) optional.add_argument("-i", help=i_desc, default=INPUT_STREAM) optional.add_argument("-d", help=d_desc, default='CPU') optional.add_argument("-c", help=c_desc, default='BLUE') optional.add_argument("-ct", help=ct_desc, default=0.5) args = parser.parse_args() return args def convert_color(color_string): ''' Get the BGR value of the desired bounding box color. Defaults to Blue if an invalid color is given. ''' colors = {"BLUE": (255,0,0), "GREEN": (0,255,0), "RED": (0,0,255)} out_color = colors.get(color_string) if out_color: return out_color else: return colors['BLUE'] def draw_boxes(frame, result, args, width, height): ''' Draw bounding boxes onto the frame. ''' for box in result[0][0]: # Output shape is 1x1x100x7 conf = box[2] if conf >= args.ct: xmin = int(box[3] * width) ymin = int(box[4] * height) xmax = int(box[5] * width) ymax = int(box[6] * height) cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), args.c, 1) return frame def infer_on_video(args): # Convert the args for color and confidence args.c = convert_color(args.c) args.ct = float(args.ct) ### TODO: Initialize the Inference Engine plugin = Network() ### TODO: Load the network model into the IE plugin.load_model(args.m, args.d, CPU_EXTENSION) net_input_shape = plugin.get_input_shape() # Get and open video capture cap = cv2.VideoCapture(args.i) cap.open(args.i) # Grab the shape of the input width = int(cap.get(3)) height = int(cap.get(4)) # Create a video writer for the output video # The second argument should be `cv2.VideoWriter_fourcc('M','J','P','G')` # on Mac, and `0x00000021` on Linux out = cv2.VideoWriter('out.mp4', 0x00000021, 30, (width,height)) # Process frames until the video ends, or process is exited while cap.isOpened(): # Read the next frame flag, frame = cap.read() if not flag: break key_pressed = cv2.waitKey(60) ### TODO: Pre-process the frame p_frame = cv2.resize(frame, (net_input_shape[3], net_input_shape[2])) p_frame = p_frame.transpose((2,0,1)) p_frame = p_frame.reshape(1, *p_frame.shape) ### TODO: Perform inference on the frame plugin.async_inference(p_frame) ### TODO: Get the output of inference if plugin.wait() == 0: result = plugin.extract_output() ### TODO: Update the frame to include detected bounding boxes frame = draw_boxes(frame, result, args, width, height) # Write out the frame out.write(frame) # Break if escape key pressed if key_pressed == 27: break # Release the out writer, capture, and destroy any OpenCV windows out.release() cap.release() cv2.destroyAllWindows() def main(): args = get_args() infer_on_video(args) if __name__ == "__main__": main() =============================================interface.py ''' Contains code for working with the Inference Engine. You'll learn how to implement this code and more in the related lesson on the topic. ''' import os import sys import logging as log from openvino.inference_engine import IENetwork, IECore class Network: ''' Load and store information for working with the Inference Engine, and any loaded models. ''' def __init__(self): self.plugin = None self.network = None self.input_blob = None self.output_blob = None self.exec_network = None self.infer_request = None def load_model(self, model, device="CPU", cpu_extension=None): ''' Load the model given IR files. Defaults to CPU as device for use in the workspace. Synchronous requests made within. ''' model_xml = model model_bin = os.path.splitext(model_xml)[0] + ".bin" # Initialize the plugin self.plugin = IECore() # Add a CPU extension, if applicable if cpu_extension and "CPU" in device: self.plugin.add_extension(cpu_extension, device) # Read the IR as a IENetwork self.network = IENetwork(model=model_xml, weights=model_bin) # Load the IENetwork into the plugin self.exec_network = self.plugin.load_network(self.network, device) # Get the input layer self.input_blob = next(iter(self.network.inputs)) self.output_blob = next(iter(self.network.outputs)) return def get_input_shape(self): ''' Gets the input shape of the network ''' return self.network.inputs[self.input_blob].shape def async_inference(self, image): ''' Makes an asynchronous inference request, given an input image. ''' self.exec_network.start_async(request_id=0, inputs={self.input_blob: image}) return def wait(self): ''' Checks the status of the inference request. ''' status = self.exec_network.requests[0].wait(-1) return status def extract_output(self): ''' Returns a list of the results for the output layer of the network. ''' return self.exec_network.requests[0].outputs[self.output_blob] ====================================== The second is just a small adjustment to the `cv2.rectangle` function that draws the bounding boxes we found to be above `args.ct`. I actually added a function to match the different potential colors up to their RGB values first, due to how I took them in from the command line: ``` def convert_color(color_string): ''' Get the BGR value of the desired bounding box color. Defaults to Blue if an invalid color is given. ''' colors = {"BLUE": (255,0,0), "GREEN": (0,255,0), "RED": (0,0,255)} out_color = colors.get(color_string) if out_color: return out_color else: return colors['BLUE'] ``` I can also add the tuple returned from this function as an additional `color` argument to feed to `draw_boxes`. Then, the line where the bounding boxes are drawn becomes: ``` cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), color, 1) ``` I was able to run my app, if I was using the converted TF model from earlier (and placed in the current directory), using the below: ```bash python app.py -m frozen_inference_graph.xml ``` Or, if I added additional customization with a confidence threshold of 0.6 and blue boxes: ```bash python app.py -m frozen_inference_graph.xml -ct 0.6 -c BLUE ``` [Note that I placed my customized app actually in `app-custom.py`] Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/G7JKnoBc5s5bG3WK7alpRCEIdQazcLj2L1DLGACGDrsMeHOK9CTS5fh5v74shZzmMJ8YN6hl77hXFxOIDH_8b3M=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/G7JKnoBc5s5bG3WK7alpRCEIdQazcLj2L1DLGACGDrsMeHOK9CTS5fh5v74shZzmMJ8YN6hl77hXFxOIDH_8b3M=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Cloud-Native Master Cloud-Native infrastructure with Kubernetes I) Learning Docker (best *****) I) Learning Docker -ti = terminal interactive docker run -ti ubuntu:latest bash cat /etc/lsb-release = which distribution docker ps -format $FORMAT => which docker ... running docker ps -l => the last docker exited docker commit ID => new image id docker tag imageID my-image docker run --rm -ti ubuntu sleep 5 => \--rm to remove after using docker run -ti ubuntu bash -c "sleep 3; echo all done"" => -c : some commands docker run -d -ti ubuntu bash => -d is detached run in background => docker attach nameDocker => ctrl+p /q docker logs continaers_name -p => publish nc -lp docker images Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/ssWXVQN4wWnp524T8UjHyQEdArMpTIaM4OalIHhpfDv0Sv6HC6tLnwxjfrdaemWa002sbPUNKYrtuvBhEcn6DIQ=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/ssWXVQN4wWnp524T8UjHyQEdArMpTIaM4OalIHhpfDv0Sv6HC6tLnwxjfrdaemWa002sbPUNKYrtuvBhEcn6DIQ=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Advanced Programming with Modern C++ 23 for Image Processing My GitHub about Advanced Programming with Modern C++ 23 for Image Processing Important commands Compile CUDA for Jetson Nano (JetPack 4.5, CUDA 10.2) compile c++ 20; based on GCC 12, CLang 13 commands Tools Links appendix Update March 2022 - 1401 ## [My GitHub about Advanced Programming with Modern C++ 23 for Image Processing ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest&sa=D&sntz=1&usg=AOvVaw3nCk6-1QOY0tGcAL6U5LmN) make industrial process scale-ups successful #c++23 #optimization #imageprocessing #deeplearning #AI #IoT You need latest version of C++ compiler in order to use C++ 20 standard. GCC>12 or CLang>13\. CUDA 11 support C++17 by nvcc; Cmake. [Book: writing solid code](http://www.google.com/url?q=http%3A%2F%2Fcs.brown.edu%2Fcourses%2Fcs190%2F2008%2Fdocuments%2Frestricted%2FWriting%2520Solid%2520Code.pdf&sa=D&sntz=1&usg=AOvVaw0wE6jx0_UIoJA1DyYUPz2U) mind map call by value: on stack * void f(int a) { a++; } //a in main not change call by reference: * void f(int *p); // f(&i); * void f(int &i);// f(i) * void func(const std::string & s) { s.c_str() } // func(s); * **struct default is public (use when we have only data members) and class default is private (when also have function members) .** * **function 's signature** // int getvalue() const; * **// ! this is a very critical comment** * **//** ***** **this is a** **highlighted** **comment** * **//** **TODO:** **this is a** **TODO** **comment** * **//** **?** **this is a** **question** **comment** [ **Creational design patterns:**](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Flearning- login%2Fshare%3Faccount%3D2154545%26forceAccount%3Dfalse%26redirect%3Dhttps%253A%252F%252Fwww.linkedin.com%252Flearning%252Fc- plus-plus-design-patterns- creational%253Ftrk%253Dshare_ent_url%2526shareId%253DMkeJou%25252BkSoupfOSfTa1SsQ%25253D%25253D&sa=D&sntz=1&usg=AOvVaw3D1OovqRzVzh02_b62IHrx) **** * **flexible, maintainable, extensible** * **gang of four "design patterns: elements of reusable object oriented software"** * **23 patterns** 1. **creational (5) : object instantiation** 1. **factory method** * **composition: property referenced by another class** * **inheritance: class extends another class** 2. **abstract factory** 3. **builder : compl** **ex** 4. **prototype: clone** 5. **singleton: only one instance** 2. **structural : class relationships and hierarchies; class pattern: is; structural object patterns: has** 1. **adapter** 2. **bridge** 3. **composite** 4. **decorator** 5. **facde** 6. **flyweight** 7. **proxy** 3. [ **behavioral (12): object intercommunication :**](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Flearning-login%2Fshare%3Faccount%3D2154545%26forceAccount%3Dfalse%26redirect%3Dhttps%253A%252F%252Fwww.linkedin.com%252Flearning%252Fc-plus-plus-design-patterns-behavioral%253Ftrk%253Dshare_ent_url%2526shareId%253DrUkPrp6VQjKydR4M1TxKWw%25253D%25253D&sa=D&sntz=1&usg=AOvVaw2CJkeXiIw8fjndK6UXHgzP) 1. **chain of responsibility** * **password check** 2. **command** * **one button for all command** 3. **mediator** * **reduce dependency : married - > spouse name -> ....** 4. **observer** * **std::vector subscribers;** * **this- >subscribers.push_back(subscriber);** * **void unsubscribe(Subscriber *subscriber) override { subscribers.erase(std::remove_if(subscribers.begin(),subscribers.end(), [subscriber](Subscriber *s){{return s->getName() == subscriber->getName(); }), subscribers.end());** 5. **interpreter** * **1+(2+3)** 6. **state** * **order** * 7. **strategy** 8. **template method** 9. **visitor** 10. **iterator pattern** 11. **memento** * **undo** 12. **null-object** * **default** **UML: unified modeling language** **abstract and concrete classes** [LinkedIn](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Fin%2Fpirahansiah%2F&sa=D&sntz=1&usg=AOvVaw0ETpuSejDWH6Dz0IId5L5j): * int x=5; * size_t y=sizeof x; or sizeof(int) * printf("sizeof x is %zd\n",y*8); //change bit to byte * func(){ static int i=5; }// it will change ++ // it will be on static storage not stack * void (*pfunc)()=func; * (*pfunc)(); * #include * #include // variadic argument * double average(const int count, ...) { va_list ap; va_start(ap,count); va_arg(ap,double); va_end(ap); } * template * larger executables * confusing error messages * longer compile times * #include * perror("");// * v.begin(); v.end(); v.size(); v.back(); v[5]; v.at(5) * string: s.size(); s.length(); s.find(); * std::hex,showbase, oct, fixed, scientific,setprecision(3), floatfield,setw, setfill('-'); std::cin.getline(buf,sizeof(buf)); * #include try { ; } catch (std::exception & e) { e.what() } * class1 * o1= new(nothrow) c1[5]; if (o1==nullptr){}; delete [] o1; * if we don't want to create base class we can put constructor in the private part classname(){} * then use constructor in the protected: classname( ): _name(value) {} * using friend class nameofsubclass; to use private functions ; friend class base; * virtual : maybe overloaded and maybe write in subclass; we need ~ * #include * std::unique_ptr a(new struct()); * auto b=std::make_unique(); * a.reset(new structure()); // delete * auto c=std::move(b); // b is null * c.release(); * auto a=std::make_shared(); * auto w1=std::weak_ptr(struct>(); * * T & x => lvalie reference * T && y => rvalue reference * rule of five (if you define any of these functions you need to define all) * ~class(); * class(class &); * class(class &&); * class & operator = (class &); * class & operator = (class &&); * []()->char{} * auto fp=[](const T & n)-> T {return n*5; }; * #define MAX(a,b) (a>b ? a:b) * constexpr int ONE =1; * unit tests * * * virtual Class *clone()= 0 ; * * * * * * template * struct B { /* ... */ }; * ![](https://lh6.googleusercontent.com/NGiYCCbFAfHWT2GIFOKYsaDKexyoPpNG59E7eS1ytZqQUCfTmlf1YojdF7x6Txxnb5LpfzW2n50KRtDB20bqdqwjrL_Ra0a6wDrNn4HLEVd0nn50Xt5rGdZKbXPFBzCOlA=w1280) ![](https://lh3.googleusercontent.com/AjBiQtlDLVVB_HgX98AR8CsYJS5yefPvG59xIxRwKXrNXq2Se5AjmNNWFB73sfgRiYam- hHsNHEWhl0uEjfNMrXMbx5DI-2L3HIIGc-3xW3p2Agk_z21jBnU2jeaHb2MSg=w1280) ![lambda capture](https://lh5.googleusercontent.com/PLptpoAkCKMjfCPKvxIXC8FGQSBuX5ZBnxLMZSAFFzUwsNcEHX0haCqpmCmZipb1zmL2AFS18V1j-4kygbYun3g=w1280) Module Interface Unit : *.cppm Module Implementation Unit: *.cpp # Important commands ### Compile CUDA for Jetson Nano (JetPack 4.5, CUDA 10.2) nvcc -std=c++14 -arch=sm_62 -o main.run main.cu ### compile c++ 20; based on GCC 12, CLang 13 clang++ -std=c++2a -c helloworld.cpp -Xclang -emit-module-interface -o helloworld.pcm clang++ -std=c++2a -stdlib=libc++ -fimplicit-modules -fimplicit-module-maps -fprebuilt-module-path=. main.cpp helloworld.cpp ### commands 1. echo "export PATH=.:"$PATH"" >> ~/.bashrc 2. source ~/.bashrc * htop * ulimit -a * git submodule add (githuburl external/glfw) ### Tools brew install --HEAD LouisBrunner/valgrind/valgrind valgrind ./a.out CppCon 2016: John Lakos "Advanced Levelization Techniques (part 1 of 3) * Large Scale C++ software design * retain control of your dependency graph * keep concerns separated * make modules reusable in other contexts at minimal cost ## Links [https://en.cppreference.com/w/cpp/23](https://www.google.com/url?q=https%3A%2F%2Fen.cppreference.com%2Fw%2Fcpp%2F23&sa=D&sntz=1&usg=AOvVaw3NxKhzYIbT-p70q0v8DuXy) [https://imfing.medium.com/hands-on-modules- in-c-20-abc3cd333133](https://www.google.com/url?q=https%3A%2F%2Fimfing.medium.com%2Fhands- on-modules-in-c-20-abc3cd333133&sa=D&sntz=1&usg=AOvVaw3AWw5eka5EpTdXAVfyGqE1) [CppCon 2016: John Lakos "Advanced Levelization Techniques (part 1 of 3)](https://www.youtube.com/watch?v=QjFpKJ8Xx78) [Modern CMake](https://www.youtube.com/watch?v=eC9-iRN2b04) (CppCon 2017) [CMake Tutorial ](https://www.youtube.com/watch?v=nlKcXPUJGwA&list=PLalVdRk2RC6o5GHu618ARWh0VO0bFlif4) # appendix C++ design patterns: factory method * class c1 * { * public: * void c1_test() * { * cout << "main class" << endl; * } * * }; * class c2:public c1 * { * public: * c2() * { * cout << "c2" << endl; * } * * }; * class c3 :public c1 * { * public: * c3() * { * cout << "c3" << endl; * } * * }; * class factory * { * private: * * c1 * _c1; * public: * c1 * function_factory(int i) * { * switch (i) * { * case 1: * return new c2; * break; * case 2: * return new c3; * break; * * * } * } * * * }; * int main() * { * factory f; * c1* c; * c = f.function_factory(2); * return 1; * } * 1 * * error : Access violation writing location for clone image from std::vector and findHomography * can not use std::vector imagesF; imagesF.at(0) or imagesF[0] or imagesF[0].clone() * Mat is some kind of **smart pointer** for the pixels, so Mat a=b will have shared pixels for a and b. similar situation for push_back() * if you need a 'deep copy', use Mat::clone(): imagesF.push_back(imageMat.clone()); [https://stackoverflow.com/a/19524261/3533188](https://www.google.com/url?q=https%3A%2F%2Fstackoverflow.com%2Fa%2F19524261%2F3533188&sa=D&sntz=1&usg=AOvVaw11jUWr1dHy3ukdjM7et6le) When you are using vector to store image from OpenCV Mat you need to use deep copy because cv::Mat is like **smart pointer.** * std::vector imagesVector; * imagesVector.push_back(imageMat.clone()); * cv::Mat im_in = imagesVector[0] #OpenCV #C++ #pirahansiah Download first draft for OpenCV 5 book : [_https://docs.google.com/document/d/1v3qRJE8d0rYXrfDf_BjJHXANTKsPu2lw6In32gD88wY/edit?usp=sharing_](https://docs.google.com/document/d/1v3qRJE8d0rYXrfDf_BjJHXANTKsPu2lw6In32gD88wY/edit?usp=sharing) __ Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/ssWXVQN4wWnp524T8UjHyQEdArMpTIaM4OalIHhpfDv0Sv6HC6tLnwxjfrdaemWa002sbPUNKYrtuvBhEcn6DIQ=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/ssWXVQN4wWnp524T8UjHyQEdArMpTIaM4OalIHhpfDv0Sv6HC6tLnwxjfrdaemWa002sbPUNKYrtuvBhEcn6DIQ=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Advanced Programming with Modern C++ 23 for Image Processing My GitHub about Advanced Programming with Modern C++ 23 for Image Processing Important commands Compile CUDA for Jetson Nano (JetPack 4.5, CUDA 10.2) compile c++ 20; based on GCC 12, CLang 13 commands Tools Links appendix Update March 2022 - 1401 ## [My GitHub about Advanced Programming with Modern C++ 23 for Image Processing ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest&sa=D&sntz=1&usg=AOvVaw3nCk6-1QOY0tGcAL6U5LmN) make industrial process scale-ups successful #c++23 #optimization #imageprocessing #deeplearning #AI #IoT You need latest version of C++ compiler in order to use C++ 20 standard. GCC>12 or CLang>13\. CUDA 11 support C++17 by nvcc; Cmake. [Book: writing solid code](http://www.google.com/url?q=http%3A%2F%2Fcs.brown.edu%2Fcourses%2Fcs190%2F2008%2Fdocuments%2Frestricted%2FWriting%2520Solid%2520Code.pdf&sa=D&sntz=1&usg=AOvVaw0wE6jx0_UIoJA1DyYUPz2U) mind map call by value: on stack * void f(int a) { a++; } //a in main not change call by reference: * void f(int *p); // f(&i); * void f(int &i);// f(i) * void func(const std::string & s) { s.c_str() } // func(s); * **struct default is public (use when we have only data members) and class default is private (when also have function members) .** * **function 's signature** // int getvalue() const; * **// ! this is a very critical comment** * **//** ***** **this is a** **highlighted** **comment** * **//** **TODO:** **this is a** **TODO** **comment** * **//** **?** **this is a** **question** **comment** [ **Creational design patterns:**](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Flearning- login%2Fshare%3Faccount%3D2154545%26forceAccount%3Dfalse%26redirect%3Dhttps%253A%252F%252Fwww.linkedin.com%252Flearning%252Fc- plus-plus-design-patterns- creational%253Ftrk%253Dshare_ent_url%2526shareId%253DMkeJou%25252BkSoupfOSfTa1SsQ%25253D%25253D&sa=D&sntz=1&usg=AOvVaw3D1OovqRzVzh02_b62IHrx) **** * **flexible, maintainable, extensible** * **gang of four "design patterns: elements of reusable object oriented software"** * **23 patterns** 1. **creational (5) : object instantiation** 1. **factory method** * **composition: property referenced by another class** * **inheritance: class extends another class** 2. **abstract factory** 3. **builder : compl** **ex** 4. **prototype: clone** 5. **singleton: only one instance** 2. **structural : class relationships and hierarchies; class pattern: is; structural object patterns: has** 1. **adapter** 2. **bridge** 3. **composite** 4. **decorator** 5. **facde** 6. **flyweight** 7. **proxy** 3. [ **behavioral (12): object intercommunication :**](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Flearning-login%2Fshare%3Faccount%3D2154545%26forceAccount%3Dfalse%26redirect%3Dhttps%253A%252F%252Fwww.linkedin.com%252Flearning%252Fc-plus-plus-design-patterns-behavioral%253Ftrk%253Dshare_ent_url%2526shareId%253DrUkPrp6VQjKydR4M1TxKWw%25253D%25253D&sa=D&sntz=1&usg=AOvVaw2CJkeXiIw8fjndK6UXHgzP) 1. **chain of responsibility** * **password check** 2. **command** * **one button for all command** 3. **mediator** * **reduce dependency : married - > spouse name -> ....** 4. **observer** * **std::vector subscribers;** * **this- >subscribers.push_back(subscriber);** * **void unsubscribe(Subscriber *subscriber) override { subscribers.erase(std::remove_if(subscribers.begin(),subscribers.end(), [subscriber](Subscriber *s){{return s->getName() == subscriber->getName(); }), subscribers.end());** 5. **interpreter** * **1+(2+3)** 6. **state** * **order** * 7. **strategy** 8. **template method** 9. **visitor** 10. **iterator pattern** 11. **memento** * **undo** 12. **null-object** * **default** **UML: unified modeling language** **abstract and concrete classes** [LinkedIn](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Fin%2Fpirahansiah%2F&sa=D&sntz=1&usg=AOvVaw0ETpuSejDWH6Dz0IId5L5j): * int x=5; * size_t y=sizeof x; or sizeof(int) * printf("sizeof x is %zd\n",y*8); //change bit to byte * func(){ static int i=5; }// it will change ++ // it will be on static storage not stack * void (*pfunc)()=func; * (*pfunc)(); * #include * #include // variadic argument * double average(const int count, ...) { va_list ap; va_start(ap,count); va_arg(ap,double); va_end(ap); } * template * larger executables * confusing error messages * longer compile times * #include * perror("");// * v.begin(); v.end(); v.size(); v.back(); v[5]; v.at(5) * string: s.size(); s.length(); s.find(); * std::hex,showbase, oct, fixed, scientific,setprecision(3), floatfield,setw, setfill('-'); std::cin.getline(buf,sizeof(buf)); * #include try { ; } catch (std::exception & e) { e.what() } * class1 * o1= new(nothrow) c1[5]; if (o1==nullptr){}; delete [] o1; * if we don't want to create base class we can put constructor in the private part classname(){} * then use constructor in the protected: classname( ): _name(value) {} * using friend class nameofsubclass; to use private functions ; friend class base; * virtual : maybe overloaded and maybe write in subclass; we need ~ * #include * std::unique_ptr a(new struct()); * auto b=std::make_unique(); * a.reset(new structure()); // delete * auto c=std::move(b); // b is null * c.release(); * auto a=std::make_shared(); * auto w1=std::weak_ptr(struct>(); * * T & x => lvalie reference * T && y => rvalue reference * rule of five (if you define any of these functions you need to define all) * ~class(); * class(class &); * class(class &&); * class & operator = (class &); * class & operator = (class &&); * []()->char{} * auto fp=[](const T & n)-> T {return n*5; }; * #define MAX(a,b) (a>b ? a:b) * constexpr int ONE =1; * unit tests * * * virtual Class *clone()= 0 ; * * * * * * template * struct B { /* ... */ }; * ![](https://lh6.googleusercontent.com/NGiYCCbFAfHWT2GIFOKYsaDKexyoPpNG59E7eS1ytZqQUCfTmlf1YojdF7x6Txxnb5LpfzW2n50KRtDB20bqdqwjrL_Ra0a6wDrNn4HLEVd0nn50Xt5rGdZKbXPFBzCOlA=w1280) ![](https://lh3.googleusercontent.com/AjBiQtlDLVVB_HgX98AR8CsYJS5yefPvG59xIxRwKXrNXq2Se5AjmNNWFB73sfgRiYam- hHsNHEWhl0uEjfNMrXMbx5DI-2L3HIIGc-3xW3p2Agk_z21jBnU2jeaHb2MSg=w1280) ![lambda capture](https://lh5.googleusercontent.com/PLptpoAkCKMjfCPKvxIXC8FGQSBuX5ZBnxLMZSAFFzUwsNcEHX0haCqpmCmZipb1zmL2AFS18V1j-4kygbYun3g=w1280) Module Interface Unit : *.cppm Module Implementation Unit: *.cpp # Important commands ### Compile CUDA for Jetson Nano (JetPack 4.5, CUDA 10.2) nvcc -std=c++14 -arch=sm_62 -o main.run main.cu ### compile c++ 20; based on GCC 12, CLang 13 clang++ -std=c++2a -c helloworld.cpp -Xclang -emit-module-interface -o helloworld.pcm clang++ -std=c++2a -stdlib=libc++ -fimplicit-modules -fimplicit-module-maps -fprebuilt-module-path=. main.cpp helloworld.cpp ### commands 1. echo "export PATH=.:"$PATH"" >> ~/.bashrc 2. source ~/.bashrc * htop * ulimit -a * git submodule add (githuburl external/glfw) ### Tools brew install --HEAD LouisBrunner/valgrind/valgrind valgrind ./a.out CppCon 2016: John Lakos "Advanced Levelization Techniques (part 1 of 3) * Large Scale C++ software design * retain control of your dependency graph * keep concerns separated * make modules reusable in other contexts at minimal cost ## Links [https://en.cppreference.com/w/cpp/23](https://www.google.com/url?q=https%3A%2F%2Fen.cppreference.com%2Fw%2Fcpp%2F23&sa=D&sntz=1&usg=AOvVaw3NxKhzYIbT-p70q0v8DuXy) [https://imfing.medium.com/hands-on-modules- in-c-20-abc3cd333133](https://www.google.com/url?q=https%3A%2F%2Fimfing.medium.com%2Fhands- on-modules-in-c-20-abc3cd333133&sa=D&sntz=1&usg=AOvVaw3AWw5eka5EpTdXAVfyGqE1) [CppCon 2016: John Lakos "Advanced Levelization Techniques (part 1 of 3)](https://www.youtube.com/watch?v=QjFpKJ8Xx78) [Modern CMake](https://www.youtube.com/watch?v=eC9-iRN2b04) (CppCon 2017) [CMake Tutorial ](https://www.youtube.com/watch?v=nlKcXPUJGwA&list=PLalVdRk2RC6o5GHu618ARWh0VO0bFlif4) # appendix C++ design patterns: factory method * class c1 * { * public: * void c1_test() * { * cout << "main class" << endl; * } * * }; * class c2:public c1 * { * public: * c2() * { * cout << "c2" << endl; * } * * }; * class c3 :public c1 * { * public: * c3() * { * cout << "c3" << endl; * } * * }; * class factory * { * private: * * c1 * _c1; * public: * c1 * function_factory(int i) * { * switch (i) * { * case 1: * return new c2; * break; * case 2: * return new c3; * break; * * * } * } * * * }; * int main() * { * factory f; * c1* c; * c = f.function_factory(2); * return 1; * } * 1 * * error : Access violation writing location for clone image from std::vector and findHomography * can not use std::vector imagesF; imagesF.at(0) or imagesF[0] or imagesF[0].clone() * Mat is some kind of **smart pointer** for the pixels, so Mat a=b will have shared pixels for a and b. similar situation for push_back() * if you need a 'deep copy', use Mat::clone(): imagesF.push_back(imageMat.clone()); [https://stackoverflow.com/a/19524261/3533188](https://www.google.com/url?q=https%3A%2F%2Fstackoverflow.com%2Fa%2F19524261%2F3533188&sa=D&sntz=1&usg=AOvVaw11jUWr1dHy3ukdjM7et6le) When you are using vector to store image from OpenCV Mat you need to use deep copy because cv::Mat is like **smart pointer.** * std::vector imagesVector; * imagesVector.push_back(imageMat.clone()); * cv::Mat im_in = imagesVector[0] #OpenCV #C++ #pirahansiah Download first draft for OpenCV 5 book : [_https://docs.google.com/document/d/1v3qRJE8d0rYXrfDf_BjJHXANTKsPu2lw6In32gD88wY/edit?usp=sharing_](https://docs.google.com/document/d/1v3qRJE8d0rYXrfDf_BjJHXANTKsPu2lw6In32gD88wY/edit?usp=sharing) __ Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/ssWXVQN4wWnp524T8UjHyQEdArMpTIaM4OalIHhpfDv0Sv6HC6tLnwxjfrdaemWa002sbPUNKYrtuvBhEcn6DIQ=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/ssWXVQN4wWnp524T8UjHyQEdArMpTIaM4OalIHhpfDv0Sv6HC6tLnwxjfrdaemWa002sbPUNKYrtuvBhEcn6DIQ=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Advanced Programming with Modern C++ 23 for Image Processing My GitHub about Advanced Programming with Modern C++ 23 for Image Processing Important commands Compile CUDA for Jetson Nano (JetPack 4.5, CUDA 10.2) compile c++ 20; based on GCC 12, CLang 13 commands Tools Links appendix Update March 2022 - 1401 ## [My GitHub about Advanced Programming with Modern C++ 23 for Image Processing ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest&sa=D&sntz=1&usg=AOvVaw3nCk6-1QOY0tGcAL6U5LmN) make industrial process scale-ups successful #c++23 #optimization #imageprocessing #deeplearning #AI #IoT You need latest version of C++ compiler in order to use C++ 20 standard. GCC>12 or CLang>13\. CUDA 11 support C++17 by nvcc; Cmake. [Book: writing solid code](http://www.google.com/url?q=http%3A%2F%2Fcs.brown.edu%2Fcourses%2Fcs190%2F2008%2Fdocuments%2Frestricted%2FWriting%2520Solid%2520Code.pdf&sa=D&sntz=1&usg=AOvVaw0wE6jx0_UIoJA1DyYUPz2U) mind map call by value: on stack * void f(int a) { a++; } //a in main not change call by reference: * void f(int *p); // f(&i); * void f(int &i);// f(i) * void func(const std::string & s) { s.c_str() } // func(s); * **struct default is public (use when we have only data members) and class default is private (when also have function members) .** * **function 's signature** // int getvalue() const; * **// ! this is a very critical comment** * **//** ***** **this is a** **highlighted** **comment** * **//** **TODO:** **this is a** **TODO** **comment** * **//** **?** **this is a** **question** **comment** [ **Creational design patterns:**](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Flearning- login%2Fshare%3Faccount%3D2154545%26forceAccount%3Dfalse%26redirect%3Dhttps%253A%252F%252Fwww.linkedin.com%252Flearning%252Fc- plus-plus-design-patterns- creational%253Ftrk%253Dshare_ent_url%2526shareId%253DMkeJou%25252BkSoupfOSfTa1SsQ%25253D%25253D&sa=D&sntz=1&usg=AOvVaw3D1OovqRzVzh02_b62IHrx) **** * **flexible, maintainable, extensible** * **gang of four "design patterns: elements of reusable object oriented software"** * **23 patterns** 1. **creational (5) : object instantiation** 1. **factory method** * **composition: property referenced by another class** * **inheritance: class extends another class** 2. **abstract factory** 3. **builder : compl** **ex** 4. **prototype: clone** 5. **singleton: only one instance** 2. **structural : class relationships and hierarchies; class pattern: is; structural object patterns: has** 1. **adapter** 2. **bridge** 3. **composite** 4. **decorator** 5. **facde** 6. **flyweight** 7. **proxy** 3. [ **behavioral (12): object intercommunication :**](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Flearning-login%2Fshare%3Faccount%3D2154545%26forceAccount%3Dfalse%26redirect%3Dhttps%253A%252F%252Fwww.linkedin.com%252Flearning%252Fc-plus-plus-design-patterns-behavioral%253Ftrk%253Dshare_ent_url%2526shareId%253DrUkPrp6VQjKydR4M1TxKWw%25253D%25253D&sa=D&sntz=1&usg=AOvVaw2CJkeXiIw8fjndK6UXHgzP) 1. **chain of responsibility** * **password check** 2. **command** * **one button for all command** 3. **mediator** * **reduce dependency : married - > spouse name -> ....** 4. **observer** * **std::vector subscribers;** * **this- >subscribers.push_back(subscriber);** * **void unsubscribe(Subscriber *subscriber) override { subscribers.erase(std::remove_if(subscribers.begin(),subscribers.end(), [subscriber](Subscriber *s){{return s->getName() == subscriber->getName(); }), subscribers.end());** 5. **interpreter** * **1+(2+3)** 6. **state** * **order** * 7. **strategy** 8. **template method** 9. **visitor** 10. **iterator pattern** 11. **memento** * **undo** 12. **null-object** * **default** **UML: unified modeling language** **abstract and concrete classes** [LinkedIn](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Fin%2Fpirahansiah%2F&sa=D&sntz=1&usg=AOvVaw0ETpuSejDWH6Dz0IId5L5j): * int x=5; * size_t y=sizeof x; or sizeof(int) * printf("sizeof x is %zd\n",y*8); //change bit to byte * func(){ static int i=5; }// it will change ++ // it will be on static storage not stack * void (*pfunc)()=func; * (*pfunc)(); * #include * #include // variadic argument * double average(const int count, ...) { va_list ap; va_start(ap,count); va_arg(ap,double); va_end(ap); } * template * larger executables * confusing error messages * longer compile times * #include * perror("");// * v.begin(); v.end(); v.size(); v.back(); v[5]; v.at(5) * string: s.size(); s.length(); s.find(); * std::hex,showbase, oct, fixed, scientific,setprecision(3), floatfield,setw, setfill('-'); std::cin.getline(buf,sizeof(buf)); * #include try { ; } catch (std::exception & e) { e.what() } * class1 * o1= new(nothrow) c1[5]; if (o1==nullptr){}; delete [] o1; * if we don't want to create base class we can put constructor in the private part classname(){} * then use constructor in the protected: classname( ): _name(value) {} * using friend class nameofsubclass; to use private functions ; friend class base; * virtual : maybe overloaded and maybe write in subclass; we need ~ * #include * std::unique_ptr a(new struct()); * auto b=std::make_unique(); * a.reset(new structure()); // delete * auto c=std::move(b); // b is null * c.release(); * auto a=std::make_shared(); * auto w1=std::weak_ptr(struct>(); * * T & x => lvalie reference * T && y => rvalue reference * rule of five (if you define any of these functions you need to define all) * ~class(); * class(class &); * class(class &&); * class & operator = (class &); * class & operator = (class &&); * []()->char{} * auto fp=[](const T & n)-> T {return n*5; }; * #define MAX(a,b) (a>b ? a:b) * constexpr int ONE =1; * unit tests * * * virtual Class *clone()= 0 ; * * * * * * template * struct B { /* ... */ }; * ![](https://lh6.googleusercontent.com/NGiYCCbFAfHWT2GIFOKYsaDKexyoPpNG59E7eS1ytZqQUCfTmlf1YojdF7x6Txxnb5LpfzW2n50KRtDB20bqdqwjrL_Ra0a6wDrNn4HLEVd0nn50Xt5rGdZKbXPFBzCOlA=w1280) ![](https://lh3.googleusercontent.com/AjBiQtlDLVVB_HgX98AR8CsYJS5yefPvG59xIxRwKXrNXq2Se5AjmNNWFB73sfgRiYam- hHsNHEWhl0uEjfNMrXMbx5DI-2L3HIIGc-3xW3p2Agk_z21jBnU2jeaHb2MSg=w1280) ![lambda capture](https://lh5.googleusercontent.com/PLptpoAkCKMjfCPKvxIXC8FGQSBuX5ZBnxLMZSAFFzUwsNcEHX0haCqpmCmZipb1zmL2AFS18V1j-4kygbYun3g=w1280) Module Interface Unit : *.cppm Module Implementation Unit: *.cpp # Important commands ### Compile CUDA for Jetson Nano (JetPack 4.5, CUDA 10.2) nvcc -std=c++14 -arch=sm_62 -o main.run main.cu ### compile c++ 20; based on GCC 12, CLang 13 clang++ -std=c++2a -c helloworld.cpp -Xclang -emit-module-interface -o helloworld.pcm clang++ -std=c++2a -stdlib=libc++ -fimplicit-modules -fimplicit-module-maps -fprebuilt-module-path=. main.cpp helloworld.cpp ### commands 1. echo "export PATH=.:"$PATH"" >> ~/.bashrc 2. source ~/.bashrc * htop * ulimit -a * git submodule add (githuburl external/glfw) ### Tools brew install --HEAD LouisBrunner/valgrind/valgrind valgrind ./a.out CppCon 2016: John Lakos "Advanced Levelization Techniques (part 1 of 3) * Large Scale C++ software design * retain control of your dependency graph * keep concerns separated * make modules reusable in other contexts at minimal cost ## Links [https://en.cppreference.com/w/cpp/23](https://www.google.com/url?q=https%3A%2F%2Fen.cppreference.com%2Fw%2Fcpp%2F23&sa=D&sntz=1&usg=AOvVaw3NxKhzYIbT-p70q0v8DuXy) [https://imfing.medium.com/hands-on-modules- in-c-20-abc3cd333133](https://www.google.com/url?q=https%3A%2F%2Fimfing.medium.com%2Fhands- on-modules-in-c-20-abc3cd333133&sa=D&sntz=1&usg=AOvVaw3AWw5eka5EpTdXAVfyGqE1) [CppCon 2016: John Lakos "Advanced Levelization Techniques (part 1 of 3)](https://www.youtube.com/watch?v=QjFpKJ8Xx78) [Modern CMake](https://www.youtube.com/watch?v=eC9-iRN2b04) (CppCon 2017) [CMake Tutorial ](https://www.youtube.com/watch?v=nlKcXPUJGwA&list=PLalVdRk2RC6o5GHu618ARWh0VO0bFlif4) # appendix C++ design patterns: factory method * class c1 * { * public: * void c1_test() * { * cout << "main class" << endl; * } * * }; * class c2:public c1 * { * public: * c2() * { * cout << "c2" << endl; * } * * }; * class c3 :public c1 * { * public: * c3() * { * cout << "c3" << endl; * } * * }; * class factory * { * private: * * c1 * _c1; * public: * c1 * function_factory(int i) * { * switch (i) * { * case 1: * return new c2; * break; * case 2: * return new c3; * break; * * * } * } * * * }; * int main() * { * factory f; * c1* c; * c = f.function_factory(2); * return 1; * } * 1 * * error : Access violation writing location for clone image from std::vector and findHomography * can not use std::vector imagesF; imagesF.at(0) or imagesF[0] or imagesF[0].clone() * Mat is some kind of **smart pointer** for the pixels, so Mat a=b will have shared pixels for a and b. similar situation for push_back() * if you need a 'deep copy', use Mat::clone(): imagesF.push_back(imageMat.clone()); [https://stackoverflow.com/a/19524261/3533188](https://www.google.com/url?q=https%3A%2F%2Fstackoverflow.com%2Fa%2F19524261%2F3533188&sa=D&sntz=1&usg=AOvVaw11jUWr1dHy3ukdjM7et6le) When you are using vector to store image from OpenCV Mat you need to use deep copy because cv::Mat is like **smart pointer.** * std::vector imagesVector; * imagesVector.push_back(imageMat.clone()); * cv::Mat im_in = imagesVector[0] #OpenCV #C++ #pirahansiah Download first draft for OpenCV 5 book : [_https://docs.google.com/document/d/1v3qRJE8d0rYXrfDf_BjJHXANTKsPu2lw6In32gD88wY/edit?usp=sharing_](https://docs.google.com/document/d/1v3qRJE8d0rYXrfDf_BjJHXANTKsPu2lw6In32gD88wY/edit?usp=sharing) __ Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/ssWXVQN4wWnp524T8UjHyQEdArMpTIaM4OalIHhpfDv0Sv6HC6tLnwxjfrdaemWa002sbPUNKYrtuvBhEcn6DIQ=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/ssWXVQN4wWnp524T8UjHyQEdArMpTIaM4OalIHhpfDv0Sv6HC6tLnwxjfrdaemWa002sbPUNKYrtuvBhEcn6DIQ=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Advanced Programming with Modern C++ 23 for Image Processing My GitHub about Advanced Programming with Modern C++ 23 for Image Processing Important commands Compile CUDA for Jetson Nano (JetPack 4.5, CUDA 10.2) compile c++ 20; based on GCC 12, CLang 13 commands Tools Links appendix Update March 2022 - 1401 ## [My GitHub about Advanced Programming with Modern C++ 23 for Image Processing ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest&sa=D&sntz=1&usg=AOvVaw3nCk6-1QOY0tGcAL6U5LmN) make industrial process scale-ups successful #c++23 #optimization #imageprocessing #deeplearning #AI #IoT You need latest version of C++ compiler in order to use C++ 20 standard. GCC>12 or CLang>13\. CUDA 11 support C++17 by nvcc; Cmake. [Book: writing solid code](http://www.google.com/url?q=http%3A%2F%2Fcs.brown.edu%2Fcourses%2Fcs190%2F2008%2Fdocuments%2Frestricted%2FWriting%2520Solid%2520Code.pdf&sa=D&sntz=1&usg=AOvVaw0wE6jx0_UIoJA1DyYUPz2U) mind map call by value: on stack * void f(int a) { a++; } //a in main not change call by reference: * void f(int *p); // f(&i); * void f(int &i);// f(i) * void func(const std::string & s) { s.c_str() } // func(s); * **struct default is public (use when we have only data members) and class default is private (when also have function members) .** * **function 's signature** // int getvalue() const; * **// ! this is a very critical comment** * **//** ***** **this is a** **highlighted** **comment** * **//** **TODO:** **this is a** **TODO** **comment** * **//** **?** **this is a** **question** **comment** [ **Creational design patterns:**](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Flearning- login%2Fshare%3Faccount%3D2154545%26forceAccount%3Dfalse%26redirect%3Dhttps%253A%252F%252Fwww.linkedin.com%252Flearning%252Fc- plus-plus-design-patterns- creational%253Ftrk%253Dshare_ent_url%2526shareId%253DMkeJou%25252BkSoupfOSfTa1SsQ%25253D%25253D&sa=D&sntz=1&usg=AOvVaw3D1OovqRzVzh02_b62IHrx) **** * **flexible, maintainable, extensible** * **gang of four "design patterns: elements of reusable object oriented software"** * **23 patterns** 1. **creational (5) : object instantiation** 1. **factory method** * **composition: property referenced by another class** * **inheritance: class extends another class** 2. **abstract factory** 3. **builder : compl** **ex** 4. **prototype: clone** 5. **singleton: only one instance** 2. **structural : class relationships and hierarchies; class pattern: is; structural object patterns: has** 1. **adapter** 2. **bridge** 3. **composite** 4. **decorator** 5. **facde** 6. **flyweight** 7. **proxy** 3. [ **behavioral (12): object intercommunication :**](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Flearning-login%2Fshare%3Faccount%3D2154545%26forceAccount%3Dfalse%26redirect%3Dhttps%253A%252F%252Fwww.linkedin.com%252Flearning%252Fc-plus-plus-design-patterns-behavioral%253Ftrk%253Dshare_ent_url%2526shareId%253DrUkPrp6VQjKydR4M1TxKWw%25253D%25253D&sa=D&sntz=1&usg=AOvVaw2CJkeXiIw8fjndK6UXHgzP) 1. **chain of responsibility** * **password check** 2. **command** * **one button for all command** 3. **mediator** * **reduce dependency : married - > spouse name -> ....** 4. **observer** * **std::vector subscribers;** * **this- >subscribers.push_back(subscriber);** * **void unsubscribe(Subscriber *subscriber) override { subscribers.erase(std::remove_if(subscribers.begin(),subscribers.end(), [subscriber](Subscriber *s){{return s->getName() == subscriber->getName(); }), subscribers.end());** 5. **interpreter** * **1+(2+3)** 6. **state** * **order** * 7. **strategy** 8. **template method** 9. **visitor** 10. **iterator pattern** 11. **memento** * **undo** 12. **null-object** * **default** **UML: unified modeling language** **abstract and concrete classes** [LinkedIn](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Fin%2Fpirahansiah%2F&sa=D&sntz=1&usg=AOvVaw0ETpuSejDWH6Dz0IId5L5j): * int x=5; * size_t y=sizeof x; or sizeof(int) * printf("sizeof x is %zd\n",y*8); //change bit to byte * func(){ static int i=5; }// it will change ++ // it will be on static storage not stack * void (*pfunc)()=func; * (*pfunc)(); * #include * #include // variadic argument * double average(const int count, ...) { va_list ap; va_start(ap,count); va_arg(ap,double); va_end(ap); } * template * larger executables * confusing error messages * longer compile times * #include * perror("");// * v.begin(); v.end(); v.size(); v.back(); v[5]; v.at(5) * string: s.size(); s.length(); s.find(); * std::hex,showbase, oct, fixed, scientific,setprecision(3), floatfield,setw, setfill('-'); std::cin.getline(buf,sizeof(buf)); * #include try { ; } catch (std::exception & e) { e.what() } * class1 * o1= new(nothrow) c1[5]; if (o1==nullptr){}; delete [] o1; * if we don't want to create base class we can put constructor in the private part classname(){} * then use constructor in the protected: classname( ): _name(value) {} * using friend class nameofsubclass; to use private functions ; friend class base; * virtual : maybe overloaded and maybe write in subclass; we need ~ * #include * std::unique_ptr a(new struct()); * auto b=std::make_unique(); * a.reset(new structure()); // delete * auto c=std::move(b); // b is null * c.release(); * auto a=std::make_shared(); * auto w1=std::weak_ptr(struct>(); * * T & x => lvalie reference * T && y => rvalue reference * rule of five (if you define any of these functions you need to define all) * ~class(); * class(class &); * class(class &&); * class & operator = (class &); * class & operator = (class &&); * []()->char{} * auto fp=[](const T & n)-> T {return n*5; }; * #define MAX(a,b) (a>b ? a:b) * constexpr int ONE =1; * unit tests * * * virtual Class *clone()= 0 ; * * * * * * template * struct B { /* ... */ }; * ![](https://lh6.googleusercontent.com/NGiYCCbFAfHWT2GIFOKYsaDKexyoPpNG59E7eS1ytZqQUCfTmlf1YojdF7x6Txxnb5LpfzW2n50KRtDB20bqdqwjrL_Ra0a6wDrNn4HLEVd0nn50Xt5rGdZKbXPFBzCOlA=w1280) ![](https://lh3.googleusercontent.com/AjBiQtlDLVVB_HgX98AR8CsYJS5yefPvG59xIxRwKXrNXq2Se5AjmNNWFB73sfgRiYam- hHsNHEWhl0uEjfNMrXMbx5DI-2L3HIIGc-3xW3p2Agk_z21jBnU2jeaHb2MSg=w1280) ![lambda capture](https://lh5.googleusercontent.com/PLptpoAkCKMjfCPKvxIXC8FGQSBuX5ZBnxLMZSAFFzUwsNcEHX0haCqpmCmZipb1zmL2AFS18V1j-4kygbYun3g=w1280) Module Interface Unit : *.cppm Module Implementation Unit: *.cpp # Important commands ### Compile CUDA for Jetson Nano (JetPack 4.5, CUDA 10.2) nvcc -std=c++14 -arch=sm_62 -o main.run main.cu ### compile c++ 20; based on GCC 12, CLang 13 clang++ -std=c++2a -c helloworld.cpp -Xclang -emit-module-interface -o helloworld.pcm clang++ -std=c++2a -stdlib=libc++ -fimplicit-modules -fimplicit-module-maps -fprebuilt-module-path=. main.cpp helloworld.cpp ### commands 1. echo "export PATH=.:"$PATH"" >> ~/.bashrc 2. source ~/.bashrc * htop * ulimit -a * git submodule add (githuburl external/glfw) ### Tools brew install --HEAD LouisBrunner/valgrind/valgrind valgrind ./a.out CppCon 2016: John Lakos "Advanced Levelization Techniques (part 1 of 3) * Large Scale C++ software design * retain control of your dependency graph * keep concerns separated * make modules reusable in other contexts at minimal cost ## Links [https://en.cppreference.com/w/cpp/23](https://www.google.com/url?q=https%3A%2F%2Fen.cppreference.com%2Fw%2Fcpp%2F23&sa=D&sntz=1&usg=AOvVaw3NxKhzYIbT-p70q0v8DuXy) [https://imfing.medium.com/hands-on-modules- in-c-20-abc3cd333133](https://www.google.com/url?q=https%3A%2F%2Fimfing.medium.com%2Fhands- on-modules-in-c-20-abc3cd333133&sa=D&sntz=1&usg=AOvVaw3AWw5eka5EpTdXAVfyGqE1) [CppCon 2016: John Lakos "Advanced Levelization Techniques (part 1 of 3)](https://www.youtube.com/watch?v=QjFpKJ8Xx78) [Modern CMake](https://www.youtube.com/watch?v=eC9-iRN2b04) (CppCon 2017) [CMake Tutorial ](https://www.youtube.com/watch?v=nlKcXPUJGwA&list=PLalVdRk2RC6o5GHu618ARWh0VO0bFlif4) # appendix C++ design patterns: factory method * class c1 * { * public: * void c1_test() * { * cout << "main class" << endl; * } * * }; * class c2:public c1 * { * public: * c2() * { * cout << "c2" << endl; * } * * }; * class c3 :public c1 * { * public: * c3() * { * cout << "c3" << endl; * } * * }; * class factory * { * private: * * c1 * _c1; * public: * c1 * function_factory(int i) * { * switch (i) * { * case 1: * return new c2; * break; * case 2: * return new c3; * break; * * * } * } * * * }; * int main() * { * factory f; * c1* c; * c = f.function_factory(2); * return 1; * } * 1 * * error : Access violation writing location for clone image from std::vector and findHomography * can not use std::vector imagesF; imagesF.at(0) or imagesF[0] or imagesF[0].clone() * Mat is some kind of **smart pointer** for the pixels, so Mat a=b will have shared pixels for a and b. similar situation for push_back() * if you need a 'deep copy', use Mat::clone(): imagesF.push_back(imageMat.clone()); [https://stackoverflow.com/a/19524261/3533188](https://www.google.com/url?q=https%3A%2F%2Fstackoverflow.com%2Fa%2F19524261%2F3533188&sa=D&sntz=1&usg=AOvVaw11jUWr1dHy3ukdjM7et6le) When you are using vector to store image from OpenCV Mat you need to use deep copy because cv::Mat is like **smart pointer.** * std::vector imagesVector; * imagesVector.push_back(imageMat.clone()); * cv::Mat im_in = imagesVector[0] #OpenCV #C++ #pirahansiah Download first draft for OpenCV 5 book : [_https://docs.google.com/document/d/1v3qRJE8d0rYXrfDf_BjJHXANTKsPu2lw6In32gD88wY/edit?usp=sharing_](https://docs.google.com/document/d/1v3qRJE8d0rYXrfDf_BjJHXANTKsPu2lw6In32gD88wY/edit?usp=sharing) __ Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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GCC>12 or CLang>13\. CUDA 11 support C++17 by nvcc; Cmake. [Book: writing solid code](http://www.google.com/url?q=http%3A%2F%2Fcs.brown.edu%2Fcourses%2Fcs190%2F2008%2Fdocuments%2Frestricted%2FWriting%2520Solid%2520Code.pdf&sa=D&sntz=1&usg=AOvVaw0wE6jx0_UIoJA1DyYUPz2U) mind map call by value: on stack * void f(int a) { a++; } //a in main not change call by reference: * void f(int *p); // f(&i); * void f(int &i);// f(i) * void func(const std::string & s) { s.c_str() } // func(s); * **struct default is public (use when we have only data members) and class default is private (when also have function members) .** * **function 's signature** // int getvalue() const; * **// ! this is a very critical comment** * **//** ***** **this is a** **highlighted** **comment** * **//** **TODO:** **this is a** **TODO** **comment** * **//** **?** **this is a** **question** **comment** [ **Creational design patterns:**](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Flearning- login%2Fshare%3Faccount%3D2154545%26forceAccount%3Dfalse%26redirect%3Dhttps%253A%252F%252Fwww.linkedin.com%252Flearning%252Fc- plus-plus-design-patterns- creational%253Ftrk%253Dshare_ent_url%2526shareId%253DMkeJou%25252BkSoupfOSfTa1SsQ%25253D%25253D&sa=D&sntz=1&usg=AOvVaw3D1OovqRzVzh02_b62IHrx) **** * **flexible, maintainable, extensible** * **gang of four "design patterns: elements of reusable object oriented software"** * **23 patterns** 1. **creational (5) : object instantiation** 1. **factory method** * **composition: property referenced by another class** * **inheritance: class extends another class** 2. **abstract factory** 3. **builder : compl** **ex** 4. **prototype: clone** 5. **singleton: only one instance** 2. **structural : class relationships and hierarchies; class pattern: is; structural object patterns: has** 1. **adapter** 2. **bridge** 3. **composite** 4. **decorator** 5. **facde** 6. **flyweight** 7. **proxy** 3. [ **behavioral (12): object intercommunication :**](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Flearning-login%2Fshare%3Faccount%3D2154545%26forceAccount%3Dfalse%26redirect%3Dhttps%253A%252F%252Fwww.linkedin.com%252Flearning%252Fc-plus-plus-design-patterns-behavioral%253Ftrk%253Dshare_ent_url%2526shareId%253DrUkPrp6VQjKydR4M1TxKWw%25253D%25253D&sa=D&sntz=1&usg=AOvVaw2CJkeXiIw8fjndK6UXHgzP) 1. **chain of responsibility** * **password check** 2. **command** * **one button for all command** 3. **mediator** * **reduce dependency : married - > spouse name -> ....** 4. **observer** * **std::vector subscribers;** * **this- >subscribers.push_back(subscriber);** * **void unsubscribe(Subscriber *subscriber) override { subscribers.erase(std::remove_if(subscribers.begin(),subscribers.end(), [subscriber](Subscriber *s){{return s->getName() == subscriber->getName(); }), subscribers.end());** 5. **interpreter** * **1+(2+3)** 6. **state** * **order** * 7. **strategy** 8. **template method** 9. **visitor** 10. **iterator pattern** 11. **memento** * **undo** 12. **null-object** * **default** **UML: unified modeling language** **abstract and concrete classes** [LinkedIn](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Fin%2Fpirahansiah%2F&sa=D&sntz=1&usg=AOvVaw0ETpuSejDWH6Dz0IId5L5j): * int x=5; * size_t y=sizeof x; or sizeof(int) * printf("sizeof x is %zd\n",y*8); //change bit to byte * func(){ static int i=5; }// it will change ++ // it will be on static storage not stack * void (*pfunc)()=func; * (*pfunc)(); * #include * #include // variadic argument * double average(const int count, ...) { va_list ap; va_start(ap,count); va_arg(ap,double); va_end(ap); } * template * larger executables * confusing error messages * longer compile times * #include * perror("");// * v.begin(); v.end(); v.size(); v.back(); v[5]; v.at(5) * string: s.size(); s.length(); s.find(); * std::hex,showbase, oct, fixed, scientific,setprecision(3), floatfield,setw, setfill('-'); std::cin.getline(buf,sizeof(buf)); * #include try { ; } catch (std::exception & e) { e.what() } * class1 * o1= new(nothrow) c1[5]; if (o1==nullptr){}; delete [] o1; * if we don't want to create base class we can put constructor in the private part classname(){} * then use constructor in the protected: classname( ): _name(value) {} * using friend class nameofsubclass; to use private functions ; friend class base; * virtual : maybe overloaded and maybe write in subclass; we need ~ * #include * std::unique_ptr a(new struct()); * auto b=std::make_unique(); * a.reset(new structure()); // delete * auto c=std::move(b); // b is null * c.release(); * auto a=std::make_shared(); * auto w1=std::weak_ptr(struct>(); * * T & x => lvalie reference * T && y => rvalue reference * rule of five (if you define any of these functions you need to define all) * ~class(); * class(class &); * class(class &&); * class & operator = (class &); * class & operator = (class &&); * []()->char{} * auto fp=[](const T & n)-> T {return n*5; }; * #define MAX(a,b) (a>b ? a:b) * constexpr int ONE =1; * unit tests * * * virtual Class *clone()= 0 ; * * * * * * template * struct B { /* ... */ }; * ![](https://lh6.googleusercontent.com/NGiYCCbFAfHWT2GIFOKYsaDKexyoPpNG59E7eS1ytZqQUCfTmlf1YojdF7x6Txxnb5LpfzW2n50KRtDB20bqdqwjrL_Ra0a6wDrNn4HLEVd0nn50Xt5rGdZKbXPFBzCOlA=w1280) ![](https://lh3.googleusercontent.com/AjBiQtlDLVVB_HgX98AR8CsYJS5yefPvG59xIxRwKXrNXq2Se5AjmNNWFB73sfgRiYam- hHsNHEWhl0uEjfNMrXMbx5DI-2L3HIIGc-3xW3p2Agk_z21jBnU2jeaHb2MSg=w1280) ![lambda capture](https://lh5.googleusercontent.com/PLptpoAkCKMjfCPKvxIXC8FGQSBuX5ZBnxLMZSAFFzUwsNcEHX0haCqpmCmZipb1zmL2AFS18V1j-4kygbYun3g=w1280) Module Interface Unit : *.cppm Module Implementation Unit: *.cpp # Important commands ### Compile CUDA for Jetson Nano (JetPack 4.5, CUDA 10.2) nvcc -std=c++14 -arch=sm_62 -o main.run main.cu ### compile c++ 20; based on GCC 12, CLang 13 clang++ -std=c++2a -c helloworld.cpp -Xclang -emit-module-interface -o helloworld.pcm clang++ -std=c++2a -stdlib=libc++ -fimplicit-modules -fimplicit-module-maps -fprebuilt-module-path=. main.cpp helloworld.cpp ### commands 1. echo "export PATH=.:"$PATH"" >> ~/.bashrc 2. source ~/.bashrc * htop * ulimit -a * git submodule add (githuburl external/glfw) ### Tools brew install --HEAD LouisBrunner/valgrind/valgrind valgrind ./a.out CppCon 2016: John Lakos "Advanced Levelization Techniques (part 1 of 3) * Large Scale C++ software design * retain control of your dependency graph * keep concerns separated * make modules reusable in other contexts at minimal cost ## Links [https://en.cppreference.com/w/cpp/23](https://www.google.com/url?q=https%3A%2F%2Fen.cppreference.com%2Fw%2Fcpp%2F23&sa=D&sntz=1&usg=AOvVaw3NxKhzYIbT-p70q0v8DuXy) [https://imfing.medium.com/hands-on-modules- in-c-20-abc3cd333133](https://www.google.com/url?q=https%3A%2F%2Fimfing.medium.com%2Fhands- on-modules-in-c-20-abc3cd333133&sa=D&sntz=1&usg=AOvVaw3AWw5eka5EpTdXAVfyGqE1) [CppCon 2016: John Lakos "Advanced Levelization Techniques (part 1 of 3)](https://www.youtube.com/watch?v=QjFpKJ8Xx78) [Modern CMake](https://www.youtube.com/watch?v=eC9-iRN2b04) (CppCon 2017) [CMake Tutorial ](https://www.youtube.com/watch?v=nlKcXPUJGwA&list=PLalVdRk2RC6o5GHu618ARWh0VO0bFlif4) # appendix C++ design patterns: factory method * class c1 * { * public: * void c1_test() * { * cout << "main class" << endl; * } * * }; * class c2:public c1 * { * public: * c2() * { * cout << "c2" << endl; * } * * }; * class c3 :public c1 * { * public: * c3() * { * cout << "c3" << endl; * } * * }; * class factory * { * private: * * c1 * _c1; * public: * c1 * function_factory(int i) * { * switch (i) * { * case 1: * return new c2; * break; * case 2: * return new c3; * break; * * * } * } * * * }; * int main() * { * factory f; * c1* c; * c = f.function_factory(2); * return 1; * } * 1 * * error : Access violation writing location for clone image from std::vector and findHomography * can not use std::vector imagesF; imagesF.at(0) or imagesF[0] or imagesF[0].clone() * Mat is some kind of **smart pointer** for the pixels, so Mat a=b will have shared pixels for a and b. similar situation for push_back() * if you need a 'deep copy', use Mat::clone(): imagesF.push_back(imageMat.clone()); [https://stackoverflow.com/a/19524261/3533188](https://www.google.com/url?q=https%3A%2F%2Fstackoverflow.com%2Fa%2F19524261%2F3533188&sa=D&sntz=1&usg=AOvVaw11jUWr1dHy3ukdjM7et6le) When you are using vector to store image from OpenCV Mat you need to use deep copy because cv::Mat is like **smart pointer.** * std::vector imagesVector; * imagesVector.push_back(imageMat.clone()); * cv::Mat im_in = imagesVector[0] #OpenCV #C++ #pirahansiah Download first draft for OpenCV 5 book : [_https://docs.google.com/document/d/1v3qRJE8d0rYXrfDf_BjJHXANTKsPu2lw6In32gD88wY/edit?usp=sharing_](https://docs.google.com/document/d/1v3qRJE8d0rYXrfDf_BjJHXANTKsPu2lw6In32gD88wY/edit?usp=sharing) __ Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/ssWXVQN4wWnp524T8UjHyQEdArMpTIaM4OalIHhpfDv0Sv6HC6tLnwxjfrdaemWa002sbPUNKYrtuvBhEcn6DIQ=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/ssWXVQN4wWnp524T8UjHyQEdArMpTIaM4OalIHhpfDv0Sv6HC6tLnwxjfrdaemWa002sbPUNKYrtuvBhEcn6DIQ=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Advanced Programming with Modern C++ 23 for Image Processing My GitHub about Advanced Programming with Modern C++ 23 for Image Processing Important commands Compile CUDA for Jetson Nano (JetPack 4.5, CUDA 10.2) compile c++ 20; based on GCC 12, CLang 13 commands Tools Links appendix Update March 2022 - 1401 ## [My GitHub about Advanced Programming with Modern C++ 23 for Image Processing ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest&sa=D&sntz=1&usg=AOvVaw3nCk6-1QOY0tGcAL6U5LmN) make industrial process scale-ups successful #c++23 #optimization #imageprocessing #deeplearning #AI #IoT You need latest version of C++ compiler in order to use C++ 20 standard. GCC>12 or CLang>13\. CUDA 11 support C++17 by nvcc; Cmake. [Book: writing solid code](http://www.google.com/url?q=http%3A%2F%2Fcs.brown.edu%2Fcourses%2Fcs190%2F2008%2Fdocuments%2Frestricted%2FWriting%2520Solid%2520Code.pdf&sa=D&sntz=1&usg=AOvVaw0wE6jx0_UIoJA1DyYUPz2U) mind map call by value: on stack * void f(int a) { a++; } //a in main not change call by reference: * void f(int *p); // f(&i); * void f(int &i);// f(i) * void func(const std::string & s) { s.c_str() } // func(s); * **struct default is public (use when we have only data members) and class default is private (when also have function members) .** * **function 's signature** // int getvalue() const; * **// ! this is a very critical comment** * **//** ***** **this is a** **highlighted** **comment** * **//** **TODO:** **this is a** **TODO** **comment** * **//** **?** **this is a** **question** **comment** [ **Creational design patterns:**](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Flearning- login%2Fshare%3Faccount%3D2154545%26forceAccount%3Dfalse%26redirect%3Dhttps%253A%252F%252Fwww.linkedin.com%252Flearning%252Fc- plus-plus-design-patterns- creational%253Ftrk%253Dshare_ent_url%2526shareId%253DMkeJou%25252BkSoupfOSfTa1SsQ%25253D%25253D&sa=D&sntz=1&usg=AOvVaw3D1OovqRzVzh02_b62IHrx) **** * **flexible, maintainable, extensible** * **gang of four "design patterns: elements of reusable object oriented software"** * **23 patterns** 1. **creational (5) : object instantiation** 1. **factory method** * **composition: property referenced by another class** * **inheritance: class extends another class** 2. **abstract factory** 3. **builder : compl** **ex** 4. **prototype: clone** 5. **singleton: only one instance** 2. **structural : class relationships and hierarchies; class pattern: is; structural object patterns: has** 1. **adapter** 2. **bridge** 3. **composite** 4. **decorator** 5. **facde** 6. **flyweight** 7. **proxy** 3. [ **behavioral (12): object intercommunication :**](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Flearning-login%2Fshare%3Faccount%3D2154545%26forceAccount%3Dfalse%26redirect%3Dhttps%253A%252F%252Fwww.linkedin.com%252Flearning%252Fc-plus-plus-design-patterns-behavioral%253Ftrk%253Dshare_ent_url%2526shareId%253DrUkPrp6VQjKydR4M1TxKWw%25253D%25253D&sa=D&sntz=1&usg=AOvVaw2CJkeXiIw8fjndK6UXHgzP) 1. **chain of responsibility** * **password check** 2. **command** * **one button for all command** 3. **mediator** * **reduce dependency : married - > spouse name -> ....** 4. **observer** * **std::vector subscribers;** * **this- >subscribers.push_back(subscriber);** * **void unsubscribe(Subscriber *subscriber) override { subscribers.erase(std::remove_if(subscribers.begin(),subscribers.end(), [subscriber](Subscriber *s){{return s->getName() == subscriber->getName(); }), subscribers.end());** 5. **interpreter** * **1+(2+3)** 6. **state** * **order** * 7. **strategy** 8. **template method** 9. **visitor** 10. **iterator pattern** 11. **memento** * **undo** 12. **null-object** * **default** **UML: unified modeling language** **abstract and concrete classes** [LinkedIn](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Fin%2Fpirahansiah%2F&sa=D&sntz=1&usg=AOvVaw0ETpuSejDWH6Dz0IId5L5j): * int x=5; * size_t y=sizeof x; or sizeof(int) * printf("sizeof x is %zd\n",y*8); //change bit to byte * func(){ static int i=5; }// it will change ++ // it will be on static storage not stack * void (*pfunc)()=func; * (*pfunc)(); * #include * #include // variadic argument * double average(const int count, ...) { va_list ap; va_start(ap,count); va_arg(ap,double); va_end(ap); } * template * larger executables * confusing error messages * longer compile times * #include * perror("");// * v.begin(); v.end(); v.size(); v.back(); v[5]; v.at(5) * string: s.size(); s.length(); s.find(); * std::hex,showbase, oct, fixed, scientific,setprecision(3), floatfield,setw, setfill('-'); std::cin.getline(buf,sizeof(buf)); * #include try { ; } catch (std::exception & e) { e.what() } * class1 * o1= new(nothrow) c1[5]; if (o1==nullptr){}; delete [] o1; * if we don't want to create base class we can put constructor in the private part classname(){} * then use constructor in the protected: classname( ): _name(value) {} * using friend class nameofsubclass; to use private functions ; friend class base; * virtual : maybe overloaded and maybe write in subclass; we need ~ * #include * std::unique_ptr a(new struct()); * auto b=std::make_unique(); * a.reset(new structure()); // delete * auto c=std::move(b); // b is null * c.release(); * auto a=std::make_shared(); * auto w1=std::weak_ptr(struct>(); * * T & x => lvalie reference * T && y => rvalue reference * rule of five (if you define any of these functions you need to define all) * ~class(); * class(class &); * class(class &&); * class & operator = (class &); * class & operator = (class &&); * []()->char{} * auto fp=[](const T & n)-> T {return n*5; }; * #define MAX(a,b) (a>b ? a:b) * constexpr int ONE =1; * unit tests * * * virtual Class *clone()= 0 ; * * * * * * template * struct B { /* ... */ }; * ![](https://lh6.googleusercontent.com/NGiYCCbFAfHWT2GIFOKYsaDKexyoPpNG59E7eS1ytZqQUCfTmlf1YojdF7x6Txxnb5LpfzW2n50KRtDB20bqdqwjrL_Ra0a6wDrNn4HLEVd0nn50Xt5rGdZKbXPFBzCOlA=w1280) ![](https://lh3.googleusercontent.com/AjBiQtlDLVVB_HgX98AR8CsYJS5yefPvG59xIxRwKXrNXq2Se5AjmNNWFB73sfgRiYam- hHsNHEWhl0uEjfNMrXMbx5DI-2L3HIIGc-3xW3p2Agk_z21jBnU2jeaHb2MSg=w1280) ![lambda capture](https://lh5.googleusercontent.com/PLptpoAkCKMjfCPKvxIXC8FGQSBuX5ZBnxLMZSAFFzUwsNcEHX0haCqpmCmZipb1zmL2AFS18V1j-4kygbYun3g=w1280) Module Interface Unit : *.cppm Module Implementation Unit: *.cpp # Important commands ### Compile CUDA for Jetson Nano (JetPack 4.5, CUDA 10.2) nvcc -std=c++14 -arch=sm_62 -o main.run main.cu ### compile c++ 20; based on GCC 12, CLang 13 clang++ -std=c++2a -c helloworld.cpp -Xclang -emit-module-interface -o helloworld.pcm clang++ -std=c++2a -stdlib=libc++ -fimplicit-modules -fimplicit-module-maps -fprebuilt-module-path=. main.cpp helloworld.cpp ### commands 1. echo "export PATH=.:"$PATH"" >> ~/.bashrc 2. source ~/.bashrc * htop * ulimit -a * git submodule add (githuburl external/glfw) ### Tools brew install --HEAD LouisBrunner/valgrind/valgrind valgrind ./a.out CppCon 2016: John Lakos "Advanced Levelization Techniques (part 1 of 3) * Large Scale C++ software design * retain control of your dependency graph * keep concerns separated * make modules reusable in other contexts at minimal cost ## Links [https://en.cppreference.com/w/cpp/23](https://www.google.com/url?q=https%3A%2F%2Fen.cppreference.com%2Fw%2Fcpp%2F23&sa=D&sntz=1&usg=AOvVaw3NxKhzYIbT-p70q0v8DuXy) [https://imfing.medium.com/hands-on-modules- in-c-20-abc3cd333133](https://www.google.com/url?q=https%3A%2F%2Fimfing.medium.com%2Fhands- on-modules-in-c-20-abc3cd333133&sa=D&sntz=1&usg=AOvVaw3AWw5eka5EpTdXAVfyGqE1) [CppCon 2016: John Lakos "Advanced Levelization Techniques (part 1 of 3)](https://www.youtube.com/watch?v=QjFpKJ8Xx78) [Modern CMake](https://www.youtube.com/watch?v=eC9-iRN2b04) (CppCon 2017) [CMake Tutorial ](https://www.youtube.com/watch?v=nlKcXPUJGwA&list=PLalVdRk2RC6o5GHu618ARWh0VO0bFlif4) # appendix C++ design patterns: factory method * class c1 * { * public: * void c1_test() * { * cout << "main class" << endl; * } * * }; * class c2:public c1 * { * public: * c2() * { * cout << "c2" << endl; * } * * }; * class c3 :public c1 * { * public: * c3() * { * cout << "c3" << endl; * } * * }; * class factory * { * private: * * c1 * _c1; * public: * c1 * function_factory(int i) * { * switch (i) * { * case 1: * return new c2; * break; * case 2: * return new c3; * break; * * * } * } * * * }; * int main() * { * factory f; * c1* c; * c = f.function_factory(2); * return 1; * } * 1 * * error : Access violation writing location for clone image from std::vector and findHomography * can not use std::vector imagesF; imagesF.at(0) or imagesF[0] or imagesF[0].clone() * Mat is some kind of **smart pointer** for the pixels, so Mat a=b will have shared pixels for a and b. similar situation for push_back() * if you need a 'deep copy', use Mat::clone(): imagesF.push_back(imageMat.clone()); [https://stackoverflow.com/a/19524261/3533188](https://www.google.com/url?q=https%3A%2F%2Fstackoverflow.com%2Fa%2F19524261%2F3533188&sa=D&sntz=1&usg=AOvVaw11jUWr1dHy3ukdjM7et6le) When you are using vector to store image from OpenCV Mat you need to use deep copy because cv::Mat is like **smart pointer.** * std::vector imagesVector; * imagesVector.push_back(imageMat.clone()); * cv::Mat im_in = imagesVector[0] #OpenCV #C++ #pirahansiah Download first draft for OpenCV 5 book : [_https://docs.google.com/document/d/1v3qRJE8d0rYXrfDf_BjJHXANTKsPu2lw6In32gD88wY/edit?usp=sharing_](https://docs.google.com/document/d/1v3qRJE8d0rYXrfDf_BjJHXANTKsPu2lw6In32gD88wY/edit?usp=sharing) __ Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/ssWXVQN4wWnp524T8UjHyQEdArMpTIaM4OalIHhpfDv0Sv6HC6tLnwxjfrdaemWa002sbPUNKYrtuvBhEcn6DIQ=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/ssWXVQN4wWnp524T8UjHyQEdArMpTIaM4OalIHhpfDv0Sv6HC6tLnwxjfrdaemWa002sbPUNKYrtuvBhEcn6DIQ=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Advanced Programming with Modern C++ 23 for Image Processing My GitHub about Advanced Programming with Modern C++ 23 for Image Processing Important commands Compile CUDA for Jetson Nano (JetPack 4.5, CUDA 10.2) compile c++ 20; based on GCC 12, CLang 13 commands Tools Links appendix Update March 2022 - 1401 ## [My GitHub about Advanced Programming with Modern C++ 23 for Image Processing ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest&sa=D&sntz=1&usg=AOvVaw3nCk6-1QOY0tGcAL6U5LmN) make industrial process scale-ups successful #c++23 #optimization #imageprocessing #deeplearning #AI #IoT You need latest version of C++ compiler in order to use C++ 20 standard. GCC>12 or CLang>13\. CUDA 11 support C++17 by nvcc; Cmake. [Book: writing solid code](http://www.google.com/url?q=http%3A%2F%2Fcs.brown.edu%2Fcourses%2Fcs190%2F2008%2Fdocuments%2Frestricted%2FWriting%2520Solid%2520Code.pdf&sa=D&sntz=1&usg=AOvVaw0wE6jx0_UIoJA1DyYUPz2U) mind map call by value: on stack * void f(int a) { a++; } //a in main not change call by reference: * void f(int *p); // f(&i); * void f(int &i);// f(i) * void func(const std::string & s) { s.c_str() } // func(s); * **struct default is public (use when we have only data members) and class default is private (when also have function members) .** * **function 's signature** // int getvalue() const; * **// ! this is a very critical comment** * **//** ***** **this is a** **highlighted** **comment** * **//** **TODO:** **this is a** **TODO** **comment** * **//** **?** **this is a** **question** **comment** [ **Creational design patterns:**](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Flearning- login%2Fshare%3Faccount%3D2154545%26forceAccount%3Dfalse%26redirect%3Dhttps%253A%252F%252Fwww.linkedin.com%252Flearning%252Fc- plus-plus-design-patterns- creational%253Ftrk%253Dshare_ent_url%2526shareId%253DMkeJou%25252BkSoupfOSfTa1SsQ%25253D%25253D&sa=D&sntz=1&usg=AOvVaw3D1OovqRzVzh02_b62IHrx) **** * **flexible, maintainable, extensible** * **gang of four "design patterns: elements of reusable object oriented software"** * **23 patterns** 1. **creational (5) : object instantiation** 1. **factory method** * **composition: property referenced by another class** * **inheritance: class extends another class** 2. **abstract factory** 3. **builder : compl** **ex** 4. **prototype: clone** 5. **singleton: only one instance** 2. **structural : class relationships and hierarchies; class pattern: is; structural object patterns: has** 1. **adapter** 2. **bridge** 3. **composite** 4. **decorator** 5. **facde** 6. **flyweight** 7. **proxy** 3. [ **behavioral (12): object intercommunication :**](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Flearning-login%2Fshare%3Faccount%3D2154545%26forceAccount%3Dfalse%26redirect%3Dhttps%253A%252F%252Fwww.linkedin.com%252Flearning%252Fc-plus-plus-design-patterns-behavioral%253Ftrk%253Dshare_ent_url%2526shareId%253DrUkPrp6VQjKydR4M1TxKWw%25253D%25253D&sa=D&sntz=1&usg=AOvVaw2CJkeXiIw8fjndK6UXHgzP) 1. **chain of responsibility** * **password check** 2. **command** * **one button for all command** 3. **mediator** * **reduce dependency : married - > spouse name -> ....** 4. **observer** * **std::vector subscribers;** * **this- >subscribers.push_back(subscriber);** * **void unsubscribe(Subscriber *subscriber) override { subscribers.erase(std::remove_if(subscribers.begin(),subscribers.end(), [subscriber](Subscriber *s){{return s->getName() == subscriber->getName(); }), subscribers.end());** 5. **interpreter** * **1+(2+3)** 6. **state** * **order** * 7. **strategy** 8. **template method** 9. **visitor** 10. **iterator pattern** 11. **memento** * **undo** 12. **null-object** * **default** **UML: unified modeling language** **abstract and concrete classes** [LinkedIn](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Fin%2Fpirahansiah%2F&sa=D&sntz=1&usg=AOvVaw0ETpuSejDWH6Dz0IId5L5j): * int x=5; * size_t y=sizeof x; or sizeof(int) * printf("sizeof x is %zd\n",y*8); //change bit to byte * func(){ static int i=5; }// it will change ++ // it will be on static storage not stack * void (*pfunc)()=func; * (*pfunc)(); * #include * #include // variadic argument * double average(const int count, ...) { va_list ap; va_start(ap,count); va_arg(ap,double); va_end(ap); } * template * larger executables * confusing error messages * longer compile times * #include * perror("");// * v.begin(); v.end(); v.size(); v.back(); v[5]; v.at(5) * string: s.size(); s.length(); s.find(); * std::hex,showbase, oct, fixed, scientific,setprecision(3), floatfield,setw, setfill('-'); std::cin.getline(buf,sizeof(buf)); * #include try { ; } catch (std::exception & e) { e.what() } * class1 * o1= new(nothrow) c1[5]; if (o1==nullptr){}; delete [] o1; * if we don't want to create base class we can put constructor in the private part classname(){} * then use constructor in the protected: classname( ): _name(value) {} * using friend class nameofsubclass; to use private functions ; friend class base; * virtual : maybe overloaded and maybe write in subclass; we need ~ * #include * std::unique_ptr a(new struct()); * auto b=std::make_unique(); * a.reset(new structure()); // delete * auto c=std::move(b); // b is null * c.release(); * auto a=std::make_shared(); * auto w1=std::weak_ptr(struct>(); * * T & x => lvalie reference * T && y => rvalue reference * rule of five (if you define any of these functions you need to define all) * ~class(); * class(class &); * class(class &&); * class & operator = (class &); * class & operator = (class &&); * []()->char{} * auto fp=[](const T & n)-> T {return n*5; }; * #define MAX(a,b) (a>b ? a:b) * constexpr int ONE =1; * unit tests * * * virtual Class *clone()= 0 ; * * * * * * template * struct B { /* ... */ }; * ![](https://lh6.googleusercontent.com/NGiYCCbFAfHWT2GIFOKYsaDKexyoPpNG59E7eS1ytZqQUCfTmlf1YojdF7x6Txxnb5LpfzW2n50KRtDB20bqdqwjrL_Ra0a6wDrNn4HLEVd0nn50Xt5rGdZKbXPFBzCOlA=w1280) ![](https://lh3.googleusercontent.com/AjBiQtlDLVVB_HgX98AR8CsYJS5yefPvG59xIxRwKXrNXq2Se5AjmNNWFB73sfgRiYam- hHsNHEWhl0uEjfNMrXMbx5DI-2L3HIIGc-3xW3p2Agk_z21jBnU2jeaHb2MSg=w1280) ![lambda capture](https://lh5.googleusercontent.com/PLptpoAkCKMjfCPKvxIXC8FGQSBuX5ZBnxLMZSAFFzUwsNcEHX0haCqpmCmZipb1zmL2AFS18V1j-4kygbYun3g=w1280) Module Interface Unit : *.cppm Module Implementation Unit: *.cpp # Important commands ### Compile CUDA for Jetson Nano (JetPack 4.5, CUDA 10.2) nvcc -std=c++14 -arch=sm_62 -o main.run main.cu ### compile c++ 20; based on GCC 12, CLang 13 clang++ -std=c++2a -c helloworld.cpp -Xclang -emit-module-interface -o helloworld.pcm clang++ -std=c++2a -stdlib=libc++ -fimplicit-modules -fimplicit-module-maps -fprebuilt-module-path=. main.cpp helloworld.cpp ### commands 1. echo "export PATH=.:"$PATH"" >> ~/.bashrc 2. source ~/.bashrc * htop * ulimit -a * git submodule add (githuburl external/glfw) ### Tools brew install --HEAD LouisBrunner/valgrind/valgrind valgrind ./a.out CppCon 2016: John Lakos "Advanced Levelization Techniques (part 1 of 3) * Large Scale C++ software design * retain control of your dependency graph * keep concerns separated * make modules reusable in other contexts at minimal cost ## Links [https://en.cppreference.com/w/cpp/23](https://www.google.com/url?q=https%3A%2F%2Fen.cppreference.com%2Fw%2Fcpp%2F23&sa=D&sntz=1&usg=AOvVaw3NxKhzYIbT-p70q0v8DuXy) [https://imfing.medium.com/hands-on-modules- in-c-20-abc3cd333133](https://www.google.com/url?q=https%3A%2F%2Fimfing.medium.com%2Fhands- on-modules-in-c-20-abc3cd333133&sa=D&sntz=1&usg=AOvVaw3AWw5eka5EpTdXAVfyGqE1) [CppCon 2016: John Lakos "Advanced Levelization Techniques (part 1 of 3)](https://www.youtube.com/watch?v=QjFpKJ8Xx78) [Modern CMake](https://www.youtube.com/watch?v=eC9-iRN2b04) (CppCon 2017) [CMake Tutorial ](https://www.youtube.com/watch?v=nlKcXPUJGwA&list=PLalVdRk2RC6o5GHu618ARWh0VO0bFlif4) # appendix C++ design patterns: factory method * class c1 * { * public: * void c1_test() * { * cout << "main class" << endl; * } * * }; * class c2:public c1 * { * public: * c2() * { * cout << "c2" << endl; * } * * }; * class c3 :public c1 * { * public: * c3() * { * cout << "c3" << endl; * } * * }; * class factory * { * private: * * c1 * _c1; * public: * c1 * function_factory(int i) * { * switch (i) * { * case 1: * return new c2; * break; * case 2: * return new c3; * break; * * * } * } * * * }; * int main() * { * factory f; * c1* c; * c = f.function_factory(2); * return 1; * } * 1 * * error : Access violation writing location for clone image from std::vector and findHomography * can not use std::vector imagesF; imagesF.at(0) or imagesF[0] or imagesF[0].clone() * Mat is some kind of **smart pointer** for the pixels, so Mat a=b will have shared pixels for a and b. similar situation for push_back() * if you need a 'deep copy', use Mat::clone(): imagesF.push_back(imageMat.clone()); [https://stackoverflow.com/a/19524261/3533188](https://www.google.com/url?q=https%3A%2F%2Fstackoverflow.com%2Fa%2F19524261%2F3533188&sa=D&sntz=1&usg=AOvVaw11jUWr1dHy3ukdjM7et6le) When you are using vector to store image from OpenCV Mat you need to use deep copy because cv::Mat is like **smart pointer.** * std::vector imagesVector; * imagesVector.push_back(imageMat.clone()); * cv::Mat im_in = imagesVector[0] #OpenCV #C++ #pirahansiah Download first draft for OpenCV 5 book : [_https://docs.google.com/document/d/1v3qRJE8d0rYXrfDf_BjJHXANTKsPu2lw6In32gD88wY/edit?usp=sharing_](https://docs.google.com/document/d/1v3qRJE8d0rYXrfDf_BjJHXANTKsPu2lw6In32gD88wY/edit?usp=sharing) __ Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/ssWXVQN4wWnp524T8UjHyQEdArMpTIaM4OalIHhpfDv0Sv6HC6tLnwxjfrdaemWa002sbPUNKYrtuvBhEcn6DIQ=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/ssWXVQN4wWnp524T8UjHyQEdArMpTIaM4OalIHhpfDv0Sv6HC6tLnwxjfrdaemWa002sbPUNKYrtuvBhEcn6DIQ=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Advanced Programming with Modern C++ 23 for Image Processing My GitHub about Advanced Programming with Modern C++ 23 for Image Processing Important commands Compile CUDA for Jetson Nano (JetPack 4.5, CUDA 10.2) compile c++ 20; based on GCC 12, CLang 13 commands Tools Links appendix Update March 2022 - 1401 ## [My GitHub about Advanced Programming with Modern C++ 23 for Image Processing ](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest&sa=D&sntz=1&usg=AOvVaw3nCk6-1QOY0tGcAL6U5LmN) make industrial process scale-ups successful #c++23 #optimization #imageprocessing #deeplearning #AI #IoT You need latest version of C++ compiler in order to use C++ 20 standard. GCC>12 or CLang>13\. CUDA 11 support C++17 by nvcc; Cmake. [Book: writing solid code](http://www.google.com/url?q=http%3A%2F%2Fcs.brown.edu%2Fcourses%2Fcs190%2F2008%2Fdocuments%2Frestricted%2FWriting%2520Solid%2520Code.pdf&sa=D&sntz=1&usg=AOvVaw0wE6jx0_UIoJA1DyYUPz2U) mind map call by value: on stack * void f(int a) { a++; } //a in main not change call by reference: * void f(int *p); // f(&i); * void f(int &i);// f(i) * void func(const std::string & s) { s.c_str() } // func(s); * **struct default is public (use when we have only data members) and class default is private (when also have function members) .** * **function 's signature** // int getvalue() const; * **// ! this is a very critical comment** * **//** ***** **this is a** **highlighted** **comment** * **//** **TODO:** **this is a** **TODO** **comment** * **//** **?** **this is a** **question** **comment** [ **Creational design patterns:**](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Flearning- login%2Fshare%3Faccount%3D2154545%26forceAccount%3Dfalse%26redirect%3Dhttps%253A%252F%252Fwww.linkedin.com%252Flearning%252Fc- plus-plus-design-patterns- creational%253Ftrk%253Dshare_ent_url%2526shareId%253DMkeJou%25252BkSoupfOSfTa1SsQ%25253D%25253D&sa=D&sntz=1&usg=AOvVaw3D1OovqRzVzh02_b62IHrx) **** * **flexible, maintainable, extensible** * **gang of four "design patterns: elements of reusable object oriented software"** * **23 patterns** 1. **creational (5) : object instantiation** 1. **factory method** * **composition: property referenced by another class** * **inheritance: class extends another class** 2. **abstract factory** 3. **builder : compl** **ex** 4. **prototype: clone** 5. **singleton: only one instance** 2. **structural : class relationships and hierarchies; class pattern: is; structural object patterns: has** 1. **adapter** 2. **bridge** 3. **composite** 4. **decorator** 5. **facde** 6. **flyweight** 7. **proxy** 3. [ **behavioral (12): object intercommunication :**](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Flearning-login%2Fshare%3Faccount%3D2154545%26forceAccount%3Dfalse%26redirect%3Dhttps%253A%252F%252Fwww.linkedin.com%252Flearning%252Fc-plus-plus-design-patterns-behavioral%253Ftrk%253Dshare_ent_url%2526shareId%253DrUkPrp6VQjKydR4M1TxKWw%25253D%25253D&sa=D&sntz=1&usg=AOvVaw2CJkeXiIw8fjndK6UXHgzP) 1. **chain of responsibility** * **password check** 2. **command** * **one button for all command** 3. **mediator** * **reduce dependency : married - > spouse name -> ....** 4. **observer** * **std::vector subscribers;** * **this- >subscribers.push_back(subscriber);** * **void unsubscribe(Subscriber *subscriber) override { subscribers.erase(std::remove_if(subscribers.begin(),subscribers.end(), [subscriber](Subscriber *s){{return s->getName() == subscriber->getName(); }), subscribers.end());** 5. **interpreter** * **1+(2+3)** 6. **state** * **order** * 7. **strategy** 8. **template method** 9. **visitor** 10. **iterator pattern** 11. **memento** * **undo** 12. **null-object** * **default** **UML: unified modeling language** **abstract and concrete classes** [LinkedIn](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Fin%2Fpirahansiah%2F&sa=D&sntz=1&usg=AOvVaw0ETpuSejDWH6Dz0IId5L5j): * int x=5; * size_t y=sizeof x; or sizeof(int) * printf("sizeof x is %zd\n",y*8); //change bit to byte * func(){ static int i=5; }// it will change ++ // it will be on static storage not stack * void (*pfunc)()=func; * (*pfunc)(); * #include * #include // variadic argument * double average(const int count, ...) { va_list ap; va_start(ap,count); va_arg(ap,double); va_end(ap); } * template * larger executables * confusing error messages * longer compile times * #include * perror("");// * v.begin(); v.end(); v.size(); v.back(); v[5]; v.at(5) * string: s.size(); s.length(); s.find(); * std::hex,showbase, oct, fixed, scientific,setprecision(3), floatfield,setw, setfill('-'); std::cin.getline(buf,sizeof(buf)); * #include try { ; } catch (std::exception & e) { e.what() } * class1 * o1= new(nothrow) c1[5]; if (o1==nullptr){}; delete [] o1; * if we don't want to create base class we can put constructor in the private part classname(){} * then use constructor in the protected: classname( ): _name(value) {} * using friend class nameofsubclass; to use private functions ; friend class base; * virtual : maybe overloaded and maybe write in subclass; we need ~ * #include * std::unique_ptr a(new struct()); * auto b=std::make_unique(); * a.reset(new structure()); // delete * auto c=std::move(b); // b is null * c.release(); * auto a=std::make_shared(); * auto w1=std::weak_ptr(struct>(); * * T & x => lvalie reference * T && y => rvalue reference * rule of five (if you define any of these functions you need to define all) * ~class(); * class(class &); * class(class &&); * class & operator = (class &); * class & operator = (class &&); * []()->char{} * auto fp=[](const T & n)-> T {return n*5; }; * #define MAX(a,b) (a>b ? a:b) * constexpr int ONE =1; * unit tests * * * virtual Class *clone()= 0 ; * * * * * * template * struct B { /* ... */ }; * ![](https://lh6.googleusercontent.com/NGiYCCbFAfHWT2GIFOKYsaDKexyoPpNG59E7eS1ytZqQUCfTmlf1YojdF7x6Txxnb5LpfzW2n50KRtDB20bqdqwjrL_Ra0a6wDrNn4HLEVd0nn50Xt5rGdZKbXPFBzCOlA=w1280) ![](https://lh3.googleusercontent.com/AjBiQtlDLVVB_HgX98AR8CsYJS5yefPvG59xIxRwKXrNXq2Se5AjmNNWFB73sfgRiYam- hHsNHEWhl0uEjfNMrXMbx5DI-2L3HIIGc-3xW3p2Agk_z21jBnU2jeaHb2MSg=w1280) ![lambda capture](https://lh5.googleusercontent.com/PLptpoAkCKMjfCPKvxIXC8FGQSBuX5ZBnxLMZSAFFzUwsNcEHX0haCqpmCmZipb1zmL2AFS18V1j-4kygbYun3g=w1280) Module Interface Unit : *.cppm Module Implementation Unit: *.cpp # Important commands ### Compile CUDA for Jetson Nano (JetPack 4.5, CUDA 10.2) nvcc -std=c++14 -arch=sm_62 -o main.run main.cu ### compile c++ 20; based on GCC 12, CLang 13 clang++ -std=c++2a -c helloworld.cpp -Xclang -emit-module-interface -o helloworld.pcm clang++ -std=c++2a -stdlib=libc++ -fimplicit-modules -fimplicit-module-maps -fprebuilt-module-path=. main.cpp helloworld.cpp ### commands 1. echo "export PATH=.:"$PATH"" >> ~/.bashrc 2. source ~/.bashrc * htop * ulimit -a * git submodule add (githuburl external/glfw) ### Tools brew install --HEAD LouisBrunner/valgrind/valgrind valgrind ./a.out CppCon 2016: John Lakos "Advanced Levelization Techniques (part 1 of 3) * Large Scale C++ software design * retain control of your dependency graph * keep concerns separated * make modules reusable in other contexts at minimal cost ## Links [https://en.cppreference.com/w/cpp/23](https://www.google.com/url?q=https%3A%2F%2Fen.cppreference.com%2Fw%2Fcpp%2F23&sa=D&sntz=1&usg=AOvVaw3NxKhzYIbT-p70q0v8DuXy) [https://imfing.medium.com/hands-on-modules- in-c-20-abc3cd333133](https://www.google.com/url?q=https%3A%2F%2Fimfing.medium.com%2Fhands- on-modules-in-c-20-abc3cd333133&sa=D&sntz=1&usg=AOvVaw3AWw5eka5EpTdXAVfyGqE1) [CppCon 2016: John Lakos "Advanced Levelization Techniques (part 1 of 3)](https://www.youtube.com/watch?v=QjFpKJ8Xx78) [Modern CMake](https://www.youtube.com/watch?v=eC9-iRN2b04) (CppCon 2017) [CMake Tutorial ](https://www.youtube.com/watch?v=nlKcXPUJGwA&list=PLalVdRk2RC6o5GHu618ARWh0VO0bFlif4) # appendix C++ design patterns: factory method * class c1 * { * public: * void c1_test() * { * cout << "main class" << endl; * } * * }; * class c2:public c1 * { * public: * c2() * { * cout << "c2" << endl; * } * * }; * class c3 :public c1 * { * public: * c3() * { * cout << "c3" << endl; * } * * }; * class factory * { * private: * * c1 * _c1; * public: * c1 * function_factory(int i) * { * switch (i) * { * case 1: * return new c2; * break; * case 2: * return new c3; * break; * * * } * } * * * }; * int main() * { * factory f; * c1* c; * c = f.function_factory(2); * return 1; * } * 1 * * error : Access violation writing location for clone image from std::vector and findHomography * can not use std::vector imagesF; imagesF.at(0) or imagesF[0] or imagesF[0].clone() * Mat is some kind of **smart pointer** for the pixels, so Mat a=b will have shared pixels for a and b. similar situation for push_back() * if you need a 'deep copy', use Mat::clone(): imagesF.push_back(imageMat.clone()); [https://stackoverflow.com/a/19524261/3533188](https://www.google.com/url?q=https%3A%2F%2Fstackoverflow.com%2Fa%2F19524261%2F3533188&sa=D&sntz=1&usg=AOvVaw11jUWr1dHy3ukdjM7et6le) When you are using vector to store image from OpenCV Mat you need to use deep copy because cv::Mat is like **smart pointer.** * std::vector imagesVector; * imagesVector.push_back(imageMat.clone()); * cv::Mat im_in = imagesVector[0] #OpenCV #C++ #pirahansiah Download first draft for OpenCV 5 book : [_https://docs.google.com/document/d/1v3qRJE8d0rYXrfDf_BjJHXANTKsPu2lw6In32gD88wY/edit?usp=sharing_](https://docs.google.com/document/d/1v3qRJE8d0rYXrfDf_BjJHXANTKsPu2lw6In32gD88wY/edit?usp=sharing) __ Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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GCC>12 or CLang>13\. CUDA 11 support C++17 by nvcc; Cmake. [Book: writing solid code](http://www.google.com/url?q=http%3A%2F%2Fcs.brown.edu%2Fcourses%2Fcs190%2F2008%2Fdocuments%2Frestricted%2FWriting%2520Solid%2520Code.pdf&sa=D&sntz=1&usg=AOvVaw0wE6jx0_UIoJA1DyYUPz2U) mind map call by value: on stack * void f(int a) { a++; } //a in main not change call by reference: * void f(int *p); // f(&i); * void f(int &i);// f(i) * void func(const std::string & s) { s.c_str() } // func(s); * **struct default is public (use when we have only data members) and class default is private (when also have function members) .** * **function 's signature** // int getvalue() const; * **// ! this is a very critical comment** * **//** ***** **this is a** **highlighted** **comment** * **//** **TODO:** **this is a** **TODO** **comment** * **//** **?** **this is a** **question** **comment** [ **Creational design patterns:**](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Flearning- login%2Fshare%3Faccount%3D2154545%26forceAccount%3Dfalse%26redirect%3Dhttps%253A%252F%252Fwww.linkedin.com%252Flearning%252Fc- plus-plus-design-patterns- creational%253Ftrk%253Dshare_ent_url%2526shareId%253DMkeJou%25252BkSoupfOSfTa1SsQ%25253D%25253D&sa=D&sntz=1&usg=AOvVaw3D1OovqRzVzh02_b62IHrx) **** * **flexible, maintainable, extensible** * **gang of four "design patterns: elements of reusable object oriented software"** * **23 patterns** 1. **creational (5) : object instantiation** 1. **factory method** * **composition: property referenced by another class** * **inheritance: class extends another class** 2. **abstract factory** 3. **builder : compl** **ex** 4. **prototype: clone** 5. **singleton: only one instance** 2. **structural : class relationships and hierarchies; class pattern: is; structural object patterns: has** 1. **adapter** 2. **bridge** 3. **composite** 4. **decorator** 5. **facde** 6. **flyweight** 7. **proxy** 3. [ **behavioral (12): object intercommunication :**](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Flearning-login%2Fshare%3Faccount%3D2154545%26forceAccount%3Dfalse%26redirect%3Dhttps%253A%252F%252Fwww.linkedin.com%252Flearning%252Fc-plus-plus-design-patterns-behavioral%253Ftrk%253Dshare_ent_url%2526shareId%253DrUkPrp6VQjKydR4M1TxKWw%25253D%25253D&sa=D&sntz=1&usg=AOvVaw2CJkeXiIw8fjndK6UXHgzP) 1. **chain of responsibility** * **password check** 2. **command** * **one button for all command** 3. **mediator** * **reduce dependency : married - > spouse name -> ....** 4. **observer** * **std::vector subscribers;** * **this- >subscribers.push_back(subscriber);** * **void unsubscribe(Subscriber *subscriber) override { subscribers.erase(std::remove_if(subscribers.begin(),subscribers.end(), [subscriber](Subscriber *s){{return s->getName() == subscriber->getName(); }), subscribers.end());** 5. **interpreter** * **1+(2+3)** 6. **state** * **order** * 7. **strategy** 8. **template method** 9. **visitor** 10. **iterator pattern** 11. **memento** * **undo** 12. **null-object** * **default** **UML: unified modeling language** **abstract and concrete classes** [LinkedIn](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Fin%2Fpirahansiah%2F&sa=D&sntz=1&usg=AOvVaw0ETpuSejDWH6Dz0IId5L5j): * int x=5; * size_t y=sizeof x; or sizeof(int) * printf("sizeof x is %zd\n",y*8); //change bit to byte * func(){ static int i=5; }// it will change ++ // it will be on static storage not stack * void (*pfunc)()=func; * (*pfunc)(); * #include * #include // variadic argument * double average(const int count, ...) { va_list ap; va_start(ap,count); va_arg(ap,double); va_end(ap); } * template * larger executables * confusing error messages * longer compile times * #include * perror("");// * v.begin(); v.end(); v.size(); v.back(); v[5]; v.at(5) * string: s.size(); s.length(); s.find(); * std::hex,showbase, oct, fixed, scientific,setprecision(3), floatfield,setw, setfill('-'); std::cin.getline(buf,sizeof(buf)); * #include try { ; } catch (std::exception & e) { e.what() } * class1 * o1= new(nothrow) c1[5]; if (o1==nullptr){}; delete [] o1; * if we don't want to create base class we can put constructor in the private part classname(){} * then use constructor in the protected: classname( ): _name(value) {} * using friend class nameofsubclass; to use private functions ; friend class base; * virtual : maybe overloaded and maybe write in subclass; we need ~ * #include * std::unique_ptr a(new struct()); * auto b=std::make_unique(); * a.reset(new structure()); // delete * auto c=std::move(b); // b is null * c.release(); * auto a=std::make_shared(); * auto w1=std::weak_ptr(struct>(); * * T & x => lvalie reference * T && y => rvalue reference * rule of five (if you define any of these functions you need to define all) * ~class(); * class(class &); * class(class &&); * class & operator = (class &); * class & operator = (class &&); * []()->char{} * auto fp=[](const T & n)-> T {return n*5; }; * #define MAX(a,b) (a>b ? a:b) * constexpr int ONE =1; * unit tests * * * virtual Class *clone()= 0 ; * * * * * * template * struct B { /* ... */ }; * ![](https://lh4.googleusercontent.com/1AlGMJjGVawP1QDY9VM8wnlECSczIbuWQvx-1Pwo6lknWXGSyMauJpstYgQSbUeS8JO0tsn_iNwryxGC_1_Iozq- SBKRSuvb_wv5lUE9Va0iRfMn20CO7qHiIM6Gg9ZkTw=w1280) ![](https://lh6.googleusercontent.com/7MouEMbb6sCVA9_PRoiI2vu2uayLUsXtk5zi6gBRRlgLV4CCkCnunfzmYKp4iywvXU_DQ0w-0c76yqFkU- oxqfOq9koowCvK1a_P46ydR45ocBuqjZxyMKBRyQuc6RFXWA=w1280) ![lambda capture](https://lh4.googleusercontent.com/LVzEn7w3pTDsLq5EHtZIxbMxf3-D-1wyjURrAFRRYNwfGkKd8zpBVYJ9PfmrSsoh0gTeaSjGBWOhI074_sFzCDI=w1280) Module Interface Unit : *.cppm Module Implementation Unit: *.cpp # Important commands ### Compile CUDA for Jetson Nano (JetPack 4.5, CUDA 10.2) nvcc -std=c++14 -arch=sm_62 -o main.run main.cu ### compile c++ 20; based on GCC 12, CLang 13 clang++ -std=c++2a -c helloworld.cpp -Xclang -emit-module-interface -o helloworld.pcm clang++ -std=c++2a -stdlib=libc++ -fimplicit-modules -fimplicit-module-maps -fprebuilt-module-path=. main.cpp helloworld.cpp ### commands 1. echo "export PATH=.:"$PATH"" >> ~/.bashrc 2. source ~/.bashrc * htop * ulimit -a * git submodule add (githuburl external/glfw) ### Tools brew install --HEAD LouisBrunner/valgrind/valgrind valgrind ./a.out CppCon 2016: John Lakos "Advanced Levelization Techniques (part 1 of 3) * Large Scale C++ software design * retain control of your dependency graph * keep concerns separated * make modules reusable in other contexts at minimal cost ## Links [https://en.cppreference.com/w/cpp/23](https://www.google.com/url?q=https%3A%2F%2Fen.cppreference.com%2Fw%2Fcpp%2F23&sa=D&sntz=1&usg=AOvVaw3NxKhzYIbT-p70q0v8DuXy) [https://imfing.medium.com/hands-on-modules- in-c-20-abc3cd333133](https://www.google.com/url?q=https%3A%2F%2Fimfing.medium.com%2Fhands- on-modules-in-c-20-abc3cd333133&sa=D&sntz=1&usg=AOvVaw3AWw5eka5EpTdXAVfyGqE1) [CppCon 2016: John Lakos "Advanced Levelization Techniques (part 1 of 3)](https://www.youtube.com/watch?v=QjFpKJ8Xx78) [Modern CMake](https://www.youtube.com/watch?v=eC9-iRN2b04) (CppCon 2017) [CMake Tutorial ](https://www.youtube.com/watch?v=nlKcXPUJGwA&list=PLalVdRk2RC6o5GHu618ARWh0VO0bFlif4) # appendix C++ design patterns: factory method * class c1 * { * public: * void c1_test() * { * cout << "main class" << endl; * } * * }; * class c2:public c1 * { * public: * c2() * { * cout << "c2" << endl; * } * * }; * class c3 :public c1 * { * public: * c3() * { * cout << "c3" << endl; * } * * }; * class factory * { * private: * * c1 * _c1; * public: * c1 * function_factory(int i) * { * switch (i) * { * case 1: * return new c2; * break; * case 2: * return new c3; * break; * * * } * } * * * }; * int main() * { * factory f; * c1* c; * c = f.function_factory(2); * return 1; * } * 1 * * error : Access violation writing location for clone image from std::vector and findHomography * can not use std::vector imagesF; imagesF.at(0) or imagesF[0] or imagesF[0].clone() * Mat is some kind of **smart pointer** for the pixels, so Mat a=b will have shared pixels for a and b. similar situation for push_back() * if you need a 'deep copy', use Mat::clone(): imagesF.push_back(imageMat.clone()); [https://stackoverflow.com/a/19524261/3533188](https://www.google.com/url?q=https%3A%2F%2Fstackoverflow.com%2Fa%2F19524261%2F3533188&sa=D&sntz=1&usg=AOvVaw11jUWr1dHy3ukdjM7et6le) When you are using vector to store image from OpenCV Mat you need to use deep copy because cv::Mat is like **smart pointer.** * std::vector imagesVector; * imagesVector.push_back(imageMat.clone()); * cv::Mat im_in = imagesVector[0] #OpenCV #C++ #pirahansiah Download first draft for OpenCV 5 book : [_https://docs.google.com/document/d/1v3qRJE8d0rYXrfDf_BjJHXANTKsPu2lw6In32gD88wY/edit?usp=sharing_](https://docs.google.com/document/d/1v3qRJE8d0rYXrfDf_BjJHXANTKsPu2lw6In32gD88wY/edit?usp=sharing) __ Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[https://www.pirahansiah.com/about/fqa](https://www.pirahansiah.com/about/fqa) # Open Source Projects ## OpenCV NuGet [https://www.nuget.org/profiles/Farshid_Pirahansiah](https://www.nuget.org/profiles/Farshid_Pirahansiah) NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022 [https://www.pirahansiah.com/topics/opencv](https://www.pirahansiah.com/topics/opencv) Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static OpenCV library for visual studio 2022 by using NuGet package manager just in a few minutes [https://www.youtube.com/watch?v=AEqZO_fZHZ8](https://www.youtube.com/watch?v=AEqZO_fZHZ8) #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, download source code (GitHub):[ https://github.com/pirahansiah/](https://github.com/pirahansiah/opencv-cpp) #OpenCV #Farshid_PirahanSiah #pirahansiah My NuGet packages comprised of two versions for different VS versions. * Visual Studio 2019 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet) * Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7 * Visual Studio 2022 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet) * Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7 more:[ https://www.pirahansiah.com/topics/opencv](https://www.pirahansiah.com/topics/opencv) * cvTest * * * cvtest: Computer Vision Test: Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning * Do you want to test your output of computer vision application which is video or images? * Standard test for computer vision application * There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. * Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? * [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://github.com/pirahansiah/cvtest/blob/main/README.md) * Multi-Class Multi-object Video Tracking * computer vision with deep learning in IoT devices * Multi Camera (Stereo Vision) Calibration for AR/VR headset (extended reality/mixed reality) 3D Image Processing with Deep Learning * End to End solution for computer vision applications in industry (cloud and IoT) * Download all mind map sources * [https://github.com/pirahansiah/pirahansiah.github.io](https://github.com/pirahansiah/pirahansiah.github.io) ## LinkedIn: (around 12K members) [Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) The Computer Vision LinkedIn group: reached to around 8000 members. This group is a wonderful place for support if you have a question, need inspiration, encouragement, and cutting edge research. Computer Vision, Deep Learning, extended reality; Metaverse; Deep Reinforcement Learning, GANs, OpenCV, TensorFlow, PyTorch. [https://www.linkedin.com/groups/10320678/](https://www.linkedin.com/groups/10320678/) ## Facebook Group: (around 14K members) Deep Reinforcement Learning, Computer Vision with Deep Learning, IoT, Robot [https://www.facebook.com/groups/185926728115336](https://www.facebook.com/groups/185926728115336) We help scale and build artificially intelligent driven start-ups with Al Researchers & Engineers! [Computer Vision] (Berlin, Germany) [Please use calendly appointment slots](https://calendly.com/pirahansiah) press . in github and open web visual studio code My LaTex Papers [https://www.overleaf.com/read/cmvgxfqxfdqm](https://www.overleaf.com/read/cmvgxfqxfdqm) This site is provided to everyone for free, however if you would like to say thanks or help support continued R&D, Mind Map, development and etc. , consider getting me a coffee. It keeps my work going. [](https://docs.google.com/forms/d/e/1FAIpQLSdiQprY0yS25LBVixQnsjkoUTjOtzx1oJye1C77At4Ur2oqTg/viewform "Open Google Forms, Contact Information in new window") ![](https://lh6.googleusercontent.com/KhHLNKbzb8yibpBYuAtKK5sVcc6x_K_aqsryMuKNVHetpz5ZM_te0K84x3yBFEgp1i4xdtfZ1ooKPkTxqXmllKfIJt7oYEx_qQuGr4P1-ibIdnLFddaIV61OH3S4FbKDwg=w1280) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[https://www.pirahansiah.com/about/fqa](https://www.pirahansiah.com/about/fqa) # Open Source Projects ## OpenCV NuGet [https://www.nuget.org/profiles/Farshid_Pirahansiah](https://www.nuget.org/profiles/Farshid_Pirahansiah) NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022 [https://www.pirahansiah.com/topics/opencv](https://www.pirahansiah.com/topics/opencv) Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static OpenCV library for visual studio 2022 by using NuGet package manager just in a few minutes [https://www.youtube.com/watch?v=AEqZO_fZHZ8](https://www.youtube.com/watch?v=AEqZO_fZHZ8) #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, download source code (GitHub):[ https://github.com/pirahansiah/](https://github.com/pirahansiah/opencv-cpp) #OpenCV #Farshid_PirahanSiah #pirahansiah My NuGet packages comprised of two versions for different VS versions. * Visual Studio 2019 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet) * Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7 * Visual Studio 2022 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet) * Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7 more:[ https://www.pirahansiah.com/topics/opencv](https://www.pirahansiah.com/topics/opencv) * cvTest * * * cvtest: Computer Vision Test: Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning * Do you want to test your output of computer vision application which is video or images? * Standard test for computer vision application * There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. * Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? * [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://github.com/pirahansiah/cvtest/blob/main/README.md) * Multi-Class Multi-object Video Tracking * computer vision with deep learning in IoT devices * Multi Camera (Stereo Vision) Calibration for AR/VR headset (extended reality/mixed reality) 3D Image Processing with Deep Learning * End to End solution for computer vision applications in industry (cloud and IoT) * Download all mind map sources * [https://github.com/pirahansiah/pirahansiah.github.io](https://github.com/pirahansiah/pirahansiah.github.io) ## LinkedIn: (around 12K members) [Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) The Computer Vision LinkedIn group: reached to around 8000 members. This group is a wonderful place for support if you have a question, need inspiration, encouragement, and cutting edge research. Computer Vision, Deep Learning, extended reality; Metaverse; Deep Reinforcement Learning, GANs, OpenCV, TensorFlow, PyTorch. [https://www.linkedin.com/groups/10320678/](https://www.linkedin.com/groups/10320678/) ## Facebook Group: (around 14K members) Deep Reinforcement Learning, Computer Vision with Deep Learning, IoT, Robot [https://www.facebook.com/groups/185926728115336](https://www.facebook.com/groups/185926728115336) We help scale and build artificially intelligent driven start-ups with Al Researchers & Engineers! [Computer Vision] (Berlin, Germany) [Please use calendly appointment slots](https://calendly.com/pirahansiah) press . in github and open web visual studio code My LaTex Papers [https://www.overleaf.com/read/cmvgxfqxfdqm](https://www.overleaf.com/read/cmvgxfqxfdqm) This site is provided to everyone for free, however if you would like to say thanks or help support continued R&D, Mind Map, development and etc. , consider getting me a coffee. It keeps my work going. [](https://docs.google.com/forms/d/e/1FAIpQLSdiQprY0yS25LBVixQnsjkoUTjOtzx1oJye1C77At4Ur2oqTg/viewform "Open Google Forms, Contact Information in new window") ![](https://lh6.googleusercontent.com/KhHLNKbzb8yibpBYuAtKK5sVcc6x_K_aqsryMuKNVHetpz5ZM_te0K84x3yBFEgp1i4xdtfZ1ooKPkTxqXmllKfIJt7oYEx_qQuGr4P1-ibIdnLFddaIV61OH3S4FbKDwg=w1280) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/HUYuTC4m2RdbH5Q_CS7Ojv6Huy1k616BL9vWsGKK7LCyUYE3s5qOrknHyVOdfj57OCUHTFqQnT0RVMWywECV0Vg=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/HUYuTC4m2RdbH5Q_CS7Ojv6Huy1k616BL9vWsGKK7LCyUYE3s5qOrknHyVOdfj57OCUHTFqQnT0RVMWywECV0Vg=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # pirahansiah ## Image Processing * Artificial SuperIntelligence (ASI) * Artificial General Intelligence (AGI) * Medical Image Processing * Robotic * AR, VR, extended reality * 3D SLAM * Computer Vision in IoT ## Machine Learning * Performance engineering in deep learning applications * End-to-End pipeline for machine learning programs * Reduce cost and development time with Amazon * Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload. * Continuous Deployment of Machine Learning Pipelines We deliver end-to-end hyper-automation solutions using computer vision & deep learning to enable AI-Powered Enterprise orchestration of various technologies and workflows to streamline and execute a process automatically. 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start/software](https://www.pirahansiah.com/topics-and-projects/how-to- start/software) [https://www.pirahansiah.com/topics-and-projects/how-to-start/roadmap-for-image- processing](https://www.pirahansiah.com/topics-and-projects/how-to-start/roadmap- for-image-processing) [https://www.pirahansiah.com/topics-and-projects/source- code](https://www.pirahansiah.com/topics-and-projects/source-code) [https://www.pirahansiah.com/topics-and-projects/source- code/python](https://www.pirahansiah.com/topics-and-projects/source-code/python) [https://www.pirahansiah.com/topics-and-projects/source- code/compile](https://www.pirahansiah.com/topics-and-projects/source-code/compile) [https://www.pirahansiah.com/topics-and- projects/share](https://www.pirahansiah.com/topics-and-projects/share) [https://www.pirahansiah.com/topics-and-projects/video- tracking](https://www.pirahansiah.com/topics-and-projects/video-tracking) [https://www.pirahansiah.com/topics-and- 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[https://www.pirahansiah.com/about/fqa](https://www.pirahansiah.com/about/fqa) # Open Source Projects ## OpenCV NuGet [https://www.nuget.org/profiles/Farshid_Pirahansiah](https://www.nuget.org/profiles/Farshid_Pirahansiah) NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022 [https://www.pirahansiah.com/topics/opencv](https://www.pirahansiah.com/topics/opencv) Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static OpenCV library for visual studio 2022 by using NuGet package manager just in a few minutes [https://www.youtube.com/watch?v=AEqZO_fZHZ8](https://www.youtube.com/watch?v=AEqZO_fZHZ8) #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, download source code (GitHub):[ https://github.com/pirahansiah/](https://github.com/pirahansiah/opencv-cpp) #OpenCV #Farshid_PirahanSiah #pirahansiah My NuGet packages comprised of two versions for different VS versions. * Visual Studio 2019 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet) * Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7 * Visual Studio 2022 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet) * Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7 more:[ https://www.pirahansiah.com/topics/opencv](https://www.pirahansiah.com/topics/opencv) * cvTest * * * cvtest: Computer Vision Test: Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning * Do you want to test your output of computer vision application which is video or images? * Standard test for computer vision application * There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. * Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? * [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://github.com/pirahansiah/cvtest/blob/main/README.md) * Multi-Class Multi-object Video Tracking * computer vision with deep learning in IoT devices * Multi Camera (Stereo Vision) Calibration for AR/VR headset (extended reality/mixed reality) 3D Image Processing with Deep Learning * End to End solution for computer vision applications in industry (cloud and IoT) * Download all mind map sources * [https://github.com/pirahansiah/pirahansiah.github.io](https://github.com/pirahansiah/pirahansiah.github.io) ## LinkedIn: (around 12K members) [Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) The Computer Vision LinkedIn group: reached to around 8000 members. This group is a wonderful place for support if you have a question, need inspiration, encouragement, and cutting edge research. Computer Vision, Deep Learning, extended reality; Metaverse; Deep Reinforcement Learning, GANs, OpenCV, TensorFlow, PyTorch. [https://www.linkedin.com/groups/10320678/](https://www.linkedin.com/groups/10320678/) ## Facebook Group: (around 14K members) Deep Reinforcement Learning, Computer Vision with Deep Learning, IoT, Robot [https://www.facebook.com/groups/185926728115336](https://www.facebook.com/groups/185926728115336) We help scale and build artificially intelligent driven start-ups with Al Researchers & Engineers! [Computer Vision] (Berlin, Germany) [Please use calendly appointment slots](https://calendly.com/pirahansiah) press . in github and open web visual studio code My LaTex Papers [https://www.overleaf.com/read/cmvgxfqxfdqm](https://www.overleaf.com/read/cmvgxfqxfdqm) This site is provided to everyone for free, however if you would like to say thanks or help support continued R&D, Mind Map, development and etc. , consider getting me a coffee. It keeps my work going. [](https://docs.google.com/forms/d/e/1FAIpQLSdiQprY0yS25LBVixQnsjkoUTjOtzx1oJye1C77At4Ur2oqTg/viewform "Open Google Forms, Contact Information in new window") ![](https://lh6.googleusercontent.com/KhHLNKbzb8yibpBYuAtKK5sVcc6x_K_aqsryMuKNVHetpz5ZM_te0K84x3yBFEgp1i4xdtfZ1ooKPkTxqXmllKfIJt7oYEx_qQuGr4P1-ibIdnLFddaIV61OH3S4FbKDwg=w1280) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[https://www.pirahansiah.com/about/fqa](https://www.pirahansiah.com/about/fqa) # Open Source Projects ## OpenCV NuGet [https://www.nuget.org/profiles/Farshid_Pirahansiah](https://www.nuget.org/profiles/Farshid_Pirahansiah) NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022 [https://www.pirahansiah.com/topics/opencv](https://www.pirahansiah.com/topics/opencv) Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static OpenCV library for visual studio 2022 by using NuGet package manager just in a few minutes [https://www.youtube.com/watch?v=AEqZO_fZHZ8](https://www.youtube.com/watch?v=AEqZO_fZHZ8) #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, download source code (GitHub):[ https://github.com/pirahansiah/](https://github.com/pirahansiah/opencv-cpp) #OpenCV #Farshid_PirahanSiah #pirahansiah My NuGet packages comprised of two versions for different VS versions. * Visual Studio 2019 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet) * Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7 * Visual Studio 2022 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet) * Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7 more:[ https://www.pirahansiah.com/topics/opencv](https://www.pirahansiah.com/topics/opencv) * cvTest * * * cvtest: Computer Vision Test: Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning * Do you want to test your output of computer vision application which is video or images? * Standard test for computer vision application * There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. * Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? * [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://github.com/pirahansiah/cvtest/blob/main/README.md) * Multi-Class Multi-object Video Tracking * computer vision with deep learning in IoT devices * Multi Camera (Stereo Vision) Calibration for AR/VR headset (extended reality/mixed reality) 3D Image Processing with Deep Learning * End to End solution for computer vision applications in industry (cloud and IoT) * Download all mind map sources * [https://github.com/pirahansiah/pirahansiah.github.io](https://github.com/pirahansiah/pirahansiah.github.io) ## LinkedIn: (around 12K members) [Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) The Computer Vision LinkedIn group: reached to around 8000 members. This group is a wonderful place for support if you have a question, need inspiration, encouragement, and cutting edge research. Computer Vision, Deep Learning, extended reality; Metaverse; Deep Reinforcement Learning, GANs, OpenCV, TensorFlow, PyTorch. [https://www.linkedin.com/groups/10320678/](https://www.linkedin.com/groups/10320678/) ## Facebook Group: (around 14K members) Deep Reinforcement Learning, Computer Vision with Deep Learning, IoT, Robot [https://www.facebook.com/groups/185926728115336](https://www.facebook.com/groups/185926728115336) We help scale and build artificially intelligent driven start-ups with Al Researchers & Engineers! [Computer Vision] (Berlin, Germany) [Please use calendly appointment slots](https://calendly.com/pirahansiah) press . in github and open web visual studio code My LaTex Papers [https://www.overleaf.com/read/cmvgxfqxfdqm](https://www.overleaf.com/read/cmvgxfqxfdqm) This site is provided to everyone for free, however if you would like to say thanks or help support continued R&D, Mind Map, development and etc. , consider getting me a coffee. It keeps my work going. [](https://docs.google.com/forms/d/e/1FAIpQLSdiQprY0yS25LBVixQnsjkoUTjOtzx1oJye1C77At4Ur2oqTg/viewform "Open Google Forms, Contact Information in new window") ![](https://lh6.googleusercontent.com/KhHLNKbzb8yibpBYuAtKK5sVcc6x_K_aqsryMuKNVHetpz5ZM_te0K84x3yBFEgp1i4xdtfZ1ooKPkTxqXmllKfIJt7oYEx_qQuGr4P1-ibIdnLFddaIV61OH3S4FbKDwg=w1280) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[https://www.pirahansiah.com/about/fqa](https://www.pirahansiah.com/about/fqa) # Open Source Projects ## OpenCV NuGet [https://www.nuget.org/profiles/Farshid_Pirahansiah](https://www.nuget.org/profiles/Farshid_Pirahansiah) NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022 [https://www.pirahansiah.com/topics/opencv](https://www.pirahansiah.com/topics/opencv) Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static OpenCV library for visual studio 2022 by using NuGet package manager just in a few minutes [https://www.youtube.com/watch?v=AEqZO_fZHZ8](https://www.youtube.com/watch?v=AEqZO_fZHZ8) #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, download source code (GitHub):[ https://github.com/pirahansiah/](https://github.com/pirahansiah/opencv-cpp) #OpenCV #Farshid_PirahanSiah #pirahansiah My NuGet packages comprised of two versions for different VS versions. * Visual Studio 2019 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet) * Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7 * Visual Studio 2022 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet) * Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7 more:[ https://www.pirahansiah.com/topics/opencv](https://www.pirahansiah.com/topics/opencv) * cvTest * * * cvtest: Computer Vision Test: Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning * Do you want to test your output of computer vision application which is video or images? * Standard test for computer vision application * There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. * Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? * [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://github.com/pirahansiah/cvtest/blob/main/README.md) * Multi-Class Multi-object Video Tracking * computer vision with deep learning in IoT devices * Multi Camera (Stereo Vision) Calibration for AR/VR headset (extended reality/mixed reality) 3D Image Processing with Deep Learning * End to End solution for computer vision applications in industry (cloud and IoT) * Download all mind map sources * [https://github.com/pirahansiah/pirahansiah.github.io](https://github.com/pirahansiah/pirahansiah.github.io) ## LinkedIn: (around 12K members) [Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) The Computer Vision LinkedIn group: reached to around 8000 members. This group is a wonderful place for support if you have a question, need inspiration, encouragement, and cutting edge research. Computer Vision, Deep Learning, extended reality; Metaverse; Deep Reinforcement Learning, GANs, OpenCV, TensorFlow, PyTorch. [https://www.linkedin.com/groups/10320678/](https://www.linkedin.com/groups/10320678/) ## Facebook Group: (around 14K members) Deep Reinforcement Learning, Computer Vision with Deep Learning, IoT, Robot [https://www.facebook.com/groups/185926728115336](https://www.facebook.com/groups/185926728115336) We help scale and build artificially intelligent driven start-ups with Al Researchers & Engineers! [Computer Vision] (Berlin, Germany) [Please use calendly appointment slots](https://calendly.com/pirahansiah) press . in github and open web visual studio code My LaTex Papers [https://www.overleaf.com/read/cmvgxfqxfdqm](https://www.overleaf.com/read/cmvgxfqxfdqm) This site is provided to everyone for free, however if you would like to say thanks or help support continued R&D, Mind Map, development and etc. , consider getting me a coffee. It keeps my work going. [](https://docs.google.com/forms/d/e/1FAIpQLSdiQprY0yS25LBVixQnsjkoUTjOtzx1oJye1C77At4Ur2oqTg/viewform "Open Google Forms, Contact Information in new window") ![](https://lh6.googleusercontent.com/qK7kZ4wtDo-2aIt1QTo8jo-mNvOIMNDBchi- sJJXVeEwQ_NhoTu2PRL6j9iCqhVgfyJX0f0AE- QGmUzPVczg1tEwdOrUOXGUeuwk8RCnmjb9lA58RCCji3NXe6abv371Vg=w1280) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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start/software](https://www.pirahansiah.com/topics-and-projects/how-to- start/software) [https://www.pirahansiah.com/topics-and-projects/how-to-start/roadmap-for-image- processing](https://www.pirahansiah.com/topics-and-projects/how-to-start/roadmap- for-image-processing) [https://www.pirahansiah.com/topics-and-projects/source- code](https://www.pirahansiah.com/topics-and-projects/source-code) [https://www.pirahansiah.com/topics-and-projects/source- code/python](https://www.pirahansiah.com/topics-and-projects/source-code/python) [https://www.pirahansiah.com/topics-and-projects/source- code/compile](https://www.pirahansiah.com/topics-and-projects/source-code/compile) [https://www.pirahansiah.com/topics-and- projects/share](https://www.pirahansiah.com/topics-and-projects/share) [https://www.pirahansiah.com/topics-and-projects/video- tracking](https://www.pirahansiah.com/topics-and-projects/video-tracking) [https://www.pirahansiah.com/topics-and- projects/opencv](https://www.pirahansiah.com/topics-and-projects/opencv) [https://www.pirahansiah.com/topics-and- projects/camera_calibration](https://www.pirahansiah.com/topics-and- projects/camera_calibration) [https://www.pirahansiah.com/topics-and- projects/drl](https://www.pirahansiah.com/topics-and-projects/drl) [https://www.pirahansiah.com/topics-and- projects/hardware](https://www.pirahansiah.com/topics-and-projects/hardware) [https://www.pirahansiah.com/topics-and-projects/quantum- computing](https://www.pirahansiah.com/topics-and-projects/quantum-computing) [https://www.pirahansiah.com/topics-and- projects/altcoin](https://www.pirahansiah.com/topics-and-projects/altcoin) [https://www.pirahansiah.com/topics-and- projects/resume_cv](https://www.pirahansiah.com/topics-and-projects/resume_cv) [https://www.pirahansiah.com/links](https://www.pirahansiah.com/links) [https://www.pirahansiah.com/about](https://www.pirahansiah.com/about) [https://www.pirahansiah.com/about/fqa](https://www.pirahansiah.com/about/fqa) # Open Source Projects ## OpenCV NuGet [https://www.nuget.org/profiles/Farshid_Pirahansiah](https://www.nuget.org/profiles/Farshid_Pirahansiah) NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022 [https://www.pirahansiah.com/topics/opencv](https://www.pirahansiah.com/topics/opencv) Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static OpenCV library for visual studio 2022 by using NuGet package manager just in a few minutes [https://www.youtube.com/watch?v=AEqZO_fZHZ8](https://www.youtube.com/watch?v=AEqZO_fZHZ8) #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, download source code (GitHub):[ https://github.com/pirahansiah/](https://github.com/pirahansiah/opencv-cpp) #OpenCV #Farshid_PirahanSiah #pirahansiah My NuGet packages comprised of two versions for different VS versions. * Visual Studio 2019 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet) * Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7 * Visual Studio 2022 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet) * Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7 more:[ https://www.pirahansiah.com/topics/opencv](https://www.pirahansiah.com/topics/opencv) * cvTest * * * cvtest: Computer Vision Test: Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning * Do you want to test your output of computer vision application which is video or images? * Standard test for computer vision application * There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. * Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? * [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://github.com/pirahansiah/cvtest/blob/main/README.md) * Multi-Class Multi-object Video Tracking * computer vision with deep learning in IoT devices * Multi Camera (Stereo Vision) Calibration for AR/VR headset (extended reality/mixed reality) 3D Image Processing with Deep Learning * End to End solution for computer vision applications in industry (cloud and IoT) * Download all mind map sources * [https://github.com/pirahansiah/pirahansiah.github.io](https://github.com/pirahansiah/pirahansiah.github.io) ## LinkedIn: (around 12K members) [Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) The Computer Vision LinkedIn group: reached to around 8000 members. This group is a wonderful place for support if you have a question, need inspiration, encouragement, and cutting edge research. Computer Vision, Deep Learning, extended reality; Metaverse; Deep Reinforcement Learning, GANs, OpenCV, TensorFlow, PyTorch. [https://www.linkedin.com/groups/10320678/](https://www.linkedin.com/groups/10320678/) ## Facebook Group: (around 14K members) Deep Reinforcement Learning, Computer Vision with Deep Learning, IoT, Robot [https://www.facebook.com/groups/185926728115336](https://www.facebook.com/groups/185926728115336) We help scale and build artificially intelligent driven start-ups with Al Researchers & Engineers! [Computer Vision] (Berlin, Germany) [Please use calendly appointment slots](https://calendly.com/pirahansiah) press . in github and open web visual studio code My LaTex Papers [https://www.overleaf.com/read/cmvgxfqxfdqm](https://www.overleaf.com/read/cmvgxfqxfdqm) This site is provided to everyone for free, however if you would like to say thanks or help support continued R&D, Mind Map, development and etc. , consider getting me a coffee. It keeps my work going. [](https://docs.google.com/forms/d/e/1FAIpQLSdiQprY0yS25LBVixQnsjkoUTjOtzx1oJye1C77At4Ur2oqTg/viewform "Open Google Forms, Contact Information in new window") ![](https://lh6.googleusercontent.com/qK7kZ4wtDo-2aIt1QTo8jo-mNvOIMNDBchi- sJJXVeEwQ_NhoTu2PRL6j9iCqhVgfyJX0f0AE- QGmUzPVczg1tEwdOrUOXGUeuwk8RCnmjb9lA58RCCji3NXe6abv371Vg=w1280) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[https://www.pirahansiah.com/about/fqa](https://www.pirahansiah.com/about/fqa) # Open Source Projects ## OpenCV NuGet [https://www.nuget.org/profiles/Farshid_Pirahansiah](https://www.nuget.org/profiles/Farshid_Pirahansiah) NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022 [https://www.pirahansiah.com/topics/opencv](https://www.pirahansiah.com/topics/opencv) Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static OpenCV library for visual studio 2022 by using NuGet package manager just in a few minutes [https://www.youtube.com/watch?v=AEqZO_fZHZ8](https://www.youtube.com/watch?v=AEqZO_fZHZ8) #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, download source code (GitHub):[ https://github.com/pirahansiah/](https://github.com/pirahansiah/opencv-cpp) #OpenCV #Farshid_PirahanSiah #pirahansiah My NuGet packages comprised of two versions for different VS versions. * Visual Studio 2019 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet) * Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7 * Visual Studio 2022 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet) * Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7 more:[ https://www.pirahansiah.com/topics/opencv](https://www.pirahansiah.com/topics/opencv) * cvTest * * * cvtest: Computer Vision Test: Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning * Do you want to test your output of computer vision application which is video or images? * Standard test for computer vision application * There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. * Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? * [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://github.com/pirahansiah/cvtest/blob/main/README.md) * Multi-Class Multi-object Video Tracking * computer vision with deep learning in IoT devices * Multi Camera (Stereo Vision) Calibration for AR/VR headset (extended reality/mixed reality) 3D Image Processing with Deep Learning * End to End solution for computer vision applications in industry (cloud and IoT) * Download all mind map sources * [https://github.com/pirahansiah/pirahansiah.github.io](https://github.com/pirahansiah/pirahansiah.github.io) ## LinkedIn: (around 12K members) [Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) The Computer Vision LinkedIn group: reached to around 8000 members. This group is a wonderful place for support if you have a question, need inspiration, encouragement, and cutting edge research. Computer Vision, Deep Learning, extended reality; Metaverse; Deep Reinforcement Learning, GANs, OpenCV, TensorFlow, PyTorch. [https://www.linkedin.com/groups/10320678/](https://www.linkedin.com/groups/10320678/) ## Facebook Group: (around 14K members) Deep Reinforcement Learning, Computer Vision with Deep Learning, IoT, Robot [https://www.facebook.com/groups/185926728115336](https://www.facebook.com/groups/185926728115336) We help scale and build artificially intelligent driven start-ups with Al Researchers & Engineers! [Computer Vision] (Berlin, Germany) [Please use calendly appointment slots](https://calendly.com/pirahansiah) press . in github and open web visual studio code My LaTex Papers [https://www.overleaf.com/read/cmvgxfqxfdqm](https://www.overleaf.com/read/cmvgxfqxfdqm) This site is provided to everyone for free, however if you would like to say thanks or help support continued R&D, Mind Map, development and etc. , consider getting me a coffee. It keeps my work going. [](https://docs.google.com/forms/d/e/1FAIpQLSdiQprY0yS25LBVixQnsjkoUTjOtzx1oJye1C77At4Ur2oqTg/viewform "Open Google Forms, Contact Information in new window") ![](https://lh6.googleusercontent.com/qK7kZ4wtDo-2aIt1QTo8jo-mNvOIMNDBchi- sJJXVeEwQ_NhoTu2PRL6j9iCqhVgfyJX0f0AE- QGmUzPVczg1tEwdOrUOXGUeuwk8RCnmjb9lA58RCCji3NXe6abv371Vg=w1280) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[https://www.pirahansiah.com/about/fqa](https://www.pirahansiah.com/about/fqa) # Open Source Projects ## OpenCV NuGet [https://www.nuget.org/profiles/Farshid_Pirahansiah](https://www.nuget.org/profiles/Farshid_Pirahansiah) NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022 [https://www.pirahansiah.com/topics/opencv](https://www.pirahansiah.com/topics/opencv) Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static OpenCV library for visual studio 2022 by using NuGet package manager just in a few minutes [https://www.youtube.com/watch?v=AEqZO_fZHZ8](https://www.youtube.com/watch?v=AEqZO_fZHZ8) #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, download source code (GitHub):[ https://github.com/pirahansiah/](https://github.com/pirahansiah/opencv-cpp) #OpenCV #Farshid_PirahanSiah #pirahansiah My NuGet packages comprised of two versions for different VS versions. * Visual Studio 2019 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet) * Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7 * Visual Studio 2022 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet) * Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7 more:[ https://www.pirahansiah.com/topics/opencv](https://www.pirahansiah.com/topics/opencv) * cvTest * * * cvtest: Computer Vision Test: Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning * Do you want to test your output of computer vision application which is video or images? * Standard test for computer vision application * There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. * Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? * [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://github.com/pirahansiah/cvtest/blob/main/README.md) * Multi-Class Multi-object Video Tracking * computer vision with deep learning in IoT devices * Multi Camera (Stereo Vision) Calibration for AR/VR headset (extended reality/mixed reality) 3D Image Processing with Deep Learning * End to End solution for computer vision applications in industry (cloud and IoT) * Download all mind map sources * [https://github.com/pirahansiah/pirahansiah.github.io](https://github.com/pirahansiah/pirahansiah.github.io) ## LinkedIn: (around 12K members) [Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) The Computer Vision LinkedIn group: reached to around 8000 members. This group is a wonderful place for support if you have a question, need inspiration, encouragement, and cutting edge research. Computer Vision, Deep Learning, extended reality; Metaverse; Deep Reinforcement Learning, GANs, OpenCV, TensorFlow, PyTorch. [https://www.linkedin.com/groups/10320678/](https://www.linkedin.com/groups/10320678/) ## Facebook Group: (around 14K members) Deep Reinforcement Learning, Computer Vision with Deep Learning, IoT, Robot [https://www.facebook.com/groups/185926728115336](https://www.facebook.com/groups/185926728115336) We help scale and build artificially intelligent driven start-ups with Al Researchers & Engineers! [Computer Vision] (Berlin, Germany) [Please use calendly appointment slots](https://calendly.com/pirahansiah) press . in github and open web visual studio code My LaTex Papers [https://www.overleaf.com/read/cmvgxfqxfdqm](https://www.overleaf.com/read/cmvgxfqxfdqm) This site is provided to everyone for free, however if you would like to say thanks or help support continued R&D, Mind Map, development and etc. , consider getting me a coffee. It keeps my work going. [](https://docs.google.com/forms/d/e/1FAIpQLSdiQprY0yS25LBVixQnsjkoUTjOtzx1oJye1C77At4Ur2oqTg/viewform "Open Google Forms, Contact Information in new window") ![](https://lh6.googleusercontent.com/qK7kZ4wtDo-2aIt1QTo8jo-mNvOIMNDBchi- sJJXVeEwQ_NhoTu2PRL6j9iCqhVgfyJX0f0AE- QGmUzPVczg1tEwdOrUOXGUeuwk8RCnmjb9lA58RCCji3NXe6abv371Vg=w1280) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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With the help of different examples, the course should provide a good starting point for students to work with robots. They learn how to create software including simulation, to interface sensors and actuators, and to integrate control algorithms. #ROSarchitecture #Navigating #ROSsystem #ROSpackages #ROS #SimulatingROS #Gazebo #simulator #robotmodels #URDF #simulationenvironments #SDF #visualizations #RViz #rqt [#computervision](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dcomputervision%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0cCdJ3Vmk5N1l3cp6O_su8) [#AI](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dai%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2dPEMgAJEghAGxPRWF43xJ) [#objectdetection](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dobjectdetection%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3iPjh0s9Xx0kiUns1ngiIB) [#objecttracking](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dobjecttracking%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0c5NH2HyRE4LM81xtfyMGC) [#ml](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dml%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw1_WgU3yRidQpKEvm5A-z8l) [#research](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dresearch%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw1vKlf5iOpwLhK-0F4foQ3e) [#CNN](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dcnn%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw19QWtSDWm3hhBhz3-AChAa) [#gans](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dgans%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw1lk1ECXOJN- mXDWUOJNSyq) [#convolutionalneuralnetworks](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dconvolutionalneuralnetworks%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0c8noF_OiCn- zFjustxEUJ) [#ai](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dai%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2dPEMgAJEghAGxPRWF43xJ) [#vr](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dvr%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2emO4DfNb- fRcZrRa5KMFg) [#reinforcementlearning](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dreinforcementlearning%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0P0YF-D2TdSHbkavX52nx2) [#mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dmlops%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3dvE9cPCHjH0CkdyuCTIA8) [#aiforbusiness](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Daiforbusiness%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw16X1M4iwrzdjYdU_ulFK3T) [#science](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dscience%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw1vmLH__ugmE11ZB7WjmLmx) [#researcher](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dresearcher%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3pIEq9N1emXExu0-191RiG) [#phd](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dphd%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2OcifGSB6XNuqH2gKzK6YY) [#cameracalibration](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dcameracalibration%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2RlWcAsgtAUNVg6OBGbkKd) [#opticalflow](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dopticalflow%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw18QjZi7x9SLhVukcmZAVgD) [#videostablization](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dvideostablization%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3WSAJkfYr1vIjbcnfpodFl) [#humanoidrobot](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dhumanoidrobot%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2xFO1kRd2oXLQJq10M-_XJ) [#localization](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dlocalization%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0MIyR__Y8eRDB3dZTecf2Z) [#3dSLAM](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3D3dslam%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2UY6pRlrLvoOiDP8URxRz5) [#reconstruction](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dreconstruction%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0N_h3Cd2AY7DVCXLQUSZ_r) [#pointcloud](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dpointcloud%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0S3yinkSJeDwnOoX27HKOr) [#mixedreality](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dmixedreality%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3BC3QpLp-w3P2WCrusaB02) [#edgecomputing](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dedgecomputing%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2do7CB7fc5a8IDPUINj916) [#raspberrypi](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Draspberrypi%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw05lTlcUMZAvgLJvCrdYOT0) [#intelstick](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dintelstick%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3nSDy4bef8ItMQDBY1AHc2) [#googlecoral](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dgooglecoral%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2flvDoGrRtzJ6AVKvsqwsF) [#jetsonnano](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Djetsonnano%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3FBZ8F6yWzRF85Qc1isy2O) [#nvidiavgpu](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dnvidiavgpu%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw1X0Zxn25NXWOT0YlLcUlPP) [#tensorflowjs](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dtensorflowjs%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2K-Wq_3mt-X1pq4mJWurnU) [#pytorch](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dpytorch%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0MKjo_xUTSXMHwpeG5pU9I) [#opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dopencv%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2WzPIfJb8SPE6hE0LcvqKp) [#aikit](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Daikit%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3rsc3lNPLGvTgNgYKccJQu) [#caffee](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dcaffee%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0VhKNPDM34JSJZe4QtqcXc) [#DIGITS](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Ddigits%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0UivwkkpqApDtx2okylxxR) [#c](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dc%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0jrMZfs5pBJdwrTFWjNWJ8)++ [#python](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dpython%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0-vYAEwDIVuEH- Fq-OrgIb) [#ubuntu](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dubuntu%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw1R_p- FRFOxdmZ9CYFTmYzL) [#farshidpirahansiah](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dfarshidpirahansiah%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw1qvC98TN9G7Di_rOxuWWk7) [#pirahansiah](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dpirahansiah%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0R-BbyLh11PJ2jVzgdk7zn).com [#farshid](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dfarshid%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2hrd3c0cgU-0ePNJwPxscm) [#pirahansiah](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dpirahansiah%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0AbYCv97yWC3MZG0QBI3L-) [#robotics](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Drobotics%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0c-v7d0S979BakkNasVEmb) #pirahansiah.com #farshid #pirahansiah [#MultiCameraMultiClassMultiObjectTracking](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dmulticameramulticlassmultiobjecttracking%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0xHni8xf5NFjd9IvccxvdN) [#deeplearning](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Ddeeplearning%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0_4BL9ENEZAPN9I_ZyTHfw) [#machinelearning](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dmachinelearning%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0GJQE0pYJLn1KpAlBULTve) [#artificialintelligence](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dartificialintelligence%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0kghSLOS6QnRK1JyVmLYgX) [#tensorflow](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dtensorflow%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw1esVtdflQf4ZZx0624h1N9) [#robotics](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Drobotics%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0c-v7d0S979BakkNasVEmb) [#3dvision](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3D3dvision%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2ZJO6ohCZ1NgSqX0wvFNlX) [#sterovision](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dsterovision%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0AmcHHfNGY_zeMJbmBcbur) [#depthmap](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Ddepthmap%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3wM --ayMcpa0-XdKt_RPO8) [#RCNN](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Drcnn%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2m0g4VZrmYsjPcW7wB_ZTR) [#machinevision](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dmachinevision%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2liIXvJ6DKQRJXtztRKEG3) [#imageprocessing](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dimageprocessing%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw1L71Lp1-CV50XPHcixp9hE) [#patternrecognition](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dpatternrecognition%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw1WoEPUJMhUpSCRh9Ta4Mta) [#compiler](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dcompiler%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw12szcaIf6dpkJAjPYM7V-e) [#RISC](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Drisc%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3YM_foJf7Hf3jZFwteLIfj)-V [#RNN](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Drnn%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3bCj47R1qTzd4MNk-1exEF) [#fullStackDeepLearning](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dfullstackdeeplearning%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw23eDRV9AhY4o007CEKu2X9) [#productinnovation](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dproductinnovation%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2AsIcbuD5dqUJA28qtDgSe) [#patents](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dpatents%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3kv-k5ws7NEzqF9ML5lbHd) [#TensorRT](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dtensorrt%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw34rToqV4FEI6a8qUEvgY_i) [#ApacheTVM](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dapachetvm%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw231JTCeb5OyZgt6ck89BnA) [#TFLite](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dtflite%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2iX_x5Fj5RXQNur3YCdCmx) [#PyTorchmobile](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dpytorchmobile%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw37QEC00u5OJDhhHOXqXpfa) [#dockers](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Ddockers%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2f-TuKUh24azBqsvKlVBmI) [#gRPC](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dgrpc%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw11hYAjKgAPw1MTu7ihz9De) [#RESTAPIs](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Drestapis%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3DKEVm0FUJzdhKMLl6QoTd) [#GRPC](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dgrpc%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw11hYAjKgAPw1MTu7ihz9De) [#GraphQL](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dgraphql%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw30 --htOGW4sG7y2BjM2He5) #imageprocessing #patternrecognition ![](https://lh3.googleusercontent.com/MNIQ8ZlUpYVpnVBiVba_fUX6LBHAHLEr0atDxuPsMqCjfdnRpYNsO17NN1-2f7SSfOJh6P4RNhSSWboGj8TIJaioSFSBnluqAIwr4katQ9MMzMMdfMB6nPj7DwfptqSj-Q=w1280) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/_hck3K2JGQ1ghYoXZ7iBAh6UrcJe4h-XNeLuiyiCVHdw5j1X2qmMgL8doj8geGzck7rU2DmtQXU2cDjW4Jc0qCo=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/_hck3K2JGQ1ghYoXZ7iBAh6UrcJe4h-XNeLuiyiCVHdw5j1X2qmMgL8doj8geGzck7rU2DmtQXU2cDjW4Jc0qCo=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # MLOps update : December 2021 Download complete summary of Machine Learning Engineering for Production (MLOps) Specialisation from Coursera: COURSE 1 Introduction to Machine Learning in Production COURSE 2 Machine Learning Data Lifecycle in Production COURSE 3 Machine Learning Modeling Pipelines in Production COURSE 4 Deploying Machine Learning Models in Production Download full resolution images: # update : December 2021 # Download complete summary of [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera: Download link: ## COURSE 1 Introduction to Machine Learning in Production In the first course of Machine Learning Engineering for Production Specialization, you will identify the various components and design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment constraints and requirements; and learn how to establish a model baseline, address concept drift, and prototype the process for developing, deploying, and continuously improving a productionized ML application. Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills. Week 1: Overview of the ML Lifecycle and Deployment Week 2: Selecting and Training a Model Week 3: Data Definition and Baseline ## COURSE 2 Machine Learning Data Lifecycle in Production In the second course of Machine Learning Engineering for Production Specialization, you will build data pipelines by gathering, cleaning, and validating datasets and assessing data quality; implement feature engineering, transformation, and selection with TensorFlow Extended and get the most predictive power out of your data; and establish the data lifecycle by leveraging data lineage and provenance metadata tools and follow data evolution with enterprise data schemas. Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills. Week 1: Collecting, Labeling, and Validating data Week 2: Feature Engineering, Transformation, and Selection Week 3: Data Journey and Data Storage Week 4: Advanced Data Labeling Methods, Data Augmentation, and Preprocessing Different Data Types ## COURSE 3 Machine Learning Modeling Pipelines in Production In the third course of Machine Learning Engineering for Production Specialization, you will build models for different serving environments; implement tools and techniques to effectively manage your modeling resources and best serve offline and online inference requests; and use analytics tools and performance metrics to address model fairness, explainability issues, and mitigate bottlenecks. Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills. Week 1: Neural Architecture Search Week 2: Model Resource Management Techniques Week 3: High-Performance Modeling Week 4: Model Analysis Week 5: Interpretability ## COURSE 4 Deploying Machine Learning Models in Production In the fourth course of Machine Learning Engineering for Production Specialization, you will deliver deployment pipelines by productionizing, scaling, and monitoring model serving that require different infrastructure; establish procedures to mitigate model decay and performance drops; and establish best practices and apply progressive delivery techniques to maintain and monitor a continuously operating production system. Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills. Week 1: Model Serving: Introduction Week 2: Model Serving: Patterns and Infrastructure Week 3: Model Management and Delivery Week 4: Model Monitoring and Logging # Download full resolution images: Download complete summary of Machine Learning Engineering for Production (MLOps) Specialisation from Coursera: * C1 * W1: * W2: * W3: * C2 * W1: * W2: * W3: * W4: _**C1+C2:**_[ _ **https://drive.google.com/file/d/1W0iv1V8T5ylRp2G5kxKKbuhKrd57S0ys/view?usp=sharing**_](https://drive.google.com/file/d/1W0iv1V8T5ylRp2G5kxKKbuhKrd57S0ys/view?usp=sharing) _ ****_ * C3 * W1: * W2: * W3: * W4: * W5: * C4 * W1 + W2 : * W3 : * W4: Monitoring and observability, End to End solution for computer vision applications in industry my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera \+ very good practice & lab COURSE 4 Deploying Machine Learning Models in Production: Week 4: Model Monitoring and Logging Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) : you can download my complete summary of [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8): [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) . #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C4-W4.png tools for experiment tracking, Logging metrics using TensorBoard, vertex tensorboard, progressive delivery End to End solution for computer vision applications in industry my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 4 Deploying Machine Learning Models in Production: Week 3: Model Management and Delivery Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C4-W3.png model serving, balance cost, latency and throughput, improving prediction latency and reducing resource costs, tensorflow serving, torchserve, KF serving, triton inference server, scaling infrastructure, pre processing operations needed before inference, ETL: extract , transform, load; kafka, pub sub, cloud dataflow, beam, spark streaming; End to End solution for computer vision applications in industry my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 4 Deploying Machine Learning Models in Production: Week 1: Model Serving: Introduction + Week 2: Model Serving: Patterns and Infrastructure + Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C4-W1+W2.png explainable AI, model interpretation methods, TensorFlow Lattice, understanding model predictions, PDP: partial dependence plots, permutation feature importance, SHAP: SHapley Additive exPlanation, testing concept activation vectors, testing concept activation vectors, LIME: local interpretable model agnostic explanations, Google cloud AI explanations for AI platform, XRAI: eXplanation with Ranked Area Integrals, End to End solution for computer vision applications in industry my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 3 Machine Learning Modeling Pipelines in Production: Week 5: Interpretability Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C3-W5.png End to End solution for computer vision applications in industry my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 3 Machine Learning Modeling Pipelines in Production: Week 4: Model Analysis tensorflow model analysis, TFMA, TFX, model debugging, TFMAL: TensorFlow Model Analysis, model remediation techniques, continuous evaluation and monitoring, Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C3-W4.png End to End solution for computer vision applications in industry my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 3 Machine Learning Modeling Pipelines in Production: Week 3: High- Performance Modeling distributed training, Gpipe: Open-source tensorflow library (using lingvo), teacher and student networks, idea: create a simple ;student; model that learns from a complex ;teacher; model. make efficientNets robust to noise with distillation Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C3-W3.png my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 3 Machine Learning Modeling Pipelines in Production: Week 2: Model Resource Management Techniques PCA, PLS, LDA, latent semantic indexing/analysis (LSI and LSA) (SVD) [removes redundant features from the dataset], independent component analysis (ICA), non-negative matrix factorization (NMF), latent dirichlet allocation (LDA), Quantization [make models run faster and use less power with low-precision] and pruning, ML kit, Core ML, TensorFlow Lite (TFX), post training quantization, quantization aware training (QAT), Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C3-W2.png my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 3 Machine Learning Modeling Pipelines in Production: Week 1: Neural Architecture Search NAS, Keras autotuner, AutoML, Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C3-W1.png my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 1 Introduction to Machine Learning in Production + COURSE 2 Machine Learning Data Lifecycle in Production Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C1+C2.png my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 2 Machine Learning Data Lifecycle in Production: Week 4: Advanced Data Labeling Methods, Data Augmentation, and Preprocessing Different Data Types semi-supervised data augmentation: UDA, semi-supervised learning with GANs; policy-based data augmentation: AutoAugment; time series, advanced labeling; active learning; human activity recognition (HAR); Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://lh4.googleusercontent.com/61yafDXW28u0J7tswhsKbkQz4rm8as4yLZ01mopeGtE4mViOAiDpGBfnErKFWmK_p6rQWgL72vlrVaq9Y0Yxfi5uX4g9veyUSKPNCNRHIzbIBJEUatsp85kRWmpkxVToMg=w1280) my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 2 Machine Learning Data Lifecycle in Production: Week 3: Data Journey and Data Storage !pip install ml-metadata, Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://lh3.googleusercontent.com/tvarCgVn1dbsdr8dVxV8ZRSC36VxBnzxevqYuQfc23B4rKuNg5z-fra70yB_2SJUR2VF_WbjjDSISDxz_J6DMtcmKo_GIOVV8unOwy4jPPxUKhqxn- kUgraOLpvvwLic=w1280) ![](https://www.google.com/images/icons/product/drive-32.png)C1-W1.png ![](https://www.google.com/images/icons/product/drive-32.png)C1-W2.png ![](https://www.google.com/images/icons/product/drive-32.png)C1-W3.png Week 1: Collecting, Labeling, and Validating data ML modeling vs production ML; data collection, labeling, validating ; TFDV; ![](https://www.google.com/images/icons/product/drive-32.png)C2-W1.pdf w2 COURSE 1 Introduction to Machine Learning in Production: Course 2: Week1, Week 2: Selecting and Training a Model error analysis example for speech recognition example; iterative process of error analysis; prioritising what to work on; skewed datasets; performance auditions; F1 score based on precision and recall; data augmentation; Week 3: Data Definition and Baseline data definition; label ambiguity; type of data; HLP; meta-data; data pipeline; balanced train/dev/test splits in small dataset; Dilligence: assess the feasibility and value of potential solution; Course 2: Week 3: ![](https://lh4.googleusercontent.com/myRQzI05PtU2U1DpsoyvpPeD9p7aFKcYNoFf6_zKsb- TugqYOaovSYnmLy4E14FSz_uOkPzV0joyxezrf5fD5AAGCipGHkm0uI3uuBywmJV6f-zs4rsrdsrrsy37J4Ftxg=w1280) Course 2: Week 4: ![](https://lh5.googleusercontent.com/ohSok7QQrbr4fnUV7k9EZQU4M95EBZpNCNmMm6zfUT2IfItKwE3Ghv3Q9T39-bCebbAS0ARMlCIITi4NcNBwIQHFIX9Q3K5-x2JQjzAL-2d0yz0lHPoXtyXCABX6kE1qHw=w1280) Machine Learning Engineering for Production (MLOps) Specialization by DeepLearning.AI COURSE 3 Machine Learning Modeling Pipelines in Production - Week 1: Neural Architecture Search (NAS) - AutoML, Hyperparameter tuning, Cloud AutoML #autoML #NAS #hyperparameter ![](https://lh5.googleusercontent.com/kCBj7SlmWg8qU17AK2vwqaI3_LLwBOV8WD8mZyIdnEIFjz3cJyhG8PxJxGZA6is2sy8w-sCPZtGuT7JpSiyhaCqn1duYJGO4nYDIYuyoKzEIH-v0DYASylN- XcgxgizLBA=w1280) Machine Learning Engineering for Production (MLOps) Specialization by DeepLearning.AI COURSE 3 Machine Learning Modeling Pipelines in Production Week 3: High-Performance Modeling PCA, ICA, SVD, QAT: quantization aware training, my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera COURSE 2 Machine Learning Data Lifecycle in Production: Week 2: Feature Engineering, Transformation, and Selection feature scaling, normalization and standaridization; TensorFlow extended; TensorFlow Transform; tf.transform analyzers; TensorFlow Ops; Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/_hck3K2JGQ1ghYoXZ7iBAh6UrcJe4h-XNeLuiyiCVHdw5j1X2qmMgL8doj8geGzck7rU2DmtQXU2cDjW4Jc0qCo=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/_hck3K2JGQ1ghYoXZ7iBAh6UrcJe4h-XNeLuiyiCVHdw5j1X2qmMgL8doj8geGzck7rU2DmtQXU2cDjW4Jc0qCo=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # MLOps update : December 2021 Download complete summary of Machine Learning Engineering for Production (MLOps) Specialisation from Coursera: COURSE 1 Introduction to Machine Learning in Production COURSE 2 Machine Learning Data Lifecycle in Production COURSE 3 Machine Learning Modeling Pipelines in Production COURSE 4 Deploying Machine Learning Models in Production Download full resolution images: # update : December 2021 # Download complete summary of [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera: Download link: ## COURSE 1 Introduction to Machine Learning in Production In the first course of Machine Learning Engineering for Production Specialization, you will identify the various components and design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment constraints and requirements; and learn how to establish a model baseline, address concept drift, and prototype the process for developing, deploying, and continuously improving a productionized ML application. Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills. Week 1: Overview of the ML Lifecycle and Deployment Week 2: Selecting and Training a Model Week 3: Data Definition and Baseline ## COURSE 2 Machine Learning Data Lifecycle in Production In the second course of Machine Learning Engineering for Production Specialization, you will build data pipelines by gathering, cleaning, and validating datasets and assessing data quality; implement feature engineering, transformation, and selection with TensorFlow Extended and get the most predictive power out of your data; and establish the data lifecycle by leveraging data lineage and provenance metadata tools and follow data evolution with enterprise data schemas. Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills. Week 1: Collecting, Labeling, and Validating data Week 2: Feature Engineering, Transformation, and Selection Week 3: Data Journey and Data Storage Week 4: Advanced Data Labeling Methods, Data Augmentation, and Preprocessing Different Data Types ## COURSE 3 Machine Learning Modeling Pipelines in Production In the third course of Machine Learning Engineering for Production Specialization, you will build models for different serving environments; implement tools and techniques to effectively manage your modeling resources and best serve offline and online inference requests; and use analytics tools and performance metrics to address model fairness, explainability issues, and mitigate bottlenecks. Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills. Week 1: Neural Architecture Search Week 2: Model Resource Management Techniques Week 3: High-Performance Modeling Week 4: Model Analysis Week 5: Interpretability ## COURSE 4 Deploying Machine Learning Models in Production In the fourth course of Machine Learning Engineering for Production Specialization, you will deliver deployment pipelines by productionizing, scaling, and monitoring model serving that require different infrastructure; establish procedures to mitigate model decay and performance drops; and establish best practices and apply progressive delivery techniques to maintain and monitor a continuously operating production system. Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills. Week 1: Model Serving: Introduction Week 2: Model Serving: Patterns and Infrastructure Week 3: Model Management and Delivery Week 4: Model Monitoring and Logging # Download full resolution images: Download complete summary of Machine Learning Engineering for Production (MLOps) Specialisation from Coursera: * C1 * W1: * W2: * W3: * C2 * W1: * W2: * W3: * W4: _**C1+C2:**_[ _ **https://drive.google.com/file/d/1W0iv1V8T5ylRp2G5kxKKbuhKrd57S0ys/view?usp=sharing**_](https://drive.google.com/file/d/1W0iv1V8T5ylRp2G5kxKKbuhKrd57S0ys/view?usp=sharing) _ ****_ * C3 * W1: * W2: * W3: * W4: * W5: * C4 * W1 + W2 : * W3 : * W4: Monitoring and observability, End to End solution for computer vision applications in industry my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera \+ very good practice & lab COURSE 4 Deploying Machine Learning Models in Production: Week 4: Model Monitoring and Logging Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) : you can download my complete summary of [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8): [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) . #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C4-W4.png tools for experiment tracking, Logging metrics using TensorBoard, vertex tensorboard, progressive delivery End to End solution for computer vision applications in industry my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 4 Deploying Machine Learning Models in Production: Week 3: Model Management and Delivery Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C4-W3.png model serving, balance cost, latency and throughput, improving prediction latency and reducing resource costs, tensorflow serving, torchserve, KF serving, triton inference server, scaling infrastructure, pre processing operations needed before inference, ETL: extract , transform, load; kafka, pub sub, cloud dataflow, beam, spark streaming; End to End solution for computer vision applications in industry my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 4 Deploying Machine Learning Models in Production: Week 1: Model Serving: Introduction + Week 2: Model Serving: Patterns and Infrastructure + Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C4-W1+W2.png explainable AI, model interpretation methods, TensorFlow Lattice, understanding model predictions, PDP: partial dependence plots, permutation feature importance, SHAP: SHapley Additive exPlanation, testing concept activation vectors, testing concept activation vectors, LIME: local interpretable model agnostic explanations, Google cloud AI explanations for AI platform, XRAI: eXplanation with Ranked Area Integrals, End to End solution for computer vision applications in industry my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 3 Machine Learning Modeling Pipelines in Production: Week 5: Interpretability Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C3-W5.png End to End solution for computer vision applications in industry my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 3 Machine Learning Modeling Pipelines in Production: Week 4: Model Analysis tensorflow model analysis, TFMA, TFX, model debugging, TFMAL: TensorFlow Model Analysis, model remediation techniques, continuous evaluation and monitoring, Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C3-W4.png End to End solution for computer vision applications in industry my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 3 Machine Learning Modeling Pipelines in Production: Week 3: High- Performance Modeling distributed training, Gpipe: Open-source tensorflow library (using lingvo), teacher and student networks, idea: create a simple ;student; model that learns from a complex ;teacher; model. make efficientNets robust to noise with distillation Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C3-W3.png my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 3 Machine Learning Modeling Pipelines in Production: Week 2: Model Resource Management Techniques PCA, PLS, LDA, latent semantic indexing/analysis (LSI and LSA) (SVD) [removes redundant features from the dataset], independent component analysis (ICA), non-negative matrix factorization (NMF), latent dirichlet allocation (LDA), Quantization [make models run faster and use less power with low-precision] and pruning, ML kit, Core ML, TensorFlow Lite (TFX), post training quantization, quantization aware training (QAT), Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C3-W2.png my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 3 Machine Learning Modeling Pipelines in Production: Week 1: Neural Architecture Search NAS, Keras autotuner, AutoML, Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C3-W1.png my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 1 Introduction to Machine Learning in Production + COURSE 2 Machine Learning Data Lifecycle in Production Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C1+C2.png my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 2 Machine Learning Data Lifecycle in Production: Week 4: Advanced Data Labeling Methods, Data Augmentation, and Preprocessing Different Data Types semi-supervised data augmentation: UDA, semi-supervised learning with GANs; policy-based data augmentation: AutoAugment; time series, advanced labeling; active learning; human activity recognition (HAR); Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://lh4.googleusercontent.com/61yafDXW28u0J7tswhsKbkQz4rm8as4yLZ01mopeGtE4mViOAiDpGBfnErKFWmK_p6rQWgL72vlrVaq9Y0Yxfi5uX4g9veyUSKPNCNRHIzbIBJEUatsp85kRWmpkxVToMg=w1280) my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 2 Machine Learning Data Lifecycle in Production: Week 3: Data Journey and Data Storage !pip install ml-metadata, Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://lh3.googleusercontent.com/tvarCgVn1dbsdr8dVxV8ZRSC36VxBnzxevqYuQfc23B4rKuNg5z-fra70yB_2SJUR2VF_WbjjDSISDxz_J6DMtcmKo_GIOVV8unOwy4jPPxUKhqxn- kUgraOLpvvwLic=w1280) ![](https://www.google.com/images/icons/product/drive-32.png)C1-W1.png ![](https://www.google.com/images/icons/product/drive-32.png)C1-W2.png ![](https://www.google.com/images/icons/product/drive-32.png)C1-W3.png Week 1: Collecting, Labeling, and Validating data ML modeling vs production ML; data collection, labeling, validating ; TFDV; ![](https://www.google.com/images/icons/product/drive-32.png)C2-W1.pdf w2 COURSE 1 Introduction to Machine Learning in Production: Course 2: Week1, Week 2: Selecting and Training a Model error analysis example for speech recognition example; iterative process of error analysis; prioritising what to work on; skewed datasets; performance auditions; F1 score based on precision and recall; data augmentation; Week 3: Data Definition and Baseline data definition; label ambiguity; type of data; HLP; meta-data; data pipeline; balanced train/dev/test splits in small dataset; Dilligence: assess the feasibility and value of potential solution; Course 2: Week 3: ![](https://lh4.googleusercontent.com/myRQzI05PtU2U1DpsoyvpPeD9p7aFKcYNoFf6_zKsb- TugqYOaovSYnmLy4E14FSz_uOkPzV0joyxezrf5fD5AAGCipGHkm0uI3uuBywmJV6f-zs4rsrdsrrsy37J4Ftxg=w1280) Course 2: Week 4: ![](https://lh5.googleusercontent.com/ohSok7QQrbr4fnUV7k9EZQU4M95EBZpNCNmMm6zfUT2IfItKwE3Ghv3Q9T39-bCebbAS0ARMlCIITi4NcNBwIQHFIX9Q3K5-x2JQjzAL-2d0yz0lHPoXtyXCABX6kE1qHw=w1280) Machine Learning Engineering for Production (MLOps) Specialization by DeepLearning.AI COURSE 3 Machine Learning Modeling Pipelines in Production - Week 1: Neural Architecture Search (NAS) - AutoML, Hyperparameter tuning, Cloud AutoML #autoML #NAS #hyperparameter ![](https://lh5.googleusercontent.com/kCBj7SlmWg8qU17AK2vwqaI3_LLwBOV8WD8mZyIdnEIFjz3cJyhG8PxJxGZA6is2sy8w-sCPZtGuT7JpSiyhaCqn1duYJGO4nYDIYuyoKzEIH-v0DYASylN- XcgxgizLBA=w1280) Machine Learning Engineering for Production (MLOps) Specialization by DeepLearning.AI COURSE 3 Machine Learning Modeling Pipelines in Production Week 3: High-Performance Modeling PCA, ICA, SVD, QAT: quantization aware training, my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera COURSE 2 Machine Learning Data Lifecycle in Production: Week 2: Feature Engineering, Transformation, and Selection feature scaling, normalization and standaridization; TensorFlow extended; TensorFlow Transform; tf.transform analyzers; TensorFlow Ops; Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/_hck3K2JGQ1ghYoXZ7iBAh6UrcJe4h-XNeLuiyiCVHdw5j1X2qmMgL8doj8geGzck7rU2DmtQXU2cDjW4Jc0qCo=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/_hck3K2JGQ1ghYoXZ7iBAh6UrcJe4h-XNeLuiyiCVHdw5j1X2qmMgL8doj8geGzck7rU2DmtQXU2cDjW4Jc0qCo=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # MLOps update : December 2021 Download complete summary of Machine Learning Engineering for Production (MLOps) Specialisation from Coursera: COURSE 1 Introduction to Machine Learning in Production COURSE 2 Machine Learning Data Lifecycle in Production COURSE 3 Machine Learning Modeling Pipelines in Production COURSE 4 Deploying Machine Learning Models in Production Download full resolution images: # update : December 2021 # Download complete summary of [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera: Download link: ## COURSE 1 Introduction to Machine Learning in Production In the first course of Machine Learning Engineering for Production Specialization, you will identify the various components and design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment constraints and requirements; and learn how to establish a model baseline, address concept drift, and prototype the process for developing, deploying, and continuously improving a productionized ML application. Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills. Week 1: Overview of the ML Lifecycle and Deployment Week 2: Selecting and Training a Model Week 3: Data Definition and Baseline ## COURSE 2 Machine Learning Data Lifecycle in Production In the second course of Machine Learning Engineering for Production Specialization, you will build data pipelines by gathering, cleaning, and validating datasets and assessing data quality; implement feature engineering, transformation, and selection with TensorFlow Extended and get the most predictive power out of your data; and establish the data lifecycle by leveraging data lineage and provenance metadata tools and follow data evolution with enterprise data schemas. Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills. Week 1: Collecting, Labeling, and Validating data Week 2: Feature Engineering, Transformation, and Selection Week 3: Data Journey and Data Storage Week 4: Advanced Data Labeling Methods, Data Augmentation, and Preprocessing Different Data Types ## COURSE 3 Machine Learning Modeling Pipelines in Production In the third course of Machine Learning Engineering for Production Specialization, you will build models for different serving environments; implement tools and techniques to effectively manage your modeling resources and best serve offline and online inference requests; and use analytics tools and performance metrics to address model fairness, explainability issues, and mitigate bottlenecks. Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills. Week 1: Neural Architecture Search Week 2: Model Resource Management Techniques Week 3: High-Performance Modeling Week 4: Model Analysis Week 5: Interpretability ## COURSE 4 Deploying Machine Learning Models in Production In the fourth course of Machine Learning Engineering for Production Specialization, you will deliver deployment pipelines by productionizing, scaling, and monitoring model serving that require different infrastructure; establish procedures to mitigate model decay and performance drops; and establish best practices and apply progressive delivery techniques to maintain and monitor a continuously operating production system. Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills. Week 1: Model Serving: Introduction Week 2: Model Serving: Patterns and Infrastructure Week 3: Model Management and Delivery Week 4: Model Monitoring and Logging # Download full resolution images: Download complete summary of Machine Learning Engineering for Production (MLOps) Specialisation from Coursera: * C1 * W1: * W2: * W3: * C2 * W1: * W2: * W3: * W4: _**C1+C2:**_[ _ **https://drive.google.com/file/d/1W0iv1V8T5ylRp2G5kxKKbuhKrd57S0ys/view?usp=sharing**_](https://drive.google.com/file/d/1W0iv1V8T5ylRp2G5kxKKbuhKrd57S0ys/view?usp=sharing) _ ****_ * C3 * W1: * W2: * W3: * W4: * W5: * C4 * W1 + W2 : * W3 : * W4: Monitoring and observability, End to End solution for computer vision applications in industry my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera \+ very good practice & lab COURSE 4 Deploying Machine Learning Models in Production: Week 4: Model Monitoring and Logging Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) : you can download my complete summary of [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8): [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) . #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C4-W4.png tools for experiment tracking, Logging metrics using TensorBoard, vertex tensorboard, progressive delivery End to End solution for computer vision applications in industry my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 4 Deploying Machine Learning Models in Production: Week 3: Model Management and Delivery Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C4-W3.png model serving, balance cost, latency and throughput, improving prediction latency and reducing resource costs, tensorflow serving, torchserve, KF serving, triton inference server, scaling infrastructure, pre processing operations needed before inference, ETL: extract , transform, load; kafka, pub sub, cloud dataflow, beam, spark streaming; End to End solution for computer vision applications in industry my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 4 Deploying Machine Learning Models in Production: Week 1: Model Serving: Introduction + Week 2: Model Serving: Patterns and Infrastructure + Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C4-W1+W2.png explainable AI, model interpretation methods, TensorFlow Lattice, understanding model predictions, PDP: partial dependence plots, permutation feature importance, SHAP: SHapley Additive exPlanation, testing concept activation vectors, testing concept activation vectors, LIME: local interpretable model agnostic explanations, Google cloud AI explanations for AI platform, XRAI: eXplanation with Ranked Area Integrals, End to End solution for computer vision applications in industry my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 3 Machine Learning Modeling Pipelines in Production: Week 5: Interpretability Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C3-W5.png End to End solution for computer vision applications in industry my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 3 Machine Learning Modeling Pipelines in Production: Week 4: Model Analysis tensorflow model analysis, TFMA, TFX, model debugging, TFMAL: TensorFlow Model Analysis, model remediation techniques, continuous evaluation and monitoring, Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C3-W4.png End to End solution for computer vision applications in industry my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 3 Machine Learning Modeling Pipelines in Production: Week 3: High- Performance Modeling distributed training, Gpipe: Open-source tensorflow library (using lingvo), teacher and student networks, idea: create a simple ;student; model that learns from a complex ;teacher; model. make efficientNets robust to noise with distillation Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C3-W3.png my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 3 Machine Learning Modeling Pipelines in Production: Week 2: Model Resource Management Techniques PCA, PLS, LDA, latent semantic indexing/analysis (LSI and LSA) (SVD) [removes redundant features from the dataset], independent component analysis (ICA), non-negative matrix factorization (NMF), latent dirichlet allocation (LDA), Quantization [make models run faster and use less power with low-precision] and pruning, ML kit, Core ML, TensorFlow Lite (TFX), post training quantization, quantization aware training (QAT), Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C3-W2.png my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 3 Machine Learning Modeling Pipelines in Production: Week 1: Neural Architecture Search NAS, Keras autotuner, AutoML, Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C3-W1.png my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 1 Introduction to Machine Learning in Production + COURSE 2 Machine Learning Data Lifecycle in Production Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C1+C2.png my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 2 Machine Learning Data Lifecycle in Production: Week 4: Advanced Data Labeling Methods, Data Augmentation, and Preprocessing Different Data Types semi-supervised data augmentation: UDA, semi-supervised learning with GANs; policy-based data augmentation: AutoAugment; time series, advanced labeling; active learning; human activity recognition (HAR); Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://lh4.googleusercontent.com/61yafDXW28u0J7tswhsKbkQz4rm8as4yLZ01mopeGtE4mViOAiDpGBfnErKFWmK_p6rQWgL72vlrVaq9Y0Yxfi5uX4g9veyUSKPNCNRHIzbIBJEUatsp85kRWmpkxVToMg=w1280) my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 2 Machine Learning Data Lifecycle in Production: Week 3: Data Journey and Data Storage !pip install ml-metadata, Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://lh3.googleusercontent.com/tvarCgVn1dbsdr8dVxV8ZRSC36VxBnzxevqYuQfc23B4rKuNg5z-fra70yB_2SJUR2VF_WbjjDSISDxz_J6DMtcmKo_GIOVV8unOwy4jPPxUKhqxn- kUgraOLpvvwLic=w1280) ![](https://www.google.com/images/icons/product/drive-32.png)C1-W1.png ![](https://www.google.com/images/icons/product/drive-32.png)C1-W2.png ![](https://www.google.com/images/icons/product/drive-32.png)C1-W3.png Week 1: Collecting, Labeling, and Validating data ML modeling vs production ML; data collection, labeling, validating ; TFDV; ![](https://www.google.com/images/icons/product/drive-32.png)C2-W1.pdf w2 COURSE 1 Introduction to Machine Learning in Production: Course 2: Week1, Week 2: Selecting and Training a Model error analysis example for speech recognition example; iterative process of error analysis; prioritising what to work on; skewed datasets; performance auditions; F1 score based on precision and recall; data augmentation; Week 3: Data Definition and Baseline data definition; label ambiguity; type of data; HLP; meta-data; data pipeline; balanced train/dev/test splits in small dataset; Dilligence: assess the feasibility and value of potential solution; Course 2: Week 3: ![](https://lh4.googleusercontent.com/myRQzI05PtU2U1DpsoyvpPeD9p7aFKcYNoFf6_zKsb- TugqYOaovSYnmLy4E14FSz_uOkPzV0joyxezrf5fD5AAGCipGHkm0uI3uuBywmJV6f-zs4rsrdsrrsy37J4Ftxg=w1280) Course 2: Week 4: ![](https://lh5.googleusercontent.com/ohSok7QQrbr4fnUV7k9EZQU4M95EBZpNCNmMm6zfUT2IfItKwE3Ghv3Q9T39-bCebbAS0ARMlCIITi4NcNBwIQHFIX9Q3K5-x2JQjzAL-2d0yz0lHPoXtyXCABX6kE1qHw=w1280) Machine Learning Engineering for Production (MLOps) Specialization by DeepLearning.AI COURSE 3 Machine Learning Modeling Pipelines in Production - Week 1: Neural Architecture Search (NAS) - AutoML, Hyperparameter tuning, Cloud AutoML #autoML #NAS #hyperparameter ![](https://lh5.googleusercontent.com/kCBj7SlmWg8qU17AK2vwqaI3_LLwBOV8WD8mZyIdnEIFjz3cJyhG8PxJxGZA6is2sy8w-sCPZtGuT7JpSiyhaCqn1duYJGO4nYDIYuyoKzEIH-v0DYASylN- XcgxgizLBA=w1280) Machine Learning Engineering for Production (MLOps) Specialization by DeepLearning.AI COURSE 3 Machine Learning Modeling Pipelines in Production Week 3: High-Performance Modeling PCA, ICA, SVD, QAT: quantization aware training, my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera COURSE 2 Machine Learning Data Lifecycle in Production: Week 2: Feature Engineering, Transformation, and Selection feature scaling, normalization and standaridization; TensorFlow extended; TensorFlow Transform; tf.transform analyzers; TensorFlow Ops; Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/_hck3K2JGQ1ghYoXZ7iBAh6UrcJe4h-XNeLuiyiCVHdw5j1X2qmMgL8doj8geGzck7rU2DmtQXU2cDjW4Jc0qCo=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/_hck3K2JGQ1ghYoXZ7iBAh6UrcJe4h-XNeLuiyiCVHdw5j1X2qmMgL8doj8geGzck7rU2DmtQXU2cDjW4Jc0qCo=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # MLOps update : December 2021 Download complete summary of Machine Learning Engineering for Production (MLOps) Specialisation from Coursera: COURSE 1 Introduction to Machine Learning in Production COURSE 2 Machine Learning Data Lifecycle in Production COURSE 3 Machine Learning Modeling Pipelines in Production COURSE 4 Deploying Machine Learning Models in Production Download full resolution images: # update : December 2021 # Download complete summary of [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera: Download link: ## COURSE 1 Introduction to Machine Learning in Production In the first course of Machine Learning Engineering for Production Specialization, you will identify the various components and design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment constraints and requirements; and learn how to establish a model baseline, address concept drift, and prototype the process for developing, deploying, and continuously improving a productionized ML application. Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills. Week 1: Overview of the ML Lifecycle and Deployment Week 2: Selecting and Training a Model Week 3: Data Definition and Baseline ## COURSE 2 Machine Learning Data Lifecycle in Production In the second course of Machine Learning Engineering for Production Specialization, you will build data pipelines by gathering, cleaning, and validating datasets and assessing data quality; implement feature engineering, transformation, and selection with TensorFlow Extended and get the most predictive power out of your data; and establish the data lifecycle by leveraging data lineage and provenance metadata tools and follow data evolution with enterprise data schemas. Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills. Week 1: Collecting, Labeling, and Validating data Week 2: Feature Engineering, Transformation, and Selection Week 3: Data Journey and Data Storage Week 4: Advanced Data Labeling Methods, Data Augmentation, and Preprocessing Different Data Types ## COURSE 3 Machine Learning Modeling Pipelines in Production In the third course of Machine Learning Engineering for Production Specialization, you will build models for different serving environments; implement tools and techniques to effectively manage your modeling resources and best serve offline and online inference requests; and use analytics tools and performance metrics to address model fairness, explainability issues, and mitigate bottlenecks. Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills. Week 1: Neural Architecture Search Week 2: Model Resource Management Techniques Week 3: High-Performance Modeling Week 4: Model Analysis Week 5: Interpretability ## COURSE 4 Deploying Machine Learning Models in Production In the fourth course of Machine Learning Engineering for Production Specialization, you will deliver deployment pipelines by productionizing, scaling, and monitoring model serving that require different infrastructure; establish procedures to mitigate model decay and performance drops; and establish best practices and apply progressive delivery techniques to maintain and monitor a continuously operating production system. Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills. Week 1: Model Serving: Introduction Week 2: Model Serving: Patterns and Infrastructure Week 3: Model Management and Delivery Week 4: Model Monitoring and Logging # Download full resolution images: Download complete summary of Machine Learning Engineering for Production (MLOps) Specialisation from Coursera: * C1 * W1: * W2: * W3: * C2 * W1: * W2: * W3: * W4: _**C1+C2:**_[ _ **https://drive.google.com/file/d/1W0iv1V8T5ylRp2G5kxKKbuhKrd57S0ys/view?usp=sharing**_](https://drive.google.com/file/d/1W0iv1V8T5ylRp2G5kxKKbuhKrd57S0ys/view?usp=sharing) _ ****_ * C3 * W1: * W2: * W3: * W4: * W5: * C4 * W1 + W2 : * W3 : * W4: Monitoring and observability, End to End solution for computer vision applications in industry my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera \+ very good practice & lab COURSE 4 Deploying Machine Learning Models in Production: Week 4: Model Monitoring and Logging Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) : you can download my complete summary of [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8): [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) . #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C4-W4.png tools for experiment tracking, Logging metrics using TensorBoard, vertex tensorboard, progressive delivery End to End solution for computer vision applications in industry my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 4 Deploying Machine Learning Models in Production: Week 3: Model Management and Delivery Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C4-W3.png model serving, balance cost, latency and throughput, improving prediction latency and reducing resource costs, tensorflow serving, torchserve, KF serving, triton inference server, scaling infrastructure, pre processing operations needed before inference, ETL: extract , transform, load; kafka, pub sub, cloud dataflow, beam, spark streaming; End to End solution for computer vision applications in industry my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 4 Deploying Machine Learning Models in Production: Week 1: Model Serving: Introduction + Week 2: Model Serving: Patterns and Infrastructure + Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C4-W1+W2.png explainable AI, model interpretation methods, TensorFlow Lattice, understanding model predictions, PDP: partial dependence plots, permutation feature importance, SHAP: SHapley Additive exPlanation, testing concept activation vectors, testing concept activation vectors, LIME: local interpretable model agnostic explanations, Google cloud AI explanations for AI platform, XRAI: eXplanation with Ranked Area Integrals, End to End solution for computer vision applications in industry my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 3 Machine Learning Modeling Pipelines in Production: Week 5: Interpretability Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C3-W5.png End to End solution for computer vision applications in industry my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 3 Machine Learning Modeling Pipelines in Production: Week 4: Model Analysis tensorflow model analysis, TFMA, TFX, model debugging, TFMAL: TensorFlow Model Analysis, model remediation techniques, continuous evaluation and monitoring, Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C3-W4.png End to End solution for computer vision applications in industry my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 3 Machine Learning Modeling Pipelines in Production: Week 3: High- Performance Modeling distributed training, Gpipe: Open-source tensorflow library (using lingvo), teacher and student networks, idea: create a simple ;student; model that learns from a complex ;teacher; model. make efficientNets robust to noise with distillation Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C3-W3.png my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 3 Machine Learning Modeling Pipelines in Production: Week 2: Model Resource Management Techniques PCA, PLS, LDA, latent semantic indexing/analysis (LSI and LSA) (SVD) [removes redundant features from the dataset], independent component analysis (ICA), non-negative matrix factorization (NMF), latent dirichlet allocation (LDA), Quantization [make models run faster and use less power with low-precision] and pruning, ML kit, Core ML, TensorFlow Lite (TFX), post training quantization, quantization aware training (QAT), Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C3-W2.png my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 3 Machine Learning Modeling Pipelines in Production: Week 1: Neural Architecture Search NAS, Keras autotuner, AutoML, Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C3-W1.png my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 1 Introduction to Machine Learning in Production + COURSE 2 Machine Learning Data Lifecycle in Production Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C1+C2.png my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 2 Machine Learning Data Lifecycle in Production: Week 4: Advanced Data Labeling Methods, Data Augmentation, and Preprocessing Different Data Types semi-supervised data augmentation: UDA, semi-supervised learning with GANs; policy-based data augmentation: AutoAugment; time series, advanced labeling; active learning; human activity recognition (HAR); Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://lh4.googleusercontent.com/61yafDXW28u0J7tswhsKbkQz4rm8as4yLZ01mopeGtE4mViOAiDpGBfnErKFWmK_p6rQWgL72vlrVaq9Y0Yxfi5uX4g9veyUSKPNCNRHIzbIBJEUatsp85kRWmpkxVToMg=w1280) my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 2 Machine Learning Data Lifecycle in Production: Week 3: Data Journey and Data Storage !pip install ml-metadata, Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://lh3.googleusercontent.com/tvarCgVn1dbsdr8dVxV8ZRSC36VxBnzxevqYuQfc23B4rKuNg5z-fra70yB_2SJUR2VF_WbjjDSISDxz_J6DMtcmKo_GIOVV8unOwy4jPPxUKhqxn- kUgraOLpvvwLic=w1280) ![](https://www.google.com/images/icons/product/drive-32.png)C1-W1.png ![](https://www.google.com/images/icons/product/drive-32.png)C1-W2.png ![](https://www.google.com/images/icons/product/drive-32.png)C1-W3.png Week 1: Collecting, Labeling, and Validating data ML modeling vs production ML; data collection, labeling, validating ; TFDV; ![](https://www.google.com/images/icons/product/drive-32.png)C2-W1.pdf w2 COURSE 1 Introduction to Machine Learning in Production: Course 2: Week1, Week 2: Selecting and Training a Model error analysis example for speech recognition example; iterative process of error analysis; prioritising what to work on; skewed datasets; performance auditions; F1 score based on precision and recall; data augmentation; Week 3: Data Definition and Baseline data definition; label ambiguity; type of data; HLP; meta-data; data pipeline; balanced train/dev/test splits in small dataset; Dilligence: assess the feasibility and value of potential solution; Course 2: Week 3: ![](https://lh4.googleusercontent.com/myRQzI05PtU2U1DpsoyvpPeD9p7aFKcYNoFf6_zKsb- TugqYOaovSYnmLy4E14FSz_uOkPzV0joyxezrf5fD5AAGCipGHkm0uI3uuBywmJV6f-zs4rsrdsrrsy37J4Ftxg=w1280) Course 2: Week 4: ![](https://lh5.googleusercontent.com/ohSok7QQrbr4fnUV7k9EZQU4M95EBZpNCNmMm6zfUT2IfItKwE3Ghv3Q9T39-bCebbAS0ARMlCIITi4NcNBwIQHFIX9Q3K5-x2JQjzAL-2d0yz0lHPoXtyXCABX6kE1qHw=w1280) Machine Learning Engineering for Production (MLOps) Specialization by DeepLearning.AI COURSE 3 Machine Learning Modeling Pipelines in Production - Week 1: Neural Architecture Search (NAS) - AutoML, Hyperparameter tuning, Cloud AutoML #autoML #NAS #hyperparameter ![](https://lh5.googleusercontent.com/kCBj7SlmWg8qU17AK2vwqaI3_LLwBOV8WD8mZyIdnEIFjz3cJyhG8PxJxGZA6is2sy8w-sCPZtGuT7JpSiyhaCqn1duYJGO4nYDIYuyoKzEIH-v0DYASylN- XcgxgizLBA=w1280) Machine Learning Engineering for Production (MLOps) Specialization by DeepLearning.AI COURSE 3 Machine Learning Modeling Pipelines in Production Week 3: High-Performance Modeling PCA, ICA, SVD, QAT: quantization aware training, my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera COURSE 2 Machine Learning Data Lifecycle in Production: Week 2: Feature Engineering, Transformation, and Selection feature scaling, normalization and standaridization; TensorFlow extended; TensorFlow Transform; tf.transform analyzers; TensorFlow Ops; Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/_hck3K2JGQ1ghYoXZ7iBAh6UrcJe4h-XNeLuiyiCVHdw5j1X2qmMgL8doj8geGzck7rU2DmtQXU2cDjW4Jc0qCo=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/_hck3K2JGQ1ghYoXZ7iBAh6UrcJe4h-XNeLuiyiCVHdw5j1X2qmMgL8doj8geGzck7rU2DmtQXU2cDjW4Jc0qCo=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # MLOps update : December 2021 Download complete summary of Machine Learning Engineering for Production (MLOps) Specialisation from Coursera: COURSE 1 Introduction to Machine Learning in Production COURSE 2 Machine Learning Data Lifecycle in Production COURSE 3 Machine Learning Modeling Pipelines in Production COURSE 4 Deploying Machine Learning Models in Production Download full resolution images: # update : December 2021 # Download complete summary of [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera: Download link: ## COURSE 1 Introduction to Machine Learning in Production In the first course of Machine Learning Engineering for Production Specialization, you will identify the various components and design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment constraints and requirements; and learn how to establish a model baseline, address concept drift, and prototype the process for developing, deploying, and continuously improving a productionized ML application. Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills. Week 1: Overview of the ML Lifecycle and Deployment Week 2: Selecting and Training a Model Week 3: Data Definition and Baseline ## COURSE 2 Machine Learning Data Lifecycle in Production In the second course of Machine Learning Engineering for Production Specialization, you will build data pipelines by gathering, cleaning, and validating datasets and assessing data quality; implement feature engineering, transformation, and selection with TensorFlow Extended and get the most predictive power out of your data; and establish the data lifecycle by leveraging data lineage and provenance metadata tools and follow data evolution with enterprise data schemas. Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills. Week 1: Collecting, Labeling, and Validating data Week 2: Feature Engineering, Transformation, and Selection Week 3: Data Journey and Data Storage Week 4: Advanced Data Labeling Methods, Data Augmentation, and Preprocessing Different Data Types ## COURSE 3 Machine Learning Modeling Pipelines in Production In the third course of Machine Learning Engineering for Production Specialization, you will build models for different serving environments; implement tools and techniques to effectively manage your modeling resources and best serve offline and online inference requests; and use analytics tools and performance metrics to address model fairness, explainability issues, and mitigate bottlenecks. Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills. Week 1: Neural Architecture Search Week 2: Model Resource Management Techniques Week 3: High-Performance Modeling Week 4: Model Analysis Week 5: Interpretability ## COURSE 4 Deploying Machine Learning Models in Production In the fourth course of Machine Learning Engineering for Production Specialization, you will deliver deployment pipelines by productionizing, scaling, and monitoring model serving that require different infrastructure; establish procedures to mitigate model decay and performance drops; and establish best practices and apply progressive delivery techniques to maintain and monitor a continuously operating production system. Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills. Week 1: Model Serving: Introduction Week 2: Model Serving: Patterns and Infrastructure Week 3: Model Management and Delivery Week 4: Model Monitoring and Logging # Download full resolution images: Download complete summary of Machine Learning Engineering for Production (MLOps) Specialisation from Coursera: * C1 * W1: * W2: * W3: * C2 * W1: * W2: * W3: * W4: _**C1+C2:**_[ _ **https://drive.google.com/file/d/1W0iv1V8T5ylRp2G5kxKKbuhKrd57S0ys/view?usp=sharing**_](https://drive.google.com/file/d/1W0iv1V8T5ylRp2G5kxKKbuhKrd57S0ys/view?usp=sharing) _ ****_ * C3 * W1: * W2: * W3: * W4: * W5: * C4 * W1 + W2 : * W3 : * W4: Monitoring and observability, End to End solution for computer vision applications in industry my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera \+ very good practice & lab COURSE 4 Deploying Machine Learning Models in Production: Week 4: Model Monitoring and Logging Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) : you can download my complete summary of [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8): [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) . #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C4-W4.png tools for experiment tracking, Logging metrics using TensorBoard, vertex tensorboard, progressive delivery End to End solution for computer vision applications in industry my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 4 Deploying Machine Learning Models in Production: Week 3: Model Management and Delivery Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C4-W3.png model serving, balance cost, latency and throughput, improving prediction latency and reducing resource costs, tensorflow serving, torchserve, KF serving, triton inference server, scaling infrastructure, pre processing operations needed before inference, ETL: extract , transform, load; kafka, pub sub, cloud dataflow, beam, spark streaming; End to End solution for computer vision applications in industry my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 4 Deploying Machine Learning Models in Production: Week 1: Model Serving: Introduction + Week 2: Model Serving: Patterns and Infrastructure + Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C4-W1+W2.png explainable AI, model interpretation methods, TensorFlow Lattice, understanding model predictions, PDP: partial dependence plots, permutation feature importance, SHAP: SHapley Additive exPlanation, testing concept activation vectors, testing concept activation vectors, LIME: local interpretable model agnostic explanations, Google cloud AI explanations for AI platform, XRAI: eXplanation with Ranked Area Integrals, End to End solution for computer vision applications in industry my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 3 Machine Learning Modeling Pipelines in Production: Week 5: Interpretability Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C3-W5.png End to End solution for computer vision applications in industry my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 3 Machine Learning Modeling Pipelines in Production: Week 4: Model Analysis tensorflow model analysis, TFMA, TFX, model debugging, TFMAL: TensorFlow Model Analysis, model remediation techniques, continuous evaluation and monitoring, Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C3-W4.png End to End solution for computer vision applications in industry my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 3 Machine Learning Modeling Pipelines in Production: Week 3: High- Performance Modeling distributed training, Gpipe: Open-source tensorflow library (using lingvo), teacher and student networks, idea: create a simple ;student; model that learns from a complex ;teacher; model. make efficientNets robust to noise with distillation Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C3-W3.png my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 3 Machine Learning Modeling Pipelines in Production: Week 2: Model Resource Management Techniques PCA, PLS, LDA, latent semantic indexing/analysis (LSI and LSA) (SVD) [removes redundant features from the dataset], independent component analysis (ICA), non-negative matrix factorization (NMF), latent dirichlet allocation (LDA), Quantization [make models run faster and use less power with low-precision] and pruning, ML kit, Core ML, TensorFlow Lite (TFX), post training quantization, quantization aware training (QAT), Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C3-W2.png my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 3 Machine Learning Modeling Pipelines in Production: Week 1: Neural Architecture Search NAS, Keras autotuner, AutoML, Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C3-W1.png my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 1 Introduction to Machine Learning in Production + COURSE 2 Machine Learning Data Lifecycle in Production Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C1+C2.png my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 2 Machine Learning Data Lifecycle in Production: Week 4: Advanced Data Labeling Methods, Data Augmentation, and Preprocessing Different Data Types semi-supervised data augmentation: UDA, semi-supervised learning with GANs; policy-based data augmentation: AutoAugment; time series, advanced labeling; active learning; human activity recognition (HAR); Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://lh4.googleusercontent.com/61yafDXW28u0J7tswhsKbkQz4rm8as4yLZ01mopeGtE4mViOAiDpGBfnErKFWmK_p6rQWgL72vlrVaq9Y0Yxfi5uX4g9veyUSKPNCNRHIzbIBJEUatsp85kRWmpkxVToMg=w1280) my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 2 Machine Learning Data Lifecycle in Production: Week 3: Data Journey and Data Storage !pip install ml-metadata, Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://lh3.googleusercontent.com/tvarCgVn1dbsdr8dVxV8ZRSC36VxBnzxevqYuQfc23B4rKuNg5z-fra70yB_2SJUR2VF_WbjjDSISDxz_J6DMtcmKo_GIOVV8unOwy4jPPxUKhqxn- kUgraOLpvvwLic=w1280) ![](https://www.google.com/images/icons/product/drive-32.png)C1-W1.png ![](https://www.google.com/images/icons/product/drive-32.png)C1-W2.png ![](https://www.google.com/images/icons/product/drive-32.png)C1-W3.png Week 1: Collecting, Labeling, and Validating data ML modeling vs production ML; data collection, labeling, validating ; TFDV; ![](https://www.google.com/images/icons/product/drive-32.png)C2-W1.pdf w2 COURSE 1 Introduction to Machine Learning in Production: Course 2: Week1, Week 2: Selecting and Training a Model error analysis example for speech recognition example; iterative process of error analysis; prioritising what to work on; skewed datasets; performance auditions; F1 score based on precision and recall; data augmentation; Week 3: Data Definition and Baseline data definition; label ambiguity; type of data; HLP; meta-data; data pipeline; balanced train/dev/test splits in small dataset; Dilligence: assess the feasibility and value of potential solution; Course 2: Week 3: ![](https://lh4.googleusercontent.com/myRQzI05PtU2U1DpsoyvpPeD9p7aFKcYNoFf6_zKsb- TugqYOaovSYnmLy4E14FSz_uOkPzV0joyxezrf5fD5AAGCipGHkm0uI3uuBywmJV6f-zs4rsrdsrrsy37J4Ftxg=w1280) Course 2: Week 4: ![](https://lh5.googleusercontent.com/ohSok7QQrbr4fnUV7k9EZQU4M95EBZpNCNmMm6zfUT2IfItKwE3Ghv3Q9T39-bCebbAS0ARMlCIITi4NcNBwIQHFIX9Q3K5-x2JQjzAL-2d0yz0lHPoXtyXCABX6kE1qHw=w1280) Machine Learning Engineering for Production (MLOps) Specialization by DeepLearning.AI COURSE 3 Machine Learning Modeling Pipelines in Production - Week 1: Neural Architecture Search (NAS) - AutoML, Hyperparameter tuning, Cloud AutoML #autoML #NAS #hyperparameter ![](https://lh5.googleusercontent.com/kCBj7SlmWg8qU17AK2vwqaI3_LLwBOV8WD8mZyIdnEIFjz3cJyhG8PxJxGZA6is2sy8w-sCPZtGuT7JpSiyhaCqn1duYJGO4nYDIYuyoKzEIH-v0DYASylN- XcgxgizLBA=w1280) Machine Learning Engineering for Production (MLOps) Specialization by DeepLearning.AI COURSE 3 Machine Learning Modeling Pipelines in Production Week 3: High-Performance Modeling PCA, ICA, SVD, QAT: quantization aware training, my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera COURSE 2 Machine Learning Data Lifecycle in Production: Week 2: Feature Engineering, Transformation, and Selection feature scaling, normalization and standaridization; TensorFlow extended; TensorFlow Transform; tf.transform analyzers; TensorFlow Ops; Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/HUYuTC4m2RdbH5Q_CS7Ojv6Huy1k616BL9vWsGKK7LCyUYE3s5qOrknHyVOdfj57OCUHTFqQnT0RVMWywECV0Vg=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/HUYuTC4m2RdbH5Q_CS7Ojv6Huy1k616BL9vWsGKK7LCyUYE3s5qOrknHyVOdfj57OCUHTFqQnT0RVMWywECV0Vg=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # MLOps update : December 2021 Download complete summary of Machine Learning Engineering for Production (MLOps) Specialisation from Coursera: COURSE 1 Introduction to Machine Learning in Production COURSE 2 Machine Learning Data Lifecycle in Production COURSE 3 Machine Learning Modeling Pipelines in Production COURSE 4 Deploying Machine Learning Models in Production Download full resolution images: # update : December 2021 # Download complete summary of [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera: Download link: ## COURSE 1 Introduction to Machine Learning in Production In the first course of Machine Learning Engineering for Production Specialization, you will identify the various components and design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment constraints and requirements; and learn how to establish a model baseline, address concept drift, and prototype the process for developing, deploying, and continuously improving a productionized ML application. Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills. Week 1: Overview of the ML Lifecycle and Deployment Week 2: Selecting and Training a Model Week 3: Data Definition and Baseline ## COURSE 2 Machine Learning Data Lifecycle in Production In the second course of Machine Learning Engineering for Production Specialization, you will build data pipelines by gathering, cleaning, and validating datasets and assessing data quality; implement feature engineering, transformation, and selection with TensorFlow Extended and get the most predictive power out of your data; and establish the data lifecycle by leveraging data lineage and provenance metadata tools and follow data evolution with enterprise data schemas. Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills. Week 1: Collecting, Labeling, and Validating data Week 2: Feature Engineering, Transformation, and Selection Week 3: Data Journey and Data Storage Week 4: Advanced Data Labeling Methods, Data Augmentation, and Preprocessing Different Data Types ## COURSE 3 Machine Learning Modeling Pipelines in Production In the third course of Machine Learning Engineering for Production Specialization, you will build models for different serving environments; implement tools and techniques to effectively manage your modeling resources and best serve offline and online inference requests; and use analytics tools and performance metrics to address model fairness, explainability issues, and mitigate bottlenecks. Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills. Week 1: Neural Architecture Search Week 2: Model Resource Management Techniques Week 3: High-Performance Modeling Week 4: Model Analysis Week 5: Interpretability ## COURSE 4 Deploying Machine Learning Models in Production In the fourth course of Machine Learning Engineering for Production Specialization, you will deliver deployment pipelines by productionizing, scaling, and monitoring model serving that require different infrastructure; establish procedures to mitigate model decay and performance drops; and establish best practices and apply progressive delivery techniques to maintain and monitor a continuously operating production system. Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills. Week 1: Model Serving: Introduction Week 2: Model Serving: Patterns and Infrastructure Week 3: Model Management and Delivery Week 4: Model Monitoring and Logging # Download full resolution images: Download complete summary of Machine Learning Engineering for Production (MLOps) Specialisation from Coursera: * C1 * W1: * W2: * W3: * C2 * W1: * W2: * W3: * W4: _**C1+C2:**_[ _ **https://drive.google.com/file/d/1W0iv1V8T5ylRp2G5kxKKbuhKrd57S0ys/view?usp=sharing**_](https://drive.google.com/file/d/1W0iv1V8T5ylRp2G5kxKKbuhKrd57S0ys/view?usp=sharing) _ ****_ * C3 * W1: * W2: * W3: * W4: * W5: * C4 * W1 + W2 : * W3 : * W4: Monitoring and observability, End to End solution for computer vision applications in industry my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera \+ very good practice & lab COURSE 4 Deploying Machine Learning Models in Production: Week 4: Model Monitoring and Logging Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) : you can download my complete summary of [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8): [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) . #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C4-W4.png tools for experiment tracking, Logging metrics using TensorBoard, vertex tensorboard, progressive delivery End to End solution for computer vision applications in industry my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 4 Deploying Machine Learning Models in Production: Week 3: Model Management and Delivery Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C4-W3.png model serving, balance cost, latency and throughput, improving prediction latency and reducing resource costs, tensorflow serving, torchserve, KF serving, triton inference server, scaling infrastructure, pre processing operations needed before inference, ETL: extract , transform, load; kafka, pub sub, cloud dataflow, beam, spark streaming; End to End solution for computer vision applications in industry my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 4 Deploying Machine Learning Models in Production: Week 1: Model Serving: Introduction + Week 2: Model Serving: Patterns and Infrastructure + Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C4-W1+W2.png explainable AI, model interpretation methods, TensorFlow Lattice, understanding model predictions, PDP: partial dependence plots, permutation feature importance, SHAP: SHapley Additive exPlanation, testing concept activation vectors, testing concept activation vectors, LIME: local interpretable model agnostic explanations, Google cloud AI explanations for AI platform, XRAI: eXplanation with Ranked Area Integrals, End to End solution for computer vision applications in industry my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 3 Machine Learning Modeling Pipelines in Production: Week 5: Interpretability Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C3-W5.png End to End solution for computer vision applications in industry my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 3 Machine Learning Modeling Pipelines in Production: Week 4: Model Analysis tensorflow model analysis, TFMA, TFX, model debugging, TFMAL: TensorFlow Model Analysis, model remediation techniques, continuous evaluation and monitoring, Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C3-W4.png End to End solution for computer vision applications in industry my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 3 Machine Learning Modeling Pipelines in Production: Week 3: High- Performance Modeling distributed training, Gpipe: Open-source tensorflow library (using lingvo), teacher and student networks, idea: create a simple ;student; model that learns from a complex ;teacher; model. make efficientNets robust to noise with distillation Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C3-W3.png my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 3 Machine Learning Modeling Pipelines in Production: Week 2: Model Resource Management Techniques PCA, PLS, LDA, latent semantic indexing/analysis (LSI and LSA) (SVD) [removes redundant features from the dataset], independent component analysis (ICA), non-negative matrix factorization (NMF), latent dirichlet allocation (LDA), Quantization [make models run faster and use less power with low-precision] and pruning, ML kit, Core ML, TensorFlow Lite (TFX), post training quantization, quantization aware training (QAT), Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C3-W2.png my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 3 Machine Learning Modeling Pipelines in Production: Week 1: Neural Architecture Search NAS, Keras autotuner, AutoML, Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C3-W1.png my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 1 Introduction to Machine Learning in Production + COURSE 2 Machine Learning Data Lifecycle in Production Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C1+C2.png my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 2 Machine Learning Data Lifecycle in Production: Week 4: Advanced Data Labeling Methods, Data Augmentation, and Preprocessing Different Data Types semi-supervised data augmentation: UDA, semi-supervised learning with GANs; policy-based data augmentation: AutoAugment; time series, advanced labeling; active learning; human activity recognition (HAR); Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://lh4.googleusercontent.com/ui-- TTgTphBOgNMM9eUZCyImbSgGKz4E5Gc1UHgoNGZGJAMvm47X_nU4oxLVG1-j55Nxjh8LxXYW51lTMPzdnpD_uEyeYncF9meKcKZ6zlf8PE4lo- nbkSKMuBoU3AFliA=w1280) my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 2 Machine Learning Data Lifecycle in Production: Week 3: Data Journey and Data Storage !pip install ml-metadata, Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://lh6.googleusercontent.com/OHCvcY0c4ku1OciHMpgdQqdPkT6ZegKp36UU6Ym_ILRU6nSV3tPw8BW_e_pSDnWG8-FOz7VVUR4yH-50m2Fp_zsd76JQnNiVh3am381SapDI29JXXLj0Q8mfgh9-pnnT=w1280) ![](https://www.google.com/images/icons/product/drive-32.png)C1-W1.png ![](https://www.google.com/images/icons/product/drive-32.png)C1-W2.png ![](https://www.google.com/images/icons/product/drive-32.png)C1-W3.png Week 1: Collecting, Labeling, and Validating data ML modeling vs production ML; data collection, labeling, validating ; TFDV; ![](https://www.google.com/images/icons/product/drive-32.png)C2-W1.pdf w2 COURSE 1 Introduction to Machine Learning in Production: Course 2: Week1, Week 2: Selecting and Training a Model error analysis example for speech recognition example; iterative process of error analysis; prioritising what to work on; skewed datasets; performance auditions; F1 score based on precision and recall; data augmentation; Week 3: Data Definition and Baseline data definition; label ambiguity; type of data; HLP; meta-data; data pipeline; balanced train/dev/test splits in small dataset; Dilligence: assess the feasibility and value of potential solution; Course 2: Week 3: ![](https://lh5.googleusercontent.com/ZgCm2g9przRO_VKdUCNAsGXXbJzZbrYvMGUyws97azObsTq6HqP8knZFkMozcvuPPdXLytZs0sZPYLTie2zhtLdrB7URzl3AQL3Fn36Ro52z5UOJQByi6INTXTO_DXdk2A=w1280) Course 2: Week 4: ![](https://lh6.googleusercontent.com/q1idESMQmJZFJzTG5jm_nFLJFk3rGFPNA7zoCpcZc-W15ItlIpK5JoUr7EZIq6LTjVDPXoCYJImMQDlDAawPVITymhu42DAWxqpjkx7zHg7KfYbSLl7mRGGrstDxcGWtGg=w1280) Machine Learning Engineering for Production (MLOps) Specialization by DeepLearning.AI COURSE 3 Machine Learning Modeling Pipelines in Production - Week 1: Neural Architecture Search (NAS) - AutoML, Hyperparameter tuning, Cloud AutoML #autoML #NAS #hyperparameter ![](https://lh5.googleusercontent.com/MZnILEMNb_rNLrgCgk6f5HgQLhoJVzJhj7MqCOCY6yzLKk6rd2DWfk2HrIhOr8RrfkZ23NXPs9o3CM-7-GGEkufOhkpb0QMf3RIUNeJv5DhU6XXYIBWopMBukEPmkDUx-Q=w1280) Machine Learning Engineering for Production (MLOps) Specialization by DeepLearning.AI COURSE 3 Machine Learning Modeling Pipelines in Production Week 3: High-Performance Modeling PCA, ICA, SVD, QAT: quantization aware training, my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera COURSE 2 Machine Learning Data Lifecycle in Production: Week 2: Feature Engineering, Transformation, and Selection feature scaling, normalization and standaridization; TensorFlow extended; TensorFlow Transform; tf.transform analyzers; TensorFlow Ops; Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/HUYuTC4m2RdbH5Q_CS7Ojv6Huy1k616BL9vWsGKK7LCyUYE3s5qOrknHyVOdfj57OCUHTFqQnT0RVMWywECV0Vg=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/HUYuTC4m2RdbH5Q_CS7Ojv6Huy1k616BL9vWsGKK7LCyUYE3s5qOrknHyVOdfj57OCUHTFqQnT0RVMWywECV0Vg=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # MLOps update : December 2021 Download complete summary of Machine Learning Engineering for Production (MLOps) Specialisation from Coursera: COURSE 1 Introduction to Machine Learning in Production COURSE 2 Machine Learning Data Lifecycle in Production COURSE 3 Machine Learning Modeling Pipelines in Production COURSE 4 Deploying Machine Learning Models in Production Download full resolution images: # update : December 2021 # Download complete summary of [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera: Download link: ## COURSE 1 Introduction to Machine Learning in Production In the first course of Machine Learning Engineering for Production Specialization, you will identify the various components and design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment constraints and requirements; and learn how to establish a model baseline, address concept drift, and prototype the process for developing, deploying, and continuously improving a productionized ML application. Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills. Week 1: Overview of the ML Lifecycle and Deployment Week 2: Selecting and Training a Model Week 3: Data Definition and Baseline ## COURSE 2 Machine Learning Data Lifecycle in Production In the second course of Machine Learning Engineering for Production Specialization, you will build data pipelines by gathering, cleaning, and validating datasets and assessing data quality; implement feature engineering, transformation, and selection with TensorFlow Extended and get the most predictive power out of your data; and establish the data lifecycle by leveraging data lineage and provenance metadata tools and follow data evolution with enterprise data schemas. Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills. Week 1: Collecting, Labeling, and Validating data Week 2: Feature Engineering, Transformation, and Selection Week 3: Data Journey and Data Storage Week 4: Advanced Data Labeling Methods, Data Augmentation, and Preprocessing Different Data Types ## COURSE 3 Machine Learning Modeling Pipelines in Production In the third course of Machine Learning Engineering for Production Specialization, you will build models for different serving environments; implement tools and techniques to effectively manage your modeling resources and best serve offline and online inference requests; and use analytics tools and performance metrics to address model fairness, explainability issues, and mitigate bottlenecks. Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills. Week 1: Neural Architecture Search Week 2: Model Resource Management Techniques Week 3: High-Performance Modeling Week 4: Model Analysis Week 5: Interpretability ## COURSE 4 Deploying Machine Learning Models in Production In the fourth course of Machine Learning Engineering for Production Specialization, you will deliver deployment pipelines by productionizing, scaling, and monitoring model serving that require different infrastructure; establish procedures to mitigate model decay and performance drops; and establish best practices and apply progressive delivery techniques to maintain and monitor a continuously operating production system. Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills. Week 1: Model Serving: Introduction Week 2: Model Serving: Patterns and Infrastructure Week 3: Model Management and Delivery Week 4: Model Monitoring and Logging # Download full resolution images: Download complete summary of Machine Learning Engineering for Production (MLOps) Specialisation from Coursera: * C1 * W1: * W2: * W3: * C2 * W1: * W2: * W3: * W4: _**C1+C2:**_[ _ **https://drive.google.com/file/d/1W0iv1V8T5ylRp2G5kxKKbuhKrd57S0ys/view?usp=sharing**_](https://drive.google.com/file/d/1W0iv1V8T5ylRp2G5kxKKbuhKrd57S0ys/view?usp=sharing) _ ****_ * C3 * W1: * W2: * W3: * W4: * W5: * C4 * W1 + W2 : * W3 : * W4: Monitoring and observability, End to End solution for computer vision applications in industry my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera \+ very good practice & lab COURSE 4 Deploying Machine Learning Models in Production: Week 4: Model Monitoring and Logging Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) : you can download my complete summary of [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8): [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) . #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C4-W4.png tools for experiment tracking, Logging metrics using TensorBoard, vertex tensorboard, progressive delivery End to End solution for computer vision applications in industry my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 4 Deploying Machine Learning Models in Production: Week 3: Model Management and Delivery Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C4-W3.png model serving, balance cost, latency and throughput, improving prediction latency and reducing resource costs, tensorflow serving, torchserve, KF serving, triton inference server, scaling infrastructure, pre processing operations needed before inference, ETL: extract , transform, load; kafka, pub sub, cloud dataflow, beam, spark streaming; End to End solution for computer vision applications in industry my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 4 Deploying Machine Learning Models in Production: Week 1: Model Serving: Introduction + Week 2: Model Serving: Patterns and Infrastructure + Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C4-W1+W2.png explainable AI, model interpretation methods, TensorFlow Lattice, understanding model predictions, PDP: partial dependence plots, permutation feature importance, SHAP: SHapley Additive exPlanation, testing concept activation vectors, testing concept activation vectors, LIME: local interpretable model agnostic explanations, Google cloud AI explanations for AI platform, XRAI: eXplanation with Ranked Area Integrals, End to End solution for computer vision applications in industry my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 3 Machine Learning Modeling Pipelines in Production: Week 5: Interpretability Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C3-W5.png End to End solution for computer vision applications in industry my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 3 Machine Learning Modeling Pipelines in Production: Week 4: Model Analysis tensorflow model analysis, TFMA, TFX, model debugging, TFMAL: TensorFlow Model Analysis, model remediation techniques, continuous evaluation and monitoring, Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C3-W4.png End to End solution for computer vision applications in industry my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 3 Machine Learning Modeling Pipelines in Production: Week 3: High- Performance Modeling distributed training, Gpipe: Open-source tensorflow library (using lingvo), teacher and student networks, idea: create a simple ;student; model that learns from a complex ;teacher; model. make efficientNets robust to noise with distillation Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C3-W3.png my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 3 Machine Learning Modeling Pipelines in Production: Week 2: Model Resource Management Techniques PCA, PLS, LDA, latent semantic indexing/analysis (LSI and LSA) (SVD) [removes redundant features from the dataset], independent component analysis (ICA), non-negative matrix factorization (NMF), latent dirichlet allocation (LDA), Quantization [make models run faster and use less power with low-precision] and pruning, ML kit, Core ML, TensorFlow Lite (TFX), post training quantization, quantization aware training (QAT), Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C3-W2.png my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 3 Machine Learning Modeling Pipelines in Production: Week 1: Neural Architecture Search NAS, Keras autotuner, AutoML, Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C3-W1.png my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 1 Introduction to Machine Learning in Production + COURSE 2 Machine Learning Data Lifecycle in Production Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C1+C2.png my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 2 Machine Learning Data Lifecycle in Production: Week 4: Advanced Data Labeling Methods, Data Augmentation, and Preprocessing Different Data Types semi-supervised data augmentation: UDA, semi-supervised learning with GANs; policy-based data augmentation: AutoAugment; time series, advanced labeling; active learning; human activity recognition (HAR); Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://lh4.googleusercontent.com/ui-- TTgTphBOgNMM9eUZCyImbSgGKz4E5Gc1UHgoNGZGJAMvm47X_nU4oxLVG1-j55Nxjh8LxXYW51lTMPzdnpD_uEyeYncF9meKcKZ6zlf8PE4lo- nbkSKMuBoU3AFliA=w1280) my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 2 Machine Learning Data Lifecycle in Production: Week 3: Data Journey and Data Storage !pip install ml-metadata, Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://lh6.googleusercontent.com/OHCvcY0c4ku1OciHMpgdQqdPkT6ZegKp36UU6Ym_ILRU6nSV3tPw8BW_e_pSDnWG8-FOz7VVUR4yH-50m2Fp_zsd76JQnNiVh3am381SapDI29JXXLj0Q8mfgh9-pnnT=w1280) ![](https://www.google.com/images/icons/product/drive-32.png)C1-W1.png ![](https://www.google.com/images/icons/product/drive-32.png)C1-W2.png ![](https://www.google.com/images/icons/product/drive-32.png)C1-W3.png Week 1: Collecting, Labeling, and Validating data ML modeling vs production ML; data collection, labeling, validating ; TFDV; ![](https://www.google.com/images/icons/product/drive-32.png)C2-W1.pdf w2 COURSE 1 Introduction to Machine Learning in Production: Course 2: Week1, Week 2: Selecting and Training a Model error analysis example for speech recognition example; iterative process of error analysis; prioritising what to work on; skewed datasets; performance auditions; F1 score based on precision and recall; data augmentation; Week 3: Data Definition and Baseline data definition; label ambiguity; type of data; HLP; meta-data; data pipeline; balanced train/dev/test splits in small dataset; Dilligence: assess the feasibility and value of potential solution; Course 2: Week 3: ![](https://lh5.googleusercontent.com/ZgCm2g9przRO_VKdUCNAsGXXbJzZbrYvMGUyws97azObsTq6HqP8knZFkMozcvuPPdXLytZs0sZPYLTie2zhtLdrB7URzl3AQL3Fn36Ro52z5UOJQByi6INTXTO_DXdk2A=w1280) Course 2: Week 4: ![](https://lh6.googleusercontent.com/q1idESMQmJZFJzTG5jm_nFLJFk3rGFPNA7zoCpcZc-W15ItlIpK5JoUr7EZIq6LTjVDPXoCYJImMQDlDAawPVITymhu42DAWxqpjkx7zHg7KfYbSLl7mRGGrstDxcGWtGg=w1280) Machine Learning Engineering for Production (MLOps) Specialization by DeepLearning.AI COURSE 3 Machine Learning Modeling Pipelines in Production - Week 1: Neural Architecture Search (NAS) - AutoML, Hyperparameter tuning, Cloud AutoML #autoML #NAS #hyperparameter ![](https://lh5.googleusercontent.com/MZnILEMNb_rNLrgCgk6f5HgQLhoJVzJhj7MqCOCY6yzLKk6rd2DWfk2HrIhOr8RrfkZ23NXPs9o3CM-7-GGEkufOhkpb0QMf3RIUNeJv5DhU6XXYIBWopMBukEPmkDUx-Q=w1280) Machine Learning Engineering for Production (MLOps) Specialization by DeepLearning.AI COURSE 3 Machine Learning Modeling Pipelines in Production Week 3: High-Performance Modeling PCA, ICA, SVD, QAT: quantization aware training, my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera COURSE 2 Machine Learning Data Lifecycle in Production: Week 2: Feature Engineering, Transformation, and Selection feature scaling, normalization and standaridization; TensorFlow extended; TensorFlow Transform; tf.transform analyzers; TensorFlow Ops; Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/G7JKnoBc5s5bG3WK7alpRCEIdQazcLj2L1DLGACGDrsMeHOK9CTS5fh5v74shZzmMJ8YN6hl77hXFxOIDH_8b3M=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/G7JKnoBc5s5bG3WK7alpRCEIdQazcLj2L1DLGACGDrsMeHOK9CTS5fh5v74shZzmMJ8YN6hl77hXFxOIDH_8b3M=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # MLOps update : December 2021 Download complete summary of Machine Learning Engineering for Production (MLOps) Specialisation from Coursera: COURSE 1 Introduction to Machine Learning in Production COURSE 2 Machine Learning Data Lifecycle in Production COURSE 3 Machine Learning Modeling Pipelines in Production COURSE 4 Deploying Machine Learning Models in Production Download full resolution images: # update : December 2021 # Download complete summary of [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera: Download link: ## COURSE 1 Introduction to Machine Learning in Production In the first course of Machine Learning Engineering for Production Specialization, you will identify the various components and design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment constraints and requirements; and learn how to establish a model baseline, address concept drift, and prototype the process for developing, deploying, and continuously improving a productionized ML application. Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills. Week 1: Overview of the ML Lifecycle and Deployment Week 2: Selecting and Training a Model Week 3: Data Definition and Baseline ## COURSE 2 Machine Learning Data Lifecycle in Production In the second course of Machine Learning Engineering for Production Specialization, you will build data pipelines by gathering, cleaning, and validating datasets and assessing data quality; implement feature engineering, transformation, and selection with TensorFlow Extended and get the most predictive power out of your data; and establish the data lifecycle by leveraging data lineage and provenance metadata tools and follow data evolution with enterprise data schemas. Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills. Week 1: Collecting, Labeling, and Validating data Week 2: Feature Engineering, Transformation, and Selection Week 3: Data Journey and Data Storage Week 4: Advanced Data Labeling Methods, Data Augmentation, and Preprocessing Different Data Types ## COURSE 3 Machine Learning Modeling Pipelines in Production In the third course of Machine Learning Engineering for Production Specialization, you will build models for different serving environments; implement tools and techniques to effectively manage your modeling resources and best serve offline and online inference requests; and use analytics tools and performance metrics to address model fairness, explainability issues, and mitigate bottlenecks. Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills. Week 1: Neural Architecture Search Week 2: Model Resource Management Techniques Week 3: High-Performance Modeling Week 4: Model Analysis Week 5: Interpretability ## COURSE 4 Deploying Machine Learning Models in Production In the fourth course of Machine Learning Engineering for Production Specialization, you will deliver deployment pipelines by productionizing, scaling, and monitoring model serving that require different infrastructure; establish procedures to mitigate model decay and performance drops; and establish best practices and apply progressive delivery techniques to maintain and monitor a continuously operating production system. Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills. Week 1: Model Serving: Introduction Week 2: Model Serving: Patterns and Infrastructure Week 3: Model Management and Delivery Week 4: Model Monitoring and Logging # Download full resolution images: Download complete summary of Machine Learning Engineering for Production (MLOps) Specialisation from Coursera: * C1 * W1: * W2: * W3: * C2 * W1: * W2: * W3: * W4: _**C1+C2:**_[ _ **https://drive.google.com/file/d/1W0iv1V8T5ylRp2G5kxKKbuhKrd57S0ys/view?usp=sharing**_](https://drive.google.com/file/d/1W0iv1V8T5ylRp2G5kxKKbuhKrd57S0ys/view?usp=sharing) _ ****_ * C3 * W1: * W2: * W3: * W4: * W5: * C4 * W1 + W2 : * W3 : * W4: Monitoring and observability, End to End solution for computer vision applications in industry my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera \+ very good practice & lab COURSE 4 Deploying Machine Learning Models in Production: Week 4: Model Monitoring and Logging Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) : you can download my complete summary of [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8): [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) . #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C4-W4.png tools for experiment tracking, Logging metrics using TensorBoard, vertex tensorboard, progressive delivery End to End solution for computer vision applications in industry my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 4 Deploying Machine Learning Models in Production: Week 3: Model Management and Delivery Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C4-W3.png model serving, balance cost, latency and throughput, improving prediction latency and reducing resource costs, tensorflow serving, torchserve, KF serving, triton inference server, scaling infrastructure, pre processing operations needed before inference, ETL: extract , transform, load; kafka, pub sub, cloud dataflow, beam, spark streaming; End to End solution for computer vision applications in industry my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 4 Deploying Machine Learning Models in Production: Week 1: Model Serving: Introduction + Week 2: Model Serving: Patterns and Infrastructure + Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C4-W1+W2.png explainable AI, model interpretation methods, TensorFlow Lattice, understanding model predictions, PDP: partial dependence plots, permutation feature importance, SHAP: SHapley Additive exPlanation, testing concept activation vectors, testing concept activation vectors, LIME: local interpretable model agnostic explanations, Google cloud AI explanations for AI platform, XRAI: eXplanation with Ranked Area Integrals, End to End solution for computer vision applications in industry my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 3 Machine Learning Modeling Pipelines in Production: Week 5: Interpretability Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C3-W5.png End to End solution for computer vision applications in industry my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 3 Machine Learning Modeling Pipelines in Production: Week 4: Model Analysis tensorflow model analysis, TFMA, TFX, model debugging, TFMAL: TensorFlow Model Analysis, model remediation techniques, continuous evaluation and monitoring, Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C3-W4.png End to End solution for computer vision applications in industry my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 3 Machine Learning Modeling Pipelines in Production: Week 3: High- Performance Modeling distributed training, Gpipe: Open-source tensorflow library (using lingvo), teacher and student networks, idea: create a simple ;student; model that learns from a complex ;teacher; model. make efficientNets robust to noise with distillation Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C3-W3.png my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 3 Machine Learning Modeling Pipelines in Production: Week 2: Model Resource Management Techniques PCA, PLS, LDA, latent semantic indexing/analysis (LSI and LSA) (SVD) [removes redundant features from the dataset], independent component analysis (ICA), non-negative matrix factorization (NMF), latent dirichlet allocation (LDA), Quantization [make models run faster and use less power with low-precision] and pruning, ML kit, Core ML, TensorFlow Lite (TFX), post training quantization, quantization aware training (QAT), Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C3-W2.png my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 3 Machine Learning Modeling Pipelines in Production: Week 1: Neural Architecture Search NAS, Keras autotuner, AutoML, Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C3-W1.png my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 1 Introduction to Machine Learning in Production + COURSE 2 Machine Learning Data Lifecycle in Production Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://www.google.com/images/icons/product/drive-32.png)C1+C2.png my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 2 Machine Learning Data Lifecycle in Production: Week 4: Advanced Data Labeling Methods, Data Augmentation, and Preprocessing Different Data Types semi-supervised data augmentation: UDA, semi-supervised learning with GANs; policy-based data augmentation: AutoAugment; time series, advanced labeling; active learning; human activity recognition (HAR); Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://lh4.googleusercontent.com/fPFTiEykJIV2FKabWa_EZJt2cUq_m-r3aRRqcGuE2_pww38jNy33cC1H7Af7Kyhy_PPQ3joTLyahyznGzLm4nXHKQRzfEzZJv03KGtMuc4oFnz6CZVuiq6C3iUn1yy8Qpw=w1280) my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera + very good practice & lab COURSE 2 Machine Learning Data Lifecycle in Production: Week 3: Data Journey and Data Storage !pip install ml-metadata, Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah ![](https://lh5.googleusercontent.com/wIlP0s6WOF1IkyJnFsTO2ihEWSlh1C0SEuXJDPfPDGpoA_p00uBWhYuLIy7jo61sX7Pux59bqBKc7jKYdwdsXpfqTQBYh2tlkmsPehRT2_7ut4WJU8mX6sFGg5zyZUaw=w1280) ![](https://www.google.com/images/icons/product/drive-32.png)C1-W1.png ![](https://www.google.com/images/icons/product/drive-32.png)C1-W2.png ![](https://www.google.com/images/icons/product/drive-32.png)C1-W3.png Week 1: Collecting, Labeling, and Validating data ML modeling vs production ML; data collection, labeling, validating ; TFDV; ![](https://www.google.com/images/icons/product/drive-32.png)C2-W1.pdf w2 COURSE 1 Introduction to Machine Learning in Production: Course 2: Week1, Week 2: Selecting and Training a Model error analysis example for speech recognition example; iterative process of error analysis; prioritising what to work on; skewed datasets; performance auditions; F1 score based on precision and recall; data augmentation; Week 3: Data Definition and Baseline data definition; label ambiguity; type of data; HLP; meta-data; data pipeline; balanced train/dev/test splits in small dataset; Dilligence: assess the feasibility and value of potential solution; Course 2: Week 3: ![](https://lh4.googleusercontent.com/NlQoctz4unb1yFzdhXtiUYuHZS7r_7Acfcsm4sq5kvRN2U1I7dRAa0qNQIQFCJF9-bz1VeykDVPKDKmKUbGmoiOwqg16S0UduCwZOzvQMjnjGzi3p37nZWaKlCYQxOvysg=w1280) Course 2: Week 4: ![](https://lh3.googleusercontent.com/0X5maS5P1ByoDFHYXfZmxNIPc58OozlY- By5aAi3VxQqskCTQvbdmDfYNwgJPKqtR0j_3sZmhR2_mOwvAAecySf8pPXTCp7fVKpBSq78NlH3Lgj1fN8x9ml- nw_vfNvOnA=w1280) Machine Learning Engineering for Production (MLOps) Specialization by DeepLearning.AI COURSE 3 Machine Learning Modeling Pipelines in Production - Week 1: Neural Architecture Search (NAS) - AutoML, Hyperparameter tuning, Cloud AutoML #autoML #NAS #hyperparameter ![](https://lh6.googleusercontent.com/VPo1QoDxHblecPzhFP60pittmbsW4FPSipmEf- jqDMTYZr_rruXsc7t6zKnVqG_TFcROSlw8dN66clomQY2gc9QdT9-F2Pdn9iPZqOjCRJFErEWvoaDD-1bpKS- fEME93A=w1280) Machine Learning Engineering for Production (MLOps) Specialization by DeepLearning.AI COURSE 3 Machine Learning Modeling Pipelines in Production Week 3: High-Performance Modeling PCA, ICA, SVD, QAT: quantization aware training, my note on [Machine Learning Engineering for Production (MLOps) Specialisation](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Fspecializations%2Fmachine- learning-engineering-for-production- mlops&sa=D&sntz=1&usg=AOvVaw3RpS9vXYOPo7bTeLDBv9S8) from Coursera COURSE 2 Machine Learning Data Lifecycle in Production: Week 2: Feature Engineering, Transformation, and Selection feature scaling, normalization and standaridization; TensorFlow extended; TensorFlow Transform; tf.transform analyzers; TensorFlow Ops; Download and see more: [https://www.pirahansiah.com/topics/courses/mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmlops&sa=D&sntz=1&usg=AOvVaw1Qyz4DQBdRlWR0fQhlix5h) #MLOps #ComputerVision #pirahansiah Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[https://www.pirahansiah.com/](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2F&sa=D&sntz=1&usg=AOvVaw3LBiNBDVpmVZuS4znrqtOI) #StackDeepLearning [#computervision](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dcomputervision%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0cCdJ3Vmk5N1l3cp6O_su8) [#AI](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dai%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2dPEMgAJEghAGxPRWF43xJ) [#objectdetection](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dobjectdetection%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3iPjh0s9Xx0kiUns1ngiIB) [#objecttracking](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dobjecttracking%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0c5NH2HyRE4LM81xtfyMGC) [#ml](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dml%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw1_WgU3yRidQpKEvm5A-z8l) [#research](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dresearch%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw1vKlf5iOpwLhK-0F4foQ3e) [#CNN](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dcnn%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw19QWtSDWm3hhBhz3-AChAa) [#gans](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dgans%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw1lk1ECXOJN- mXDWUOJNSyq) [#convolutionalneuralnetworks](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dconvolutionalneuralnetworks%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0c8noF_OiCn- zFjustxEUJ) [#ai](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dai%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2dPEMgAJEghAGxPRWF43xJ) [#vr](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dvr%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2emO4DfNb- fRcZrRa5KMFg) [#reinforcementlearning](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dreinforcementlearning%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0P0YF-D2TdSHbkavX52nx2) [#mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dmlops%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3dvE9cPCHjH0CkdyuCTIA8) [#aiforbusiness](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Daiforbusiness%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw16X1M4iwrzdjYdU_ulFK3T) [#science](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dscience%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw1vmLH__ugmE11ZB7WjmLmx) [#researcher](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dresearcher%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3pIEq9N1emXExu0-191RiG) [#phd](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dphd%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2OcifGSB6XNuqH2gKzK6YY) [#cameracalibration](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dcameracalibration%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2RlWcAsgtAUNVg6OBGbkKd) [#opticalflow](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dopticalflow%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw18QjZi7x9SLhVukcmZAVgD) [#videostablization](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dvideostablization%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3WSAJkfYr1vIjbcnfpodFl) [#humanoidrobot](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dhumanoidrobot%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2xFO1kRd2oXLQJq10M-_XJ) [#localization](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dlocalization%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0MIyR__Y8eRDB3dZTecf2Z) [#3dSLAM](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3D3dslam%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2UY6pRlrLvoOiDP8URxRz5) [#reconstruction](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dreconstruction%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0N_h3Cd2AY7DVCXLQUSZ_r) [#pointcloud](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dpointcloud%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0S3yinkSJeDwnOoX27HKOr) [#mixedreality](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dmixedreality%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3BC3QpLp-w3P2WCrusaB02) [#edgecomputing](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dedgecomputing%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2do7CB7fc5a8IDPUINj916) [#raspberrypi](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Draspberrypi%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw05lTlcUMZAvgLJvCrdYOT0) [#intelstick](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dintelstick%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3nSDy4bef8ItMQDBY1AHc2) [#googlecoral](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dgooglecoral%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2flvDoGrRtzJ6AVKvsqwsF) [#jetsonnano](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Djetsonnano%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3FBZ8F6yWzRF85Qc1isy2O) [#nvidiavgpu](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dnvidiavgpu%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw1X0Zxn25NXWOT0YlLcUlPP) [#tensorflowjs](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dtensorflowjs%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2K-Wq_3mt-X1pq4mJWurnU) [#pytorch](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dpytorch%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0MKjo_xUTSXMHwpeG5pU9I) [#opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dopencv%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2WzPIfJb8SPE6hE0LcvqKp) [#aikit](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Daikit%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3rsc3lNPLGvTgNgYKccJQu) [#caffee](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dcaffee%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0VhKNPDM34JSJZe4QtqcXc) [#DIGITS](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Ddigits%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0UivwkkpqApDtx2okylxxR) [#c](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dc%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0jrMZfs5pBJdwrTFWjNWJ8)pp #C [#python](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dpython%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0-vYAEwDIVuEH- Fq-OrgIb) [#ubuntu](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dubuntu%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw1R_p- FRFOxdmZ9CYFTmYzL) [#farshidpirahansiah](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dfarshidpirahansiah%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw1qvC98TN9G7Di_rOxuWWk7) [#farshid](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dfarshid%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2hrd3c0cgU-0ePNJwPxscm) [#pirahansiah](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dpirahansiah%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0AbYCv97yWC3MZG0QBI3L-) [#robotics](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Drobotics%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0c-v7d0S979BakkNasVEmb) #pirahansiah [#MultiCameraMultiClassMultiObjectTracking](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dmulticameramulticlassmultiobjecttracking%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0xHni8xf5NFjd9IvccxvdN) [#deeplearning](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Ddeeplearning%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0_4BL9ENEZAPN9I_ZyTHfw) [#machinelearning](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dmachinelearning%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0GJQE0pYJLn1KpAlBULTve) [#artificialintelligence](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dartificialintelligence%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0kghSLOS6QnRK1JyVmLYgX) [#tensorflow](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dtensorflow%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw1esVtdflQf4ZZx0624h1N9) [#robotics](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Drobotics%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0c-v7d0S979BakkNasVEmb) [#3dvision](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3D3dvision%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2ZJO6ohCZ1NgSqX0wvFNlX) [#sterovision](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dsterovision%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0AmcHHfNGY_zeMJbmBcbur) [#depthmap](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Ddepthmap%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3wM --ayMcpa0-XdKt_RPO8) [#RCNN](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Drcnn%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2m0g4VZrmYsjPcW7wB_ZTR) [#machinevision](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dmachinevision%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2liIXvJ6DKQRJXtztRKEG3) [#imageprocessing](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dimageprocessing%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw1L71Lp1-CV50XPHcixp9hE) [#patternrecognition](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dpatternrecognition%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw1WoEPUJMhUpSCRh9Ta4Mta) [#compiler](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dcompiler%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw12szcaIf6dpkJAjPYM7V-e) [#RISC](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Drisc%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3YM_foJf7Hf3jZFwteLIfj)-V [#RNN](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Drnn%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3bCj47R1qTzd4MNk-1exEF) [#fullStackDeepLearning](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dfullstackdeeplearning%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw23eDRV9AhY4o007CEKu2X9) [#productinnovation](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dproductinnovation%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2AsIcbuD5dqUJA28qtDgSe) [#patents](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dpatents%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3kv-k5ws7NEzqF9ML5lbHd) [#TensorRT](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dtensorrt%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw34rToqV4FEI6a8qUEvgY_i) [#ApacheTVM](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dapachetvm%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw231JTCeb5OyZgt6ck89BnA) [#TFLite](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dtflite%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2iX_x5Fj5RXQNur3YCdCmx) [#PyTorchmobile](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dpytorchmobile%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw37QEC00u5OJDhhHOXqXpfa) [#dockers](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Ddockers%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2f-TuKUh24azBqsvKlVBmI) [#gRPC](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dgrpc%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw11hYAjKgAPw1MTu7ihz9De) [#RESTAPIs](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Drestapis%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3DKEVm0FUJzdhKMLl6QoTd) [#GRPC](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dgrpc%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw11hYAjKgAPw1MTu7ihz9De) [#GraphQL](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dgraphql%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw30 --htOGW4sG7y2BjM2He5) #imageprocessing #patternrecognition #Full_Stack_Deep_Learning #overfit #underfit ![](https://lh3.googleusercontent.com/tFPFXWwAVCT9LPMM9UUT5Jp7cOBMVteADB92pfut79hCMeVfCHE5KCxqJn- _F7P7btan9kxGDVZTIj41nbvXd2OCX4RudQ5Mi-CuUD47ZcOjPfNsz42kfj9O1qrdIMzMmA=w1280) ![](https://lh3.googleusercontent.com/ltcIgw- yo0ILhErPJp8lFDSeGkvc8XIKz596RougxDr9iLOaLpFUV5dD8NPVNb_9vVwlenh_kUA19U1TYTAeOg0wHCARwRzGuFcLuQEW_JIrXlRooCKKHjG2GxR74y9nTg=w1280) ![](https://lh6.googleusercontent.com/-vdpOZR-Y6IxSzBo526q77NBOBCSf47DhTzfgob- Hn_OvF95CvoKkQXZcQ-pdhKSljs42zX5__u- HdxjeCpuGIwBCt7z34xc3p0SLOczcEMLSV1AipAhLCLtCPxKLEtw=w1280) ![](https://lh6.googleusercontent.com/FKMWnIDbPpc9hL5yEvJVeE7vTxei- yYybp6UWpwOQlRjKAMyrhIyr0GA0nTZSk1Az96xN-hv-- PT8FIRIoYHutkso2glQW5ycstYNADPATMXFAW2IYUwCaq0rcBvMLcsaw=w1280) ![](https://lh6.googleusercontent.com/1LkSVMO98DQLetxl5wb- oB8497ulas44rtTKVGyHCN8-Xk4Yv5x43Gp2n1eIoFOzwEAv35B-DDMw52qfkhL0Cqg153MrXqN- EX3JCBokkUw_elez8bQjs4QEafY7DIf2PQ=w1280) ![](https://lh5.googleusercontent.com/dBKtc9W9yIWTtC79mQoF0Qsskmzu1T6Cz33kKHo3xNnkdR3jJf24ega6j5fNUhfmVPAaXw2ApPQGJGWepBCnqgKTJxGxTMARtjsVNWK9gXkodM47E0XV7_yfaccIq34eRQ=w1280) ![](https://lh5.googleusercontent.com/DD7SczdgI-0vmUwBPIO2Wv3MPW9Vr4Uka178RipChty_Q_oFKLF2yhPDfDsLbxBdCb3SKX9JJ9cpKPNMxdwfMWRYQ6oesn6ZldvPr3xlvT8_ynp7C8rzc3Xh98OuHg- SAw=w1280) ![](https://lh6.googleusercontent.com/5ELjsHRBeq-- BQR08SkAyrCZfRN8vCqJx_9shm_HwtS6yci_EO3TWwcu- JYx-f4JG4kXp2yEY5GsNgWf0_zV3gW-8aRC89wKE-M1EH_2TkAet2ocDlU0ykmmYnrPcgIMuA=w1280) ![](https://lh3.googleusercontent.com/GkZKK9cuJa9E-- JD7Qo2_O1pMLsgMPQsyeG18uMetQDV4Q-C0zUssb40xLPSxtQtGl_tDG9LatPzsFbcLkXY- JS1zlKaj0_idJTQS8O-DFXJ45R5xj1JgRNDCEw8ePCc0g=w1280) ![](https://lh4.googleusercontent.com/rDxc06t-Vax_4WvxEw1RyedYBjpAg1KYnDYkP6Y3_kEoZzIACiak-l9tAH0S4gTm8Y3ORgcmT4FcbVVflPQlU2KoJAIGimBGY2_2ytOUzMkj7DLdz07M6ZD0ft6cDeuXNQ=w1280) ![](https://lh6.googleusercontent.com/79D_2GUiJ0tSFcmEvp96wzF0MkkYl2PNPzwQdxMgKTPjcz_K26wrz5XlEjGHc4g0XCPdPamy6wm0M8ahmY0k-qI2nbtHXWksAk9k6YuXFKRj0AB2m4QRHGhqIEBHHGtszw=w1280) ![](https://lh4.googleusercontent.com/3bcJwMV- USqLuSc8li1OCZjgbW6fRPJtZgifOKeWXCV1AHXt1JdHfR- kBRKRecGmse35OCBDTliFyu9k2Qk9AOLtXCOdd4gmjrXA7gOSU_TfFPNI894RBLEeUBH0JNpB3Q=w1280) ![](https://lh6.googleusercontent.com/2y-duXbPX9rMbTrLZpAxaJHpjcXzurDeByjP0Rt_YmRyG8bdnS64PUdvhHoYvXDjOJLs4MKy4ODX6xRhOIOEwx6BKtL0RumceGORLauKyKtLg1eK6oTRkLAWlvQzuv9k8g=w1280) Common solution for under-fitting or over-fitting: check data-set, error analysis, choose a different model architecture, hyper-parameter tuning Under-fitting (reducing bias): ⬆️ bigger model ⬇️ reduce regularization 🤔 error analysis 🤔 different model architecture 🤔 tune hyper-parameters ⬆️ add features over-fitting (reducing variance): ⬆️ add more training data ⬆️ add normalization (batch norm, layer norm) ⬆️ add data augmentation ⬆️ increase regularization (dropout, L2, weight decay) 🤔 error analysis 🤔 choose a different model architecture 🤔 tune hyper-parameters ⬇️ early stopping ⬇️ remove features ⬇️ reduce model size ![](https://lh5.googleusercontent.com/rJt4fuB5pU1virR8piVIrHpU26e8bdCvOUkXBZZOg73o4ZhMEJHjkYFKX4pAp- AEbY9ITT8LjgRzsFyzncx6_NbgHvyEvDxFL8r2pcVPLLkZikfrTJkiEogqi-gUAaBjQQ=w1280) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[https://www.pirahansiah.com/](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2F&sa=D&sntz=1&usg=AOvVaw3LBiNBDVpmVZuS4znrqtOI) #StackDeepLearning [#computervision](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dcomputervision%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0cCdJ3Vmk5N1l3cp6O_su8) [#AI](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dai%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2dPEMgAJEghAGxPRWF43xJ) [#objectdetection](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dobjectdetection%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3iPjh0s9Xx0kiUns1ngiIB) [#objecttracking](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dobjecttracking%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0c5NH2HyRE4LM81xtfyMGC) [#ml](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dml%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw1_WgU3yRidQpKEvm5A-z8l) [#research](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dresearch%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw1vKlf5iOpwLhK-0F4foQ3e) [#CNN](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dcnn%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw19QWtSDWm3hhBhz3-AChAa) [#gans](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dgans%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw1lk1ECXOJN- mXDWUOJNSyq) [#convolutionalneuralnetworks](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dconvolutionalneuralnetworks%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0c8noF_OiCn- zFjustxEUJ) [#ai](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dai%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2dPEMgAJEghAGxPRWF43xJ) [#vr](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dvr%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2emO4DfNb- fRcZrRa5KMFg) [#reinforcementlearning](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dreinforcementlearning%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0P0YF-D2TdSHbkavX52nx2) [#mlops](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dmlops%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3dvE9cPCHjH0CkdyuCTIA8) [#aiforbusiness](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Daiforbusiness%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw16X1M4iwrzdjYdU_ulFK3T) [#science](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dscience%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw1vmLH__ugmE11ZB7WjmLmx) [#researcher](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dresearcher%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3pIEq9N1emXExu0-191RiG) [#phd](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dphd%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2OcifGSB6XNuqH2gKzK6YY) [#cameracalibration](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dcameracalibration%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2RlWcAsgtAUNVg6OBGbkKd) [#opticalflow](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dopticalflow%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw18QjZi7x9SLhVukcmZAVgD) [#videostablization](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dvideostablization%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3WSAJkfYr1vIjbcnfpodFl) [#humanoidrobot](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dhumanoidrobot%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2xFO1kRd2oXLQJq10M-_XJ) [#localization](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dlocalization%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0MIyR__Y8eRDB3dZTecf2Z) [#3dSLAM](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3D3dslam%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2UY6pRlrLvoOiDP8URxRz5) [#reconstruction](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dreconstruction%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0N_h3Cd2AY7DVCXLQUSZ_r) [#pointcloud](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dpointcloud%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0S3yinkSJeDwnOoX27HKOr) [#mixedreality](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dmixedreality%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3BC3QpLp-w3P2WCrusaB02) [#edgecomputing](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dedgecomputing%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2do7CB7fc5a8IDPUINj916) [#raspberrypi](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Draspberrypi%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw05lTlcUMZAvgLJvCrdYOT0) [#intelstick](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dintelstick%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3nSDy4bef8ItMQDBY1AHc2) [#googlecoral](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dgooglecoral%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2flvDoGrRtzJ6AVKvsqwsF) [#jetsonnano](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Djetsonnano%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3FBZ8F6yWzRF85Qc1isy2O) [#nvidiavgpu](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dnvidiavgpu%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw1X0Zxn25NXWOT0YlLcUlPP) [#tensorflowjs](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dtensorflowjs%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2K-Wq_3mt-X1pq4mJWurnU) [#pytorch](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dpytorch%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0MKjo_xUTSXMHwpeG5pU9I) [#opencv](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dopencv%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2WzPIfJb8SPE6hE0LcvqKp) [#aikit](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Daikit%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3rsc3lNPLGvTgNgYKccJQu) [#caffee](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dcaffee%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0VhKNPDM34JSJZe4QtqcXc) [#DIGITS](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Ddigits%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0UivwkkpqApDtx2okylxxR) [#c](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dc%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0jrMZfs5pBJdwrTFWjNWJ8)pp #C [#python](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dpython%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0-vYAEwDIVuEH- Fq-OrgIb) [#ubuntu](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dubuntu%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw1R_p- FRFOxdmZ9CYFTmYzL) [#farshidpirahansiah](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dfarshidpirahansiah%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw1qvC98TN9G7Di_rOxuWWk7) [#farshid](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dfarshid%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2hrd3c0cgU-0ePNJwPxscm) [#pirahansiah](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dpirahansiah%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0AbYCv97yWC3MZG0QBI3L-) [#robotics](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Drobotics%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0c-v7d0S979BakkNasVEmb) #pirahansiah [#MultiCameraMultiClassMultiObjectTracking](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dmulticameramulticlassmultiobjecttracking%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0xHni8xf5NFjd9IvccxvdN) [#deeplearning](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Ddeeplearning%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0_4BL9ENEZAPN9I_ZyTHfw) [#machinelearning](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dmachinelearning%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0GJQE0pYJLn1KpAlBULTve) [#artificialintelligence](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dartificialintelligence%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0kghSLOS6QnRK1JyVmLYgX) [#tensorflow](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dtensorflow%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw1esVtdflQf4ZZx0624h1N9) [#robotics](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Drobotics%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0c-v7d0S979BakkNasVEmb) [#3dvision](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3D3dvision%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2ZJO6ohCZ1NgSqX0wvFNlX) [#sterovision](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dsterovision%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw0AmcHHfNGY_zeMJbmBcbur) [#depthmap](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Ddepthmap%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3wM --ayMcpa0-XdKt_RPO8) [#RCNN](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Drcnn%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2m0g4VZrmYsjPcW7wB_ZTR) [#machinevision](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dmachinevision%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2liIXvJ6DKQRJXtztRKEG3) [#imageprocessing](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dimageprocessing%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw1L71Lp1-CV50XPHcixp9hE) [#patternrecognition](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dpatternrecognition%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw1WoEPUJMhUpSCRh9Ta4Mta) [#compiler](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dcompiler%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw12szcaIf6dpkJAjPYM7V-e) [#RISC](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Drisc%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3YM_foJf7Hf3jZFwteLIfj)-V [#RNN](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Drnn%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3bCj47R1qTzd4MNk-1exEF) [#fullStackDeepLearning](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dfullstackdeeplearning%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw23eDRV9AhY4o007CEKu2X9) [#productinnovation](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dproductinnovation%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2AsIcbuD5dqUJA28qtDgSe) [#patents](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dpatents%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3kv-k5ws7NEzqF9ML5lbHd) [#TensorRT](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dtensorrt%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw34rToqV4FEI6a8qUEvgY_i) [#ApacheTVM](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dapachetvm%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw231JTCeb5OyZgt6ck89BnA) [#TFLite](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dtflite%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2iX_x5Fj5RXQNur3YCdCmx) [#PyTorchmobile](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dpytorchmobile%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw37QEC00u5OJDhhHOXqXpfa) [#dockers](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Ddockers%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw2f-TuKUh24azBqsvKlVBmI) [#gRPC](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dgrpc%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw11hYAjKgAPw1MTu7ihz9De) [#RESTAPIs](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Drestapis%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw3DKEVm0FUJzdhKMLl6QoTd) [#GRPC](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dgrpc%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw11hYAjKgAPw1MTu7ihz9De) [#GraphQL](https://www.google.com/url?q=https%3A%2F%2Fwww.linkedin.com%2Ffeed%2Fhashtag%2F%3Fkeywords%3Dgraphql%26highlightedUpdateUrns%3Durn%253Ali%253Aactivity%253A6787871627586625536&sa=D&sntz=1&usg=AOvVaw30 --htOGW4sG7y2BjM2He5) #imageprocessing #patternrecognition #Full_Stack_Deep_Learning #overfit #underfit ![](https://lh6.googleusercontent.com/6dehcQyE_4ynfRykTc36WjFZjH8OMtJutzI05eXXcVXwBPudiqjEVBG24FghWG4fUEpyshEcVeJc7KyNylipJ6chtfHSga7Vtb9EiLJ30nJVLo47aOm3pXeqID_hb3uzGg=w1280) ![](https://lh5.googleusercontent.com/FaevWDam- VcXGaeO2de9uEn_eqOz9MX2nh2ZxjJAdvHA7M69InMmtG2t7SxIiqErvonmPIdqyOPXjZZybZt62-8xC2ykHr9aNvatcOApxJO6emhsGwWXBf8-DUTIs3g50Q=w1280) ![](https://lh6.googleusercontent.com/h0wsPFa5DArerke9iEPpbuNBLl2GDBRei0dQ1OkWfSXZK1tMC8xYla4qPZh7UPNtpqJDgYkcIzvGhdV0_-JLhUgkK- DrmZ9jxlqD88oPE2p3fdDCHyX1oV1MyxjXI-77=w1280) ![](https://lh3.googleusercontent.com/SU8y4DZpFKo6DMkG- vzIigfJVAN6fORbgliSY5xJeVpcicJDP1M7de1Htk9r4fZbUen6_Tmp7LY5Kd7ZPL3lZDPqaNtSR2bYOwe6is1jrlqJO- u_UquAyeutKeMpVhkWVQ=w1280) ![](https://lh5.googleusercontent.com/vJNt-2d2-I0qsiB3J5iJVlU6SdK0nNmeZ9ubaFurF7AGcy8Fh9Rya4-bqU7U-3vgpuDv4FXww2gAruFSn14d5TYVlMkCcNJobYAPfuCUh3J0RFL6AA03WIYUV5BFrazQyg=w1280) ![](https://lh3.googleusercontent.com/AzwczZya1tRhNBgW43SGYKq75ydfBupcQOdDbhL-5bEkeXefgk_la24eRY7TU3LSKXAvqQuLxUVavtpSRTvd3G2HyVE9UiDuSAVWUXTl2JEPjL4Wd18-BZQa3ipqrsSbow=w1280) ![](https://lh5.googleusercontent.com/Xzm4ztQU3OQH0B7Q-8VBCd90R_qb2ScfohcRb8dF9V2zPlCtBrRB_W26_rfhJatBS65GXC8s-tHlCIvGmNqov59VqNYPYMYr4l2t9wLu2HrF44jJMov3L7zEH1k9W4Ch0g=w1280) ![](https://lh4.googleusercontent.com/8ye- gGd0kWZLomcpkIMnmSirUeddQIo5ZOUgcTuqg-0oLbjYJgtgi4j8lo6WC3JG8SfniRbbIx1feTI4G_314QTh5dtPmaowJSH3752gBI3Bxz2MdbL0zed3K4E3BZTTRA=w1280) ![](https://lh5.googleusercontent.com/cDyO7gFRFwOxduxzqHeDiuPOqUG2VHYUuW3NWbl4t48ZtAQAphmI8GKRjxRuC7J1ufOCDv1snkKRbR_80RL0evKokc4mt4vfOu8V_1oXehIqrrpZm9XdoSa- kwiquCkmvA=w1280) ![](https://lh5.googleusercontent.com/97GQhT3uNE8jtLaWQfd5l3ulFk5IpV6xbfRbhZS1g9phBk4rUg3CzGSQjNLWeYJqKUplmfdPc3I3B-dFO2ACpfNqzKN7kcNvG2_tVRw5g4ufsmBXYOpi1om65NYi9XuwIQ=w1280) ![](https://lh6.googleusercontent.com/R1VMD7tPBJY-Ybexcf3QdfqqKVg28ud- ygYWkNZXRkhwMEHhUvuC0ABNvw6P5TrffqLm2PWkyXffAhE76mYPHKvu- aHzV6yQdjqBdDggxypaGTDoEnHoaW_BaC2nKnzO-A=w1280) ![](https://lh6.googleusercontent.com/Gf4V4rYer8WmB0_gSJGjqLTa9dlFawqx7fT5KOsB7x2CH3_dpFiA7hZ9tnm2ZGJ7SpVrkTxMfcHeUYkg9f5QWbDW8akU_HVciJ5Jz721DoGc2QuKwjeT8pwXA6Aafxgtzw=w1280) ![](https://lh6.googleusercontent.com/_f4FoO8x8bCIF3LBCv1TT- PUJI6idI2WieW5a2kjkRS3W4VOIB7MkstgrC-klA3P_Y5N7X3L9ezDvvNjaRbFQGZxTfRKmWMd- AgV0i4ixsu_YqWNHL3BIvVjXV3s87IRaw=w1280) Common solution for under-fitting or over-fitting: check data-set, error analysis, choose a different model architecture, hyper-parameter tuning Under-fitting (reducing bias): ⬆️ bigger model ⬇️ reduce regularization 🤔 error analysis 🤔 different model architecture 🤔 tune hyper-parameters ⬆️ add features over-fitting (reducing variance): ⬆️ add more training data ⬆️ add normalization (batch norm, layer norm) ⬆️ add data augmentation ⬆️ increase regularization (dropout, L2, weight decay) 🤔 error analysis 🤔 choose a different model architecture 🤔 tune hyper-parameters ⬇️ early stopping ⬇️ remove features ⬇️ reduce model size ![](https://lh3.googleusercontent.com/_J- axGQuO6XhNaTl1eNEWg9QFSGpr0ts4t1CG4jqAtPit8wFrIyJNuN7VhqvYUnlWdvbgALmqnaIPJI1qGiGOfp79uz98mgvNfex_CF6Aml3xQIQvwySEU4dJkKJeEEOqQ=w1280) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/64TqwjhC5SmyL8C2PAzPTeZ1eymQjar7GANkNJZpwnamRumOFEpuqaCmqVBV4_c_1_k_QhPKrZ3BtlOwEWMtPXc=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/64TqwjhC5SmyL8C2PAzPTeZ1eymQjar7GANkNJZpwnamRumOFEpuqaCmqVBV4_c_1_k_QhPKrZ3BtlOwEWMtPXc=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Full Stack Deep Learning FSDL 2022 Lecture 07 [https://www.pirahansiah.com/topics/courses/fsdl](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Ffsdl&sa=D&sntz=1&usg=AOvVaw0NzI497w7Cu8RwtruHofT3) Note on Full Stack Deep Learning FSDL 2022; #Full_Stack_Deep_Learning #pirahansiah #Farshid_PirahanSiah more: [https://www.pirahansiah.com/topics/courses/fsdl](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Ffsdl&sa=D&sntz=1&usg=AOvVaw0NzI497w7Cu8RwtruHofT3) Lecture 06 [https://www.pirahansiah.com/topics/courses/fsdl](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Ffsdl&sa=D&sntz=1&usg=AOvVaw0NzI497w7Cu8RwtruHofT3) Note on Full Stack Deep Learning FSDL 2022; #Full_Stack_Deep_Learning #pirahansiah #Farshid_PirahanSiah more: [https://www.pirahansiah.com/topics/courses/fsdl](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Ffsdl&sa=D&sntz=1&usg=AOvVaw0NzI497w7Cu8RwtruHofT3) Lecture 05 [https://www.pirahansiah.com/topics/courses/fsdl](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Ffsdl&sa=D&sntz=1&usg=AOvVaw0NzI497w7Cu8RwtruHofT3) Note on Full Stack Deep Learning FSDL 2022; #Full_Stack_Deep_Learning #pirahansiah #Farshid_PirahanSiah Lecture 06: Continual Learning (FSDL 2022) \- what metrics to monitor \- Outcomes and feedback from users \- Model performance metrics \- Proxy metrics \- Data quality testing \- accuracy \- completeness \- consistency \- timeliness \- validity \- integrity \- Distribution drift \- type \- instantaneous drift like \- gradual drift \- periodic drifts \- temporary drift \- measure \- reference window \- metric \- 1D : KL , KS \- dealing with high-dimensional data \- *projections* \- system metrics \- how to tell if those metrics are "bad" \- KS-Test \- good \- 1 fixed rule \- 2 specified range \- 3 predicted range \- 4 unsupervised detection \- \- tools for monitoring \- system monitoring tools \- datadog \- honeycomb.io \- NewRelic \- amazon cloudwatch \- OSS ML monitoring: evidently AI, why logs \- 1 logging \- profiling \- sampling \- 2 curation \- L1: just sample randomly \- L2: stratified sampling \- L3: curate "interesting" data \- manually \- similarity-based curation \- projection-based curation \- automatically curating data using active learning \- scoring function \- most uncertain \- highest predicted loss \- most different from labels \- most representative \- big impact on training \- tools: scale nucleus, data-centric ML tools \- 3 retraining triggers \- based on performance \- online learning \- 4 dataset formation \- 1: train on all available data \- 2: sliding window \- 3: online batch selection \- 4: continual fine-tuning \- 5: offline testing \- dynamic \- expectation tests \- 6: online testing \- shadow mode, AB test, roll out gradually, roll back, ... \- trying it all more: [https://www.pirahansiah.com/topics/courses/fsdl](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Ffsdl&sa=D&sntz=1&usg=AOvVaw0NzI497w7Cu8RwtruHofT3) ![](https://lh5.googleusercontent.com/d5hRt9zT3Rhv6cniRmHA748tOz7m1KvLqSIxpipztBGni9H-Qd4ooevwQAWOOOGHi2R1dGD_sfBVCJ7D6gUFgDE=w1280) Lecture 04 [https://www.pirahansiah.com/topics/courses/fsdl](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Ffsdl&sa=D&sntz=1&usg=AOvVaw0NzI497w7Cu8RwtruHofT3) Note on Full Stack Deep Learning FSDL 2022; #Full_Stack_Deep_Learning #pirahansiah #Farshid_PirahanSiah Lecture 04: Data Management (FSDL 2022) \- fixing/adding/augmenting data: keep it simple \- data sources \- filesystem: local disk speeds: NVME M.2 SSD, latency: nice visualization - \- object storage: usually binary object can versioning, redundancy "S3", \- database: persistent, fast, scalable, in RAM, object-store URLs, - postgres, SQLite \- data warehouse: OLAP, OLTP, ETL \- data lake: unstructured: ELT, \- SQL and DataFrames \- SQL: structured \- Pandas is DataFrames: DASK parallelize pandas, RAPIDS pandas on GPUs \- Airflow: specify the DAG of tasks using python \- Prefect \- Dagster \- feature stores \- tecton.ai \- FEAST \- Featureform \- Hu \- Activeloop \- Labeling \- self-supervised learning \- image data augmentation \- HIVE \- scale.ai \- labelbox \- label studio ** \- diffgram \- aquarium and scale nucleus \- weak supervision : snorkel.ai - rubrix \- data versioning: level 1 - level 3: DVC \- privacy more: [https://www.pirahansiah.com/topics/courses/fsdl](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Ffsdl&sa=D&sntz=1&usg=AOvVaw0NzI497w7Cu8RwtruHofT3) ![](https://lh5.googleusercontent.com/rPh9wynj_TZ54mUCKXv54RYcDjUsU0TjDKsu8ZCCttN4gY1VPuqNRZ01ZG6R4GaJTqa7JgfV6F62f1T9mEukfEgWkngt95a_qSxkf- aIyFI8Sj7jp58JXtBEFi1PUVPpVg=w1280) Lecture 03: testing [https://www.pirahansiah.com/topics/courses/fsdl](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Ffsdl&sa=D&sntz=1&usg=AOvVaw0NzI497w7Cu8RwtruHofT3) Note on Full Stack Deep Learning FSDL 2022; #Full_Stack_Deep_Learning #pirahansiah #Farshid_PirahanSiah more: [https://www.pirahansiah.com/topics/courses/fsdl](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Ffsdl&sa=D&sntz=1&usg=AOvVaw0NzI497w7Cu8RwtruHofT3) ![](https://lh5.googleusercontent.com/XK7q0ygWDPi1t6i_dzhGN53zQsgpRIs8EJVT2otK- FPMxMiBiFsiRVTz9p8JtdxoaQ5efxhIShQdIlCAorLjUYqgZ3NcEd8aFfitljcRQT2hMoy5LI03EpfzCEYDZ8pQrw=w1280) Lecture 02: Development Infrastructure & Tooling (FSDL 2022) [https://www.pirahansiah.com/topics/courses/fsdl](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Ffsdl&sa=D&sntz=1&usg=AOvVaw0NzI497w7Cu8RwtruHofT3) Note on Full Stack Deep Learning FSDL 2022; #Full_Stack_Deep_Learning #pirahansiah #Farshid_PirahanSiah more: [https://www.pirahansiah.com/topics/courses/fsdl](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Ffsdl&sa=D&sntz=1&usg=AOvVaw0NzI497w7Cu8RwtruHofT3) ![](https://lh3.googleusercontent.com/fsCcqOuJiJS18KAfGpMJ1_oq962VFK0I7P4j7hyE7CYmW-4GvLo8T1XMGgZYjAXG8mIB8ck9srZKtNSNdv2BVcENc0FvQDnOeNbWi5Fhps7K4Fmm2B2PlqZsHr7a8PaAwg=w1280) Lecture 01 + Lab 1&2&3: [https://www.pirahansiah.com/topics/courses/fsdl](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Ffsdl&sa=D&sntz=1&usg=AOvVaw0NzI497w7Cu8RwtruHofT3) Note on Full Stack Deep Learning FSDL 2022; ResnetTransformer, teacher_forward, --precision 16, - --limit_train_batches 10 #Full_Stack_Deep_Learning #pirahansiah #Farshid_PirahanSiah more: [https://www.pirahansiah.com/topics/courses/fsdl](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Ffsdl&sa=D&sntz=1&usg=AOvVaw0NzI497w7Cu8RwtruHofT3) ![](https://lh5.googleusercontent.com/CyyWAe21cXRkrVPbaz-o0XRSlqCS7rAgKQRPn0Rj8B8KqB_q- SbHMIzzgfjdmrOHxrzKOhSuz2bCWcjyCDciE-3zdWsnfgJvmAjzj4fL2fSBjhZg8ZyLOJqVT6rTOzwb0w=w1280) ![](https://lh5.googleusercontent.com/TFMaVL49Fq_nJ1mvrBFnIpRvLC5HeB5-TxjtlZHbRhlLkiEId7L5IMdvCrIV5eJtdncZlHZqq4m7hElR56y6poMCnq4UotasDeEfypTOOwPouSm6BKij7w14jhYHtKrrow=w1280) Lecture 01: When to Use ML and Course Vision [https://www.pirahansiah.com/topics/courses/fsdl](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Ffsdl&sa=D&sntz=1&usg=AOvVaw0NzI497w7Cu8RwtruHofT3) #Full_Stack_Deep_Learning #pirahansiah #Farshid_PirahanSiah Formulating the problem and estimating project cost Sourcing, cleaning, processing, labeling, synthesizing, and augmenting data Picking the right framework and compute infrastructure Troubleshooting training and ensuring reproducibility Deploying the model at scale ✨ Monitoring and continually improving the deployed model ✨ ✨ How ML teams work and how to manage ML projects ✨ ✨ Building on Large Language Models and other Foundation Models ✨ Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/64TqwjhC5SmyL8C2PAzPTeZ1eymQjar7GANkNJZpwnamRumOFEpuqaCmqVBV4_c_1_k_QhPKrZ3BtlOwEWMtPXc=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/64TqwjhC5SmyL8C2PAzPTeZ1eymQjar7GANkNJZpwnamRumOFEpuqaCmqVBV4_c_1_k_QhPKrZ3BtlOwEWMtPXc=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Machine Learning Foundations: A Case Study Approach See more: [**https://www.pirahansiah.com/topics/courses/machine-learning- specialization/machine-learning-foundations-a-case-study- approach**](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmachine- learning-specialization%2Fmachine-learning-foundations-a-case-study- approach&sa=D&sntz=1&usg=AOvVaw2Qxr7mG3gMjiA5NEeF-stB) **** If you found the content informative, you may Follow me by [Farshid PirahanSiah](https://www.google.com/url?q=https%3A%2F%2Ftwitter.com%2Fpirahansiah&sa=D&sntz=1&usg=AOvVaw3hfltLQuPqA2GdOTjpZSOC), Ph.D for more! **#MachineLearning #pirahansiah #FarshidPirahanSiah** **GitHub:**[ **https://github.com/pirahansiah**](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah&sa=D&sntz=1&usg=AOvVaw117ZUj_8nRizCX3p9pQqVY) **** **Twitter:**[ **https://twitter.com/pirahansiah**](https://www.google.com/url?q=https%3A%2F%2Ftwitter.com%2Fpirahansiah&sa=D&sntz=1&usg=AOvVaw3hfltLQuPqA2GdOTjpZSOC) **** **COURSE** 1: Machine Learning Foundations: A Case Study Approach * [Week 1](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fml-foundations%2Fhome%2Fweek%2F1&sa=D&sntz=1&usg=AOvVaw302K3NSfoG4cNPa3JqN85k) * [Week 2](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fml-foundations%2Fhome%2Fweek%2F2&sa=D&sntz=1&usg=AOvVaw3aTxD8L_gyPDYDgn6aRyd8) * [Week 3](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fml-foundations%2Fhome%2Fweek%2F3&sa=D&sntz=1&usg=AOvVaw1crXPiXRD7aiv3KbF6b1fJ) * [Week 4](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fml-foundations%2Fhome%2Fweek%2F4&sa=D&sntz=1&usg=AOvVaw1TyvYtfwsqsZypgFC2d4a6) * [Week 5](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fml-foundations%2Fhome%2Fweek%2F5&sa=D&sntz=1&usg=AOvVaw2K4nKTG8renPLAqARzDDsM) * [Week 6](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fml-foundations%2Fhome%2Fweek%2F6&sa=D&sntz=1&usg=AOvVaw2uJcFrDqT5zzVGJM5KEQdc) Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains. This first course treats the machine learning method as a black box. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications. Learning Outcomes: By the end of this course, you will be able to: -Identify potential applications of machine learning in practice. -Describe the core differences in analyses enabled by regression, classification, and clustering. -Select the appropriate machine learning task for a potential application. -Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. -Represent your data as features to serve as input to machine learning models. -Assess the model quality in terms of relevant error metrics for each task. -Utilize a dataset to fit a model to analyze new data. -Build an end-to-end application that uses machine learning at its core. -Implement these techniques in Python. 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Machine Learning Specialization 2022 Coursera: Machine Learning Specialization (2022) Course 2: Advanced Learning Algorithms Week 2: Neural network training See more & download other notes: [https://www.pirahansiah.com/topics/courses/machine-learning- specialization](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmachine- learning-specialization&sa=D&sntz=1&usg=AOvVaw2DeYIlikOMmy64M0k_H_xq) #Machine_Learning_Specialization #pirahansiah #FarshidPirahanSiah ![](https://lh6.googleusercontent.com/GdmLPexR6hIwVgkxlqCqeqI09WXBxrGYxWj3c_flVuuyHmua5hN0JQmBDEsEUaEOkoQ3QUHe8rDpG09d1cOhgR-B54IRRJ_p_6lVsEjPrSuzO- nico3H-verqEpd9BlygA=w1280) Coursera: Machine Learning Specialization (2022) Course 2: Advanced Learning Algorithms Week 1: NN See more: [https://www.pirahansiah.com/topics/courses/machine-learning- specialization](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmachine- learning-specialization&sa=D&sntz=1&usg=AOvVaw2DeYIlikOMmy64M0k_H_xq) #Machine_Learning_Specialization #pirahansiah #FarshidPirahanSiah ![](https://lh3.googleusercontent.com/HR07pr6AKn5Vmuzx8B3VUKdpN1Rv48OQHLnL9sQQKDikj3JlbosijpDuiOr7vkucnMPBRfQ7v4_1QcaKAEoeFYTjheEez49BjmCFEG5bGjkAbBnUPpnNETWi5RPPR0oZ3A=w1280) Coursera: Machine Learning Specialization (2022) Course 1: Supervised Machine Learning: Regression and Classification Week 3: Classification See more: [https://www.pirahansiah.com/topics/courses/machine-learning- specialization](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmachine- learning-specialization&sa=D&sntz=1&usg=AOvVaw2DeYIlikOMmy64M0k_H_xq) #Machine_Learning_Specialization #pirahansiah #FarshidPirahanSiah ![](https://lh5.googleusercontent.com/zWSytFZKoGmf_K8jo2TTPX- lXDDhjKeWPt0mEUmwfnk1cFHMn5BdEiWyi_IzCs- iBpL0t8OCG7iZ8uKJkMsosrhynfF2-U3uimU3SNzCioV30EYoXAjRmv7sMLZwxrYcoQ=w1280) Coursera: Machine Learning Specialization Course 1: Supervised Machine Learning: Regression and Classification Week 2: Regression with multiple input variables See more: [https://www.pirahansiah.com/topics/courses/machine-learning- specialization](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmachine- learning-specialization&sa=D&sntz=1&usg=AOvVaw2DeYIlikOMmy64M0k_H_xq) #Machine_Learning_Specialization #pirahansiah #FarshidPirahanSiah ![](https://lh5.googleusercontent.com/TefpuCXLRYNEFO89Mw_1EaPxXEQPbsZSVIFEqsJcrvH9hA5tVXZGpU2T5tN4NHnHfEjofYHDJymHA2Irk8ub2mMm7uEs6-CNqnbwSqT0XpKFar23Z2Wqngfbu6xUhgHw6Q=w1280) Coursera: Machine Learning Specialization Course 1: Supervised Machine Learning: Regression and Classification Week 1: Introduction to Machine Learning See more: [https://www.pirahansiah.com/topics/courses/machine-learning- specialization](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmachine- learning-specialization&sa=D&sntz=1&usg=AOvVaw2DeYIlikOMmy64M0k_H_xq) #Machine_Learning_Specialization #pirahansiah #FarshidPirahanSiah ![](https://lh6.googleusercontent.com/zTu5IckCpMo7yWNXvWrMV-k2Hysz1i4R0ffeEDzwrywnkGTJZ_s3ZXflc8y77Y5lHEu28U5bFnH0ZkDBHaeaXUsn_WRW0ythg8dbRtUFbg3YzOsOfVlRhT_i4fPdXaezXw=w1280) Machine Learning Specialization Course 1 Week 2 See more: [https://www.pirahansiah.com/topics/courses/machine-learning- specialization](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmachine- learning-specialization&sa=D&sntz=1&usg=AOvVaw2DeYIlikOMmy64M0k_H_xq) #MachineLearning #pirahansiah #FarshidPirahanSiah If you found the content informative, you may Follow me by [https://twitter.com/pirahansiah](https://www.google.com/url?q=https%3A%2F%2Ftwitter.com%2Fpirahansiah&sa=D&sntz=1&usg=AOvVaw3hfltLQuPqA2GdOTjpZSOC) for more! **COURSE** 1 ### [Machine Learning Foundations: A Case Study Approach](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fml- foundations%3Fspecialization%3Dmachine- learning&sa=D&sntz=1&usg=AOvVaw1nrYbfZ3vhOT2Xd5KM5Isp) **4.6** **stars** 13,044 ratings • 3,104 reviews Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains. This first course treats the machine learning method as a black box. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications. Learning Outcomes: By the end of this course, you will be able to: -Identify potential applications of machine learning in practice. -Describe the core differences in analyses enabled by regression, classification, and clustering. -Select the appropriate machine learning task for a potential application. -Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. -Represent your data as features to serve as input to machine learning models. -Assess the model quality in terms of relevant error metrics for each task. -Utilize a dataset to fit a model to analyze new data. -Build an end-to-end application that uses machine learning at its core. -Implement these techniques in Python. **SHOW ALL ABOUT MACHINE LEARNING FOUNDATIONS: A CASE STUDY APPROACH** **SHOW ALL** **COURSE** 2 ### [Machine Learning: Regression](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fml- regression%3Fspecialization%3Dmachine- learning&sa=D&sntz=1&usg=AOvVaw0gPQh7m6G5PdRgWw1sLrYn) **4.8** **stars** 5,470 ratings • 1,016 reviews Case Study - Predicting Housing Prices In our first case study, predicting house prices, you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...). This is just one of the many places where regression can be applied. Other applications range from predicting health outcomes in medicine, stock prices in finance, and power usage in high- performance computing, to analyzing which regulators are important for gene expression. In this course, you will explore regularized linear regression models for the task of prediction and feature selection. You will be able to handle very large sets of features and select between models of various complexity. You will also analyze the impact of aspects of your data -- such as outliers -- on your selected models and predictions. To fit these models, you will implement optimization algorithms that scale to large datasets. Learning Outcomes: By the end of this course, you will be able to: -Describe the input and output of a regression model. -Compare and contrast bias and variance when modeling data. -Estimate model parameters using optimization algorithms. -Tune parameters with cross validation. -Analyze the performance of the model. -Describe the notion of sparsity and how LASSO leads to sparse solutions. -Deploy methods to select between models. -Exploit the model to form predictions. -Build a regression model to predict prices using a housing dataset. -Implement these techniques in Python. **SHOW ALL ABOUT MACHINE LEARNING: REGRESSION** **SHOW ALL** **COURSE** 3 ### [Machine Learning: Classification](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fml- classification%3Fspecialization%3Dmachine- learning&sa=D&sntz=1&usg=AOvVaw0DO2ATucnIpN4omP3WXrhB) **4.7** **stars** 3,662 ratings • 603 reviews Case Studies: Analyzing Sentiment & Loan Default Prediction In our case study on analyzing sentiment, you will create models that predict a class (positive/negative sentiment) from input features (text of the reviews, user profile information,...). In our second case study for this course, loan default prediction, you will tackle financial data, and predict when a loan is likely to be risky or safe for the bank. These tasks are an examples of classification, one of the most widely used areas of machine learning, with a broad array of applications, including ad targeting, spam detection, medical diagnosis and image classification. In this course, you will create classifiers that provide state-of-the-art performance on a variety of tasks. You will become familiar with the most successful techniques, which are most widely used in practice, including logistic regression, decision trees and boosting. In addition, you will be able to design and implement the underlying algorithms that can learn these models at scale, using stochastic gradient ascent. You will implement these technique on real-world, large-scale machine learning tasks. You will also address significant tasks you will face in real-world applications of ML, including handling missing data and measuring precision and recall to evaluate a classifier. This course is hands-on, action-packed, and full of visualizations and illustrations of how these techniques will behave on real data. We've also included optional content in every module, covering advanced topics for those who want to go even deeper! Learning Objectives: By the end of this course, you will be able to: -Describe the input and output of a classification model. -Tackle both binary and multiclass classification problems. -Implement a logistic regression model for large-scale classification. -Create a non-linear model using decision trees. -Improve the performance of any model using boosting. -Scale your methods with stochastic gradient ascent. -Describe the underlying decision boundaries. -Build a classification model to predict sentiment in a product review dataset. -Analyze financial data to predict loan defaults. -Use techniques for handling missing data. -Evaluate your models using precision-recall metrics. -Implement these techniques in Python (or in the language of your choice, though Python is highly recommended). **SHOW ALL ABOUT MACHINE LEARNING: CLASSIFICATION** **SHOW ALL** **COURSE** 4 ### [Machine Learning: Clustering & Retrieval](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fml- clustering-and-retrieval%3Fspecialization%3Dmachine- learning&sa=D&sntz=1&usg=AOvVaw0i5MZL56twhU3RZPbgPV3w) **4.7** **stars** 2,294 ratings • 392 reviews Case Studies: Finding Similar Documents A reader is interested in a specific news article and you want to find similar articles to recommend. What is the right notion of similarity? Moreover, what if there are millions of other documents? Each time you want to a retrieve a new document, do you need to search through all other documents? How do you group similar documents together? How do you discover new, emerging topics that the documents cover? In this third case study, finding similar documents, you will examine similarity-based algorithms for retrieval. In this course, you will also examine structured representations for describing the documents in the corpus, including clustering and mixed membership models, such as latent Dirichlet allocation (LDA). You will implement expectation maximization (EM) to learn the document clusterings, and see how to scale the methods using MapReduce. Learning Outcomes: By the end of this course, you will be able to: -Create a document retrieval system using k-nearest neighbors. -Identify various similarity metrics for text data. -Reduce computations in k-nearest neighbor search by using KD-trees. -Produce approximate nearest neighbors using locality sensitive hashing. -Compare and contrast supervised and unsupervised learning tasks. -Cluster documents by topic using k-means. -Describe how to parallelize k-means using MapReduce. -Examine probabilistic clustering approaches using mixtures models. -Fit a mixture of Gaussian model using expectation maximization (EM). -Perform mixed membership modeling using latent Dirichlet allocation (LDA). -Describe the steps of a Gibbs sampler and how to use its output to draw inferences. -Compare and contrast initialization techniques for non-convex optimization objectives. -Implement these techniques in Python. **SHOW ALL ABOUT MACHINE LEARNING: CLUSTERING & RETRIEVAL** **SHOW ALL** Download source code and full text of Mind map from [https://github.com/pirahansiah/pirahansiah.github.io/tree/main/Machine_Learning_Specialization](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fpirahansiah.github.io%2Ftree%2Fmain%2FMachine_Learning_Specialization&sa=D&sntz=1&usg=AOvVaw1L1CgVAceLsiPzKKiMkCw-) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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Machine Learning Specialization 2022 Coursera: Machine Learning Specialization (2022) Course 2: Advanced Learning Algorithms Week 2: Neural network training See more & download other notes: [https://www.pirahansiah.com/topics/courses/machine-learning- specialization](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmachine- learning-specialization&sa=D&sntz=1&usg=AOvVaw2DeYIlikOMmy64M0k_H_xq) #Machine_Learning_Specialization #pirahansiah #FarshidPirahanSiah ![](https://lh6.googleusercontent.com/GdmLPexR6hIwVgkxlqCqeqI09WXBxrGYxWj3c_flVuuyHmua5hN0JQmBDEsEUaEOkoQ3QUHe8rDpG09d1cOhgR-B54IRRJ_p_6lVsEjPrSuzO- nico3H-verqEpd9BlygA=w1280) Coursera: Machine Learning Specialization (2022) Course 2: Advanced Learning Algorithms Week 1: NN See more: [https://www.pirahansiah.com/topics/courses/machine-learning- specialization](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmachine- learning-specialization&sa=D&sntz=1&usg=AOvVaw2DeYIlikOMmy64M0k_H_xq) #Machine_Learning_Specialization #pirahansiah #FarshidPirahanSiah ![](https://lh3.googleusercontent.com/HR07pr6AKn5Vmuzx8B3VUKdpN1Rv48OQHLnL9sQQKDikj3JlbosijpDuiOr7vkucnMPBRfQ7v4_1QcaKAEoeFYTjheEez49BjmCFEG5bGjkAbBnUPpnNETWi5RPPR0oZ3A=w1280) Coursera: Machine Learning Specialization (2022) Course 1: Supervised Machine Learning: Regression and Classification Week 3: Classification See more: [https://www.pirahansiah.com/topics/courses/machine-learning- specialization](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmachine- learning-specialization&sa=D&sntz=1&usg=AOvVaw2DeYIlikOMmy64M0k_H_xq) #Machine_Learning_Specialization #pirahansiah #FarshidPirahanSiah ![](https://lh5.googleusercontent.com/zWSytFZKoGmf_K8jo2TTPX- lXDDhjKeWPt0mEUmwfnk1cFHMn5BdEiWyi_IzCs- iBpL0t8OCG7iZ8uKJkMsosrhynfF2-U3uimU3SNzCioV30EYoXAjRmv7sMLZwxrYcoQ=w1280) Coursera: Machine Learning Specialization Course 1: Supervised Machine Learning: Regression and Classification Week 2: Regression with multiple input variables See more: [https://www.pirahansiah.com/topics/courses/machine-learning- specialization](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmachine- learning-specialization&sa=D&sntz=1&usg=AOvVaw2DeYIlikOMmy64M0k_H_xq) #Machine_Learning_Specialization #pirahansiah #FarshidPirahanSiah ![](https://lh5.googleusercontent.com/TefpuCXLRYNEFO89Mw_1EaPxXEQPbsZSVIFEqsJcrvH9hA5tVXZGpU2T5tN4NHnHfEjofYHDJymHA2Irk8ub2mMm7uEs6-CNqnbwSqT0XpKFar23Z2Wqngfbu6xUhgHw6Q=w1280) Coursera: Machine Learning Specialization Course 1: Supervised Machine Learning: Regression and Classification Week 1: Introduction to Machine Learning See more: [https://www.pirahansiah.com/topics/courses/machine-learning- specialization](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmachine- learning-specialization&sa=D&sntz=1&usg=AOvVaw2DeYIlikOMmy64M0k_H_xq) #Machine_Learning_Specialization #pirahansiah #FarshidPirahanSiah ![](https://lh6.googleusercontent.com/zTu5IckCpMo7yWNXvWrMV-k2Hysz1i4R0ffeEDzwrywnkGTJZ_s3ZXflc8y77Y5lHEu28U5bFnH0ZkDBHaeaXUsn_WRW0ythg8dbRtUFbg3YzOsOfVlRhT_i4fPdXaezXw=w1280) Machine Learning Specialization Course 1 Week 2 See more: [https://www.pirahansiah.com/topics/courses/machine-learning- specialization](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmachine- learning-specialization&sa=D&sntz=1&usg=AOvVaw2DeYIlikOMmy64M0k_H_xq) #MachineLearning #pirahansiah #FarshidPirahanSiah If you found the content informative, you may Follow me by [https://twitter.com/pirahansiah](https://www.google.com/url?q=https%3A%2F%2Ftwitter.com%2Fpirahansiah&sa=D&sntz=1&usg=AOvVaw3hfltLQuPqA2GdOTjpZSOC) for more! **COURSE** 1 ### [Machine Learning Foundations: A Case Study Approach](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fml- foundations%3Fspecialization%3Dmachine- learning&sa=D&sntz=1&usg=AOvVaw1nrYbfZ3vhOT2Xd5KM5Isp) **4.6** **stars** 13,044 ratings • 3,104 reviews Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains. This first course treats the machine learning method as a black box. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications. Learning Outcomes: By the end of this course, you will be able to: -Identify potential applications of machine learning in practice. -Describe the core differences in analyses enabled by regression, classification, and clustering. -Select the appropriate machine learning task for a potential application. -Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. -Represent your data as features to serve as input to machine learning models. -Assess the model quality in terms of relevant error metrics for each task. -Utilize a dataset to fit a model to analyze new data. -Build an end-to-end application that uses machine learning at its core. -Implement these techniques in Python. **SHOW ALL ABOUT MACHINE LEARNING FOUNDATIONS: A CASE STUDY APPROACH** **SHOW ALL** **COURSE** 2 ### [Machine Learning: Regression](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fml- regression%3Fspecialization%3Dmachine- learning&sa=D&sntz=1&usg=AOvVaw0gPQh7m6G5PdRgWw1sLrYn) **4.8** **stars** 5,470 ratings • 1,016 reviews Case Study - Predicting Housing Prices In our first case study, predicting house prices, you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...). This is just one of the many places where regression can be applied. Other applications range from predicting health outcomes in medicine, stock prices in finance, and power usage in high- performance computing, to analyzing which regulators are important for gene expression. In this course, you will explore regularized linear regression models for the task of prediction and feature selection. You will be able to handle very large sets of features and select between models of various complexity. You will also analyze the impact of aspects of your data -- such as outliers -- on your selected models and predictions. To fit these models, you will implement optimization algorithms that scale to large datasets. Learning Outcomes: By the end of this course, you will be able to: -Describe the input and output of a regression model. -Compare and contrast bias and variance when modeling data. -Estimate model parameters using optimization algorithms. -Tune parameters with cross validation. -Analyze the performance of the model. -Describe the notion of sparsity and how LASSO leads to sparse solutions. -Deploy methods to select between models. -Exploit the model to form predictions. -Build a regression model to predict prices using a housing dataset. -Implement these techniques in Python. **SHOW ALL ABOUT MACHINE LEARNING: REGRESSION** **SHOW ALL** **COURSE** 3 ### [Machine Learning: Classification](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fml- classification%3Fspecialization%3Dmachine- learning&sa=D&sntz=1&usg=AOvVaw0DO2ATucnIpN4omP3WXrhB) **4.7** **stars** 3,662 ratings • 603 reviews Case Studies: Analyzing Sentiment & Loan Default Prediction In our case study on analyzing sentiment, you will create models that predict a class (positive/negative sentiment) from input features (text of the reviews, user profile information,...). In our second case study for this course, loan default prediction, you will tackle financial data, and predict when a loan is likely to be risky or safe for the bank. These tasks are an examples of classification, one of the most widely used areas of machine learning, with a broad array of applications, including ad targeting, spam detection, medical diagnosis and image classification. In this course, you will create classifiers that provide state-of-the-art performance on a variety of tasks. You will become familiar with the most successful techniques, which are most widely used in practice, including logistic regression, decision trees and boosting. In addition, you will be able to design and implement the underlying algorithms that can learn these models at scale, using stochastic gradient ascent. You will implement these technique on real-world, large-scale machine learning tasks. You will also address significant tasks you will face in real-world applications of ML, including handling missing data and measuring precision and recall to evaluate a classifier. This course is hands-on, action-packed, and full of visualizations and illustrations of how these techniques will behave on real data. We've also included optional content in every module, covering advanced topics for those who want to go even deeper! Learning Objectives: By the end of this course, you will be able to: -Describe the input and output of a classification model. -Tackle both binary and multiclass classification problems. -Implement a logistic regression model for large-scale classification. -Create a non-linear model using decision trees. -Improve the performance of any model using boosting. -Scale your methods with stochastic gradient ascent. -Describe the underlying decision boundaries. -Build a classification model to predict sentiment in a product review dataset. -Analyze financial data to predict loan defaults. -Use techniques for handling missing data. -Evaluate your models using precision-recall metrics. -Implement these techniques in Python (or in the language of your choice, though Python is highly recommended). **SHOW ALL ABOUT MACHINE LEARNING: CLASSIFICATION** **SHOW ALL** **COURSE** 4 ### [Machine Learning: Clustering & Retrieval](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fml- clustering-and-retrieval%3Fspecialization%3Dmachine- learning&sa=D&sntz=1&usg=AOvVaw0i5MZL56twhU3RZPbgPV3w) **4.7** **stars** 2,294 ratings • 392 reviews Case Studies: Finding Similar Documents A reader is interested in a specific news article and you want to find similar articles to recommend. What is the right notion of similarity? Moreover, what if there are millions of other documents? Each time you want to a retrieve a new document, do you need to search through all other documents? How do you group similar documents together? How do you discover new, emerging topics that the documents cover? In this third case study, finding similar documents, you will examine similarity-based algorithms for retrieval. In this course, you will also examine structured representations for describing the documents in the corpus, including clustering and mixed membership models, such as latent Dirichlet allocation (LDA). You will implement expectation maximization (EM) to learn the document clusterings, and see how to scale the methods using MapReduce. Learning Outcomes: By the end of this course, you will be able to: -Create a document retrieval system using k-nearest neighbors. -Identify various similarity metrics for text data. -Reduce computations in k-nearest neighbor search by using KD-trees. -Produce approximate nearest neighbors using locality sensitive hashing. -Compare and contrast supervised and unsupervised learning tasks. -Cluster documents by topic using k-means. -Describe how to parallelize k-means using MapReduce. -Examine probabilistic clustering approaches using mixtures models. -Fit a mixture of Gaussian model using expectation maximization (EM). -Perform mixed membership modeling using latent Dirichlet allocation (LDA). -Describe the steps of a Gibbs sampler and how to use its output to draw inferences. -Compare and contrast initialization techniques for non-convex optimization objectives. -Implement these techniques in Python. **SHOW ALL ABOUT MACHINE LEARNING: CLUSTERING & RETRIEVAL** **SHOW ALL** Download source code and full text of Mind map from [https://github.com/pirahansiah/pirahansiah.github.io/tree/main/Machine_Learning_Specialization](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fpirahansiah.github.io%2Ftree%2Fmain%2FMachine_Learning_Specialization&sa=D&sntz=1&usg=AOvVaw1L1CgVAceLsiPzKKiMkCw-) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/_hck3K2JGQ1ghYoXZ7iBAh6UrcJe4h-XNeLuiyiCVHdw5j1X2qmMgL8doj8geGzck7rU2DmtQXU2cDjW4Jc0qCo=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/_hck3K2JGQ1ghYoXZ7iBAh6UrcJe4h-XNeLuiyiCVHdw5j1X2qmMgL8doj8geGzck7rU2DmtQXU2cDjW4Jc0qCo=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Machine Learning Specialization 2022 Coursera: Machine Learning Specialization (2022) Course 2: Advanced Learning Algorithms Week 2: Neural network training See more & download other notes: [https://www.pirahansiah.com/topics/courses/machine-learning- specialization](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmachine- learning-specialization&sa=D&sntz=1&usg=AOvVaw2DeYIlikOMmy64M0k_H_xq) #Machine_Learning_Specialization #pirahansiah #FarshidPirahanSiah ![](https://lh6.googleusercontent.com/GdmLPexR6hIwVgkxlqCqeqI09WXBxrGYxWj3c_flVuuyHmua5hN0JQmBDEsEUaEOkoQ3QUHe8rDpG09d1cOhgR-B54IRRJ_p_6lVsEjPrSuzO- nico3H-verqEpd9BlygA=w1280) Coursera: Machine Learning Specialization (2022) Course 2: Advanced Learning Algorithms Week 1: NN See more: [https://www.pirahansiah.com/topics/courses/machine-learning- specialization](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmachine- learning-specialization&sa=D&sntz=1&usg=AOvVaw2DeYIlikOMmy64M0k_H_xq) #Machine_Learning_Specialization #pirahansiah #FarshidPirahanSiah ![](https://lh3.googleusercontent.com/HR07pr6AKn5Vmuzx8B3VUKdpN1Rv48OQHLnL9sQQKDikj3JlbosijpDuiOr7vkucnMPBRfQ7v4_1QcaKAEoeFYTjheEez49BjmCFEG5bGjkAbBnUPpnNETWi5RPPR0oZ3A=w1280) Coursera: Machine Learning Specialization (2022) Course 1: Supervised Machine Learning: Regression and Classification Week 3: Classification See more: [https://www.pirahansiah.com/topics/courses/machine-learning- specialization](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmachine- learning-specialization&sa=D&sntz=1&usg=AOvVaw2DeYIlikOMmy64M0k_H_xq) #Machine_Learning_Specialization #pirahansiah #FarshidPirahanSiah ![](https://lh5.googleusercontent.com/zWSytFZKoGmf_K8jo2TTPX- lXDDhjKeWPt0mEUmwfnk1cFHMn5BdEiWyi_IzCs- iBpL0t8OCG7iZ8uKJkMsosrhynfF2-U3uimU3SNzCioV30EYoXAjRmv7sMLZwxrYcoQ=w1280) Coursera: Machine Learning Specialization Course 1: Supervised Machine Learning: Regression and Classification Week 2: Regression with multiple input variables See more: [https://www.pirahansiah.com/topics/courses/machine-learning- specialization](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmachine- learning-specialization&sa=D&sntz=1&usg=AOvVaw2DeYIlikOMmy64M0k_H_xq) #Machine_Learning_Specialization #pirahansiah #FarshidPirahanSiah ![](https://lh5.googleusercontent.com/TefpuCXLRYNEFO89Mw_1EaPxXEQPbsZSVIFEqsJcrvH9hA5tVXZGpU2T5tN4NHnHfEjofYHDJymHA2Irk8ub2mMm7uEs6-CNqnbwSqT0XpKFar23Z2Wqngfbu6xUhgHw6Q=w1280) Coursera: Machine Learning Specialization Course 1: Supervised Machine Learning: Regression and Classification Week 1: Introduction to Machine Learning See more: [https://www.pirahansiah.com/topics/courses/machine-learning- specialization](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmachine- learning-specialization&sa=D&sntz=1&usg=AOvVaw2DeYIlikOMmy64M0k_H_xq) #Machine_Learning_Specialization #pirahansiah #FarshidPirahanSiah ![](https://lh6.googleusercontent.com/zTu5IckCpMo7yWNXvWrMV-k2Hysz1i4R0ffeEDzwrywnkGTJZ_s3ZXflc8y77Y5lHEu28U5bFnH0ZkDBHaeaXUsn_WRW0ythg8dbRtUFbg3YzOsOfVlRhT_i4fPdXaezXw=w1280) Machine Learning Specialization Course 1 Week 2 See more: [https://www.pirahansiah.com/topics/courses/machine-learning- specialization](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmachine- learning-specialization&sa=D&sntz=1&usg=AOvVaw2DeYIlikOMmy64M0k_H_xq) #MachineLearning #pirahansiah #FarshidPirahanSiah If you found the content informative, you may Follow me by [https://twitter.com/pirahansiah](https://www.google.com/url?q=https%3A%2F%2Ftwitter.com%2Fpirahansiah&sa=D&sntz=1&usg=AOvVaw3hfltLQuPqA2GdOTjpZSOC) for more! **COURSE** 1 ### [Machine Learning Foundations: A Case Study Approach](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fml- foundations%3Fspecialization%3Dmachine- learning&sa=D&sntz=1&usg=AOvVaw1nrYbfZ3vhOT2Xd5KM5Isp) **4.6** **stars** 13,044 ratings • 3,104 reviews Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains. This first course treats the machine learning method as a black box. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications. Learning Outcomes: By the end of this course, you will be able to: -Identify potential applications of machine learning in practice. -Describe the core differences in analyses enabled by regression, classification, and clustering. -Select the appropriate machine learning task for a potential application. -Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. -Represent your data as features to serve as input to machine learning models. -Assess the model quality in terms of relevant error metrics for each task. -Utilize a dataset to fit a model to analyze new data. -Build an end-to-end application that uses machine learning at its core. -Implement these techniques in Python. **SHOW ALL ABOUT MACHINE LEARNING FOUNDATIONS: A CASE STUDY APPROACH** **SHOW ALL** **COURSE** 2 ### [Machine Learning: Regression](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fml- regression%3Fspecialization%3Dmachine- learning&sa=D&sntz=1&usg=AOvVaw0gPQh7m6G5PdRgWw1sLrYn) **4.8** **stars** 5,470 ratings • 1,016 reviews Case Study - Predicting Housing Prices In our first case study, predicting house prices, you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...). This is just one of the many places where regression can be applied. Other applications range from predicting health outcomes in medicine, stock prices in finance, and power usage in high- performance computing, to analyzing which regulators are important for gene expression. In this course, you will explore regularized linear regression models for the task of prediction and feature selection. You will be able to handle very large sets of features and select between models of various complexity. You will also analyze the impact of aspects of your data -- such as outliers -- on your selected models and predictions. To fit these models, you will implement optimization algorithms that scale to large datasets. Learning Outcomes: By the end of this course, you will be able to: -Describe the input and output of a regression model. -Compare and contrast bias and variance when modeling data. -Estimate model parameters using optimization algorithms. -Tune parameters with cross validation. -Analyze the performance of the model. -Describe the notion of sparsity and how LASSO leads to sparse solutions. -Deploy methods to select between models. -Exploit the model to form predictions. -Build a regression model to predict prices using a housing dataset. -Implement these techniques in Python. **SHOW ALL ABOUT MACHINE LEARNING: REGRESSION** **SHOW ALL** **COURSE** 3 ### [Machine Learning: Classification](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fml- classification%3Fspecialization%3Dmachine- learning&sa=D&sntz=1&usg=AOvVaw0DO2ATucnIpN4omP3WXrhB) **4.7** **stars** 3,662 ratings • 603 reviews Case Studies: Analyzing Sentiment & Loan Default Prediction In our case study on analyzing sentiment, you will create models that predict a class (positive/negative sentiment) from input features (text of the reviews, user profile information,...). In our second case study for this course, loan default prediction, you will tackle financial data, and predict when a loan is likely to be risky or safe for the bank. These tasks are an examples of classification, one of the most widely used areas of machine learning, with a broad array of applications, including ad targeting, spam detection, medical diagnosis and image classification. In this course, you will create classifiers that provide state-of-the-art performance on a variety of tasks. You will become familiar with the most successful techniques, which are most widely used in practice, including logistic regression, decision trees and boosting. In addition, you will be able to design and implement the underlying algorithms that can learn these models at scale, using stochastic gradient ascent. You will implement these technique on real-world, large-scale machine learning tasks. You will also address significant tasks you will face in real-world applications of ML, including handling missing data and measuring precision and recall to evaluate a classifier. This course is hands-on, action-packed, and full of visualizations and illustrations of how these techniques will behave on real data. We've also included optional content in every module, covering advanced topics for those who want to go even deeper! Learning Objectives: By the end of this course, you will be able to: -Describe the input and output of a classification model. -Tackle both binary and multiclass classification problems. -Implement a logistic regression model for large-scale classification. -Create a non-linear model using decision trees. -Improve the performance of any model using boosting. -Scale your methods with stochastic gradient ascent. -Describe the underlying decision boundaries. -Build a classification model to predict sentiment in a product review dataset. -Analyze financial data to predict loan defaults. -Use techniques for handling missing data. -Evaluate your models using precision-recall metrics. -Implement these techniques in Python (or in the language of your choice, though Python is highly recommended). **SHOW ALL ABOUT MACHINE LEARNING: CLASSIFICATION** **SHOW ALL** **COURSE** 4 ### [Machine Learning: Clustering & Retrieval](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fml- clustering-and-retrieval%3Fspecialization%3Dmachine- learning&sa=D&sntz=1&usg=AOvVaw0i5MZL56twhU3RZPbgPV3w) **4.7** **stars** 2,294 ratings • 392 reviews Case Studies: Finding Similar Documents A reader is interested in a specific news article and you want to find similar articles to recommend. What is the right notion of similarity? Moreover, what if there are millions of other documents? Each time you want to a retrieve a new document, do you need to search through all other documents? How do you group similar documents together? How do you discover new, emerging topics that the documents cover? In this third case study, finding similar documents, you will examine similarity-based algorithms for retrieval. In this course, you will also examine structured representations for describing the documents in the corpus, including clustering and mixed membership models, such as latent Dirichlet allocation (LDA). You will implement expectation maximization (EM) to learn the document clusterings, and see how to scale the methods using MapReduce. Learning Outcomes: By the end of this course, you will be able to: -Create a document retrieval system using k-nearest neighbors. -Identify various similarity metrics for text data. -Reduce computations in k-nearest neighbor search by using KD-trees. -Produce approximate nearest neighbors using locality sensitive hashing. -Compare and contrast supervised and unsupervised learning tasks. -Cluster documents by topic using k-means. -Describe how to parallelize k-means using MapReduce. -Examine probabilistic clustering approaches using mixtures models. -Fit a mixture of Gaussian model using expectation maximization (EM). -Perform mixed membership modeling using latent Dirichlet allocation (LDA). -Describe the steps of a Gibbs sampler and how to use its output to draw inferences. -Compare and contrast initialization techniques for non-convex optimization objectives. -Implement these techniques in Python. **SHOW ALL ABOUT MACHINE LEARNING: CLUSTERING & RETRIEVAL** **SHOW ALL** Download source code and full text of Mind map from [https://github.com/pirahansiah/pirahansiah.github.io/tree/main/Machine_Learning_Specialization](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fpirahansiah.github.io%2Ftree%2Fmain%2FMachine_Learning_Specialization&sa=D&sntz=1&usg=AOvVaw1L1CgVAceLsiPzKKiMkCw-) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/_hck3K2JGQ1ghYoXZ7iBAh6UrcJe4h-XNeLuiyiCVHdw5j1X2qmMgL8doj8geGzck7rU2DmtQXU2cDjW4Jc0qCo=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/_hck3K2JGQ1ghYoXZ7iBAh6UrcJe4h-XNeLuiyiCVHdw5j1X2qmMgL8doj8geGzck7rU2DmtQXU2cDjW4Jc0qCo=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Machine Learning Specialization 2022 Coursera: Machine Learning Specialization (2022) Course 2: Advanced Learning Algorithms Week 2: Neural network training See more & download other notes: [https://www.pirahansiah.com/topics/courses/machine-learning- specialization](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmachine- learning-specialization&sa=D&sntz=1&usg=AOvVaw2DeYIlikOMmy64M0k_H_xq) #Machine_Learning_Specialization #pirahansiah #FarshidPirahanSiah ![](https://lh6.googleusercontent.com/GdmLPexR6hIwVgkxlqCqeqI09WXBxrGYxWj3c_flVuuyHmua5hN0JQmBDEsEUaEOkoQ3QUHe8rDpG09d1cOhgR-B54IRRJ_p_6lVsEjPrSuzO- nico3H-verqEpd9BlygA=w1280) Coursera: Machine Learning Specialization (2022) Course 2: Advanced Learning Algorithms Week 1: NN See more: [https://www.pirahansiah.com/topics/courses/machine-learning- specialization](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmachine- learning-specialization&sa=D&sntz=1&usg=AOvVaw2DeYIlikOMmy64M0k_H_xq) #Machine_Learning_Specialization #pirahansiah #FarshidPirahanSiah ![](https://lh3.googleusercontent.com/HR07pr6AKn5Vmuzx8B3VUKdpN1Rv48OQHLnL9sQQKDikj3JlbosijpDuiOr7vkucnMPBRfQ7v4_1QcaKAEoeFYTjheEez49BjmCFEG5bGjkAbBnUPpnNETWi5RPPR0oZ3A=w1280) Coursera: Machine Learning Specialization (2022) Course 1: Supervised Machine Learning: Regression and Classification Week 3: Classification See more: [https://www.pirahansiah.com/topics/courses/machine-learning- specialization](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmachine- learning-specialization&sa=D&sntz=1&usg=AOvVaw2DeYIlikOMmy64M0k_H_xq) #Machine_Learning_Specialization #pirahansiah #FarshidPirahanSiah ![](https://lh5.googleusercontent.com/zWSytFZKoGmf_K8jo2TTPX- lXDDhjKeWPt0mEUmwfnk1cFHMn5BdEiWyi_IzCs- iBpL0t8OCG7iZ8uKJkMsosrhynfF2-U3uimU3SNzCioV30EYoXAjRmv7sMLZwxrYcoQ=w1280) Coursera: Machine Learning Specialization Course 1: Supervised Machine Learning: Regression and Classification Week 2: Regression with multiple input variables See more: [https://www.pirahansiah.com/topics/courses/machine-learning- specialization](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmachine- learning-specialization&sa=D&sntz=1&usg=AOvVaw2DeYIlikOMmy64M0k_H_xq) #Machine_Learning_Specialization #pirahansiah #FarshidPirahanSiah ![](https://lh5.googleusercontent.com/TefpuCXLRYNEFO89Mw_1EaPxXEQPbsZSVIFEqsJcrvH9hA5tVXZGpU2T5tN4NHnHfEjofYHDJymHA2Irk8ub2mMm7uEs6-CNqnbwSqT0XpKFar23Z2Wqngfbu6xUhgHw6Q=w1280) Coursera: Machine Learning Specialization Course 1: Supervised Machine Learning: Regression and Classification Week 1: Introduction to Machine Learning See more: [https://www.pirahansiah.com/topics/courses/machine-learning- specialization](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmachine- learning-specialization&sa=D&sntz=1&usg=AOvVaw2DeYIlikOMmy64M0k_H_xq) #Machine_Learning_Specialization #pirahansiah #FarshidPirahanSiah ![](https://lh6.googleusercontent.com/zTu5IckCpMo7yWNXvWrMV-k2Hysz1i4R0ffeEDzwrywnkGTJZ_s3ZXflc8y77Y5lHEu28U5bFnH0ZkDBHaeaXUsn_WRW0ythg8dbRtUFbg3YzOsOfVlRhT_i4fPdXaezXw=w1280) Machine Learning Specialization Course 1 Week 2 See more: [https://www.pirahansiah.com/topics/courses/machine-learning- specialization](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmachine- learning-specialization&sa=D&sntz=1&usg=AOvVaw2DeYIlikOMmy64M0k_H_xq) #MachineLearning #pirahansiah #FarshidPirahanSiah If you found the content informative, you may Follow me by [https://twitter.com/pirahansiah](https://www.google.com/url?q=https%3A%2F%2Ftwitter.com%2Fpirahansiah&sa=D&sntz=1&usg=AOvVaw3hfltLQuPqA2GdOTjpZSOC) for more! **COURSE** 1 ### [Machine Learning Foundations: A Case Study Approach](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fml- foundations%3Fspecialization%3Dmachine- learning&sa=D&sntz=1&usg=AOvVaw1nrYbfZ3vhOT2Xd5KM5Isp) **4.6** **stars** 13,044 ratings • 3,104 reviews Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains. This first course treats the machine learning method as a black box. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications. Learning Outcomes: By the end of this course, you will be able to: -Identify potential applications of machine learning in practice. -Describe the core differences in analyses enabled by regression, classification, and clustering. -Select the appropriate machine learning task for a potential application. -Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. -Represent your data as features to serve as input to machine learning models. -Assess the model quality in terms of relevant error metrics for each task. -Utilize a dataset to fit a model to analyze new data. -Build an end-to-end application that uses machine learning at its core. -Implement these techniques in Python. **SHOW ALL ABOUT MACHINE LEARNING FOUNDATIONS: A CASE STUDY APPROACH** **SHOW ALL** **COURSE** 2 ### [Machine Learning: Regression](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fml- regression%3Fspecialization%3Dmachine- learning&sa=D&sntz=1&usg=AOvVaw0gPQh7m6G5PdRgWw1sLrYn) **4.8** **stars** 5,470 ratings • 1,016 reviews Case Study - Predicting Housing Prices In our first case study, predicting house prices, you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...). This is just one of the many places where regression can be applied. Other applications range from predicting health outcomes in medicine, stock prices in finance, and power usage in high- performance computing, to analyzing which regulators are important for gene expression. In this course, you will explore regularized linear regression models for the task of prediction and feature selection. You will be able to handle very large sets of features and select between models of various complexity. You will also analyze the impact of aspects of your data -- such as outliers -- on your selected models and predictions. To fit these models, you will implement optimization algorithms that scale to large datasets. Learning Outcomes: By the end of this course, you will be able to: -Describe the input and output of a regression model. -Compare and contrast bias and variance when modeling data. -Estimate model parameters using optimization algorithms. -Tune parameters with cross validation. -Analyze the performance of the model. -Describe the notion of sparsity and how LASSO leads to sparse solutions. -Deploy methods to select between models. -Exploit the model to form predictions. -Build a regression model to predict prices using a housing dataset. -Implement these techniques in Python. **SHOW ALL ABOUT MACHINE LEARNING: REGRESSION** **SHOW ALL** **COURSE** 3 ### [Machine Learning: Classification](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fml- classification%3Fspecialization%3Dmachine- learning&sa=D&sntz=1&usg=AOvVaw0DO2ATucnIpN4omP3WXrhB) **4.7** **stars** 3,662 ratings • 603 reviews Case Studies: Analyzing Sentiment & Loan Default Prediction In our case study on analyzing sentiment, you will create models that predict a class (positive/negative sentiment) from input features (text of the reviews, user profile information,...). In our second case study for this course, loan default prediction, you will tackle financial data, and predict when a loan is likely to be risky or safe for the bank. These tasks are an examples of classification, one of the most widely used areas of machine learning, with a broad array of applications, including ad targeting, spam detection, medical diagnosis and image classification. In this course, you will create classifiers that provide state-of-the-art performance on a variety of tasks. You will become familiar with the most successful techniques, which are most widely used in practice, including logistic regression, decision trees and boosting. In addition, you will be able to design and implement the underlying algorithms that can learn these models at scale, using stochastic gradient ascent. You will implement these technique on real-world, large-scale machine learning tasks. You will also address significant tasks you will face in real-world applications of ML, including handling missing data and measuring precision and recall to evaluate a classifier. This course is hands-on, action-packed, and full of visualizations and illustrations of how these techniques will behave on real data. We've also included optional content in every module, covering advanced topics for those who want to go even deeper! Learning Objectives: By the end of this course, you will be able to: -Describe the input and output of a classification model. -Tackle both binary and multiclass classification problems. -Implement a logistic regression model for large-scale classification. -Create a non-linear model using decision trees. -Improve the performance of any model using boosting. -Scale your methods with stochastic gradient ascent. -Describe the underlying decision boundaries. -Build a classification model to predict sentiment in a product review dataset. -Analyze financial data to predict loan defaults. -Use techniques for handling missing data. -Evaluate your models using precision-recall metrics. -Implement these techniques in Python (or in the language of your choice, though Python is highly recommended). **SHOW ALL ABOUT MACHINE LEARNING: CLASSIFICATION** **SHOW ALL** **COURSE** 4 ### [Machine Learning: Clustering & Retrieval](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fml- clustering-and-retrieval%3Fspecialization%3Dmachine- learning&sa=D&sntz=1&usg=AOvVaw0i5MZL56twhU3RZPbgPV3w) **4.7** **stars** 2,294 ratings • 392 reviews Case Studies: Finding Similar Documents A reader is interested in a specific news article and you want to find similar articles to recommend. What is the right notion of similarity? Moreover, what if there are millions of other documents? Each time you want to a retrieve a new document, do you need to search through all other documents? How do you group similar documents together? How do you discover new, emerging topics that the documents cover? In this third case study, finding similar documents, you will examine similarity-based algorithms for retrieval. In this course, you will also examine structured representations for describing the documents in the corpus, including clustering and mixed membership models, such as latent Dirichlet allocation (LDA). You will implement expectation maximization (EM) to learn the document clusterings, and see how to scale the methods using MapReduce. Learning Outcomes: By the end of this course, you will be able to: -Create a document retrieval system using k-nearest neighbors. -Identify various similarity metrics for text data. -Reduce computations in k-nearest neighbor search by using KD-trees. -Produce approximate nearest neighbors using locality sensitive hashing. -Compare and contrast supervised and unsupervised learning tasks. -Cluster documents by topic using k-means. -Describe how to parallelize k-means using MapReduce. -Examine probabilistic clustering approaches using mixtures models. -Fit a mixture of Gaussian model using expectation maximization (EM). -Perform mixed membership modeling using latent Dirichlet allocation (LDA). -Describe the steps of a Gibbs sampler and how to use its output to draw inferences. -Compare and contrast initialization techniques for non-convex optimization objectives. -Implement these techniques in Python. **SHOW ALL ABOUT MACHINE LEARNING: CLUSTERING & RETRIEVAL** **SHOW ALL** Download source code and full text of Mind map from [https://github.com/pirahansiah/pirahansiah.github.io/tree/main/Machine_Learning_Specialization](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fpirahansiah.github.io%2Ftree%2Fmain%2FMachine_Learning_Specialization&sa=D&sntz=1&usg=AOvVaw1L1CgVAceLsiPzKKiMkCw-) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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Machine Learning Specialization 2022 Coursera: Machine Learning Specialization (2022) Course 2: Advanced Learning Algorithms Week 2: Neural network training See more & download other notes: [https://www.pirahansiah.com/topics/courses/machine-learning- specialization](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmachine- learning-specialization&sa=D&sntz=1&usg=AOvVaw2DeYIlikOMmy64M0k_H_xq) #Machine_Learning_Specialization #pirahansiah #FarshidPirahanSiah ![](https://lh5.googleusercontent.com/_W8Fvy1c4zC- LotiIepjGrevDZirPFptWldeeNktro3Y72MJzIaA-j4O7xjvbTQxzMtDHfp5yLo18TgrqNDS6DoSm83DO4eG8B5_MMHgpnPGx0ExQKyeZW50fVVFJdlxIQ=w1280) Coursera: Machine Learning Specialization (2022) Course 2: Advanced Learning Algorithms Week 1: NN See more: [https://www.pirahansiah.com/topics/courses/machine-learning- specialization](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmachine- learning-specialization&sa=D&sntz=1&usg=AOvVaw2DeYIlikOMmy64M0k_H_xq) #Machine_Learning_Specialization #pirahansiah #FarshidPirahanSiah ![](https://lh3.googleusercontent.com/JaYdNzAbAQ0B18ByOQxDeReNUTp8AxPzfY8uzUxe8d7rMJjPtJLk3wCt0hoOY28vc4r2GjZg3dl1aAviTAlo2js8x4rGVx4yPBt22LZ55dvVPZv_uMCxac4Gs46pyYui5g=w1280) Coursera: Machine Learning Specialization (2022) Course 1: Supervised Machine Learning: Regression and Classification Week 3: Classification See more: [https://www.pirahansiah.com/topics/courses/machine-learning- specialization](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmachine- learning-specialization&sa=D&sntz=1&usg=AOvVaw2DeYIlikOMmy64M0k_H_xq) #Machine_Learning_Specialization #pirahansiah #FarshidPirahanSiah ![](https://lh6.googleusercontent.com/JLaWYjTq- xTpa3ZAS1happl85ACL_4NKhEGTA66WCtXPX1pHZTiY34rJ- Tt_n1Bz4B1U57gW7Q8qoNUcMGDDdpq8tH7LYeP_dbA9iqX5Vn7JZv-n5-cKwNXNSYe6vGg6gg=w1280) Coursera: Machine Learning Specialization Course 1: Supervised Machine Learning: Regression and Classification Week 2: Regression with multiple input variables See more: [https://www.pirahansiah.com/topics/courses/machine-learning- specialization](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmachine- learning-specialization&sa=D&sntz=1&usg=AOvVaw2DeYIlikOMmy64M0k_H_xq) #Machine_Learning_Specialization #pirahansiah #FarshidPirahanSiah ![](https://lh4.googleusercontent.com/e1gqokiDh0IXPCE5zWdcw53soLw6V2wqY-6k4qJP8x1e04u1Z20neieDGymJk61W9L_jBYMiYB- cMH2pQ6dCA8yPWPJrCZINn80s80LaMWMRe2bYMnvddqTSzHteNmnxxA=w1280) Coursera: Machine Learning Specialization Course 1: Supervised Machine Learning: Regression and Classification Week 1: Introduction to Machine Learning See more: [https://www.pirahansiah.com/topics/courses/machine-learning- specialization](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmachine- learning-specialization&sa=D&sntz=1&usg=AOvVaw2DeYIlikOMmy64M0k_H_xq) #Machine_Learning_Specialization #pirahansiah #FarshidPirahanSiah ![](https://lh6.googleusercontent.com/wzLJS6_BNehj5Fr8wBeJiMUKiHombp1bAY9ZEWvmToBbipO80vxZIs_KyzUa21QuGxeEfOH3f4OtgBf0mzFymNQuatqchpCGru- TAI6uDCvMCsEK7_WGnD8nUW_5-KM_zg=w1280) Machine Learning Specialization Course 1 Week 2 See more: [https://www.pirahansiah.com/topics/courses/machine-learning- specialization](https://www.google.com/url?q=https%3A%2F%2Fwww.pirahansiah.com%2Ftopics%2Fcourses%2Fmachine- learning-specialization&sa=D&sntz=1&usg=AOvVaw2DeYIlikOMmy64M0k_H_xq) #MachineLearning #pirahansiah #FarshidPirahanSiah If you found the content informative, you may Follow me by [https://twitter.com/pirahansiah](https://www.google.com/url?q=https%3A%2F%2Ftwitter.com%2Fpirahansiah&sa=D&sntz=1&usg=AOvVaw3hfltLQuPqA2GdOTjpZSOC) for more! **COURSE** 1 ### [Machine Learning Foundations: A Case Study Approach](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fml- foundations%3Fspecialization%3Dmachine- learning&sa=D&sntz=1&usg=AOvVaw1nrYbfZ3vhOT2Xd5KM5Isp) **4.6** **stars** 13,044 ratings • 3,104 reviews Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains. This first course treats the machine learning method as a black box. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications. Learning Outcomes: By the end of this course, you will be able to: -Identify potential applications of machine learning in practice. -Describe the core differences in analyses enabled by regression, classification, and clustering. -Select the appropriate machine learning task for a potential application. -Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. -Represent your data as features to serve as input to machine learning models. -Assess the model quality in terms of relevant error metrics for each task. -Utilize a dataset to fit a model to analyze new data. -Build an end-to-end application that uses machine learning at its core. -Implement these techniques in Python. **SHOW ALL ABOUT MACHINE LEARNING FOUNDATIONS: A CASE STUDY APPROACH** **SHOW ALL** **COURSE** 2 ### [Machine Learning: Regression](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fml- regression%3Fspecialization%3Dmachine- learning&sa=D&sntz=1&usg=AOvVaw0gPQh7m6G5PdRgWw1sLrYn) **4.8** **stars** 5,470 ratings • 1,016 reviews Case Study - Predicting Housing Prices In our first case study, predicting house prices, you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...). This is just one of the many places where regression can be applied. Other applications range from predicting health outcomes in medicine, stock prices in finance, and power usage in high- performance computing, to analyzing which regulators are important for gene expression. In this course, you will explore regularized linear regression models for the task of prediction and feature selection. You will be able to handle very large sets of features and select between models of various complexity. You will also analyze the impact of aspects of your data -- such as outliers -- on your selected models and predictions. To fit these models, you will implement optimization algorithms that scale to large datasets. Learning Outcomes: By the end of this course, you will be able to: -Describe the input and output of a regression model. -Compare and contrast bias and variance when modeling data. -Estimate model parameters using optimization algorithms. -Tune parameters with cross validation. -Analyze the performance of the model. -Describe the notion of sparsity and how LASSO leads to sparse solutions. -Deploy methods to select between models. -Exploit the model to form predictions. -Build a regression model to predict prices using a housing dataset. -Implement these techniques in Python. **SHOW ALL ABOUT MACHINE LEARNING: REGRESSION** **SHOW ALL** **COURSE** 3 ### [Machine Learning: Classification](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fml- classification%3Fspecialization%3Dmachine- learning&sa=D&sntz=1&usg=AOvVaw0DO2ATucnIpN4omP3WXrhB) **4.7** **stars** 3,662 ratings • 603 reviews Case Studies: Analyzing Sentiment & Loan Default Prediction In our case study on analyzing sentiment, you will create models that predict a class (positive/negative sentiment) from input features (text of the reviews, user profile information,...). In our second case study for this course, loan default prediction, you will tackle financial data, and predict when a loan is likely to be risky or safe for the bank. These tasks are an examples of classification, one of the most widely used areas of machine learning, with a broad array of applications, including ad targeting, spam detection, medical diagnosis and image classification. In this course, you will create classifiers that provide state-of-the-art performance on a variety of tasks. You will become familiar with the most successful techniques, which are most widely used in practice, including logistic regression, decision trees and boosting. In addition, you will be able to design and implement the underlying algorithms that can learn these models at scale, using stochastic gradient ascent. You will implement these technique on real-world, large-scale machine learning tasks. You will also address significant tasks you will face in real-world applications of ML, including handling missing data and measuring precision and recall to evaluate a classifier. This course is hands-on, action-packed, and full of visualizations and illustrations of how these techniques will behave on real data. We've also included optional content in every module, covering advanced topics for those who want to go even deeper! Learning Objectives: By the end of this course, you will be able to: -Describe the input and output of a classification model. -Tackle both binary and multiclass classification problems. -Implement a logistic regression model for large-scale classification. -Create a non-linear model using decision trees. -Improve the performance of any model using boosting. -Scale your methods with stochastic gradient ascent. -Describe the underlying decision boundaries. -Build a classification model to predict sentiment in a product review dataset. -Analyze financial data to predict loan defaults. -Use techniques for handling missing data. -Evaluate your models using precision-recall metrics. -Implement these techniques in Python (or in the language of your choice, though Python is highly recommended). **SHOW ALL ABOUT MACHINE LEARNING: CLASSIFICATION** **SHOW ALL** **COURSE** 4 ### [Machine Learning: Clustering & Retrieval](https://www.google.com/url?q=https%3A%2F%2Fwww.coursera.org%2Flearn%2Fml- clustering-and-retrieval%3Fspecialization%3Dmachine- learning&sa=D&sntz=1&usg=AOvVaw0i5MZL56twhU3RZPbgPV3w) **4.7** **stars** 2,294 ratings • 392 reviews Case Studies: Finding Similar Documents A reader is interested in a specific news article and you want to find similar articles to recommend. What is the right notion of similarity? Moreover, what if there are millions of other documents? Each time you want to a retrieve a new document, do you need to search through all other documents? How do you group similar documents together? How do you discover new, emerging topics that the documents cover? In this third case study, finding similar documents, you will examine similarity-based algorithms for retrieval. In this course, you will also examine structured representations for describing the documents in the corpus, including clustering and mixed membership models, such as latent Dirichlet allocation (LDA). You will implement expectation maximization (EM) to learn the document clusterings, and see how to scale the methods using MapReduce. Learning Outcomes: By the end of this course, you will be able to: -Create a document retrieval system using k-nearest neighbors. -Identify various similarity metrics for text data. -Reduce computations in k-nearest neighbor search by using KD-trees. -Produce approximate nearest neighbors using locality sensitive hashing. -Compare and contrast supervised and unsupervised learning tasks. -Cluster documents by topic using k-means. -Describe how to parallelize k-means using MapReduce. -Examine probabilistic clustering approaches using mixtures models. -Fit a mixture of Gaussian model using expectation maximization (EM). -Perform mixed membership modeling using latent Dirichlet allocation (LDA). -Describe the steps of a Gibbs sampler and how to use its output to draw inferences. -Compare and contrast initialization techniques for non-convex optimization objectives. -Implement these techniques in Python. **SHOW ALL ABOUT MACHINE LEARNING: CLUSTERING & RETRIEVAL** **SHOW ALL** Download source code and full text of Mind map from [https://github.com/pirahansiah/pirahansiah.github.io/tree/main/Machine_Learning_Specialization](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fpirahansiah.github.io%2Ftree%2Fmain%2FMachine_Learning_Specialization&sa=D&sntz=1&usg=AOvVaw1L1CgVAceLsiPzKKiMkCw-) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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Data: relational, network, hierarchical models - OO data models UML: 2.5 (2015) - omg.org [https://plantuml.com/download](https://www.google.com/url?q=https%3A%2F%2Fplantuml.com%2Fdownload&sa=D&sntz=1&usg=AOvVaw0Wz7_Yj1BjAH- ptWc6iPU7) UML Diagrams * Structure: static view * Class diagram * classifiers * features * relationships * Component diagram * Object diagram * Composite structure diagram * Package diagram * Behavior: dynamic view * Deployment diagram * Use case diagram * use cases * systems * actors * associations * Activity diagram * State machine diagram * Interaction * Sequence diagram * Communication diagram * Timing diagram * Interaction overview * diagram aggregation and composition * attributes = properties = characteristics = state = fields = variables * object = instance * defining a class = creating objects = instantiation * superclass=parent class=base class * subclass=child class=derived class class components: * **identity=name=type** : glass * **attributes=properties=data** : color, size, fullness * **behaviors=operations** : fill(), empty(), clean() = method 1. abstraction 2. polymorphism 3. inheritance 4. encapsulation @startuml farshid left to right direction package "High level definition for Image Processing interface" { together { interface output_data { *data } interface input_data { *data } interface image_processing_class { *input_data *output_data } } input_data "1" *-- "many" image_processing_class : contains output_data "1" *-- "many" image_processing_class : contains } newpage interface output_data { *data } note top of output_data <> DataNode end note @enduml [https://github.com/pirahansiah/cvtest](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest&sa=D&sntz=1&usg=AOvVaw3nCk6-1QOY0tGcAL6U5LmN) [https://github.com/opencv/opencv/blob/4.x/modules/highgui/test/test_gui.cpp](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fopencv%2Fopencv%2Fblob%2F4.x%2Fmodules%2Fhighgui%2Ftest%2Ftest_gui.cpp&sa=D&sntz=1&usg=AOvVaw0uL5WeAU0KKh34KohQw_bp) **functional** requirements: The module must * test opencv input/output image * allow to check grand truth image and output image * maintain a library of all comparison SSIM, PSNR, ... * allow to choose different comparison algorithms: PSNR, SSIM, ... **non functional** requirements: the module should be ... (maintainability, reliability, usability, availability) * simple library attached to project * fast * update-able **FURPS requirements:** * functionality: SSIM, PSNR, * usability: attached to the project as a class test * reliability: assert in C++ * performance: real-time * support-ability: simple class with all functions **use cases:** * title: developer test image processing functions by see ground truth image and output image differences * primary actor: computer vision developer * success scenario: check and see differences between ground truth image and output image by different matrix such as SSIM, PSNR, ... **user story:** as a computer vision developer i want to test my output image so that I can see ground truth image and my output image differences 1. classes - objects 2. relationship between objects 3. conceptual object model 4. CRC card = CRH card 1. Class (name of class) = Component : class test histogram 2. responsibility = responsibility : test two image histogram and compare them 3. collaboration = Helper= get ground truth image and compare it with output function classA *myclass=new classA() constructor : we want to set value at the beginning and not problem with null or 0 or ... initialization destructor = finalizer static variable: shared across all objects in a class = shared variable = class variable = classA.staticVariable SubClass(int foo, int bar): SuperClass(foo){} interface: list of methods = <> = ----> ## SOLID, DRY (don't repeat yourself), YAGNI (your ain't gonna need it), design pattern, ![](https://lh4.googleusercontent.com/VyNS7rCD7FKuvug70-SF40PZPG8MCxWgG0XllRqHS3AM5xobN8G898o_MENnJV-5FaySQGS6D_vUmPOAHD6ZMd- WCLOoiGR4UCA3PCrQjrKa2qxBndkJ4xjsYFMXOcbxCA=w1280) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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Data: relational, network, hierarchical models - OO data models UML: 2.5 (2015) - omg.org [https://plantuml.com/download](https://www.google.com/url?q=https%3A%2F%2Fplantuml.com%2Fdownload&sa=D&sntz=1&usg=AOvVaw0Wz7_Yj1BjAH- ptWc6iPU7) UML Diagrams * Structure: static view * Class diagram * classifiers * features * relationships * Component diagram * Object diagram * Composite structure diagram * Package diagram * Behavior: dynamic view * Deployment diagram * Use case diagram * use cases * systems * actors * associations * Activity diagram * State machine diagram * Interaction * Sequence diagram * Communication diagram * Timing diagram * Interaction overview * diagram aggregation and composition * attributes = properties = characteristics = state = fields = variables * object = instance * defining a class = creating objects = instantiation * superclass=parent class=base class * subclass=child class=derived class class components: * **identity=name=type** : glass * **attributes=properties=data** : color, size, fullness * **behaviors=operations** : fill(), empty(), clean() = method 1. abstraction 2. polymorphism 3. inheritance 4. encapsulation @startuml farshid left to right direction package "High level definition for Image Processing interface" { together { interface output_data { *data } interface input_data { *data } interface image_processing_class { *input_data *output_data } } input_data "1" *-- "many" image_processing_class : contains output_data "1" *-- "many" image_processing_class : contains } newpage interface output_data { *data } note top of output_data <> DataNode end note @enduml [https://github.com/pirahansiah/cvtest](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fpirahansiah%2Fcvtest&sa=D&sntz=1&usg=AOvVaw3nCk6-1QOY0tGcAL6U5LmN) [https://github.com/opencv/opencv/blob/4.x/modules/highgui/test/test_gui.cpp](https://www.google.com/url?q=https%3A%2F%2Fgithub.com%2Fopencv%2Fopencv%2Fblob%2F4.x%2Fmodules%2Fhighgui%2Ftest%2Ftest_gui.cpp&sa=D&sntz=1&usg=AOvVaw0uL5WeAU0KKh34KohQw_bp) **functional** requirements: The module must * test opencv input/output image * allow to check grand truth image and output image * maintain a library of all comparison SSIM, PSNR, ... * allow to choose different comparison algorithms: PSNR, SSIM, ... **non functional** requirements: the module should be ... (maintainability, reliability, usability, availability) * simple library attached to project * fast * update-able **FURPS requirements:** * functionality: SSIM, PSNR, * usability: attached to the project as a class test * reliability: assert in C++ * performance: real-time * support-ability: simple class with all functions **use cases:** * title: developer test image processing functions by see ground truth image and output image differences * primary actor: computer vision developer * success scenario: check and see differences between ground truth image and output image by different matrix such as SSIM, PSNR, ... **user story:** as a computer vision developer i want to test my output image so that I can see ground truth image and my output image differences 1. classes - objects 2. relationship between objects 3. conceptual object model 4. CRC card = CRH card 1. Class (name of class) = Component : class test histogram 2. responsibility = responsibility : test two image histogram and compare them 3. collaboration = Helper= get ground truth image and compare it with output function classA *myclass=new classA() constructor : we want to set value at the beginning and not problem with null or 0 or ... initialization destructor = finalizer static variable: shared across all objects in a class = shared variable = class variable = classA.staticVariable SubClass(int foo, int bar): SuperClass(foo){} interface: list of methods = <> = ----> ## SOLID, DRY (don't repeat yourself), YAGNI (your ain't gonna need it), design pattern, ![](https://lh5.googleusercontent.com/03n3BV- jufExdT5_rZ_VDym0-bhVd9SdhRgDsm8oKtKEXd0xkz50XcO9Iu_RCKyisBP4wNrfgmNpd0LQGoxszRx- OW3UZgg3S2HcWYObQ8I1NNHApu0hk_Bhok25pP6shQ=w1280) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/64TqwjhC5SmyL8C2PAzPTeZ1eymQjar7GANkNJZpwnamRumOFEpuqaCmqVBV4_c_1_k_QhPKrZ3BtlOwEWMtPXc=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/64TqwjhC5SmyL8C2PAzPTeZ1eymQjar7GANkNJZpwnamRumOFEpuqaCmqVBV4_c_1_k_QhPKrZ3BtlOwEWMtPXc=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # Product According to Gartner: "Democratize Data Science & Machine Learning (DSML) by applying multipersona DSML platforms, bringing its value to an ever-larger audience of less technical experts. In addition, by expanding the use of a platform from a single business department across multiple internal or external business functions, insights and experiences can be shared more effectively, enabling decision optimization." Have you ever thought about the power of automation? In everyday life, we often no longer notice the automated helpers. What if the benefits also impacted your working life? With our tools & solutions, we can accelerate standard tasks to a very large extent or even completely automate them. Valuable time left for you and other experts to complete further activities. These are the top challenges that we’re solving for other companies utilizing visual inspection and defect detection: * The Accuracy of Traditional Machine Vision (causing false positives/excess scrap) * Align teams on a defect definitions and how to label * Monitoring of edge devices, fleet management, change detection, etc. * Identify defects on complex parts with rule-based solutions * Challenges to classify defect types due to ambiguities with rule-based solutions we utilizes deep learning to improve accuracy and defect detection across a wide range of applications in industrial automation and manufacturing. We developing a solution that auto-diagnoses issues in Computer Vision models and datasets, enabling ML teams to deliver AI models that perform well in the real world. We currently help ML teams. We are Web & Mobile App Development company specializing in Staff Augmentation (React JS/React Native, Native iOS/Android, Python, Bootstrap), Digital Marketing and building custom digital solutions. some of the areas we excel in are : • AI, Machine Learning, IoT connected devices • AR/VR/XR, Metaverse, Advanced UX/UI • NFT, Blockchain (Advance payments B2B-B2C) • Enterprise/Consumer apps, Fleet tracking, Saas We have developed an ML Ops platform that helps healthcare companies to push ML innovation faster and more successful. A key aspect is collaboration. The benefits in a nutshell: • Leverage your existing ML development capacity by up to 250% via a multipersona development approach. • Remove any domain knowledge gap of your data scientists to improve quality and remove up to 75% risk of project failure -> btw. nearly 35% of ML PoCs fail because of domain knowledge gaps. • Get the best time-to-value with an integrated knowledgebase of hundreds of medical datasets and imaging AI/ML recipes, ready to use and all open source. • Our ML Ops platform is all secure, works on your premise and with high governance capabilities. Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies. [Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/_hck3K2JGQ1ghYoXZ7iBAh6UrcJe4h-XNeLuiyiCVHdw5j1X2qmMgL8doj8geGzck7rU2DmtQXU2cDjW4Jc0qCo=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/_hck3K2JGQ1ghYoXZ7iBAh6UrcJe4h-XNeLuiyiCVHdw5j1X2qmMgL8doj8geGzck7rU2DmtQXU2cDjW4Jc0qCo=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # pirahansiah ## Image Processing * Artificial SuperIntelligence (ASI) * Artificial General Intelligence (AGI) * Medical Image Processing * Robotic * AR, VR, extended reality * 3D SLAM * Computer Vision in IoT ## Machine Learning * Performance engineering in deep learning applications * End-to-End pipeline for machine learning programs * Reduce cost and development time with Amazon * Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload. * Continuous Deployment of Machine Learning Pipelines We deliver end-to-end hyper-automation solutions using computer vision & deep learning to enable AI-Powered Enterprise orchestration of various technologies and workflows to streamline and execute a process automatically. 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[https://www.pirahansiah.com/about/fqa](https://www.pirahansiah.com/about/fqa) # Open Source Projects ## OpenCV NuGet [https://www.nuget.org/profiles/Farshid_Pirahansiah](https://www.nuget.org/profiles/Farshid_Pirahansiah) NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022 [https://www.pirahansiah.com/topics/opencv](https://www.pirahansiah.com/topics/opencv) Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static OpenCV library for visual studio 2022 by using NuGet package manager just in a few minutes [https://www.youtube.com/watch?v=AEqZO_fZHZ8](https://www.youtube.com/watch?v=AEqZO_fZHZ8) #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, download source code (GitHub):[ https://github.com/pirahansiah/](https://github.com/pirahansiah/opencv-cpp) #OpenCV #Farshid_PirahanSiah #pirahansiah My NuGet packages comprised of two versions for different VS versions. * Visual Studio 2019 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet) * Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7 * Visual Studio 2022 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet) * Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7 more:[ https://www.pirahansiah.com/topics/opencv](https://www.pirahansiah.com/topics/opencv) * cvTest * * * cvtest: Computer Vision Test: Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning * Do you want to test your output of computer vision application which is video or images? * Standard test for computer vision application * There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. * Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? * [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://github.com/pirahansiah/cvtest/blob/main/README.md) * Multi-Class Multi-object Video Tracking * computer vision with deep learning in IoT devices * Multi Camera (Stereo Vision) Calibration for AR/VR headset (extended reality/mixed reality) 3D Image Processing with Deep Learning * End to End solution for computer vision applications in industry (cloud and IoT) * Download all mind map sources * [https://github.com/pirahansiah/pirahansiah.github.io](https://github.com/pirahansiah/pirahansiah.github.io) ## LinkedIn: (around 12K members) [Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) The Computer Vision LinkedIn group: reached to around 8000 members. This group is a wonderful place for support if you have a question, need inspiration, encouragement, and cutting edge research. Computer Vision, Deep Learning, extended reality; Metaverse; Deep Reinforcement Learning, GANs, OpenCV, TensorFlow, PyTorch. [https://www.linkedin.com/groups/10320678/](https://www.linkedin.com/groups/10320678/) ## Facebook Group: (around 14K members) Deep Reinforcement Learning, Computer Vision with Deep Learning, IoT, Robot [https://www.facebook.com/groups/185926728115336](https://www.facebook.com/groups/185926728115336) We help scale and build artificially intelligent driven start-ups with Al Researchers & Engineers! [Computer Vision] (Berlin, Germany) [Please use calendly appointment slots](https://calendly.com/pirahansiah) press . in github and open web visual studio code My LaTex Papers [https://www.overleaf.com/read/cmvgxfqxfdqm](https://www.overleaf.com/read/cmvgxfqxfdqm) This site is provided to everyone for free, however if you would like to say thanks or help support continued R&D, Mind Map, development and etc. , consider getting me a coffee. It keeps my work going. [](https://docs.google.com/forms/d/e/1FAIpQLSdiQprY0yS25LBVixQnsjkoUTjOtzx1oJye1C77At4Ur2oqTg/viewform "Open Google Forms, Contact Information in new window") ![](https://lh6.googleusercontent.com/J4dkBCDFO0RHrXxLWJL8AT2oOq2Q2lYfkFF0cHqnDjPybrli93-OwX8W2y9SjF7tkclad8vqIG53XfRrfLorSDjxTp7fNR1T7a25KnXNAQEAopCkcFhaPP91vV1Pi2ozrA=w1280) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[https://www.pirahansiah.com/about/fqa](https://www.pirahansiah.com/about/fqa) # Open Source Projects ## OpenCV NuGet [https://www.nuget.org/profiles/Farshid_Pirahansiah](https://www.nuget.org/profiles/Farshid_Pirahansiah) NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022 [https://www.pirahansiah.com/topics/opencv](https://www.pirahansiah.com/topics/opencv) Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static OpenCV library for visual studio 2022 by using NuGet package manager just in a few minutes [https://www.youtube.com/watch?v=AEqZO_fZHZ8](https://www.youtube.com/watch?v=AEqZO_fZHZ8) #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, download source code (GitHub):[ https://github.com/pirahansiah/](https://github.com/pirahansiah/opencv-cpp) #OpenCV #Farshid_PirahanSiah #pirahansiah My NuGet packages comprised of two versions for different VS versions. * Visual Studio 2019 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet) * Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7 * Visual Studio 2022 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet) * Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7 more:[ https://www.pirahansiah.com/topics/opencv](https://www.pirahansiah.com/topics/opencv) * cvTest * * * cvtest: Computer Vision Test: Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning * Do you want to test your output of computer vision application which is video or images? * Standard test for computer vision application * There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. * Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? * [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://github.com/pirahansiah/cvtest/blob/main/README.md) * Multi-Class Multi-object Video Tracking * computer vision with deep learning in IoT devices * Multi Camera (Stereo Vision) Calibration for AR/VR headset (extended reality/mixed reality) 3D Image Processing with Deep Learning * End to End solution for computer vision applications in industry (cloud and IoT) * Download all mind map sources * [https://github.com/pirahansiah/pirahansiah.github.io](https://github.com/pirahansiah/pirahansiah.github.io) ## LinkedIn: (around 12K members) [Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) The Computer Vision LinkedIn group: reached to around 8000 members. This group is a wonderful place for support if you have a question, need inspiration, encouragement, and cutting edge research. Computer Vision, Deep Learning, extended reality; Metaverse; Deep Reinforcement Learning, GANs, OpenCV, TensorFlow, PyTorch. [https://www.linkedin.com/groups/10320678/](https://www.linkedin.com/groups/10320678/) ## Facebook Group: (around 14K members) Deep Reinforcement Learning, Computer Vision with Deep Learning, IoT, Robot [https://www.facebook.com/groups/185926728115336](https://www.facebook.com/groups/185926728115336) We help scale and build artificially intelligent driven start-ups with Al Researchers & Engineers! [Computer Vision] (Berlin, Germany) [Please use calendly appointment slots](https://calendly.com/pirahansiah) press . in github and open web visual studio code My LaTex Papers [https://www.overleaf.com/read/cmvgxfqxfdqm](https://www.overleaf.com/read/cmvgxfqxfdqm) This site is provided to everyone for free, however if you would like to say thanks or help support continued R&D, Mind Map, development and etc. , consider getting me a coffee. It keeps my work going. [](https://docs.google.com/forms/d/e/1FAIpQLSdiQprY0yS25LBVixQnsjkoUTjOtzx1oJye1C77At4Ur2oqTg/viewform "Open Google Forms, Contact Information in new window") ![](https://lh6.googleusercontent.com/J4dkBCDFO0RHrXxLWJL8AT2oOq2Q2lYfkFF0cHqnDjPybrli93-OwX8W2y9SjF7tkclad8vqIG53XfRrfLorSDjxTp7fNR1T7a25KnXNAQEAopCkcFhaPP91vV1Pi2ozrA=w1280) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[https://www.pirahansiah.com/about/fqa](https://www.pirahansiah.com/about/fqa) # Open Source Projects ## OpenCV NuGet [https://www.nuget.org/profiles/Farshid_Pirahansiah](https://www.nuget.org/profiles/Farshid_Pirahansiah) NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022 [https://www.pirahansiah.com/topics/opencv](https://www.pirahansiah.com/topics/opencv) Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static OpenCV library for visual studio 2022 by using NuGet package manager just in a few minutes [https://www.youtube.com/watch?v=AEqZO_fZHZ8](https://www.youtube.com/watch?v=AEqZO_fZHZ8) #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, download source code (GitHub):[ https://github.com/pirahansiah/](https://github.com/pirahansiah/opencv-cpp) #OpenCV #Farshid_PirahanSiah #pirahansiah My NuGet packages comprised of two versions for different VS versions. * Visual Studio 2019 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet) * Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7 * Visual Studio 2022 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet) * Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7 more:[ https://www.pirahansiah.com/topics/opencv](https://www.pirahansiah.com/topics/opencv) * cvTest * * * cvtest: Computer Vision Test: Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning * Do you want to test your output of computer vision application which is video or images? * Standard test for computer vision application * There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. * Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? * [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://github.com/pirahansiah/cvtest/blob/main/README.md) * Multi-Class Multi-object Video Tracking * computer vision with deep learning in IoT devices * Multi Camera (Stereo Vision) Calibration for AR/VR headset (extended reality/mixed reality) 3D Image Processing with Deep Learning * End to End solution for computer vision applications in industry (cloud and IoT) * Download all mind map sources * [https://github.com/pirahansiah/pirahansiah.github.io](https://github.com/pirahansiah/pirahansiah.github.io) ## LinkedIn: (around 12K members) [Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) The Computer Vision LinkedIn group: reached to around 8000 members. This group is a wonderful place for support if you have a question, need inspiration, encouragement, and cutting edge research. Computer Vision, Deep Learning, extended reality; Metaverse; Deep Reinforcement Learning, GANs, OpenCV, TensorFlow, PyTorch. [https://www.linkedin.com/groups/10320678/](https://www.linkedin.com/groups/10320678/) ## Facebook Group: (around 14K members) Deep Reinforcement Learning, Computer Vision with Deep Learning, IoT, Robot [https://www.facebook.com/groups/185926728115336](https://www.facebook.com/groups/185926728115336) We help scale and build artificially intelligent driven start-ups with Al Researchers & Engineers! [Computer Vision] (Berlin, Germany) [Please use calendly appointment slots](https://calendly.com/pirahansiah) press . in github and open web visual studio code My LaTex Papers [https://www.overleaf.com/read/cmvgxfqxfdqm](https://www.overleaf.com/read/cmvgxfqxfdqm) This site is provided to everyone for free, however if you would like to say thanks or help support continued R&D, Mind Map, development and etc. , consider getting me a coffee. It keeps my work going. [](https://docs.google.com/forms/d/e/1FAIpQLSdiQprY0yS25LBVixQnsjkoUTjOtzx1oJye1C77At4Ur2oqTg/viewform "Open Google Forms, Contact Information in new window") ![](https://lh6.googleusercontent.com/J4dkBCDFO0RHrXxLWJL8AT2oOq2Q2lYfkFF0cHqnDjPybrli93-OwX8W2y9SjF7tkclad8vqIG53XfRrfLorSDjxTp7fNR1T7a25KnXNAQEAopCkcFhaPP91vV1Pi2ozrA=w1280) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[https://www.pirahansiah.com/about/fqa](https://www.pirahansiah.com/about/fqa) # Open Source Projects ## OpenCV NuGet [https://www.nuget.org/profiles/Farshid_Pirahansiah](https://www.nuget.org/profiles/Farshid_Pirahansiah) NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022 [https://www.pirahansiah.com/topics/opencv](https://www.pirahansiah.com/topics/opencv) Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static OpenCV library for visual studio 2022 by using NuGet package manager just in a few minutes [https://www.youtube.com/watch?v=AEqZO_fZHZ8](https://www.youtube.com/watch?v=AEqZO_fZHZ8) #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, download source code (GitHub):[ https://github.com/pirahansiah/](https://github.com/pirahansiah/opencv-cpp) #OpenCV #Farshid_PirahanSiah #pirahansiah My NuGet packages comprised of two versions for different VS versions. * Visual Studio 2019 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet) * Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7 * Visual Studio 2022 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet) * Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7 more:[ https://www.pirahansiah.com/topics/opencv](https://www.pirahansiah.com/topics/opencv) * cvTest * * * cvtest: Computer Vision Test: Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning * Do you want to test your output of computer vision application which is video or images? * Standard test for computer vision application * There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. * Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? * [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://github.com/pirahansiah/cvtest/blob/main/README.md) * Multi-Class Multi-object Video Tracking * computer vision with deep learning in IoT devices * Multi Camera (Stereo Vision) Calibration for AR/VR headset (extended reality/mixed reality) 3D Image Processing with Deep Learning * End to End solution for computer vision applications in industry (cloud and IoT) * Download all mind map sources * [https://github.com/pirahansiah/pirahansiah.github.io](https://github.com/pirahansiah/pirahansiah.github.io) ## LinkedIn: (around 12K members) [Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) The Computer Vision LinkedIn group: reached to around 8000 members. This group is a wonderful place for support if you have a question, need inspiration, encouragement, and cutting edge research. Computer Vision, Deep Learning, extended reality; Metaverse; Deep Reinforcement Learning, GANs, OpenCV, TensorFlow, PyTorch. [https://www.linkedin.com/groups/10320678/](https://www.linkedin.com/groups/10320678/) ## Facebook Group: (around 14K members) Deep Reinforcement Learning, Computer Vision with Deep Learning, IoT, Robot [https://www.facebook.com/groups/185926728115336](https://www.facebook.com/groups/185926728115336) We help scale and build artificially intelligent driven start-ups with Al Researchers & Engineers! [Computer Vision] (Berlin, Germany) [Please use calendly appointment slots](https://calendly.com/pirahansiah) press . in github and open web visual studio code My LaTex Papers [https://www.overleaf.com/read/cmvgxfqxfdqm](https://www.overleaf.com/read/cmvgxfqxfdqm) This site is provided to everyone for free, however if you would like to say thanks or help support continued R&D, Mind Map, development and etc. , consider getting me a coffee. It keeps my work going. [](https://docs.google.com/forms/d/e/1FAIpQLSdiQprY0yS25LBVixQnsjkoUTjOtzx1oJye1C77At4Ur2oqTg/viewform "Open Google Forms, Contact Information in new window") ![](https://lh6.googleusercontent.com/J4dkBCDFO0RHrXxLWJL8AT2oOq2Q2lYfkFF0cHqnDjPybrli93-OwX8W2y9SjF7tkclad8vqIG53XfRrfLorSDjxTp7fNR1T7a25KnXNAQEAopCkcFhaPP91vV1Pi2ozrA=w1280) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[https://www.pirahansiah.com/about/fqa](https://www.pirahansiah.com/about/fqa) # Open Source Projects ## OpenCV NuGet [https://www.nuget.org/profiles/Farshid_Pirahansiah](https://www.nuget.org/profiles/Farshid_Pirahansiah) NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022 [https://www.pirahansiah.com/topics/opencv](https://www.pirahansiah.com/topics/opencv) Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static OpenCV library for visual studio 2022 by using NuGet package manager just in a few minutes [https://www.youtube.com/watch?v=AEqZO_fZHZ8](https://www.youtube.com/watch?v=AEqZO_fZHZ8) #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, download source code (GitHub):[ https://github.com/pirahansiah/](https://github.com/pirahansiah/opencv-cpp) #OpenCV #Farshid_PirahanSiah #pirahansiah My NuGet packages comprised of two versions for different VS versions. * Visual Studio 2019 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet) * Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7 * Visual Studio 2022 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet) * Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7 more:[ https://www.pirahansiah.com/topics/opencv](https://www.pirahansiah.com/topics/opencv) * cvTest * * * cvtest: Computer Vision Test: Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning * Do you want to test your output of computer vision application which is video or images? * Standard test for computer vision application * There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. * Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? * [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://github.com/pirahansiah/cvtest/blob/main/README.md) * Multi-Class Multi-object Video Tracking * computer vision with deep learning in IoT devices * Multi Camera (Stereo Vision) Calibration for AR/VR headset (extended reality/mixed reality) 3D Image Processing with Deep Learning * End to End solution for computer vision applications in industry (cloud and IoT) * Download all mind map sources * [https://github.com/pirahansiah/pirahansiah.github.io](https://github.com/pirahansiah/pirahansiah.github.io) ## LinkedIn: (around 12K members) [Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) The Computer Vision LinkedIn group: reached to around 8000 members. This group is a wonderful place for support if you have a question, need inspiration, encouragement, and cutting edge research. Computer Vision, Deep Learning, extended reality; Metaverse; Deep Reinforcement Learning, GANs, OpenCV, TensorFlow, PyTorch. [https://www.linkedin.com/groups/10320678/](https://www.linkedin.com/groups/10320678/) ## Facebook Group: (around 14K members) Deep Reinforcement Learning, Computer Vision with Deep Learning, IoT, Robot [https://www.facebook.com/groups/185926728115336](https://www.facebook.com/groups/185926728115336) We help scale and build artificially intelligent driven start-ups with Al Researchers & Engineers! [Computer Vision] (Berlin, Germany) [Please use calendly appointment slots](https://calendly.com/pirahansiah) press . in github and open web visual studio code My LaTex Papers [https://www.overleaf.com/read/cmvgxfqxfdqm](https://www.overleaf.com/read/cmvgxfqxfdqm) This site is provided to everyone for free, however if you would like to say thanks or help support continued R&D, Mind Map, development and etc. , consider getting me a coffee. It keeps my work going. [](https://docs.google.com/forms/d/e/1FAIpQLSdiQprY0yS25LBVixQnsjkoUTjOtzx1oJye1C77At4Ur2oqTg/viewform "Open Google Forms, Contact Information in new window") ![](https://lh6.googleusercontent.com/J4dkBCDFO0RHrXxLWJL8AT2oOq2Q2lYfkFF0cHqnDjPybrli93-OwX8W2y9SjF7tkclad8vqIG53XfRrfLorSDjxTp7fNR1T7a25KnXNAQEAopCkcFhaPP91vV1Pi2ozrA=w1280) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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start/software](https://www.pirahansiah.com/topics-and-projects/how-to- start/software) [https://www.pirahansiah.com/topics-and-projects/how-to-start/roadmap-for-image- processing](https://www.pirahansiah.com/topics-and-projects/how-to-start/roadmap- for-image-processing) [https://www.pirahansiah.com/topics-and-projects/source- code](https://www.pirahansiah.com/topics-and-projects/source-code) [https://www.pirahansiah.com/topics-and-projects/source- code/python](https://www.pirahansiah.com/topics-and-projects/source-code/python) [https://www.pirahansiah.com/topics-and-projects/source- code/compile](https://www.pirahansiah.com/topics-and-projects/source-code/compile) [https://www.pirahansiah.com/topics-and- projects/share](https://www.pirahansiah.com/topics-and-projects/share) [https://www.pirahansiah.com/topics-and-projects/video- tracking](https://www.pirahansiah.com/topics-and-projects/video-tracking) [https://www.pirahansiah.com/topics-and- 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[https://www.pirahansiah.com/about/fqa](https://www.pirahansiah.com/about/fqa) # Open Source Projects ## OpenCV NuGet [https://www.nuget.org/profiles/Farshid_Pirahansiah](https://www.nuget.org/profiles/Farshid_Pirahansiah) NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022 [https://www.pirahansiah.com/topics/opencv](https://www.pirahansiah.com/topics/opencv) Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static OpenCV library for visual studio 2022 by using NuGet package manager just in a few minutes [https://www.youtube.com/watch?v=AEqZO_fZHZ8](https://www.youtube.com/watch?v=AEqZO_fZHZ8) #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, download source code (GitHub):[ https://github.com/pirahansiah/](https://github.com/pirahansiah/opencv-cpp) #OpenCV #Farshid_PirahanSiah #pirahansiah My NuGet packages comprised of two versions for different VS versions. * Visual Studio 2019 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet) * Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7 * Visual Studio 2022 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet) * Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7 more:[ https://www.pirahansiah.com/topics/opencv](https://www.pirahansiah.com/topics/opencv) * cvTest * * * cvtest: Computer Vision Test: Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning * Do you want to test your output of computer vision application which is video or images? * Standard test for computer vision application * There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. * Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? * [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://github.com/pirahansiah/cvtest/blob/main/README.md) * Multi-Class Multi-object Video Tracking * computer vision with deep learning in IoT devices * Multi Camera (Stereo Vision) Calibration for AR/VR headset (extended reality/mixed reality) 3D Image Processing with Deep Learning * End to End solution for computer vision applications in industry (cloud and IoT) * Download all mind map sources * [https://github.com/pirahansiah/pirahansiah.github.io](https://github.com/pirahansiah/pirahansiah.github.io) ## LinkedIn: (around 12K members) [Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) The Computer Vision LinkedIn group: reached to around 8000 members. This group is a wonderful place for support if you have a question, need inspiration, encouragement, and cutting edge research. Computer Vision, Deep Learning, extended reality; Metaverse; Deep Reinforcement Learning, GANs, OpenCV, TensorFlow, PyTorch. [https://www.linkedin.com/groups/10320678/](https://www.linkedin.com/groups/10320678/) ## Facebook Group: (around 14K members) Deep Reinforcement Learning, Computer Vision with Deep Learning, IoT, Robot [https://www.facebook.com/groups/185926728115336](https://www.facebook.com/groups/185926728115336) We help scale and build artificially intelligent driven start-ups with Al Researchers & Engineers! [Computer Vision] (Berlin, Germany) [Please use calendly appointment slots](https://calendly.com/pirahansiah) press . in github and open web visual studio code My LaTex Papers [https://www.overleaf.com/read/cmvgxfqxfdqm](https://www.overleaf.com/read/cmvgxfqxfdqm) This site is provided to everyone for free, however if you would like to say thanks or help support continued R&D, Mind Map, development and etc. , consider getting me a coffee. It keeps my work going. [](https://docs.google.com/forms/d/e/1FAIpQLSdiQprY0yS25LBVixQnsjkoUTjOtzx1oJye1C77At4Ur2oqTg/viewform "Open Google Forms, Contact Information in new window") ![](https://lh6.googleusercontent.com/J4dkBCDFO0RHrXxLWJL8AT2oOq2Q2lYfkFF0cHqnDjPybrli93-OwX8W2y9SjF7tkclad8vqIG53XfRrfLorSDjxTp7fNR1T7a25KnXNAQEAopCkcFhaPP91vV1Pi2ozrA=w1280) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/_hck3K2JGQ1ghYoXZ7iBAh6UrcJe4h-XNeLuiyiCVHdw5j1X2qmMgL8doj8geGzck7rU2DmtQXU2cDjW4Jc0qCo=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/_hck3K2JGQ1ghYoXZ7iBAh6UrcJe4h-XNeLuiyiCVHdw5j1X2qmMgL8doj8geGzck7rU2DmtQXU2cDjW4Jc0qCo=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # pirahansiah ## Image Processing * Artificial SuperIntelligence (ASI) * Artificial General Intelligence (AGI) * Medical Image Processing * Robotic * AR, VR, extended reality * 3D SLAM * Computer Vision in IoT ## Machine Learning * Performance engineering in deep learning applications * End-to-End pipeline for machine learning programs * Reduce cost and development time with Amazon * Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload. * Continuous Deployment of Machine Learning Pipelines We deliver end-to-end hyper-automation solutions using computer vision & deep learning to enable AI-Powered Enterprise orchestration of various technologies and workflows to streamline and execute a process automatically. Data labeling service remove or on site in Berlin, Germany ## Site Map [https://www.pirahansiah.com/home](https://www.pirahansiah.com/home) [https://www.pirahansiah.com/courses](https://www.pirahansiah.com/courses) [https://www.pirahansiah.com/courses/machine-learning- specialization](https://www.pirahansiah.com/courses/machine-learning- specialization) [https://www.pirahansiah.com/courses/machine-learning-specialization/machine- learning-foundations-a-case-study- approach](https://www.pirahansiah.com/courses/machine-learning- specialization/machine-learning-foundations-a-case-study-approach) [https://www.pirahansiah.com/courses/fsdl](https://www.pirahansiah.com/courses/fsdl) [https://www.pirahansiah.com/courses/full-stack-deep- learning](https://www.pirahansiah.com/courses/full-stack-deep-learning) [https://www.pirahansiah.com/courses/mlops](https://www.pirahansiah.com/courses/mlops) [https://www.pirahansiah.com/courses/ros](https://www.pirahansiah.com/courses/ros) 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[https://www.pirahansiah.com/book-summary/commonplace- book](https://www.pirahansiah.com/book-summary/commonplace-book) [https://www.pirahansiah.com/book- summary/knowledge_management](https://www.pirahansiah.com/book- summary/knowledge_management) [https://www.pirahansiah.com/book- summary/knowledge_management/pkm](https://www.pirahansiah.com/book- summary/knowledge_management/pkm) [https://www.pirahansiah.com/topics-and-projects](https://www.pirahansiah.com/topics- and-projects) [https://www.pirahansiah.com/topics-and-projects/how-to- start](https://www.pirahansiah.com/topics-and-projects/how-to-start) [https://www.pirahansiah.com/topics-and-projects/how-to- start/youtube](https://www.pirahansiah.com/topics-and-projects/how-to- start/youtube) [https://www.pirahansiah.com/topics-and-projects/how-to-start/youtube- ii](https://www.pirahansiah.com/topics-and-projects/how-to-start/youtube-ii) [https://www.pirahansiah.com/topics-and-projects/how-to- 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[https://www.pirahansiah.com/about/fqa](https://www.pirahansiah.com/about/fqa) # Open Source Projects ## OpenCV NuGet [https://www.nuget.org/profiles/Farshid_Pirahansiah](https://www.nuget.org/profiles/Farshid_Pirahansiah) NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022 [https://www.pirahansiah.com/topics/opencv](https://www.pirahansiah.com/topics/opencv) Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static OpenCV library for visual studio 2022 by using NuGet package manager just in a few minutes [https://www.youtube.com/watch?v=AEqZO_fZHZ8](https://www.youtube.com/watch?v=AEqZO_fZHZ8) #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, download source code (GitHub):[ https://github.com/pirahansiah/](https://github.com/pirahansiah/opencv-cpp) #OpenCV #Farshid_PirahanSiah #pirahansiah My NuGet packages comprised of two versions for different VS versions. * Visual Studio 2019 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet) * Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7 * Visual Studio 2022 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet) * Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7 more:[ https://www.pirahansiah.com/topics/opencv](https://www.pirahansiah.com/topics/opencv) * cvTest * * * cvtest: Computer Vision Test: Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning * Do you want to test your output of computer vision application which is video or images? * Standard test for computer vision application * There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. * Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? * [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://github.com/pirahansiah/cvtest/blob/main/README.md) * Multi-Class Multi-object Video Tracking * computer vision with deep learning in IoT devices * Multi Camera (Stereo Vision) Calibration for AR/VR headset (extended reality/mixed reality) 3D Image Processing with Deep Learning * End to End solution for computer vision applications in industry (cloud and IoT) * Download all mind map sources * [https://github.com/pirahansiah/pirahansiah.github.io](https://github.com/pirahansiah/pirahansiah.github.io) ## LinkedIn: (around 12K members) [Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) The Computer Vision LinkedIn group: reached to around 8000 members. This group is a wonderful place for support if you have a question, need inspiration, encouragement, and cutting edge research. Computer Vision, Deep Learning, extended reality; Metaverse; Deep Reinforcement Learning, GANs, OpenCV, TensorFlow, PyTorch. [https://www.linkedin.com/groups/10320678/](https://www.linkedin.com/groups/10320678/) ## Facebook Group: (around 14K members) Deep Reinforcement Learning, Computer Vision with Deep Learning, IoT, Robot [https://www.facebook.com/groups/185926728115336](https://www.facebook.com/groups/185926728115336) We help scale and build artificially intelligent driven start-ups with Al Researchers & Engineers! [Computer Vision] (Berlin, Germany) [Please use calendly appointment slots](https://calendly.com/pirahansiah) press . in github and open web visual studio code My LaTex Papers [https://www.overleaf.com/read/cmvgxfqxfdqm](https://www.overleaf.com/read/cmvgxfqxfdqm) This site is provided to everyone for free, however if you would like to say thanks or help support continued R&D, Mind Map, development and etc. , consider getting me a coffee. It keeps my work going. [](https://docs.google.com/forms/d/e/1FAIpQLSdiQprY0yS25LBVixQnsjkoUTjOtzx1oJye1C77At4Ur2oqTg/viewform "Open Google Forms, Contact Information in new window") ![](https://lh6.googleusercontent.com/J4dkBCDFO0RHrXxLWJL8AT2oOq2Q2lYfkFF0cHqnDjPybrli93-OwX8W2y9SjF7tkclad8vqIG53XfRrfLorSDjxTp7fNR1T7a25KnXNAQEAopCkcFhaPP91vV1Pi2ozrA=w1280) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[https://www.pirahansiah.com/about/fqa](https://www.pirahansiah.com/about/fqa) # Open Source Projects ## OpenCV NuGet [https://www.nuget.org/profiles/Farshid_Pirahansiah](https://www.nuget.org/profiles/Farshid_Pirahansiah) NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022 [https://www.pirahansiah.com/topics/opencv](https://www.pirahansiah.com/topics/opencv) Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static OpenCV library for visual studio 2022 by using NuGet package manager just in a few minutes [https://www.youtube.com/watch?v=AEqZO_fZHZ8](https://www.youtube.com/watch?v=AEqZO_fZHZ8) #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, download source code (GitHub):[ https://github.com/pirahansiah/](https://github.com/pirahansiah/opencv-cpp) #OpenCV #Farshid_PirahanSiah #pirahansiah My NuGet packages comprised of two versions for different VS versions. * Visual Studio 2019 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet) * Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7 * Visual Studio 2022 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet) * Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7 more:[ https://www.pirahansiah.com/topics/opencv](https://www.pirahansiah.com/topics/opencv) * cvTest * * * cvtest: Computer Vision Test: Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning * Do you want to test your output of computer vision application which is video or images? * Standard test for computer vision application * There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. * Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? * [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://github.com/pirahansiah/cvtest/blob/main/README.md) * Multi-Class Multi-object Video Tracking * computer vision with deep learning in IoT devices * Multi Camera (Stereo Vision) Calibration for AR/VR headset (extended reality/mixed reality) 3D Image Processing with Deep Learning * End to End solution for computer vision applications in industry (cloud and IoT) * Download all mind map sources * [https://github.com/pirahansiah/pirahansiah.github.io](https://github.com/pirahansiah/pirahansiah.github.io) ## LinkedIn: (around 12K members) [Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) The Computer Vision LinkedIn group: reached to around 8000 members. This group is a wonderful place for support if you have a question, need inspiration, encouragement, and cutting edge research. Computer Vision, Deep Learning, extended reality; Metaverse; Deep Reinforcement Learning, GANs, OpenCV, TensorFlow, PyTorch. [https://www.linkedin.com/groups/10320678/](https://www.linkedin.com/groups/10320678/) ## Facebook Group: (around 14K members) Deep Reinforcement Learning, Computer Vision with Deep Learning, IoT, Robot [https://www.facebook.com/groups/185926728115336](https://www.facebook.com/groups/185926728115336) We help scale and build artificially intelligent driven start-ups with Al Researchers & Engineers! [Computer Vision] (Berlin, Germany) [Please use calendly appointment slots](https://calendly.com/pirahansiah) press . in github and open web visual studio code My LaTex Papers [https://www.overleaf.com/read/cmvgxfqxfdqm](https://www.overleaf.com/read/cmvgxfqxfdqm) This site is provided to everyone for free, however if you would like to say thanks or help support continued R&D, Mind Map, development and etc. , consider getting me a coffee. It keeps my work going. [](https://docs.google.com/forms/d/e/1FAIpQLSdiQprY0yS25LBVixQnsjkoUTjOtzx1oJye1C77At4Ur2oqTg/viewform "Open Google Forms, Contact Information in new window") ![](https://lh6.googleusercontent.com/uNWjwwlNdJatOdrALrIJ26-K1biCa0UTaZs70ltCsrHtuqR9wtKszZpDR1ckbebRMW- eVa_EnnWZRJ-l07UCunph2umc-QY5CCoAeeEqtwY-srwOHueGolWUlW7PH3EFzQ=w1280) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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start/software](https://www.pirahansiah.com/topics-and-projects/how-to- start/software) [https://www.pirahansiah.com/topics-and-projects/how-to-start/roadmap-for-image- processing](https://www.pirahansiah.com/topics-and-projects/how-to-start/roadmap- for-image-processing) [https://www.pirahansiah.com/topics-and-projects/source- code](https://www.pirahansiah.com/topics-and-projects/source-code) [https://www.pirahansiah.com/topics-and-projects/source- code/python](https://www.pirahansiah.com/topics-and-projects/source-code/python) [https://www.pirahansiah.com/topics-and-projects/source- code/compile](https://www.pirahansiah.com/topics-and-projects/source-code/compile) [https://www.pirahansiah.com/topics-and- projects/share](https://www.pirahansiah.com/topics-and-projects/share) [https://www.pirahansiah.com/topics-and-projects/video- tracking](https://www.pirahansiah.com/topics-and-projects/video-tracking) [https://www.pirahansiah.com/topics-and- 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[https://www.pirahansiah.com/about/fqa](https://www.pirahansiah.com/about/fqa) # Open Source Projects ## OpenCV NuGet [https://www.nuget.org/profiles/Farshid_Pirahansiah](https://www.nuget.org/profiles/Farshid_Pirahansiah) NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022 [https://www.pirahansiah.com/topics/opencv](https://www.pirahansiah.com/topics/opencv) Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static OpenCV library for visual studio 2022 by using NuGet package manager just in a few minutes [https://www.youtube.com/watch?v=AEqZO_fZHZ8](https://www.youtube.com/watch?v=AEqZO_fZHZ8) #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, download source code (GitHub):[ https://github.com/pirahansiah/](https://github.com/pirahansiah/opencv-cpp) #OpenCV #Farshid_PirahanSiah #pirahansiah My NuGet packages comprised of two versions for different VS versions. * Visual Studio 2019 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet) * Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7 * Visual Studio 2022 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet) * Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7 more:[ https://www.pirahansiah.com/topics/opencv](https://www.pirahansiah.com/topics/opencv) * cvTest * * * cvtest: Computer Vision Test: Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning * Do you want to test your output of computer vision application which is video or images? * Standard test for computer vision application * There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. * Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? * [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://github.com/pirahansiah/cvtest/blob/main/README.md) * Multi-Class Multi-object Video Tracking * computer vision with deep learning in IoT devices * Multi Camera (Stereo Vision) Calibration for AR/VR headset (extended reality/mixed reality) 3D Image Processing with Deep Learning * End to End solution for computer vision applications in industry (cloud and IoT) * Download all mind map sources * [https://github.com/pirahansiah/pirahansiah.github.io](https://github.com/pirahansiah/pirahansiah.github.io) ## LinkedIn: (around 12K members) [Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) The Computer Vision LinkedIn group: reached to around 8000 members. This group is a wonderful place for support if you have a question, need inspiration, encouragement, and cutting edge research. Computer Vision, Deep Learning, extended reality; Metaverse; Deep Reinforcement Learning, GANs, OpenCV, TensorFlow, PyTorch. [https://www.linkedin.com/groups/10320678/](https://www.linkedin.com/groups/10320678/) ## Facebook Group: (around 14K members) Deep Reinforcement Learning, Computer Vision with Deep Learning, IoT, Robot [https://www.facebook.com/groups/185926728115336](https://www.facebook.com/groups/185926728115336) We help scale and build artificially intelligent driven start-ups with Al Researchers & Engineers! [Computer Vision] (Berlin, Germany) [Please use calendly appointment slots](https://calendly.com/pirahansiah) press . in github and open web visual studio code My LaTex Papers [https://www.overleaf.com/read/cmvgxfqxfdqm](https://www.overleaf.com/read/cmvgxfqxfdqm) This site is provided to everyone for free, however if you would like to say thanks or help support continued R&D, Mind Map, development and etc. , consider getting me a coffee. It keeps my work going. [](https://docs.google.com/forms/d/e/1FAIpQLSdiQprY0yS25LBVixQnsjkoUTjOtzx1oJye1C77At4Ur2oqTg/viewform "Open Google Forms, Contact Information in new window") ![](https://lh5.googleusercontent.com/HqDM83pPNYM3LNzVWreWzcUMYRayeevvBwsrUK7NpEMLZEDsj59vpBdP9d5cs7_M85uziv6YCrNKYo_hXahYL- eBVC2pIrAMEcOi7upVgpm18pXaTD6LpenF9o4Glje5IA=w1280) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh5.googleusercontent.com/JUG4BtkQbtzRY0jgA_26_ShjeMHmHKtfUbJPyuxQJS5M37S8-VMf- uZlLJ-FYfMZKkQ8judcIHceGQm9llDiLRo=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh5.googleusercontent.com/JUG4BtkQbtzRY0jgA_26_ShjeMHmHKtfUbJPyuxQJS5M37S8-VMf- uZlLJ-FYfMZKkQ8judcIHceGQm9llDiLRo=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # pirahansiah ## Image Processing * Artificial SuperIntelligence (ASI) * Artificial General Intelligence (AGI) * Medical Image Processing * Robotic * AR, VR, extended reality * 3D SLAM * Computer Vision in IoT ## Machine Learning * Performance engineering in deep learning applications * End-to-End pipeline for machine learning programs * Reduce cost and development time with Amazon * Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload. * Continuous Deployment of Machine Learning Pipelines We deliver end-to-end hyper-automation solutions using computer vision & deep learning to enable AI-Powered Enterprise orchestration of various technologies and workflows to streamline and execute a process automatically. Data labeling service remove or on site in Berlin, Germany ## Site Map [https://www.pirahansiah.com/home](https://www.pirahansiah.com/home) [https://www.pirahansiah.com/courses](https://www.pirahansiah.com/courses) [https://www.pirahansiah.com/courses/machine-learning- specialization](https://www.pirahansiah.com/courses/machine-learning- specialization) [https://www.pirahansiah.com/courses/machine-learning-specialization/machine- learning-foundations-a-case-study- approach](https://www.pirahansiah.com/courses/machine-learning- specialization/machine-learning-foundations-a-case-study-approach) [https://www.pirahansiah.com/courses/fsdl](https://www.pirahansiah.com/courses/fsdl) [https://www.pirahansiah.com/courses/full-stack-deep- learning](https://www.pirahansiah.com/courses/full-stack-deep-learning) [https://www.pirahansiah.com/courses/mlops](https://www.pirahansiah.com/courses/mlops) [https://www.pirahansiah.com/courses/ros](https://www.pirahansiah.com/courses/ros) 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[https://www.pirahansiah.com/book-summary/commonplace- book](https://www.pirahansiah.com/book-summary/commonplace-book) [https://www.pirahansiah.com/book- summary/knowledge_management](https://www.pirahansiah.com/book- summary/knowledge_management) [https://www.pirahansiah.com/book- summary/knowledge_management/pkm](https://www.pirahansiah.com/book- summary/knowledge_management/pkm) [https://www.pirahansiah.com/topics-and-projects](https://www.pirahansiah.com/topics- and-projects) [https://www.pirahansiah.com/topics-and-projects/how-to- start](https://www.pirahansiah.com/topics-and-projects/how-to-start) [https://www.pirahansiah.com/topics-and-projects/how-to- start/youtube](https://www.pirahansiah.com/topics-and-projects/how-to- start/youtube) [https://www.pirahansiah.com/topics-and-projects/how-to-start/youtube- ii](https://www.pirahansiah.com/topics-and-projects/how-to-start/youtube-ii) [https://www.pirahansiah.com/topics-and-projects/how-to- 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[https://www.pirahansiah.com/about/fqa](https://www.pirahansiah.com/about/fqa) # Open Source Projects ## OpenCV NuGet [https://www.nuget.org/profiles/Farshid_Pirahansiah](https://www.nuget.org/profiles/Farshid_Pirahansiah) NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022 [https://www.pirahansiah.com/topics/opencv](https://www.pirahansiah.com/topics/opencv) Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static OpenCV library for visual studio 2022 by using NuGet package manager just in a few minutes [https://www.youtube.com/watch?v=AEqZO_fZHZ8](https://www.youtube.com/watch?v=AEqZO_fZHZ8) #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, download source code (GitHub):[ https://github.com/pirahansiah/](https://github.com/pirahansiah/opencv-cpp) #OpenCV #Farshid_PirahanSiah #pirahansiah My NuGet packages comprised of two versions for different VS versions. * Visual Studio 2019 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet) * Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7 * Visual Studio 2022 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet) * Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7 more:[ https://www.pirahansiah.com/topics/opencv](https://www.pirahansiah.com/topics/opencv) * cvTest * * * cvtest: Computer Vision Test: Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning * Do you want to test your output of computer vision application which is video or images? * Standard test for computer vision application * There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. * Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? * [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://github.com/pirahansiah/cvtest/blob/main/README.md) * Multi-Class Multi-object Video Tracking * computer vision with deep learning in IoT devices * Multi Camera (Stereo Vision) Calibration for AR/VR headset (extended reality/mixed reality) 3D Image Processing with Deep Learning * End to End solution for computer vision applications in industry (cloud and IoT) * Download all mind map sources * [https://github.com/pirahansiah/pirahansiah.github.io](https://github.com/pirahansiah/pirahansiah.github.io) ## LinkedIn: (around 12K members) [Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) The Computer Vision LinkedIn group: reached to around 8000 members. This group is a wonderful place for support if you have a question, need inspiration, encouragement, and cutting edge research. Computer Vision, Deep Learning, extended reality; Metaverse; Deep Reinforcement Learning, GANs, OpenCV, TensorFlow, PyTorch. [https://www.linkedin.com/groups/10320678/](https://www.linkedin.com/groups/10320678/) ## Facebook Group: (around 14K members) Deep Reinforcement Learning, Computer Vision with Deep Learning, IoT, Robot [https://www.facebook.com/groups/185926728115336](https://www.facebook.com/groups/185926728115336) We help scale and build artificially intelligent driven start-ups with Al Researchers & Engineers! [Computer Vision] (Berlin, Germany) [Please use calendly appointment slots](https://calendly.com/pirahansiah) press . in github and open web visual studio code My LaTex Papers [https://www.overleaf.com/read/cmvgxfqxfdqm](https://www.overleaf.com/read/cmvgxfqxfdqm) This site is provided to everyone for free, however if you would like to say thanks or help support continued R&D, Mind Map, development and etc. , consider getting me a coffee. It keeps my work going. [](https://docs.google.com/forms/d/e/1FAIpQLSdiQprY0yS25LBVixQnsjkoUTjOtzx1oJye1C77At4Ur2oqTg/viewform "Open Google Forms, Contact Information in new window") ![](https://lh5.googleusercontent.com/HqDM83pPNYM3LNzVWreWzcUMYRayeevvBwsrUK7NpEMLZEDsj59vpBdP9d5cs7_M85uziv6YCrNKYo_hXahYL- eBVC2pIrAMEcOi7upVgpm18pXaTD6LpenF9o4Glje5IA=w1280) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[https://www.pirahansiah.com/about/fqa](https://www.pirahansiah.com/about/fqa) # Open Source Projects ## OpenCV NuGet [https://www.nuget.org/profiles/Farshid_Pirahansiah](https://www.nuget.org/profiles/Farshid_Pirahansiah) NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022 [https://www.pirahansiah.com/topics/opencv](https://www.pirahansiah.com/topics/opencv) Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static OpenCV library for visual studio 2022 by using NuGet package manager just in a few minutes [https://www.youtube.com/watch?v=AEqZO_fZHZ8](https://www.youtube.com/watch?v=AEqZO_fZHZ8) #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, download source code (GitHub):[ https://github.com/pirahansiah/](https://github.com/pirahansiah/opencv-cpp) #OpenCV #Farshid_PirahanSiah #pirahansiah My NuGet packages comprised of two versions for different VS versions. * Visual Studio 2019 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet) * Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7 * Visual Studio 2022 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet) * Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7 more:[ https://www.pirahansiah.com/topics/opencv](https://www.pirahansiah.com/topics/opencv) * cvTest * * * cvtest: Computer Vision Test: Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning * Do you want to test your output of computer vision application which is video or images? * Standard test for computer vision application * There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. * Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? * [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://github.com/pirahansiah/cvtest/blob/main/README.md) * Multi-Class Multi-object Video Tracking * computer vision with deep learning in IoT devices * Multi Camera (Stereo Vision) Calibration for AR/VR headset (extended reality/mixed reality) 3D Image Processing with Deep Learning * End to End solution for computer vision applications in industry (cloud and IoT) * Download all mind map sources * [https://github.com/pirahansiah/pirahansiah.github.io](https://github.com/pirahansiah/pirahansiah.github.io) ## LinkedIn: (around 12K members) [Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) The Computer Vision LinkedIn group: reached to around 8000 members. This group is a wonderful place for support if you have a question, need inspiration, encouragement, and cutting edge research. Computer Vision, Deep Learning, extended reality; Metaverse; Deep Reinforcement Learning, GANs, OpenCV, TensorFlow, PyTorch. [https://www.linkedin.com/groups/10320678/](https://www.linkedin.com/groups/10320678/) ## Facebook Group: (around 14K members) Deep Reinforcement Learning, Computer Vision with Deep Learning, IoT, Robot [https://www.facebook.com/groups/185926728115336](https://www.facebook.com/groups/185926728115336) We help scale and build artificially intelligent driven start-ups with Al Researchers & Engineers! [Computer Vision] (Berlin, Germany) [Please use calendly appointment slots](https://calendly.com/pirahansiah) press . in github and open web visual studio code My LaTex Papers [https://www.overleaf.com/read/cmvgxfqxfdqm](https://www.overleaf.com/read/cmvgxfqxfdqm) This site is provided to everyone for free, however if you would like to say thanks or help support continued R&D, Mind Map, development and etc. , consider getting me a coffee. It keeps my work going. [](https://docs.google.com/forms/d/e/1FAIpQLSdiQprY0yS25LBVixQnsjkoUTjOtzx1oJye1C77At4Ur2oqTg/viewform "Open Google Forms, Contact Information in new window") ![](https://lh6.googleusercontent.com/J4dkBCDFO0RHrXxLWJL8AT2oOq2Q2lYfkFF0cHqnDjPybrli93-OwX8W2y9SjF7tkclad8vqIG53XfRrfLorSDjxTp7fNR1T7a25KnXNAQEAopCkcFhaPP91vV1Pi2ozrA=w1280) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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start/software](https://www.pirahansiah.com/topics-and-projects/how-to- start/software) [https://www.pirahansiah.com/topics-and-projects/how-to-start/roadmap-for-image- processing](https://www.pirahansiah.com/topics-and-projects/how-to-start/roadmap- for-image-processing) [https://www.pirahansiah.com/topics-and-projects/source- code](https://www.pirahansiah.com/topics-and-projects/source-code) [https://www.pirahansiah.com/topics-and-projects/source- code/python](https://www.pirahansiah.com/topics-and-projects/source-code/python) [https://www.pirahansiah.com/topics-and-projects/source- code/compile](https://www.pirahansiah.com/topics-and-projects/source-code/compile) [https://www.pirahansiah.com/topics-and- projects/share](https://www.pirahansiah.com/topics-and-projects/share) [https://www.pirahansiah.com/topics-and-projects/video- tracking](https://www.pirahansiah.com/topics-and-projects/video-tracking) [https://www.pirahansiah.com/topics-and- 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[https://www.pirahansiah.com/about/fqa](https://www.pirahansiah.com/about/fqa) # Open Source Projects ## OpenCV NuGet [https://www.nuget.org/profiles/Farshid_Pirahansiah](https://www.nuget.org/profiles/Farshid_Pirahansiah) NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022 [https://www.pirahansiah.com/topics/opencv](https://www.pirahansiah.com/topics/opencv) Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static OpenCV library for visual studio 2022 by using NuGet package manager just in a few minutes [https://www.youtube.com/watch?v=AEqZO_fZHZ8](https://www.youtube.com/watch?v=AEqZO_fZHZ8) #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, download source code (GitHub):[ https://github.com/pirahansiah/](https://github.com/pirahansiah/opencv-cpp) #OpenCV #Farshid_PirahanSiah #pirahansiah My NuGet packages comprised of two versions for different VS versions. * Visual Studio 2019 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet) * Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7 * Visual Studio 2022 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet) * Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7 more:[ https://www.pirahansiah.com/topics/opencv](https://www.pirahansiah.com/topics/opencv) * cvTest * * * cvtest: Computer Vision Test: Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning * Do you want to test your output of computer vision application which is video or images? * Standard test for computer vision application * There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. * Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? * [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://github.com/pirahansiah/cvtest/blob/main/README.md) * Multi-Class Multi-object Video Tracking * computer vision with deep learning in IoT devices * Multi Camera (Stereo Vision) Calibration for AR/VR headset (extended reality/mixed reality) 3D Image Processing with Deep Learning * End to End solution for computer vision applications in industry (cloud and IoT) * Download all mind map sources * [https://github.com/pirahansiah/pirahansiah.github.io](https://github.com/pirahansiah/pirahansiah.github.io) ## LinkedIn: (around 12K members) [Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) The Computer Vision LinkedIn group: reached to around 8000 members. This group is a wonderful place for support if you have a question, need inspiration, encouragement, and cutting edge research. Computer Vision, Deep Learning, extended reality; Metaverse; Deep Reinforcement Learning, GANs, OpenCV, TensorFlow, PyTorch. [https://www.linkedin.com/groups/10320678/](https://www.linkedin.com/groups/10320678/) ## Facebook Group: (around 14K members) Deep Reinforcement Learning, Computer Vision with Deep Learning, IoT, Robot [https://www.facebook.com/groups/185926728115336](https://www.facebook.com/groups/185926728115336) We help scale and build artificially intelligent driven start-ups with Al Researchers & Engineers! [Computer Vision] (Berlin, Germany) [Please use calendly appointment slots](https://calendly.com/pirahansiah) press . in github and open web visual studio code My LaTex Papers [https://www.overleaf.com/read/cmvgxfqxfdqm](https://www.overleaf.com/read/cmvgxfqxfdqm) This site is provided to everyone for free, however if you would like to say thanks or help support continued R&D, Mind Map, development and etc. , consider getting me a coffee. It keeps my work going. [](https://docs.google.com/forms/d/e/1FAIpQLSdiQprY0yS25LBVixQnsjkoUTjOtzx1oJye1C77At4Ur2oqTg/viewform "Open Google Forms, Contact Information in new window") ![](https://lh6.googleusercontent.com/J4dkBCDFO0RHrXxLWJL8AT2oOq2Q2lYfkFF0cHqnDjPybrli93-OwX8W2y9SjF7tkclad8vqIG53XfRrfLorSDjxTp7fNR1T7a25KnXNAQEAopCkcFhaPP91vV1Pi2ozrA=w1280) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh3.googleusercontent.com/_hck3K2JGQ1ghYoXZ7iBAh6UrcJe4h-XNeLuiyiCVHdw5j1X2qmMgL8doj8geGzck7rU2DmtQXU2cDjW4Jc0qCo=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh3.googleusercontent.com/_hck3K2JGQ1ghYoXZ7iBAh6UrcJe4h-XNeLuiyiCVHdw5j1X2qmMgL8doj8geGzck7rU2DmtQXU2cDjW4Jc0qCo=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # pirahansiah ## Image Processing * Artificial SuperIntelligence (ASI) * Artificial General Intelligence (AGI) * Medical Image Processing * Robotic * AR, VR, extended reality * 3D SLAM * Computer Vision in IoT ## Machine Learning * Performance engineering in deep learning applications * End-to-End pipeline for machine learning programs * Reduce cost and development time with Amazon * Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload. * Continuous Deployment of Machine Learning Pipelines We deliver end-to-end hyper-automation solutions using computer vision & deep learning to enable AI-Powered Enterprise orchestration of various technologies and workflows to streamline and execute a process automatically. Data labeling service remove or on site in Berlin, Germany ## Site Map [https://www.pirahansiah.com/home](https://www.pirahansiah.com/home) [https://www.pirahansiah.com/courses](https://www.pirahansiah.com/courses) [https://www.pirahansiah.com/courses/machine-learning- specialization](https://www.pirahansiah.com/courses/machine-learning- specialization) [https://www.pirahansiah.com/courses/machine-learning-specialization/machine- learning-foundations-a-case-study- approach](https://www.pirahansiah.com/courses/machine-learning- specialization/machine-learning-foundations-a-case-study-approach) [https://www.pirahansiah.com/courses/fsdl](https://www.pirahansiah.com/courses/fsdl) [https://www.pirahansiah.com/courses/full-stack-deep- learning](https://www.pirahansiah.com/courses/full-stack-deep-learning) [https://www.pirahansiah.com/courses/mlops](https://www.pirahansiah.com/courses/mlops) [https://www.pirahansiah.com/courses/ros](https://www.pirahansiah.com/courses/ros) 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[https://www.pirahansiah.com/book-summary/commonplace- book](https://www.pirahansiah.com/book-summary/commonplace-book) [https://www.pirahansiah.com/book- summary/knowledge_management](https://www.pirahansiah.com/book- summary/knowledge_management) [https://www.pirahansiah.com/book- summary/knowledge_management/pkm](https://www.pirahansiah.com/book- summary/knowledge_management/pkm) [https://www.pirahansiah.com/topics-and-projects](https://www.pirahansiah.com/topics- and-projects) [https://www.pirahansiah.com/topics-and-projects/how-to- start](https://www.pirahansiah.com/topics-and-projects/how-to-start) [https://www.pirahansiah.com/topics-and-projects/how-to- start/youtube](https://www.pirahansiah.com/topics-and-projects/how-to- start/youtube) [https://www.pirahansiah.com/topics-and-projects/how-to-start/youtube- ii](https://www.pirahansiah.com/topics-and-projects/how-to-start/youtube-ii) [https://www.pirahansiah.com/topics-and-projects/how-to- 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[https://www.pirahansiah.com/about/fqa](https://www.pirahansiah.com/about/fqa) # Open Source Projects ## OpenCV NuGet [https://www.nuget.org/profiles/Farshid_Pirahansiah](https://www.nuget.org/profiles/Farshid_Pirahansiah) NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022 [https://www.pirahansiah.com/topics/opencv](https://www.pirahansiah.com/topics/opencv) Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static OpenCV library for visual studio 2022 by using NuGet package manager just in a few minutes [https://www.youtube.com/watch?v=AEqZO_fZHZ8](https://www.youtube.com/watch?v=AEqZO_fZHZ8) #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, download source code (GitHub):[ https://github.com/pirahansiah/](https://github.com/pirahansiah/opencv-cpp) #OpenCV #Farshid_PirahanSiah #pirahansiah My NuGet packages comprised of two versions for different VS versions. * Visual Studio 2019 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet) * Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7 * Visual Studio 2022 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet) * Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7 more:[ https://www.pirahansiah.com/topics/opencv](https://www.pirahansiah.com/topics/opencv) * cvTest * * * cvtest: Computer Vision Test: Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning * Do you want to test your output of computer vision application which is video or images? * Standard test for computer vision application * There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. * Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? * [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://github.com/pirahansiah/cvtest/blob/main/README.md) * Multi-Class Multi-object Video Tracking * computer vision with deep learning in IoT devices * Multi Camera (Stereo Vision) Calibration for AR/VR headset (extended reality/mixed reality) 3D Image Processing with Deep Learning * End to End solution for computer vision applications in industry (cloud and IoT) * Download all mind map sources * [https://github.com/pirahansiah/pirahansiah.github.io](https://github.com/pirahansiah/pirahansiah.github.io) ## LinkedIn: (around 12K members) [Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) The Computer Vision LinkedIn group: reached to around 8000 members. This group is a wonderful place for support if you have a question, need inspiration, encouragement, and cutting edge research. Computer Vision, Deep Learning, extended reality; Metaverse; Deep Reinforcement Learning, GANs, OpenCV, TensorFlow, PyTorch. [https://www.linkedin.com/groups/10320678/](https://www.linkedin.com/groups/10320678/) ## Facebook Group: (around 14K members) Deep Reinforcement Learning, Computer Vision with Deep Learning, IoT, Robot [https://www.facebook.com/groups/185926728115336](https://www.facebook.com/groups/185926728115336) We help scale and build artificially intelligent driven start-ups with Al Researchers & Engineers! [Computer Vision] (Berlin, Germany) [Please use calendly appointment slots](https://calendly.com/pirahansiah) press . in github and open web visual studio code My LaTex Papers [https://www.overleaf.com/read/cmvgxfqxfdqm](https://www.overleaf.com/read/cmvgxfqxfdqm) This site is provided to everyone for free, however if you would like to say thanks or help support continued R&D, Mind Map, development and etc. , consider getting me a coffee. It keeps my work going. [](https://docs.google.com/forms/d/e/1FAIpQLSdiQprY0yS25LBVixQnsjkoUTjOtzx1oJye1C77At4Ur2oqTg/viewform "Open Google Forms, Contact Information in new window") ![](https://lh6.googleusercontent.com/J4dkBCDFO0RHrXxLWJL8AT2oOq2Q2lYfkFF0cHqnDjPybrli93-OwX8W2y9SjF7tkclad8vqIG53XfRrfLorSDjxTp7fNR1T7a25KnXNAQEAopCkcFhaPP91vV1Pi2ozrA=w1280) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[https://www.pirahansiah.com/about/fqa](https://www.pirahansiah.com/about/fqa) # Open Source Projects ## OpenCV NuGet [https://www.nuget.org/profiles/Farshid_Pirahansiah](https://www.nuget.org/profiles/Farshid_Pirahansiah) NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022 [https://www.pirahansiah.com/topics/opencv](https://www.pirahansiah.com/topics/opencv) Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static OpenCV library for visual studio 2022 by using NuGet package manager just in a few minutes [https://www.youtube.com/watch?v=AEqZO_fZHZ8](https://www.youtube.com/watch?v=AEqZO_fZHZ8) #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, download source code (GitHub):[ https://github.com/pirahansiah/](https://github.com/pirahansiah/opencv-cpp) #OpenCV #Farshid_PirahanSiah #pirahansiah My NuGet packages comprised of two versions for different VS versions. * Visual Studio 2019 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet) * Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7 * Visual Studio 2022 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet) * Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7 more:[ https://www.pirahansiah.com/topics/opencv](https://www.pirahansiah.com/topics/opencv) * cvTest * * * cvtest: Computer Vision Test: Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning * Do you want to test your output of computer vision application which is video or images? * Standard test for computer vision application * There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. * Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? * [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://github.com/pirahansiah/cvtest/blob/main/README.md) * Multi-Class Multi-object Video Tracking * computer vision with deep learning in IoT devices * Multi Camera (Stereo Vision) Calibration for AR/VR headset (extended reality/mixed reality) 3D Image Processing with Deep Learning * End to End solution for computer vision applications in industry (cloud and IoT) * Download all mind map sources * [https://github.com/pirahansiah/pirahansiah.github.io](https://github.com/pirahansiah/pirahansiah.github.io) ## LinkedIn: (around 12K members) [Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) The Computer Vision LinkedIn group: reached to around 8000 members. This group is a wonderful place for support if you have a question, need inspiration, encouragement, and cutting edge research. Computer Vision, Deep Learning, extended reality; Metaverse; Deep Reinforcement Learning, GANs, OpenCV, TensorFlow, PyTorch. [https://www.linkedin.com/groups/10320678/](https://www.linkedin.com/groups/10320678/) ## Facebook Group: (around 14K members) Deep Reinforcement Learning, Computer Vision with Deep Learning, IoT, Robot [https://www.facebook.com/groups/185926728115336](https://www.facebook.com/groups/185926728115336) We help scale and build artificially intelligent driven start-ups with Al Researchers & Engineers! [Computer Vision] (Berlin, Germany) [Please use calendly appointment slots](https://calendly.com/pirahansiah) press . in github and open web visual studio code My LaTex Papers [https://www.overleaf.com/read/cmvgxfqxfdqm](https://www.overleaf.com/read/cmvgxfqxfdqm) This site is provided to everyone for free, however if you would like to say thanks or help support continued R&D, Mind Map, development and etc. , consider getting me a coffee. It keeps my work going. [](https://docs.google.com/forms/d/e/1FAIpQLSdiQprY0yS25LBVixQnsjkoUTjOtzx1oJye1C77At4Ur2oqTg/viewform "Open Google Forms, Contact Information in new window") ![](https://lh6.googleusercontent.com/J4dkBCDFO0RHrXxLWJL8AT2oOq2Q2lYfkFF0cHqnDjPybrli93-OwX8W2y9SjF7tkclad8vqIG53XfRrfLorSDjxTp7fNR1T7a25KnXNAQEAopCkcFhaPP91vV1Pi2ozrA=w1280) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[https://www.pirahansiah.com/about/fqa](https://www.pirahansiah.com/about/fqa) # Open Source Projects ## OpenCV NuGet [https://www.nuget.org/profiles/Farshid_Pirahansiah](https://www.nuget.org/profiles/Farshid_Pirahansiah) NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022 [https://www.pirahansiah.com/topics/opencv](https://www.pirahansiah.com/topics/opencv) Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static OpenCV library for visual studio 2022 by using NuGet package manager just in a few minutes [https://www.youtube.com/watch?v=AEqZO_fZHZ8](https://www.youtube.com/watch?v=AEqZO_fZHZ8) #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, download source code (GitHub):[ https://github.com/pirahansiah/](https://github.com/pirahansiah/opencv-cpp) #OpenCV #Farshid_PirahanSiah #pirahansiah My NuGet packages comprised of two versions for different VS versions. * Visual Studio 2019 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet) * Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7 * Visual Studio 2022 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet) * Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7 more:[ https://www.pirahansiah.com/topics/opencv](https://www.pirahansiah.com/topics/opencv) * cvTest * * * cvtest: Computer Vision Test: Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning * Do you want to test your output of computer vision application which is video or images? * Standard test for computer vision application * There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. * Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? * [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://github.com/pirahansiah/cvtest/blob/main/README.md) * Multi-Class Multi-object Video Tracking * computer vision with deep learning in IoT devices * Multi Camera (Stereo Vision) Calibration for AR/VR headset (extended reality/mixed reality) 3D Image Processing with Deep Learning * End to End solution for computer vision applications in industry (cloud and IoT) * Download all mind map sources * [https://github.com/pirahansiah/pirahansiah.github.io](https://github.com/pirahansiah/pirahansiah.github.io) ## LinkedIn: (around 12K members) [Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) The Computer Vision LinkedIn group: reached to around 8000 members. This group is a wonderful place for support if you have a question, need inspiration, encouragement, and cutting edge research. Computer Vision, Deep Learning, extended reality; Metaverse; Deep Reinforcement Learning, GANs, OpenCV, TensorFlow, PyTorch. [https://www.linkedin.com/groups/10320678/](https://www.linkedin.com/groups/10320678/) ## Facebook Group: (around 14K members) Deep Reinforcement Learning, Computer Vision with Deep Learning, IoT, Robot [https://www.facebook.com/groups/185926728115336](https://www.facebook.com/groups/185926728115336) We help scale and build artificially intelligent driven start-ups with Al Researchers & Engineers! [Computer Vision] (Berlin, Germany) [Please use calendly appointment slots](https://calendly.com/pirahansiah) press . in github and open web visual studio code My LaTex Papers [https://www.overleaf.com/read/cmvgxfqxfdqm](https://www.overleaf.com/read/cmvgxfqxfdqm) This site is provided to everyone for free, however if you would like to say thanks or help support continued R&D, Mind Map, development and etc. , consider getting me a coffee. It keeps my work going. [](https://docs.google.com/forms/d/e/1FAIpQLSdiQprY0yS25LBVixQnsjkoUTjOtzx1oJye1C77At4Ur2oqTg/viewform "Open Google Forms, Contact Information in new window") ![](https://lh6.googleusercontent.com/J4dkBCDFO0RHrXxLWJL8AT2oOq2Q2lYfkFF0cHqnDjPybrli93-OwX8W2y9SjF7tkclad8vqIG53XfRrfLorSDjxTp7fNR1T7a25KnXNAQEAopCkcFhaPP91vV1Pi2ozrA=w1280) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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[Learn more](https://www.google.com/policies/technologies/cookies/) Got it Search this site Skip to main content Skip to navigation [![](https://lh6.googleusercontent.com/64TqwjhC5SmyL8C2PAzPTeZ1eymQjar7GANkNJZpwnamRumOFEpuqaCmqVBV4_c_1_k_QhPKrZ3BtlOwEWMtPXc=w16383)](/home)[Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) * [Home](/home) * [Product](/home/product) * [Courses](/courses) * [Machine Learning Specialization](/courses/machine-learning-specialization) * [Machine Learning Foundations: A Case Study Approach](/courses/machine-learning-specialization/machine-learning-foundations-a-case-study-approach) * [FSDL](/courses/fsdl) * [Full Stack Deep Learning](/courses/full-stack-deep-learning) * [MLOps](/courses/mlops) * [ROS](/courses/ros) * [Parallel Programming ](/courses/parallel-programming) * [Modern CPP](/courses/modern-cpp) * [Cloud-Native](/courses/cloud-native) * [IoT Scholarship Foundation](/courses/iot-scholarship-foundation) * [TensorFlow: Data and Deployment Specialization ](/courses/tensorflow-data-and-deployment-specialization) * [Workshops and Events](/workshops-and-events) * [RISC-V](/workshops-and-events/risc-v) * [Edge-AI-summit](/workshops-and-events/edge-ai-summit) * [Embedded IoT](/workshops-and-events/embedded-iot) * [Tesla](/workshops-and-events/tesla) * [AI-Hardware](/workshops-and-events/ai-hardware) * [OpenVINO Deep Learning](/workshops-and-events/openvino-deep-learning) * [Metaverse](/workshops-and-events/metaverse) * [Workshops](/workshops-and-events/workshops) * [IFA2022](/workshops-and-events/ifa2022) * [Book Summary ](/book-summary) * [commonplace book](/book-summary/commonplace-book) * [knowledge_management](/book-summary/knowledge_management) * [PKM](/book-summary/knowledge_management/pkm) * [Topics and Projects](/topics-and-projects) * [AI_Hub](/topics-and-projects/ai_hub) * [ChatGPT](/topics-and-projects/chatgpt) * [How to start](/topics-and-projects/how-to-start) * [YouTube](/topics-and-projects/how-to-start/youtube) * [YouTube II](/topics-and-projects/how-to-start/youtube-ii) * [Software](/topics-and-projects/how-to-start/software) * [Roadmap for Image Processing ](/topics-and-projects/how-to-start/roadmap-for-image-processing) * [Source Code](/topics-and-projects/source-code) * [OpenCV](/topics-and-projects/source-code/opencv) * [CPP](/topics-and-projects/source-code/opencv/cpp) * [python](/topics-and-projects/source-code/opencv/python) * [MacOS+OpenCV](/topics-and-projects/source-code/opencv/macos-opencv) * [Rust](/topics-and-projects/source-code/opencv/rust) * [compile](/topics-and-projects/source-code/compile) * [IoT](/topics-and-projects/source-code/iot) * [Share](/topics-and-projects/share) * [Video Tracking](/topics-and-projects/video-tracking) * [Camera_Calibration](/topics-and-projects/camera_calibration) * [DRL](/topics-and-projects/drl) * [Hardware](/topics-and-projects/hardware) * [Quantum Computing](/topics-and-projects/quantum-computing) * [AltCoin](/topics-and-projects/altcoin) * [Resume_CV](/topics-and-projects/resume_cv) * [فارسی](/topics-and-projects/فارسی) * [Apple](/topics-and-projects/apple) * [startup](/topics-and-projects/startup) * [Links](/links) * [amazon](/links/amazon) * [About](/about) * [FQA](/about/fqa) [![](https://lh6.googleusercontent.com/64TqwjhC5SmyL8C2PAzPTeZ1eymQjar7GANkNJZpwnamRumOFEpuqaCmqVBV4_c_1_k_QhPKrZ3BtlOwEWMtPXc=w16383)Computer Vision, Deep Learning, Artificial superintelligence (ASI)](/home) # pirahansiah ## Image Processing * Artificial SuperIntelligence (ASI) * Artificial General Intelligence (AGI) * Medical Image Processing * Robotic * AR, VR, extended reality * 3D SLAM * Computer Vision in IoT ## Machine Learning * Performance engineering in deep learning applications * End-to-End pipeline for machine learning programs * Reduce cost and development time with Amazon * Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload. * Continuous Deployment of Machine Learning Pipelines We deliver end-to-end hyper-automation solutions using computer vision & deep learning to enable AI-Powered Enterprise orchestration of various technologies and workflows to streamline and execute a process automatically. Data labeling service remove or on site in Berlin, Germany ## Site Map [https://www.pirahansiah.com/home](https://www.pirahansiah.com/home) [https://www.pirahansiah.com/courses](https://www.pirahansiah.com/courses) [https://www.pirahansiah.com/courses/machine-learning- specialization](https://www.pirahansiah.com/courses/machine-learning- specialization) [https://www.pirahansiah.com/courses/machine-learning-specialization/machine- learning-foundations-a-case-study- approach](https://www.pirahansiah.com/courses/machine-learning- specialization/machine-learning-foundations-a-case-study-approach) [https://www.pirahansiah.com/courses/fsdl](https://www.pirahansiah.com/courses/fsdl) [https://www.pirahansiah.com/courses/full-stack-deep- learning](https://www.pirahansiah.com/courses/full-stack-deep-learning) [https://www.pirahansiah.com/courses/mlops](https://www.pirahansiah.com/courses/mlops) [https://www.pirahansiah.com/courses/ros](https://www.pirahansiah.com/courses/ros) 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[https://www.pirahansiah.com/about/fqa](https://www.pirahansiah.com/about/fqa) # Open Source Projects ## OpenCV NuGet [https://www.nuget.org/profiles/Farshid_Pirahansiah](https://www.nuget.org/profiles/Farshid_Pirahansiah) NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022 [https://www.pirahansiah.com/topics/opencv](https://www.pirahansiah.com/topics/opencv) Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static OpenCV library for visual studio 2022 by using NuGet package manager just in a few minutes [https://www.youtube.com/watch?v=AEqZO_fZHZ8](https://www.youtube.com/watch?v=AEqZO_fZHZ8) #YouTube #OpenCV5 #cpp #vs22 Test, C++, Computer Vision, Image Processing, download source code (GitHub):[ https://github.com/pirahansiah/](https://github.com/pirahansiah/opencv-cpp) #OpenCV #Farshid_PirahanSiah #pirahansiah My NuGet packages comprised of two versions for different VS versions. * Visual Studio 2019 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet](https://www.nuget.org/packages/OpenCV5_StaticLib_VS2019_NuGet) * Install-Package OpenCV5_StaticLib_VS2019_NuGet -Version 2022.7.7 * Visual Studio 2022 * [https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet](https://www.nuget.org/packages/OpenCV5_StaticLib_VS22_NuGet) * Install-Package OpenCV5_StaticLib_VS22_NuGet -Version 2022.7.7 more:[ https://www.pirahansiah.com/topics/opencv](https://www.pirahansiah.com/topics/opencv) * cvTest * * * cvtest: Computer Vision Test: Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning * Do you want to test your output of computer vision application which is video or images? * Standard test for computer vision application * There isn't any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware. * Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? * [https://github.com/pirahansiah/cvtest/blob/main/README.md](https://github.com/pirahansiah/cvtest/blob/main/README.md) * Multi-Class Multi-object Video Tracking * computer vision with deep learning in IoT devices * Multi Camera (Stereo Vision) Calibration for AR/VR headset (extended reality/mixed reality) 3D Image Processing with Deep Learning * End to End solution for computer vision applications in industry (cloud and IoT) * Download all mind map sources * [https://github.com/pirahansiah/pirahansiah.github.io](https://github.com/pirahansiah/pirahansiah.github.io) ## LinkedIn: (around 12K members) [Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch](https://www.linkedin.com/groups/10320678/) The Computer Vision LinkedIn group: reached to around 8000 members. This group is a wonderful place for support if you have a question, need inspiration, encouragement, and cutting edge research. Computer Vision, Deep Learning, extended reality; Metaverse; Deep Reinforcement Learning, GANs, OpenCV, TensorFlow, PyTorch. [https://www.linkedin.com/groups/10320678/](https://www.linkedin.com/groups/10320678/) ## Facebook Group: (around 14K members) Deep Reinforcement Learning, Computer Vision with Deep Learning, IoT, Robot [https://www.facebook.com/groups/185926728115336](https://www.facebook.com/groups/185926728115336) We help scale and build artificially intelligent driven start-ups with Al Researchers & Engineers! [Computer Vision] (Berlin, Germany) [Please use calendly appointment slots](https://calendly.com/pirahansiah) press . in github and open web visual studio code My LaTex Papers [https://www.overleaf.com/read/cmvgxfqxfdqm](https://www.overleaf.com/read/cmvgxfqxfdqm) This site is provided to everyone for free, however if you would like to say thanks or help support continued R&D, Mind Map, development and etc. , consider getting me a coffee. It keeps my work going. [](https://docs.google.com/forms/d/e/1FAIpQLSdiQprY0yS25LBVixQnsjkoUTjOtzx1oJye1C77At4Ur2oqTg/viewform "Open Google Forms, Contact Information in new window") ![](https://lh6.googleusercontent.com/uNWjwwlNdJatOdrALrIJ26-K1biCa0UTaZs70ltCsrHtuqR9wtKszZpDR1ckbebRMW- eVa_EnnWZRJ-l07UCunph2umc-QY5CCoAeeEqtwY-srwOHueGolWUlW7PH3EFzQ=w1280) Report abuse Page details Page updated Google Sites Report abuse This site uses cookies from Google to deliver its services and to analyze traffic. 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